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News Article | May 19, 2017
Site: phys.org

Automobile traffic in this "smart city" should move almost constantly, stopping or slowing as little as possible at traffic lights, on freeway ramps and in traffic circles, says Liu, a Ph.D. candidate in industrial engineering. Likewise, electricity should flow through power lines at an optimal rate, high enough to achieve maximum efficiency but not so high that wires overheat. These streamlined flows of people and power, says Liu, are made possible by machines that process large quantities of data in real time and learn to make intelligent decisions. Liu develops large-scale optimization algorithms, or mathematical models, and applies them to machine learning. His goal is to design a power-transmission system that meets the energy demands of a city with maximum efficiency and minimal cost. Liu recently received the IBM Ph.D. Fellowship Award to study power systems and data analytics at IBM Research-Ireland, one of 12 IBM research laboratories in the world. The fellowship is given each year to 50 outstanding students worldwide. IBM Research-Ireland conducts research into the Cognitive Internet of Things, cognitive integrated healthcare, interactive reasoning, data centric computing and the cloud and privacy. As part of the fellowship program, Liu has been assigned a mentor from IBM Research-Ireland. He will continue his research into power systems and data analytics and will likely spend part of the fall semester in Ireland or at the company's New York site. As an example of machine learning or signal processing, Liu points to E-ZPass, the electronic toll-collection system that serves motorists in the eastern half of the United States and in Ontario, Canada. E-ZPass photographs the license plates of passing cars and employs optimization techniques to read the letters and numbers of license plates, even in blurry photos. Machine learning is also used by online shopping sites such as Amazon to analyze a consumer's purchasing history and recommend similar products that the consumer might be interested in buying. Liu's research focuses on the buses, or intermediate power stations, which take electricity from a power plant and distribute it to homes and businesses in a utility company's service area. He develops models that seek to determine the optimal number, and optimal location, of intermediate stations. He is also working on the development of fast solvers for the popular deep neural networks and other graphical models. A utility company's goal, says Liu, is to transmit electricity from its intermediate stations with maximum efficiency. If a station's wires carry too much electricity, he says, they will exceed the station's voltage limits and overheat, with undesired consequences, shutting down or even igniting. If the wires carry too little electricity, the system loses efficiency. "We want to find a middle point," says Liu. "We can never reach the voltage limit. On the other hand, if the amount of electricity being transmitted is too low, efficiency is reduced. We want to be efficient but not get too close to the limit." A second goal, says Liu, is to solve problems that arise as quickly as possible. "We want to speed up optimization for power networks," he says. "Our goal is to transmit power efficiently while solving problems as quickly as possible." Power grids and other networks generate massive streams of data that must be processed and analyzed in real time. Liu and his group set up polynomial optimization problems (POPs) and solve them by randomly choosing coordinates of data, which can be solved in parallel by multiple machines at the same time to improve efficiency. This contrasts, he says, with the conventional technique, which is called the Newton method and is difficult to parallelize. "The Newton method processes all the data," says Liu, "but it is not possible to do this with a power system because it generates so much data. Our method doesn't process all the data; instead, we pick coordinates, or bits of data, randomly. This greatly reduces the total amount of time needed to solve a problem. "To solve a multidimensional problem using the traditional method took days and did not always yield a feasible solution. With our method, we can arrive at a feasible solution in several minutes to half an hour." Liu and his group reported their results recently in an article titled "Hybrid Methods in Solving Alternating-Current Optimal Power Flows." The article was coauthored with Alan C. Liddell, Jakub Mareček and Martin Takáč. Takáč, an assistant professor of industrial and systems engineering, is Liu's Lehigh Ph.D. adviser. Mareček is with IBM-Ireland, and Liddell is with Notre Dame University. Liu enrolled at Lehigh in 2013 after completing his M.S. in mathematics from the State University of New York at Buffalo. He holds a B.S. in mathematics from Nankai University in Tianjin, China. His other honors include the Dean's Doctoral Assistantship and Dean's Fellowship from Lehigh's P.C. Rossin College of Engineering and Applied Science, the Gotshall Fellowship from Lehigh, and the American Express Machine Learning Contest Award. At Lehigh, Liu is part of a research group called Optimization and Machine Learning (OptML), which includes Takáč; Katya Scheinberg, the Harvey E. Wagner Endowed Chair Professor of Industrial and Systems Engineering; and Frank Curtis, associate professor of industrial and systems engineering. Students in the OptML group receive support to present their work at international conferences. Liu's papers and posters have been accepted at conferences of Neural Information Processing Systems (NIPS), the Institute for Operations Research and the Management Sciences (INFORMS), the International Conference on Machine Learning (ICML); and the Machine Learning Symposium in New York City. Liu and his fellow students are also encouraged to do industrial internships. Liu has completed internships with Siemens Corporate Research and IBM. This summer he will work in Boston with Mitsubishi's Electricity Research Laboratories (MERL) in data analytics. In 2012, he worked with Argonnes National Laboratory near Chicago. "These are amazing opportunities," Liu says. "These are different companies, totally different. These internships connect us to industry. They give us the chance to do something we're interested in and to learn new knowledge at the same time. "The professors in our group are very supportive. They really help us learn how we can contribute and make an impact." Explore further: Scientists propose better battery system for smart home use


