text: Increasingly stringent environmental regulations, such as reducing carbon dioxide emissions and increasing fuel economy targets, have made it essential to develop vehicles with superior fuel efficiency and cleaner emissions. At the same time, the demand for improved vehicle characteristics such as driving performance and NVH are increasing. Toyota Motor Corporation has developed a new eight-speed automatic transaxle (UA80) which has realized these requirements at a high level, and is adopted into the new Sienna and Highlander. UA80 is the first Toyota transaxle which incorporates the TNGA concept. It has high level competitiveness in a wide torque range from L4 engines to V6 engines by using both optimization and standardization of main components. Additionally, UA80 has achieved an increased ease of installation by optimizing the transaxle size and arrangement while still achieving a low gravity center. UA80's low gravity center contributes to the vehicle's attractiveness by improving driving performance, NVH and design flexibility. The UA80 Torque Converter is Toyota's first use of a Multiple Disk Clutch and the smallest torus in this torque range for FWD vehicles. The Multiple Disk Clutch utilizes slip control in all torque ranges of UA80 and it has Highly Accurate and Highly Responsive Lock up Piston Mechanics. As a result, the vehicle can use the Lock up Clutch in any condition and the vehicle driver experiences an improved connection between pedal application and vehicle response and improved the fuel efficiency. To enable packaging of the Multiple Disk Clutch, The New Next Generation Flat Torus has approximately 12% smaller flow diameter and approximately 28% higher flatness than U660. Despite these restrictions, this torus keeps a high torque capacity, and a max efficiency over 80%. This paper describes the major features, adopted techniques and performance of The Multiple Disk Clutch and The New Torus.


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Strelkov was charged by Ukraine authorities with terrorism and is currently sanctioned by the European Union for his leading role in the insurgency in eastern Ukraine.
According to Ukrainian and EU authorities, Girkin is a retired Russian military intelligence GRU colonel who has previously participated in the 2014 Crimea crisis.
In early April 2014, Girkin arrived in the city of Slavyansk where the self-declared "people's mayor" Vyacheslav Ponomarev gave him the command over local militant group.
On April 15, the Security Service of Ukraine (SBU) opened a criminal proceeding against "Igor Strelkov".
He was described as a Russian recruiter and leader of armed "saboteurs" and a chief organizer of the "terror" in Ukraine's Slavyansk Raion (including an ambush that killed one and wounded three SBU officers), who had previously coordinated Russian military takeovers of Ukrainian units in Crimea during the 2014 Crimea crisis in March.
The next day, he allegedly sought to recruit Ukrainian soldiers captured at the entrance to Kramatorsk.
The SBU presented Strelkov's presence as proof of Russia's involvement in the East Ukraine crisis and released intercepted telephone conversations between "Strelkov" and his handlers in Moscow.
Russia denied any interference in Ukraine by its troops outside Crimea.
The Ukrainian government said Strelkov was behind the April 17 kidnapping, torture and murder of a local Ukrainian politician Volodymyr Rybak and a 19-year-old college student Yury Popravko.
Rybak's abduction by a group of men in Horlivka was recorded on camera.
The SBU released portions of intercepted calls in which another Russian citizen, alleged GRU officer and Girkin's subordinate Igor Bezler orders Rybak to be "neutralized", and a subsequent conversation in which "Strelkov" is heard instructing Ponomarev to dispose of Rybak's body, which is "lying here [in the basement of the separatist headquarters in Slavyansk] and beginning to smell."
Rybak's corpse with a smashed head and multiple stab wounds was found later in April in a river near Slavyansk; Popravko's body was also found nearby.
The incident helped to prompt the government's "anti-terrorist" military offensive against the pro-Russia separatists in Ukraine.
During the weekend of April 26–27, the political leader of the separatist Donetsk People's Republic and Girkin's old friend, Alexander Borodai, also a Russian national, ceded control of all separatist fighters in the entire Donetsk region to him.
On April 26, "Strelkov" made his first public appearance when he gave a video interview to the Komsomolskaya Pravda where he said that his militia in Slavyansk was formed in Crimea and consisted of volunteer veterans with combat experience from the wars in the former Yugoslavia, Iraq, and others (mostly from the Russian Armed Forces); it included many Russian and other foreign citizens but was mostly ("more than half, maybe two-thirds") locals.
He said nothing about his own background, denied receiving weapons or ammunition from Russia, and announced that his militia would not release the Organisation for Security and Co-operation in Europe (OSCE) observers that it had taken hostage unless pro-Russia activists would be freed by the Ukrainian government.
On April 28, the EU sanctioned "Igor Strelkov" as a GRU staff member believed to be a coordinator of armed actions and a security assistant to Crimea's Sergey Aksyonov.
On April 29, Girkin appointed a new police chief for Kramatorsk.
According to a report issued by the Office of the United Nations High Commissioner for Human Rights, "reportedly, on 26 May, by order of Igor Strelkov, Dmytro Slavov ('commander of a company of the people's militia') and Mykola Lukyanov ('commander of a platoon of the militia of Donetsk People's Republic') were 'executed' in Slavyansk, after they were 'sentenced' for 'looting, armed robbery, kidnapping and abandoning the battle field'.
The order, which was circulated widely and posted in the streets in Slavyansk, referred to a decree of the Presidium of the Supreme Council of the USSR of 22 June 1941 as the basis for the execution."
The report also mentions Strelkov's efforts to recruit local women into his armed formations:
"A particular call for women to join the armed groups was made on 17 May through a video released with Igor Girkin 'Strelkov', urging women of the Donetsk region to enlist in combat units."
Slavyansk's separatist "people's mayor" and former boss of Girkin, Ponomarev, was himself detained on an order of "Strelkov" on June 10 for "engaging in activities incompatible with the goals and tasks of the civil administration" and disappeared; according to rumors, he too was executed.
On the night of July 4-5, during a large-scale offensive by Ukrainian military following the end of a 10-day ceasefire on June 30, Girkin and his militants fled from Slavyansk which was then captured by Ukrainian forces, ending the separatist occupation of the city which had started on April 6.
Shortly before this happened, a video was posted on YouTube in which Girkin desperately pleaded for military aid from Russia for "Novorossiya" ("New Russia", a separatist name for eastern Ukraine) and said Slavyansk "will fall earlier than the rest."
Other rebel leaders denied Girkin's assessment that the insurgents were on the verge of collapse.
One of them, the self-proclaimed "people's governor" of Donetsk Pavel Gubarev, compared Girkin to 19th century Russian general Mikhail Kutuzov, claiming that both "Strelkov" and Kutuzov would "depart only before a decisive, victorious battle."
Other activities In late April 2014, Strelkov was identified by Ukrainian intelligence as Colonel Igor Girkin, registered as a resident of Moscow.
Journalists visiting the apartment where his mother allegedly lived were told by neighbors that a "fancy black car" had that morning picked up the woman living there; the neighbors also described him as "polite" and "quiet".
Girkin is known as a fan of military-historical movement and has participated in several reenactments connected with the Russian history.
Vice News stated that "during the 1990s, Girkin wrote for the right-wing Russian newspaper Zavtra, which is run by the anti-Semitic Russian nationalist Alexander Prokhanov" and where Borodai was an editor.
Source: Wikipedia

subset: commoncrawl


text:
PCI is a set of procedures including balloon angioplasty and stent placement that clear and prop open clogged coronary blood vessels that lead to a heart attack, a process known as reperfusion. The trial, known as DELTA-MI, was not conducted to demonstrate the effectiveness of KAI-9803, but researchers said that early data suggests it appears to be a promising compound. Dr. Matthew Roe, a cardiologist at Duke and the lead author of the study, said that many people might not realize that the heart suffers damage at two major points in a heart attack: first, when a blockage in a coronary artery prevents blood and oxygen from getting to the heart, and then again when the patient undergoes PCI and normal blood flow is restored through reperfusion.
"We may not be able to intervene in the first stage of a heart attack, but we think there may be ways to limit damage caused by reperfusion injury," he said. For the trial, the researchers randomised 154 patients who had suffered heart attacks and were eligible for PCI into either one of four dosing levels of KAI-9803 or a placebo.
Patients underwent PCI, with physicians injecting the drug directly into their coronary blood vessels during the procedure.
"The goal of the treatment is to flood the heart damaged by the heart attack with the drug immediately before blood flow is restored and then again, immediately afterwards," Roe said.
"We believe that bathing the area with this novel compound may block the damaging cascade of events that are triggered specifically by delta protein kinase C when blood is restored to the heart muscle," he added.
Previous studies in animals revealed that KAI-9803 lessened damage to the heart muscle and quickly restored its pumping function.
"We designed the DELTA MI trial to find out if KAI-9803 is safe for humans, and we accomplished that goal; we did not see any serious side effects," Roe said.
"We also found, however, many promising signs of beneficial drug activity such as lessened damage to the heart muscle and improvement in electrical conductivity in the heart that corresponded to restoration of blood flow to the heart muscle. As a result, we feel this drug has the potential to be helpful in reducing the impact of a heart attack in humans," he added.
The study is available online and to be published in the February 19 issue of the journal Circulation.


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The Economics of the World Trading System
By (author) Kyle W. Bagwell Robert W. Staiger
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Economics of the World Trading System by Kyle W. Bagwell
Book DescriptionWorld trade is governed by the rules of the World Trade Organization (WTO), the successor to the General Agreement on Tariffs and Trade (GATT). The WTO sets rules of conduct for the international trade of goods and services and for intellectual property rights, provides a forum for multinational negotiations to resolve trade problems, and has a formal mechanism for dispute settlement. It is the primary institution working, through rule-based bargaining, at freeing trade. In this book, Kyle Bagwell and Robert Staiger provide an economic analysis and justification for the purpose and design of the GATT/WTO. They summarize their own research, discuss the major features of the GATT agreement, and survey the literature on trade agreements. Their focus on the terms-of-trade externality is particularly original and ties the book together. Topics include the theory of trade agreements, the origin and design of the GATT and the WTO, the principles of reciprocity, the most favored nation principle, terms-of-trade theory, enforcement, preferential trade agreements, labor and environmental standards, competition policy, and agricultural export subsidies.
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Book DetailsISBN: 9780262524346
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Pages: 238
Imprint: MIT Press
Publisher: MIT Press Ltd
Publish Date: 3-Sep-2004
Country of Publication: United States
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Books By Author Kyle W. Bagwell
Preferential Trade Agreements, Paperback (October 2014)
Examines preferential trade agreements and the various dysfunctions that place them among the priority items for negotiation by the WTO.
Law and Economics of Contingent Protection in International Trade, Paperback (January 2014)» View all books by Kyle W. Bagwell
The book discusses the regulatory framework of contingent protection in the World Trade Organization.
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Kyle Bagwell is Professor of Economics and Senior Fellow at the Stanford Institute for Economic Policy Research. He is also Research Associate at the National Bureau of Economic Research.
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In the Footsteps of Gandhi
Revised Edition
By (author) Carolyn Ingram
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In the Footsteps of Gandhi by Carolyn Ingram
Book DescriptionIn the spring of 2003 there was a worldwide demonstration for nonviolence that was the largest in history. A growing number of people are studying the works of Gandhi, Martin Luther King, Jr., and other nonviolent leaders as the world collectively seeks to abandon the use of weapons in resolving conflict. At a time when eloquent and inspiring voices for peace are relevant more than ever, "In the Footsteps of Gandhi, " profiles contemporary leaders in this struggle. Here are people whose positive work has been part of international news for decadesin topics ranging from AIDS, to apartheid, to the Israeli-Palestine conflictall of whom embody the understanding that violence is not stopped by violence. Violence is only ended by love. This revised edition of Catherine Ingram's book features interviews with Mubarak Awad, Ram Dass, Thich Nhat Hanh, Cesar Chavez, H.H. The Dalai Lama, Desmond Tutu, Joan Baez, and others."
Buy In the Footsteps of Gandhi book by Carolyn Ingram from Australia's Online Bookstore, Boomerang Books.
Book DetailsISBN: 9781888375350
(228mm x 152mm x 20mm)
Pages: 285
Imprint: Parallax Press
Publisher: Parallax Press
Publish Date: 31-Jan-2004
Country of Publication: United States
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Catherine Ingram; Foreword by Arun Gandhi; Afterword by Michael Nagler.
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text: Most people think that Indian food is a made with curry only. Not true, of course curry is used often, but certainly not in all the dishes. We have been coming here for a few years. We can not get enough. At first we were pretty new to Indian cuisine, but our server, Shimoon made incredible recommendations to us. So... More


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Men's Alterations
By popular demand, Ron and Sandra bring you a show on doing men's alterations.
Get the best tips and techniques on getting the right fit for men!
Students please Sign In to watch the full-length class. Already Signed In? Click here to Watch. Want to become a student? Enroll.


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Cottage in village with poppies
A picture of Amberley - a Picturesque Village in the county of West Sussex
Photographer: © adam swaine (Gallery)(14th November 2009)
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A truly beautiful shot.
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Flyers are what we typically refer to as color copies, though it usually refers to a one-page (or two if double-sided) document intended specifically for promotional purposes, such as products, services and events.
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A merry Christmas to all at Satfix and all good things for 2017.
Merry Christmas to all Satfix peeps. Have a great Christmas. I hope Santa is good to all
Merry Christmas to all members and staff of Satfix!!! Have a safe and Happy holidays!!
Seasons greeting to all, whatever your religion constitutes.May health & happiness guide you into the new year & the winds of happiness fill your sails all year long.
The Noof.
Merry christmas to all
Merry Christmas To All From BigBadBrother
When fascism comes to America, it will be wrapped in the flag and carrying the cross." -- Sinclair Lewis
The answer is 42
Merry xristmas to all !
May 2017 brings all the best to all !
A very Merry Christmas to all
merry xmas all...;]
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Merry Christmas to all
HOW-TO:Install add-ons from zip files (for beginners-only)
Best kodi sources (for beginners-only)
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Hope she's naughty and nice................just saying


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If you enjoy pudding, you will love this Banana Pudding Toffee Poke Cake.
My family loves banana pudding. I have written two earlier posts about it. To try something different, I went to the Betty Crocker website and Duncan Hines website looking for the Banana Pudding Poke Cake recipe I have seen on Pinterest.