News Article | May 19, 2017
Site: www.eurekalert.org

The modern city, says Jie Liu, can be considered a web of networks that should run like a healthy, well-tuned circulatory system. Automobile traffic in this "smart city" should move almost constantly, stopping or slowing as little as possible at traffic lights, on freeway ramps and in traffic circles, says Liu, a Ph.D. candidate in industrial engineering. Likewise, electricity should flow through power lines at an optimal rate, high enough to achieve maximum efficiency but not so high that wires overheat. These streamlined flows of people and power, says Liu, are made possible by machines that process large quantities of data in real time and learn to make intelligent decisions. Liu develops large-scale optimization algorithms, or mathematical models, and applies them to machine learning. His goal is to design a power-transmission system that meets the energy demands of a city with maximum efficiency and minimal cost. Liu recently received the IBM Ph.D. Fellowship Award to study power systems and data analytics at IBM Research-Ireland, one of 12 IBM research laboratories in the world. The fellowship is given each year to 50 outstanding students worldwide. IBM Research-Ireland conducts research into the Cognitive Internet of Things, cognitive integrated healthcare, interactive reasoning, data centric computing and the cloud and privacy. As part of the fellowship program, Liu has been assigned a mentor from IBM Research-Ireland. He will continue his research into power systems and data analytics and will likely spend part of the fall semester in Ireland or at the company's New York site. As an example of machine learning or signal processing, Liu points to E-ZPass, the electronic toll-collection system that serves motorists in the eastern half of the United States and in Ontario, Canada. E-ZPass photographs the license plates of passing cars and employs optimization techniques to read the letters and numbers of license plates, even in blurry photos. Machine learning is also used by online shopping sites such as Amazon to analyze a consumer's purchasing history and recommend similar products that the consumer might be interested in buying. Liu's research focuses on the buses, or intermediate power stations, which take electricity from a power plant and distribute it to homes and businesses in a utility company's service area. He develops models that seek to determine the optimal number, and optimal location, of intermediate stations. He is also working on the development of fast solvers for the popular deep neural networks and other graphical models. A utility company's goal, says Liu, is to transmit electricity from its intermediate stations with maximum efficiency. If a station's wires carry too much electricity, he says, they will exceed the station's voltage limits and overheat, with undesired consequences, shutting down or even igniting. If the wires carry too little electricity, the system loses efficiency. "We want to find a middle point," says Liu. "We can never reach the voltage limit. On the other hand, if the amount of electricity being transmitted is too low, efficiency is reduced. We want to be efficient but not get too close to the limit." A second goal, says Liu, is to solve problems that arise as quickly as possible. "We want to speed up optimization for power networks," he