I decided to kick up the recipe a bit by adding some toffee. During Christmas, I fix Pralines that go well-crumbled up on top of banana pudding. I did not have any Pralines, so I decided to try toffee. Toffee and banana pudding are an excellent combination. This Banana Pudding Toffee Poke Cake vanished.
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By Joyce Hanson |
October 17, 2011
Vanguard, Manning & Napier, T. Rowe Price and Tweedy Browne all won multiple awards in this year's S&P Capital IQ U.S. Mutual Fund Excellence Awards Program.
By staff Writer |
April 2, 2007
Charles Schwab launched three new index funds April 2 that are based on fundamental indexes rather than cap-weighted indexes such as the S&P 500. Fundamentally weighted indexes base inclusion in the index on a company's sales, cash flow, dividends, and book value, rather than on market cap, P/E, or valuation,...
ThinkAdvisor's TechCenter is an educational resource designed to give you a competitive edge by keeping you abreast of new tech innovations and need-to-know information that can be applied to your business.
STP can be described as electronically capturing and processing transactions in one pass, from the point of first 'deal' to final settlement.
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This article is only available in the PDF format. Download the PDF to view the article, as well as its associated figures and tables.
National Convention of Charities and Corrections —Commitment and Detention of the Insane—Moral and Criminal Responsibility—How Shall Organized Charity Work—Immigration—State and Municipal Charities—Social Degradation—Summer Corps of Physicians—Floating Hospital.
In July, the Fifteenth National Convention of Charities and Correction was held at Buffalo, and the session was one of considerable interest. At the opening exercises there was an address of welcome by the Hon. Sherman S. Rogers, a welcome to the State by Gen. George S. Field, on behalf of Governor Hill, a response by the Hon. Philip C. Garrett, of Philadelphia, and the President's address, delivered by Dr. Charles S. Hoyt, Secretary of the New York State Board of Charities. In the latter the care of the dependant claims by the State was discussed, and the subject of insanity as related to pauperism received extended attention. Immigration and intemperance were spoken of as among the chief sources
P. B. P. . LETTER FROM NEW YORK.. JAMA. 1888;XI(8):284-286. doi:10.1001/jama.1888.02400600032008
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Immortalize a moment by capturing it on camera. Use the Sony Xperia Z5 Black. Boasting of a 23-megapixel camera with 5x optical zoom, the Xperia Z5 has the finest smartphone camera that Sony has released so far. And with its Hybrid Autofocus feature, that camera is sure to capture everything clearly in just one try.
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Movie poster En midsommarnatts sexkomedi (1982)
Poster 70x100cm nice condition FN original
Poster year:1982
Director:Woody Allen
Actors:Mia Farrow
3.5 / 5 from 9 ratings
Vintage Movie poster from a movie theater 1982. Rare, most were thrown away. Great quality in paper and colors, nice to frame on your wall.
Other posters you might like
Manhattan (1979)
Diane Keaton
Alice (1990)
Mia Farrow
Rosemary's Baby (1971-75)
Mia Farrow
Blind terror (1971)
Mia Farrow
John and Mary (1969)
Dustin Hoffman
Blade Runner (1982-92)
Harrison Ford
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Compact Semiconductor Lasers
Compact Semiconductor Lasers by Richard De La Rue, Jean-Michel Lourtioz and Siyuan Yu
English | 2014 | ISBN: 3527410937 | ISBN-13: 9783527410934 | 328 pages | PDF | 10,2 MB
English | 2014 | ISBN: 3527410937 | ISBN-13: 9783527410934 | 328 pages | PDF | 10,2 MB
This book brings together in a single volume a unique contribution by the top experts around the world in the field of compact semiconductor lasers to provide a comprehensive description and analysis of the current status as well as future directions in the field of micro- and nano-scale semiconductor lasers.
It is organized according to the various forms of micro- or nano-laser cavity configurations with each chapter discussing key technical issues, including semiconductor carrier recombination processes and optical gain dynamics, photonic confinement behavior and output coupling mechanisms, carrier transport considerations relevant to the injection process, and emission mode control.
Required reading for those working in and researching the area of semiconductors lasers and micro-electronics.
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Posted on 9/28/2016
Veteran John Welch spent decades of his life assuming that suicidal thoughts, depression and anger issues were simply something he had to live with — but today he knows better.
Posted on 9/1/2016
Brad Hibbard, director of training at America's VetDogs and Retired U.S. Army Major Peter Way, recipient of an America's Vet Dogs service dog.
Posted on 8/30/2016
The 7th Annual VetDogs Golf Classic kicked off at Huntington Country Club on Monday, and since its inception in 2009, the tournament has raised more than $1 million for the Smithtown nonprofit organization, which trains service dogs and places them w
Posted on 8/23/2016
North America's leading pet specialty retailer, PetSmart, and America's beloved national morning show, NBC's TODAY, has announced their collaboration on "Puppy with a Purpose."
Posted on 8/22/2016
When military veteran Joe Worley needed help as he learned to walk again after being injured in Iraq, he knew he could always lean on his service dog Benjamin - literally.
Posted on 8/22/2016
The TODAY family welcomed a paw-some new addition Monday morning — our second puppy with a purpose!
Posted on 8/22/2016
Middle School Honor Student Created the Tournament for 8-10-year-olds to Benefit America's VetDogs
Posted on 8/22/2016
If you thought you heard the patter of little puppy feet this morning on TODAY, you weren't imagining it.
Posted on 8/22/2016
The "Today" show welcomed its new "puppy with a purpose" on Monday.
Posted on 8/14/2016
When Sandy and John Ward of Greenbackville completed raising their first pup, Audrey, for America's VetDogs, they were asked if they would like to raise another.
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text:
|Publication number
|Publication type
|Application number
|Publication date
|May 3, 2011
|Filing date
|Jul 10, 2008
|Priority date
|Jul 10, 2008
|Fee status
|Also published as
|Publication number
|Johan de Kleer, Robert Price, Lukas D. Kuhn, Rong Zhou, Minh Do
|Original Assignee
|Palo Alto Research Center Incorporated
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (25), Non-Patent Citations (9), Referenced by (19), Classifications (15), Legal Events (2)
|External Links: USPTO, USPTO Assignment, Espacenet
The present exemplary embodiments relate to automated diagnosis and production in systems having multiple resources for achieving production goals. In such systems, automated diagnosis of system performance and component status can advantageously aid in improving productivity, identifying faulty or underperforming resources, scheduling repair or maintenance, etc. Accurate diagnostics requires information about the true condition of components in the production system. Such information can be obtained directly from sensors associated with individual components and/or may be inferred from a limited number of sensor readings within the production plant using a model or other knowledge of the system structure and dynamics. Providing complete sensor coverage for all possible system faults can be expensive or impractical in harsh production environments, and thus it is generally preferable to instead employ diagnostic procedures to infer the source of faults detected or suspected from limited sensors. System diagnostic information is typically gathered by one of two methods, including dedicated or explicit diagnostics with the system being exercised while holding production to perform tests and record observations without attaining any production, as well as passive diagnostics in which information is gathered from the system sensors during normal production. Although the latter technique allows inference of some information without disrupting production, the regular production mode may not sufficiently exercise the system to provide adequate diagnostic information to improve long term productivity. Moreover, while dedicated diagnostic operation generally provides better information than passive diagnostics, the cost of this information is high in terms of short term productivity reduction, particularly when diagnosing recurring intermittent system component failures that require repeated diagnostic interventions. Conventional production system diagnostics are thus largely unable to adequately yield useful diagnostic information without halting production and incurring the associated costs of system down-time, and are therefore of limited utility in achieving long term system productivity. Accordingly, a need remains for improved control systems and techniques by which both long term and short term productivity goals can be achieved in production systems having only limited sensor deployment.