says. "Our goal is to transmit power efficiently while solving problems as quickly as possible." Power grids and other networks generate massive streams of data that must be processed and analyzed in real time. Liu and his group set up polynomial optimization problems (POPs) and solve them by randomly choosing coordinates of data, which can be solved in parallel by multiple machines at the same time to improve efficiency. This contrasts, he says, with the conventional technique, which is called the Newton method and is difficult to parallelize. "The Newton method processes all the data," says Liu, "but it is not possible to do this with a power system because it generates so much data. Our method doesn't process all the data; instead, we pick coordinates, or bits of data, randomly. This greatly reduces the total amount of time needed to solve a problem. "To solve a multidimensional problem using the traditional method took days and did not always yield a feasible solution. With our method, we can arrive at a feasible solution in several minutes to half an hour." Liu and his group reported their results recently in an article titled "Hybrid Methods in Solving Alternating-Current Optimal Power Flows." The article was coauthored with Alan C. Liddell, Jakub Mareček and Martin Takáč. Takáč, an assistant professor of industrial and systems engineering, is Liu's Lehigh Ph.D. adviser. Mareček is with IBM-Ireland, and Liddell is with Notre Dame University. Liu enrolled at Lehigh in 2013 after completing his M.S. in mathematics from the State University of New York at Buffalo. He holds a B.S. in mathematics from Nankai University in Tianjin, China. His other honors include the Dean's Doctoral Assistantship and Dean's Fellowship from Lehigh's P.C. Rossin College of Engineering and Applied Science, the Gotshall Fellowship from Lehigh, and the American Express Machine Learning Contest Award. At Lehigh, Liu is part of a research group called Optimization and Machine Learning (OptML), which includes Takáč; Katya Scheinberg, the Harvey E. Wagner Endowed Chair Professor of Industrial and Systems Engineering; and Frank Curtis, associate professor of industrial and systems engineering. Students in the OptML group receive support to present their work at international conferences. Liu's papers and posters have been accepted at conferences of Neural Information Processing Systems (NIPS), the Institute for Operations Research and the Management Sciences (INFORMS), the International Conference on Machine Learning (ICML); and the Machine Learning Symposium in New York City. Liu and his fellow students are also encouraged to do industrial internships. Liu has completed internships with Siemens Corporate Research and IBM. This summer he will work in Boston with Mitsubishi's Electricity Research Laboratories (MERL) in data analytics. In 2012, he worked with Argonnes National Laboratory near Chicago. "These are amazing opportunities," Liu says. "These are different companies, totally different. These internships connect us to industry. They give us the chance to do something we're interested in and to learn new knowledge at the same time. "The professors in our group are very supportive. They really help us learn how we can contribute and make an impact." Story by Kurt Pfitzer, Editor and Writer, Lehigh University Office of Communications and Public Affairs


News Article | May 22, 2017
Site: www.eurekalert.org

Tamás Terlaky, chair of Lehigh University's Industrial and Systems Engineering department, along with fellow editors Miguel F. Anjos of Polytechnique Montréal and Shabbir Ahmed of Georgia Institute of Technology, has released a new textbook to "provide a solid foundation for engineers and mathematical optimizers alike who want to understand the importance of optimization methods to engineering, and the capabilities of these methods." Advances and Trends in Optimization with Engineering Applications, as published by the Mathematical Optimization Society and the Society for Industrial and Applied Mathematics (SIAM) is volume 24 of a prestigious MOS-SIAM series on optimization. This series, published jointly by the two organizations, includes research monographs, textbooks at all levels, books on applications, and tutorials. Topics covered by the series explore the theory and practice of optimization, discussing theory, algorithms, software, computational practice, applications, and the links among these subjects. Advances reviews 10 major areas of optimization and related engineering applications, providing a broad summary of state-of-the-art optimization techniques that are of crucial importance to engineering practice. "In recent years," reads the book's description, "the theory and methodology of optimization have seen revolutionary improvement. Moreover, the exponential growth in computational power, along with the availability of multicore computing with virtually unlimited memory and storage capacity, has fundamentally changed what engineers can do to optimize their designs." "From idea to product this was a 4-year-long project," Tamás explains. "The book includes 40 chapters, and covers all major areas of optimization and includes applications of modern optimization methodology in virtually all areas of engineering. Just designing its scope, content and structure took more than a year. Then, recruiting its 70 authors, among the most renowned experts in their respective fields, and working to unify terminology and notation across the content, was itself a major operation." Terlaky's research interests include high-performance optimization methods, optimization models, algorithms and software, and solving optimization problems in engineering sciences. Through his work, he harnesses algorithms to optimize the core refueling process of nuclear reactors, the radiation effectiveness of cancer treatment, the maintenance of oil refineries, the management of correctional facilities, and more. Over the course of his academic career, Terlaky has published 4 books and more than 160 scholarly papers. The topics of his publications include the theoretical and algorithmic foundations of Operations Research, and numerous engineering applications. He is founding editor-in-chief of the journal Optimization and Engineering and has served as associate editor of seven journals. He is a Fellow of the Toronto-based Fields Institute for Research in Mathematical Sciences, and has received the Canada's Mitacs Mentorship Award for his distinguished graduate student supervisory record. The book is available via SIAM's online bookstore, and will soon be available as an eBook in the SIAM digital library package for Universities and other venues.


PubMed | Psychiatry and Behavioral science, Industrial and Systems Engineering., University of Washington, University of California at Los Angeles and 5 more.
Type: Journal Article | Journal: Journal of neurosurgery. Pediatrics | Year: 2016

OBJECTIVE Posttraumatic seizure is a major complication following traumatic brain injury (TBI). The aim of this study was to determine the variation in seizure prophylaxis in select pediatric trauma centers. The authors hypothesized that there would be wide variation in seizure prophylaxis selection and use, within and between pediatric trauma centers. METHODS In this retrospective multicenter cohort study including 5 regional pediatric trauma centers affiliated with academic medical centers, the authors examined data from 236 children (age < 18 years) with severe TBI (admission Glasgow Coma Scale score 8, ICD-9 diagnosis codes of 800.0-801.9, 803.0-804.9, 850.0-854.1, 959.01, 950.1-950.3, 995.55, maximum head Abbreviated Injury Scale score 3) who received tracheal intubation for 48 hours in the ICU between 2007 and 2011. RESULTS Of 236 patients, 187 (79%) received seizure prophylaxis. In 2 of the 5 centers, 100% of the patients received seizure prophylaxis medication. Use of seizure prophylaxis was associated with younger patient age (p < 0.001), inflicted TBI (p < 0.001), subdural hematoma (p = 0.02), cerebral infarction (p < 0.001), and use of electroencephalography (p = 0.023), but not higher Injury Severity Score. In 63% cases in which seizure prophylaxis was used, the patients were given the first medication within 24 hours of injury, and 50% of the patients received the first dose in the prehospital or emergency department setting. Initial seizure prophylaxis was most commonly with fosphenytoin (47%), followed by phenytoin (40%). CONCLUSIONS While fosphenytoin was the most commonly used medication for seizure prophylaxis, there was large variation within and between trauma centers with respect to timing and choice of seizure prophylaxis in severe pediatric TBI. The heterogeneity in seizure prophylaxis use may explain the previously observed lack of relationship between seizure prophylaxis and outcomes.