The present disclosure provides systems and methods for implementing on-line pervasive diagnostics in a production system, in which a planner constructs/generates a plan in the form of a sequence of actions taken by one or more resources in a plant based on production goals and at least partially based on one or both of production and diagnostic objectives or metrics. Goals, as used herein include binary conditions that either are or are not achieved, wherein production goals as used herein include the production of products, modification of products, etc. Model-based production control systems and production plan construction/generation methods are provided for controlling operation of a production system, in which a planner constructs plans for execution in the plant based on production goals and diagnostic objectives, and a diagnosis engine determines a current plant condition based on a previously executed plan and corresponding observations from the plant, and provides expected information gain data to the planner. Objectives as used herein constitute aspirations toward which systems are directed, and may be achieved in whole or in part. Thus, production objectives may include, for example, production of products at a higher rate, or of higher quality, or more efficiently, etc., and diagnostic objectives may include, for instance, isolating the source of system faults, etc.
The planner constructs a plan that will achieve a given production goal based at least partially on a diagnostic objective and the expected information gain data. In systems that do not have complete sensor coverage, this novel approach provides significant advantages over conventional passive diagnostics in the amount of information that may be obtained, and is significantly more cost effective in terms of short term productivity and may also improve long run production objectives compared with conventional dedicated/explicit diagnostic techniques.
In accordance with one or more aspects of the present disclosure, a model-based production control system is provided including a model, a planner, and a diagnosis engine for controlling the operation of a production system that has one or more plants with resources to achieve production goals. The plant model includes a model of the plant and the planner provides one or more plans for execution in the plant based on at least one output objective. The diagnosis engine determines a current plant condition based on one or more previously executed plans, at least one corresponding observation from the plant, and the plant model. The diagnosis engine also provides expected information gain data to the planner for one or more possible plans based on the current plant condition and the model. The planner generates or constructs a plan that will achieve a given production goal at least partially based on a diagnostic objective and the expected information gain data. This control system facilitates the inclusion of diagnostic objectives and expected diagnostic information gain into the generation of production plans, and thus may be implemented to provide more useful diagnostic information than conventional passive diagnostic techniques, while still avoiding or mitigating system down-time.
The control system is flexible in allowing the planner to also construct or generate dedicated diagnostic plans based on one or more particular diagnostic objectives and the current plant condition, where the information to be gained may substantially improve the ability to realize long term productivity improvements. The planner may selectively interleave dedicated diagnostic plans and production plans for execution in the plant based on at least one production goal and at least one diagnostic objective. The diagnostic objective may be updated based at least partially on the expected information gain data. The control system may also include an operator interface that allows an operator to provide observations that the diagnosis engine can use in determining the current plant condition and a diagnosis job description language allowing definition of a diagnostic plan. In addition the current plant condition and the plant model may be used at least partially by the planner to generate/construct a control plan or a sequence of control plans for the plant that will achieve a set of production goals while balancing both production objectives (e.g., production cost, production time) and diagnostic objectives (e.g., diagnostic cost, information gained, repair cost) to perform diagnosis in isolating faulty resources in the plant.
Further aspects of the disclosure relate to a method of constructing or generating plans for execution in a production system with a plant having a plurality of resources to achieve one or more production goals. In this method, a current plant condition is determined based at least partially on a previously executed plan, at least one corresponding observation from the plant, and a plant model. The method also includes determining expected information gain data for one or more possible plans based on the current plant condition and the model, and constructing a plan that will achieve a given production goal based at least partially on a diagnostic objective and the expected information gain data. The plan construction may also be based on the current plant condition, and may include making a tradeoff between production objectives and diagnostic objectives and/or may further include performing diagnosis to isolate faulty resources in the plant at least partially based on the current plant condition. The method may further include constructing a dedicated or explicit diagnostic plan as well as selectively interleaving dedicated diagnostic plans and production plans based on at least one production goal and at least one diagnostic objective. In addition, the method may include allowing a user to define a diagnostic plan using a diagnosis job description language, as well as receiving user observations and constructing the plan at least partially based on the user observations.
In accordance with still other aspects of the disclosure, a computer readable medium is provided, which has computer executable instructions for performing the steps of determining a current plant condition based at least partially on a previously executed plan, at least one corresponding observation from the plant, and a plant model, and determining expected information gain data for one or more possible plans based on the current plant condition and the model. Computer executable instructions are also provided for constructing a plan for execution in the plant that will achieve a given production goal based at least partially on a diagnostic objective and the expected information gain data. The computer readable medium may also include computer executable instructions for constructing the plan based at least partially on the current plant condition.
The present subject matter may take form in various components and arrangements of components, and in various steps and arrangements of steps. The drawings are only for purposes of illustrating preferred embodiments and are not to be construed as limiting the subject matter.
Referring now to the drawing figures, several embodiments or implementations of the present disclosure are hereinafter described in conjunction with the drawings, wherein like reference numerals are used to refer to like elements throughout, and wherein the various features, structures, and graphical renderings are not necessarily drawn to scale. The disclosure relates to production systems generally and is hereinafter illustrated and described in the context of exemplary document processing systems having various printing and document transport resources. However, the concepts of the disclosure also find utility in association with product packaging systems and any other type or form of system in which a plurality of resources, whether machines, humans, software or logic components, objects, etc., may be selectively employed according to plans comprised of a series of actions to achieve one or more production goals based at least partially on one or more diagnostic metrics or objectives, wherein all such alternative or variant implementations are contemplated as falling within the scope of the present disclosure and the appended claims. The disclosure finds particular utility in constructing and scheduling plans in systems in which a given production goal can be achieved in two or more different ways, including use of different resources (e.g., two or more print engines that can each perform a given desired printing action, two different substrate routing paths that can be employed to transport a given printed substrate from one system location to another, etc.), and/or the operation of a given system resource at different operating parameter values (e.g., operating substrate feeding components at different speeds, operating print engines at different voltages, temperatures, speeds, etc.).
The model-based control system 2 and the components thereof may be implemented as hardware, software, firmware, programmable logic, or combinations thereof, and may be implemented in unitary or distributed fashion. In one possible implementation, the planner 30, the diagnosis engine 40, and the model 50 are software components and may be implemented as a set of sub-components or objects including computer executable instructions and computer readable data executing on one or more hardware platforms such as one or more computers including one or more processors, data stores, memory, etc. The components 30, 40, and 50 and sub components thereof may be executed on the same computer or in distributed fashion in two or more processing components that are operatively coupled with one another to provide the functionality and operation described herein. Likewise, the producer 10 may be implemented in any suitable hardware, software, firmware, logic, or combinations thereof, in a single system component or in distributed fashion in multiple interoperable components. In this regard, the control system 2 may be implemented using modular software components (e.g., model 50, planner 30, diagnosis engine 40 and/or sub-components thereof) to facilitate ease of debugging and testing, the ability to plug state of the art modules into any role, and distribution of operation over multiple servers, computers, hardware components, etc.