News Article | March 2, 2017
Site: www.eurekalert.org

BINGHAMTON, NY-Government agencies cannot always use social media and telecommunication to uncover the intentions of terrorists as terrorists are now more careful in utilizing these technologies for planning and preparing for attacks. A new framework developed by researchers at Binghamton University, State University of New York is able to understand future terrorist behaviors by recognizing patterns in past attacks. Researchers at Binghamton have proposed a comprehensive new framework, the Networked Pattern Recognition (NEPAR) Framework, by defining the useful patterns of attacks to understand behaviors, to analyze patterns and connections in terrorist activity, to predict terrorists' future moves, and finally, to prevent and detect potential terrorist behaviors. Using data on more than 150,000 terrorist attacks between 1970 and 2015, Binghamton University PhD student Salih Tutun developed a framework that calculates the relationships among terrorist attacks (e.g. attack time, weapon type) and detects terrorist behaviors with these connections. Mohammad Khasawneh, professor and head of the Systems Science and Industrial Engineering (SSIE) department at Binghamton University, assisted and advised Tutun with his research. Jun Zhuang, an associate professor and director of undergraduate studies in the Department of Industrial and Systems Engineering at the University at Buffalo, also contributed to this research. In the framework, there are two main phases: (1) building networks by finding connections between events, and (2) using a unified detection approach that combines proposed network topology and pattern recognition approaches. Firstly, the framework identifies the characteristics of future terrorist attacks by analyzing the relationship between past attacks. Comparing the results with existing data shows that the proposed method was able to successfully predict most of the characteristics of attacks with more than 90% accuracy. Moreover, after building the network with connections, the researchers propose a unified detection approach that applies pattern classification techniques to network topology and features of incidents to detect terrorism attacks with high accuracy, and identify the extension of attacks (90 percent accuracy), multiple attacks (96 percent accuracy) and terrorist goals (92 percent accuracy). Hence, governments can control terrorist behaviors to reduce the risk of future events. The results could potentially allow law enforcement to propose reactive strategies, said Tutun. "Terrorists are learning, but they don't know they are learning. If we can't monitor them through social media or other technologies, we need to understand the patterns. Our framework works to define which metrics are important," said Tutun. "Based on this feature, we propose a new similarity (interaction) function. Then we use the similarity (interaction) function to understand the difference (how they interact with each other) between two attacks. For example, what is the relationship between the Paris and the 9/11 attacks? When we look at that, if there's a relationship, we're making a network. Maybe one attack in the past and another attack have a big relationship, but nobody knows. We tried to extract this information." Previous studies have focused on understanding the behavior of individual terrorists (as people) rather than studying the different attacks by modeling their relationship with each other. And terrorist activity detection focuses on either individual incidents, which does not take into account the dynamic interactions among them; or network analysis, which gives a general idea about networks but sets aside functional roles of individuals and their interactions. "Predicting terrorist events is a dream, but protecting some area by using patterns is a reality. If you know the patterns, you can reduce the risks. It's not about predicting, it's about understanding," said Tutun. Tutun believes that policymakers can use these approaches for time-sensitive understanding and detection of terrorist activity, which can enable precautions to avoid against future attacks. "When you solve the problem in Baghdad, you solve the problem in Iraq. When you solve the problem in Iraq, you solve the problem in the Middle East. When you solve the problem in the Middle East, you solve the problem in the world," said Tutun. "Because when we look at Iraq, these patterns are happening in the USA, too." The paper, "New framework that uses patterns and relations to understand terrorist behaviors," was published in Expert Systems with Applications.


News Article | September 16, 2016
Site: www.rdmag.com

Researchers at North Carolina State University have developed a new type of inverter device with greater efficiency in a smaller, lighter package – which should improve the fuel-efficiency and range of hybrid and electric vehicles. Electric and hybrid vehicles rely on inverters to ensure that enough electricity is conveyed from the battery to the motor during vehicle operation. Conventional inverters rely on components made of the semiconductor material silicon. Now researchers at the Future Renewable Electric Energy Distribution and Management (FREEDM) Systems Center at NC State have developed an inverter using off-the-shelf components made of the wide-bandgap semiconductor material silicon carbide (SiC) – with promising results. “Our silicon carbide prototype inverter can transfer 99 percent of energy to the motor, which is about two percent higher than the best silicon-based inverters under normal conditions,” says Iqbal Husain, ABB Distinguished Professor of Electrical and Computer Engineering at NC State and director of the FREEDM Center. “Equally important, the silicon carbide inverters can be smaller and lighter than their silicon counterparts, further improving the range of electric vehicles,” says Husain, who co-authored two papers related to the work. “And new advances we’ve made in inverter components should allow us to make the inverters even smaller still.” Range is an important issue because so-called “range anxiety” is a major factor limiting public acceptance of electric vehicles. People are afraid they won’t be able to travel very far or that they’ll get stuck on the side of the road. The new SiC-based inverter is able to convey 12.1 kilowatts of power per liter (kW/L) – close to the U.S. Department of Energy’s goal of developing inverters that can achieve 13.4 kW/L by 2020. By way of comparison, a 2010 electric vehicle could achieve only 4.1 kW/L. “Conventional, silicon-based inverters have likely improved since 2010, but they’re still nowhere near 12.1 kW/L,” Husain says. The power density of new SiC materials allows engineers to make the inverters – and their components, such as capacitors and inductors – smaller and lighter. “But, frankly, we are pretty sure that we can improve further on the energy density that we’ve shown with this prototype,” Husain says. That’s because the new inverter prototype was made using off-the-shelf SiC components – and FREEDM researchers have recently made new, ultra-high density SiC power components that they expect will allow them to get closer to DOE’s 13.4 kW/L target once it’s incorporated into next generation inverters. What’s more, the design of the new power component is more effective at dissipating heat than previous versions. This could allow the creation of air-cooled inverters, eliminating the need for bulky (and heavy) liquid cooling systems. “We predict that we’ll be able to make an air-cooled inverter up to 35 kW using the new module, for use in motorcycles, hybrid vehicles and scooters,” Husain says. “And it will boost energy density even when used with liquid cooling systems in more powerful vehicles.” The current SiC inverter prototype was designed to go up to 55 kW – the sort of power you’d see in a hybrid vehicle. The researchers are now in the process of scaling it up to 100 kW – akin to what you’d see in a fully electric vehicle – using off-the-shelf components. And they’re also in the process of developing inverters that make use of the new, ultra-high density SiC power component that they developed on-site. A paper on the new inverter, “Design Methodology for a Planarized High Power Density EV/HEV Traction Drive using SiC Power Modules,” will be presented at the IEEE Energy Conversion Congress and Exposition (ECCE), being held Sept. 18-22 in Milwaukee. Lead author of the paper is Dhrubo Rahman, a Ph.D. student at NC State. The paper was co-authored by Adam Morgan, Yang Xu and Rui Gao, who are Ph.D. students at NC State; Wensong Yu and Douglas Hopkins, research professors in NC State’s Department of Electrical and Computer Engineering; and Husain. A paper on the new, ultra-high density SiC power component, “Development of an Ultra-high Density Power Chip on Bus Module,” will also be presented at ECCE. Lead author of the paper is Yang Xu. The paper was co-authored by Yu, Husain and Hopkins, as well as by Harvey West, a research professor in NC State’s Edward P. Fitts Department of Industrial and Systems Engineering. The research was done with the support of the PowerAmerica Institute, a public-private research initiative housed at NC State and funded by DOE’s Office of Energy Efficiency and Renewable Energy under award number DE-EE0006521. FREEDM, a National Science Foundation Engineering Research Center, is aimed at facilitating the development and implementation of new renewable electric-energy technologies.