The embodiment of
Referring also to
As further shown in
The planner 30 creates and provides plans 54 for execution in the plant 20. The plans 54 include a series of actions to facilitate one or more production and/or diagnostic objectives 34 while achieving a production goal according to the jobs 51, and in which a given action may appear more than once. The actions are taken with respect to states and resources 21-24 defined in the model 50 of the plant 20, for example, to route a given substrate through a modular printing system 20 from a starting state to a finished state as shown in
Referring also to
As further illustrated in
Referring now to
Moreover, where the plant 20 includes only limited sensing capabilities, (e.g., such as the system in
Even without utilizing dedicated diagnostic plans 54, moreover, the control system 6 significantly expands the range of diagnosis that can be done online through pervasive diagnostic aspects of this disclosure during production (e.g., above and beyond the purely passive diagnostic capabilities of the system), thereby lowering the overall cost of diagnostic information by mitigating down time, the number of service visits, and the cost of unnecessarily replacing components 21-24 in the system 20 that are actually working, without requiring complete sensor coverage. The planner 30 is further operative to use the current plant condition 58 in making a tradeoff between production objectives 34 a and diagnostic objectives 34 b in generating plans 54 for execution in the plant 20, and may also take the condition 58 into account in performing diagnosis in isolating faulty resources 21-24 in the plant 20.
The plant condition estimation and updating component 44 of the diagnosis engine 40 infers the condition of internal components 21-24 of the plant 20 at least partially from information in the form or observations 56 derived from the limited sensors 26, wherein the diagnosis engine 40 constructs the plant condition 58 in one embodiment to indicate both the condition (e.g., normal, worn, broken) and the current operational state (e.g., on, off, occupied, empty, etc.) of the individual resources 21-24 or components of the plant 20, and the belief model 42 can be updated accordingly to indicate confidence in the conditions and/or states of the resources or components 21-24. In operation of the illustrated embodiment, once the producer 10 has initiated production of one or more plans 54, the diagnosis engine 40 receives a copy of the executed plan(s) 54 and corresponding observations 56 (along with any operator-entered observations 56 a). The condition estimation and updating component 44 uses the observations 56, 56 a together with the plant model 50 to infer or estimate the condition 58 of internal components/resources 21-24 and updates the belief model 42 accordingly. The inferred plant condition information 58 is used by the planner 30 to directly improve the productivity of the system 20, such as by selectively constructing plans 54 that avoid using one or more resources/components 21-24 known (or believed with high probability) to be faulty, and/or the producer 10 may utilize the condition information 58 in scheduling jobs 51 to accomplish such avoidance of faulty resources 21-24. The exemplary diagnosis engine 40 also provides future prognostic information to update the diagnostic objectives 34 b which may be used by the planner 30 to spread utilization load over multiple redundant components 21-24 to create even wear or to facilitate other long term objectives 34.
To improve future productivity, moreover, the diagnosis engine 40 provides the data 70 to the planner 30 regarding the expected information gain of various possible production plans 54. The planner 30, in turn, can use this data 70 to construct production plans 54 that are maximally diagnostic (e.g., most likely to yield information of highest diagnostic value). In this manner, the planner 30 can implement active diagnostics or active monitoring by using carefully generated or modified production plans 54 to increase information during production (e.g., using 'diagnostic' production plans). Moreover, certain diagnostic plans 54 are non-productive with respect to the plant 20, but nevertheless may yield important diagnostic information (e.g., operating the transport mechanisms 24 in
Referring also to
Diagnostic objectives 34 b are received at 202 in the method 200. The method 200 further includes determining a current plant condition 58 at 204 based at least partially on a previously executed plan 54 and at least one corresponding observation 56 from the plant 20 using a plant model 50, and determining expected information gain data 70 at 206 based on the current plant condition 58 and the model 50. The planner 30 receives the plant conditions 58 at 208 from the diagnosis engine 40, and receives production jobs and objectives 51 at 210 from the producer 10. At 212, the planner 30 constructs a plan 54 at based at least partially on a diagnostic objective 34 b and the expected information gain data 70. At 214, the planner 30 sends the constructed plan 54 to the plant 20 for execution and the diagnosis engine 40 receives the plan 54 and the plant observations 56 at 216. At 218, the diagnosis engine 40 updates the plant condition 58 and updates the expected information gain data 70, after which further jobs and objectives 51 are serviced and the process 200 continues again at 210 as described above.
The plan construction at 212 may be based at least partially on the current plant condition 58, and may include making a tradeoff between production objectives 34 a and diagnostic objectives 34 b based at least partially on the current plant condition 58. Moreover, the plan construction at 212 may include performing prognosis to isolate faulty resources 21-24 in the plant 20 based at least partially on the current plant condition 58. In certain embodiments, a dedicated diagnostic plan 54 may be constructed for execution in the plant 20 based at least partially on at least one diagnostic objective 34 b, a diagnostic job 60, 8 b, and the current plant condition 58, and the plan construction may provide for selectively interleaving dedicated diagnostic and production plans 54 based on at least one production objective 34 a and at least one diagnostic objective 34 b. Further embodiments of the method 200 may also include allowing an operator to define a diagnostic plan 8 b using a diagnosis job description language 8 a and receiving operator observations 56 a, with the plan selection/generation at 216 being based at least partially on the operator observations 56 a.
In accordance with further aspects of the present disclosure, a computer readable medium is provided, which has computer executable instructions for performing the steps of determining a current plant condition 58 in a production system 6 with a plant 20 having a plurality of resources 21-24 based at least partially on a previously executed plan 54, at least one corresponding observation 56 from the plant 20, and a plant model 50, and computer executable instructions for determining expected information gain data 70 based on the current plant condition 58 and the model 50. The medium further includes instructions for constructing a plan 54 for execution in the plant 20 to achieve one or more production goals based at least partially on a diagnostic objective 34 b and the expected information gain data 70. In various embodiments, further computer executable instructions are included in the medium for constructing the plan 54 based at least partially on the current plant condition 58.
Referring now to
The diagnosis engine 40 in this approach advantageously provides the inputs for searching by the planner 30 in order to derive valuable information for the diagnosis of the system 20. In this embodiment, the best plans 54 with respect to diagnostic value for single persistent faults are those that have an equal probability of succeeding or failing. The diagnosis engine 40 uses this notion to develop heuristics to guide the search by the planner 30 in evaluating partial plans 54 to construct the plan 54 to be executed in the plant 20. In addition, the plan construction search may employ pruning techniques to improve search performance. By this approach, the control system 2 implements efficient on-line active or pervasive diagnosis in controlling the plant 20 through a combination of model-based probabilistic inference in the diagnosis engine 40 with decomposition of the information gain associated with executing a given plan 54 using an efficient heuristic target search in the planner 30. In this active diagnosis technique, specific inputs or control actions in the form of plans 54 are constructed by the planner 30 with the help of the diagnosis engine 40 to maximize or increase the amount and/or quality of diagnostic information obtained from the controlled system plant 20. The informative production (active or pervasive diagnosis) techniques of the present disclosure rank the set of partial plans at any given point in the construction process that achieve production goals by their potential information gain and the planner 30 operates to selectively generate the most promising strategy in that respect, in consideration of other possible production objectives 34 a and diagnosis objectives 34 b.
As discussed above in the context of the exemplary modular printing system plant 20 of
In the exemplary planner 30, a search algorithm may be employed to find and construct plans 54. The model 50 describes the plant system 20 as a state machine with all possible actions A that the plant 20 can accommodate. Actions are defined by preconditions and post-conditions over the system state. As such, an action requires the system 20 to be in a certain state in order to be executable and modifies the system state when executed. The system 20 is controlled by plan p (54) that is comprised of a sequence of actions a1, a2, . . . , an drawn from the set A of possible actions. Execution of an action potentially changes the system state, and part of the system state may represent the state of a product 52 at any given time, particularly if the action is part of a production plan 54. Further, internal constraints of the system 54 limit the set of plans 54 to a subset of all possible sequences (e.g., the plan space 100 in
One type of observable outcome 26 is defined as abnormal, denoted ab(p), in which the plan 54 fails to achieve its production goal. Another type is a normal (not abnormal) outcome, denoted
A plan p is deemed by the diagnosis engine 40 and the planner 30 as being informative if it contributes information to (e.g., reduces the uncertainty of) the diagnosis engine's beliefs 42, where the informative value can be measured as the mutual information between the system beliefs Pr(H) and the plan outcome conditioned on the plan executed, I(H; O|P=p). This mutual information may be defined in terms of entropy or uncertainty implied by a probability distribution, where a uniform probability distribution has high uncertainty and a deterministic distribution has low uncertainty. In the context of diagnostic information value, an informative plan 54 reduces the uncertainty of the system's beliefs 42, and thus plans 54 with outcomes that are hard to predict are the most informative, while execution of plans 54 that are known to succeed (or known to fail) will yield no diagnostic information gain. In this respect, an optimal (e.g., 'target') uncertainty T may be used to rate the plans 54 with respect to expected informative value, by which the diagnosis engine 40 can evaluate plans 54 accordingly and provide the expected information gain data 70 to the planner 30 to influence the plan selection/generation for preferential selection/generation of informative plans 54.
In the case of persistent single faults, a value of T=0.5 can be use as the optimal uncertainty about the outcome, and uncertainty in the case of intermittent faults may be maximized in the range of about 0.36≦T≦0.5 in one exemplary implementation. In finding a plan 54 with a given amount of uncertainty T, the diagnosis engine 40 is operative to predict the uncertainty associated with a given plan p=[a1, a2, . . . , an], where the set of unique actions in a plan Ap=Ui {ai∈p}. Presuming f failures are observable, a plan 54 will be abnormal ab(p) if one or more of its actions are abnormal, as set forth in the following equation (1):
ab(a1)ν . . . νab(an)
Given a distribution over hypotheses and the set Hp of explanatory hypotheses for a given plan p, it is possible to calculate the probability that plan p will fail. Since every hypothesis h∈Hp contains at least one abnormal action that is also in plan p, hypothesis h being true implies ab(p):
(h1νh2ν . . . νhm)
Since the hypotheses are mutually exclusive by definition, the probability of a plan failure Pr(ab(p)) can be defined as the sum of all probabilities of hypotheses which imply that the plan will fail, as in the following equation (4):
To find a plan which achieves production goals while also being informative, the planner 30 evaluates the plans 54 in terms of the probability T and uses this and the current plant state 58 to preferentially construct a plan 54 that achieves a production goal while having a positive probability of failure. While in the short term this may likely lower the productivity of the system 20, the information gained allows improvement in long term productivity. The planner 30, moreover, may construct a sequence of plans 54 for execution in the plant 20 which might not be maximally informative individually, but are maximally informative taken jointly.
As noted above, any form of search may be employed in the planner 30 that piece-wise selects/generates from suitable plans that will achieve a given production goal while yielding useful diagnostic information within the scope of the present disclosure. A simple brute force search could be employed to generate all possible action sequences and the resulting list could be filtered to yield plans 54 that achieve production goals while being informative, as per the following equation (5):
p opt=argminachievesGoal(p)∈P |Pr(ab(p))−T| (5)
However, this may be impractical in real-time control applications if the space of plans P is very large. Another possible approach employed in the exemplary system 2 is for the diagnosis engine 40 to establish a heuristic by which the planner 30 considers sets or families of plans 54 that share structure, such as by employing an A* target search using a set of partial plans pI→S
f(S n)=g(S n)+h(S n). (6)
If the heuristic function f(Sn) never overestimates the true quality of the complete the plan, then the heuristic f(Sn) is said to be admissible and an A* target search by the planner 30 should return an optimal plan 54. In this regard, the underestimation causes the A* search approach to be optimistic in the face of uncertainty, thereby ensuring that uncertain plans are explored before committing to completed plans known to be high in quality. As a result, the more accurate the heuristic function is, the more the A* target search focuses on the highly informative plans 54. In the illustrated embodiments, therefore, the planner 30 employs a heuristic function derived by the diagnosis engine 40 based at least partially on the description of the system architecture and dynamics in the plant model 50.
The diagnosis engine 40 uses the graph structure and probability estimates to construct heuristic bounds on the uncertainty that can be contributed to a plan by any plan suffix, in this example, by building up the heuristic from right to left in
The planner 30 uses these bounds with a forward A* target search to identify and construct a plan 54 that achieves or most closely approximates the target probability T. For example, one possible plan 54 begins from the start node S 402 s and includes a first action aS,A, which was part of the plan 54 that was observed to be abnormal. If the action aS,A 404 ac is added to a partial plan, it must add ⅓ probability to the chance of failure as it is a candidate itself. After aS,A, the system 20 would be in state A, and a plan 54 could be completed through D by including actions 404 ad and 404 dg to arrive at the goal state G 402 g. The action aA,D itself has a zero probability of being abnormal since it was not involved in the previously observed faulty plan. Using the heuristic bound, therefore, a completion through state node D 402 d adds zero probability of being abnormal. From node A 402 a, a plan 54 could alternatively be completed through node C, as in the originally observed plan 54. The corresponding action aA,C 404 ac adds ⅓ probability of failure to such a plan and based on the heuristic bound the completion through C 402 c must add another ⅓ probability of being abnormal.
The heuristic that is precomputed and provided by the diagnosis engine 40 therefore allows prediction of total plan abnormality probability for a possible plan 54 that moves the system 20 through the state node sequence [S, A, C, G] or [S, A, D, G]. The lower bound of the total plan is ⅓, as determined by ⅓ from aS,A plus 0 from the completion aA,D,aD,G, and the upper bound is 3/3 equal to the sum of ⅓ from aS,A plus ⅓ each from aA,C and aC,G. If this plan is computed through [aA,C,aC,G] the total plan 54 will fail with probability 1, and therefore nothing is to be learned from constructing such a plan completion. If the plan 54 is instead completed through the suffix [aA,D,aD,G] the failure probability of the total plan will be ⅓ which is closer to the optimally informative probability T=0.5. In this case, the planner 30 will construct the plan 54 [S, A, D, G] for execution in the plant 20. The plan 54 may or may not succeed, and in either case something may be learned from a diagnostic perspective. For instance, if the plan [S, A, D, G] fails, the diagnosis engine 40 learns that node aS,A was the failed action (for the assumed single persistent fault scenario), and if the plan 54 is successful, the engine 40 can further refine the belief model 42 by eliminating action 404 sa as a fault suspect.
It is noted that there is no guarantee that a plan 54 exists for any given value between the bounds. The diagnosis engine 40 recursively calculates the heuristic bounds starting from all goal states, where a goal state has an empty set of suffix plans pG→G=� and therefore has a set lower bound LG=0 and a set upper bound UG=0. For each new state Sm, the diagnosis engine 40 calculates the corresponding bounds based at least partially on the bounds of all possible successor states SUC(Sm) and the failure probability of the connecting action aSm,Sn between Sm and a successor state Sn. In this regard, a successor state Sn of a state Sm is any state that can be reached in a single step starting from the state Sm. In the case where a single fault is assumed, the failure probability added to a plan pI→S
In contrast to the computation of the heuristic in the diagnosis engine 40, the search for an informative production plan by the planner 30 starts from the initial starting state S 402 a and works recursively forward toward the goal state 402 g. The abnormality probability of the empty plan starting at the initial state S is zero plus the best completion. In general, the planner 30 computes the abnormality probability as the plan probability up to the current state plus the abnormality probability of the best completion route. Since the planner 30 is initially uncertain about the completion, its probability of abnormality is an interval that includes a lower and upper bound and the values in between. As a result, the total abnormality probability is also an interval, as set forth in the following equation (7):
I(p I→Sn)=[Pr(ab(p I→Sn))+L Sn ,Pr(ab(p I→Sn))+U Sn)] (7)
As noted above, the most informative plan 54 is one whose total failure probability is T, with T=0.5 in a preferred implementation for an assumed persistent single fault. Given an interval describing bounds on the total abnormality probability of a plan I(pI→S
|T−I(p I→S
This absolute value in equation (8) folds the range around T, and if the estimated total abnormality probability of the plan 54 straddles target probability T, then the interval |T−I(pI→S
The planner 30 can search from a whole set of partial plans P={pI→S
In a further aspect of the disclosure, the planner 30 may be operative to improve the efficiency of the target plan search using selective pruning. In this regard, the above described search heuristic in many cases may return the same value, i.e., zero, which provides the planner 30 with little guidance in making a selection. The planner 30 may accordingly be adapted to focus the search using one or more techniques. In a first focusing approach, the planner 30 prunes out dominated parts of the search space. For example, a given partial plan I(pI→S
V pI→S
The plan pI→S
In another aspect of the disclosure, the planner 30 may employ other focusing techniques to intelligently break ties in the heuristic value. As noted above, the heuristic value determines which state node will be expanded next, but it is possible that two or more nodes will receive the same heuristic value. Accordingly, the planner 30 may employ one or more rules to break the tie and hence to determine which node should be expanded first. One suitable rule in this regard is to simply pick a node randomly.
A further improvement can be implemented in the planner 30 according to this aspect of the disclosure, using the fact that VpI→S
1 If
2 If
3 If UI(pI→S
4 If UI(pI→S
5 If LI(pI→S
6 If LI(pI→S
7 otherwise pick randomly.
The planner 30 can also facilitate the selective avoidance of known faulty resources 21-24 in the plant 20 via the component 32 b, as well as generation of plans 54 so as to help determine the source of faults observed during production. For example, the planner 30 operating the above described modular printing system plant 20 of
The control system 2 can thus provide the advantages of performing diagnosis functions during production, even with limited sensor capabilities, with the flexibility to schedule dedicated diagnostic plans 54 if/when needed or highly informative. In the case of explicit dedicated diagnosis, the planner 30 focuses on the needs of the diagnosis engine 40 and thus creates/selects plans 54 that maximize information gain with respect to the fault hypotheses. The system 2 also allows the generation of plans 54 solely on the basis of production goals, for instance, where there is only one plan 54 that can perform a given production task and the planner 30 need not chose from a set of equivalent plans, thereby limiting the information gathering to the case of passive diagnosis for that plan.
In the exemplary modular printing system example 20 above, therefore, the control system 2 can choose to parallelize production to the extent possible, use specialized print engines 22 for specific printing tasks, and have the operational control to reroute sheet substrates around failed modules as these are identified. In this implementation, the planner 30 may receive a production print job 51 from a job queue (in the producer 10, or a queue in the planner 30), and one or more plans 54 are constructed as described above to implement the job 51. The observations 56 are provided to the diagnosis engine 40 upon execution of the plan(s) 54 to indicate whether the plan 54 succeeded without faults (e.g., not abnormal), or whether an abnormal fault was observed (e.g., bent corners and/or wrinkles detected by the sensors 26 in printed substrates). The diagnosis engine 30 updates the hypothesis probabilities of the belief model 42 based on the executed plan 54 and the observations 56. When a fault occurs, the planner 30 constructs the most informative plan 54 in subsequent scheduling so as to satisfy the diagnostic objectives 34 b. In this regard, there may be a delay between submitting a plan 54 to the plant 20 and receiving the observations 56, and the planner 30 may accordingly plan production jobs 51 from the job queue without optimizing for information gain until the outcome is returned in order to maintain high short term productivity in the plant 20.
Using the above described pervasive diagnosis, the plan construction in the planner 30 is biased to have an outcome probability closest to the target T, and this bias can create paths capable of isolating faults in specific actions. Prior to detection of a system fault, the plant 20 may produce products 52 at a nominal rate rnom, with diagnosis efforts beginning once some abnormal outcome is observed. The length of time required to diagnose a given fault in the system (e.g., to identify faulty plant components or resources 21-24) will be short if dedicated, explicit diagnostic plans 54 are selected, with pervasive diagnosis approaches taking somewhat longer, and passive diagnostic techniques taking much longer and possibly not being able to completely diagnose the problem(s). With regard to diagnosis cost, however, explicit dedicated diagnosis results in high production loss (production is halted), while purely passive diagnosis incurs the highest expected repair costs due to its lower quality diagnosis. The pervasive diagnosis aspects of the present disclosure advantageously integrate diagnostic objectives 34 b into production planning by operation of the planner 30, and therefore facilitate realization of a lower minimal total expected production loss in comparison to passive and explicit diagnosis.
The passive diagnostic aspects of the disclosure, moreover, are generally applicable to a wide class of production manufacturing problems in which it is important to optimize efficiency but the cost of failure for any one job is low compared to stopping the production system to perform explicit diagnosis. In addition, the disclosure finds utility in association with non-manufacturing production systems, for example, service industry organizations can employ the pervasive diagnostic techniques in systems that produce services using machines, software, and/or human resources. Moreover, the disclosure is not limited to a probability based A* search, wherein other plan construction techniques can be employed such as a SAT-solver approach in which the clauses represent failed plans and each satisfying assignment is interpreted as a valid diagnosis.
Referring also to
In the simulation, a system module (e.g., one of the print engines 22) may exhibit faulty behavior with some probability q (also referred to as an intermittency rate), resulting in plan failure, where persistent fault resources 21-24 (e.g., modules that cause a fault every time they are used) have an intermittency of 1. The simulation of costs employed a simple cost model of opportunity costs in terms of unrealized production due to efforts of isolating the faulty component (diagnosis costs) and exchanging this component (repair costs). The cost in this model represents the expected total amount of lost production due to a fault. When a fault is first observed, the cost is 1 (lost unit of production) and the belief state 42 is a uniform distribution over all fault hypothesis for all approaches. With the passive diagnostic approach, the plant 20 continues production at the normal rate rnom, whereas for dedicated (explicit) diagnostics in the simulation, the plant 20 produces products 52 at a reduced rate rperv≦rdiag during diagnosis, and since faulty products can be produced, the cost is calculated by the following equation 10:
c diag t,per=1+n faulty+(r nom −r perv)*t diag (10)
In the simulated results, the exchange time for replacing a single module 22 was assumed to be 10 minutes, and this replacement causes shutdown of only one print engine 22 at the time. Since the exemplary plant 20 employs four print engines 22, the simulated exchange time was set as texc=150 sec. The simulation also employed a nominal print rate rnom=3.1 sheets/sec, and the simulation showed a reduced rate of pervasive diagnosis rperv=1.9 sheets/sec. Moreover, the experimental results were averaged over 100 runs to reduce statistical variation. Table 1 below summarizes the simulated results for three different intermittency rates q=0.01, 0.20, and 1.00, respectively, in which the pervasive diagnostics described above results in the lowest rate of lost production (e.g., number of exchanged modules 22, the number of exchanged modules at the minimal response cost).
min. response cost
# of exch. mod.
q = 0.01
q = 0.1
q = 1.0
The instantaneous repair costs in
In the simulations, the optimal response cost of pervasive diagnosis is below those of the other two approaches, and the shortest diagnosis duration is for explicit, with pervasive and passive diagnosis techniques taking successively longer. However, the explicit diagnostic approach involves the highest production loss since production is halted during diagnosis, whereby explicit diagnosis does not result in minimal response costs. Passive diagnosis has the lowest rate of lost production, but incurs the highest expected repair costs due to its lower quality diagnosis, due to plans 54 being constructed and executed solely on the basis of production goals and objectives 34 a irrespective of diagnostic objectives 34 b. Pervasive diagnosis, on the other hand, intelligently integrates the diagnostic objectives 34 b into production planning by using the flexibility of the planner 30 and the diagnosis engine 40, and the simulated results show a lower minimal total expected production loss in comparison to passive and explicit diagnosis approaches.
Referring now to
The SAT problem formulation 814 is then provided to a SAT solver 38, such as by a query to the solver component 38 in
Compared to the heuristic A* search approach above, the SAT solver approach in
The above examples are merely illustrative of several possible embodiments of the present disclosure, wherein equivalent alterations and/or modifications will occur to others skilled in the art upon reading and understanding this specification and the annexed drawings. In particular regard to the various functions performed by the above described components (assemblies, devices, systems, circuits, and the like), the terms (including a reference to a "means") used to describe such components are intended to correspond, unless otherwise indicated, to any component, such as hardware, software, or combinations thereof, which performs the specified function of the described component (i.e., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the illustrated implementations of the disclosure. In addition, although a particular feature of the disclosure may have been disclosed with respect to only one of several embodiments, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Also, to the extent that the terms "including", "includes", "having", "has", "with", or variants thereof are used in the detailed description and/or in the claims, such terms are intended to be inclusive in a manner similar to the term "comprising". It will be appreciated that various of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications, and further that various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims.
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Owner name: PALO ALTO RESEARCH CENTER INCORPORATED, CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:DE KLEER, JOHAN;PRICE, ROBERT;KUHN, LUKAS D.;AND OTHERS;REEL/FRAME:021361/0392
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|Oct 16, 2014
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Expired Practice Squad Contracts Howard Balzer provides the following practice-squad contracts that expired on Monday: Ravens S Dezmen Southward. Bears TE Rory Anderson, TE Justin Perillo and DE Lawrence Okoye. …Read More »
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Bears Bears signed LB Josh Shirley to their practice squad. (NFLTR) Bills Bills signed DE Greg Townsend Jr. to their practice squad. (NFLTR) Broncos Broncos signed RB Zac Brooks to their practice …Read More »
Chargers Place CB Brandon Flowers On I.R. & Promote DT Kaleb Eulls
The San Diego Chargers announced Wednesday that they've placed CB Brandon Flowers on injured reserve and promoted DT Kaleb Eulls to the active roster. The Chargers signed DT Chuka Ndulue …Read More »
Chargers Offically Cut 21 Including Rookie LB James Ross
The San Diego Chargers have begun the process of trimming their roster down to 53 players. Here's the list of roster cuts up to this point: LB James Ross (Aaron Wilson) TE Tim …Read More »
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49ers 49ers signed DE Garrison Smith, WR DiAndre Campbell and DT Kaleb Ramsey to future/reserve contracts. Bengals Bengals placed LB Emmanuel Lamur on season-ending injured reserve. (NFLTR) Bengals promoted LB Trevor Roach to their active roster. (NFLTR) …Read More »
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Bears Bears promoted DB Bryce Callahan to their active roster. Bills Bills placed WR Marquise Goodwin on season-ending injured reserve. (NFLTR) Bills signed of OT Jordan Mills, who was on the …Read More »
Cowboys Worked Out 9 Nine Players Including Isaiah Pead & Kendrick Ellis
Zac Jackson of Pro Football Talk reports that the Cowboys brought in nine players for workouts on Wednesday. The list of players trying out for Dallas includes: DE Da'Quan Bowers, DE …Read More »
NFL Transactions: Tuesday 9/22
Bears Bears signed DB Demontre Hurst and WR Deonte Thompson to the practice squad. Bears waived CB Bryce Callahan. Bears waived WR Ify Umodu from their practice squad. Broncos Broncos signed …Read More »


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Essay: This is what depression feels likeI feel like the light at the end of the tunnel is a solitary candle about to blow out at any moment. At the same time, I feel like the pain will never...
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Spied on test in a camouflaged state, new Bajaj Pulsar 400 CS production ready variant has now been rendered in five vibrant monotone colours. First showcased as a concept at the 2014 Auto Expo, the launch of Bajaj Pulsar 400 CS is to take place by June 2016.
Speaking of its design, the launch ready Bajaj Pulsar 400 CS continues with similar styling cues which were showcased on the concept.
Changes which have not been carried forward from the concept are upside down front telescopic forks, alloy design and the styling of pillion seat. Instead of a single seat on the concept, the prod-ready Pulsar 400 gets a split rear seat which is raised by a few mm.
Highlights of the Pulsar 400 include LED headlamp and taillamps, new chiseled fuel tank design, wide tyres, ABS, two digital displays, an additional one is situated on the fuel tank which displays which gear is engaged and speed of the vehicle.
Power to the Bajaj Pulsar 400 CS comes from a single cylinder 373 cc liquid cooled 4 stroke engine, which has been derived from the KTM Duke / RC 390. This engine is expected to be tuned for higher low-end and mid-range torque to offer relaxed cruising experience on the highway (CS stands for Cruising Sports). On board the KTM, this engine delivers 44 PS and 35 Nm via a 6-speed gearbox.
Once launched, Bajaj Pulsar 400 CS take on the likes of Mahindra Mojo, Honda CBR 250R, KTM Duke 390, upcoming TVS Akula 310, etc. Expect the price to be in the range of INR 1.8 lakh.
Bajaj Pulsar CS 400 Concept – Photos
Renders via indianautosblog