News Article | September 7, 2016
Site: phys.org

That's according to a new University at Buffalo study that explores security vulnerabilities of 3-D printing, also called additive manufacturing, which analysts say will become a multibillion-dollar industry employed to build everything from rocket engines to heart valves. "Many companies are betting on 3-D printing to revolutionize their businesses, but there are still security unknowns associated with these machines that leave intellectual property vulnerable," said Wenyao Xu, PhD, assistant professor in UB's Department of Computer Science and Engineering, and the study's lead author. Xu and collaborators will present the research, "My Smartphone Knows What You Print: Exploring Smartphone-based Side-channel Attacks Against 3D Printers," at the Association for Computing Machinery's 23rd annual Conference on Computer and Communications Security in October in Austria. Unlike most security hacks, the researchers did not simulate a cyberattack. Many 3-D printers have features, such as encryption and watermarks, designed to foil such incursions. Instead, the researchers programmed a common smartphone's built-in sensors to measure electromagnetic energy and acoustic waves that emanate from 3-D printers. These sensors can infer the location of the print nozzle as it moves to create the three-dimensional object being printed. The smartphone, at 20 centimeters away from the printer, gathered enough data to enable the researchers to replicate printing a simple object, such as a door stop, with a 94 percent accuracy rate. For complex objects, such as an automotive part or medical device, the accuracy rate was lower but still above 90 percent. "The tests show that smartphones are quite capable of retrieving enough data to put sensitive information at risk," says Kui Ren, PhD, professor in UB's Department of Computer Science and Engineering, a co-author of the study. The richest source of information came from electromagnetic waves, which accounted for about 80 percent of the useful data. The remaining data came from acoustic waves. Ultimately, the results are eye-opening because they show how anyone with a smartphone—from a disgruntled employee to an industrial spy—might steal intellectual property from an unsuspecting business, especially "mission critical" industries where one breakdown of a system can have a serious impact on the entire organization. "Smartphones are so common that industries may let their guard down, thus creating a situation where intellectual property is ripe for theft," says Chi Zhou, PhD, assistant professor in UB's Department of Industrial and Systems Engineering, another study co-author. The researchers suggests several ways to make 3-D printing more secure. Perhaps the simplest deterrent from such an attack is distance. The ability to obtain accurate data for simple objects diminished to 87 percent at 30 centimeters, and 66 percent at 40 centimeters, according to the study. Another option is to increase the print speed. The researchers said that emerging materials may allow 3-D printers to work faster, thus making it more difficult for smartphone sensors to determine the print nozzle's movement. Other ideas include software-based solutions, such as programming the printer to operate at different speeds, and hardware-based ideas, such as acoustic and electromagnetic shields. Explore further: HP injecting Internet technology into new printers


Medal H.R.,Industrial and Systems Engineering | Pohl E.A.,University of Arkansas | Rossetti M.D.,University of Arkansas
IIE Transactions (Institute of Industrial Engineers) | Year: 2016

We study a new facility protection problem in which one must allocate scarce protection resources to a set of facilities given that allocating resources to a facility only has a probabilistic effect on the facilitys post-disruption capacity. This study seeks to test three common assumptions made in the literature on modeling infrastructure systems subject to disruptions: 1) perfect protection, e.g., protecting an element makes it fail-proof, 2) binary protection, i.e., an element is either fully protected or unprotected, and 3) binary state, i.e., disrupted elements are fully operational or non-operational. We model this facility protection problem as a two-stage stochastic program with endogenous uncertainty. Because this stochastic program is non-convex we present a greedy algorithm and show that it has a worst-case performance of 0.63. However, empirical results indicate that the average performance is much better. In addition, experimental results indicate that the mean-value version of this model, in which parameters are set to their mean values, performs close to optimal. Results also indicate that the perfect and binary protection assumptions together significantly affect the performance of a model. On the other hand, the binary state assumption was found to have a smaller effect. © 2015 "IIE".


Mandal S.,Industrial and Systems Engineering | Singh K.,Industrial and Systems Engineering | Behera R.K.,ITR Chandipur | Sahu S.K.,ITR Chandipur | And 2 more authors.
Expert Systems with Applications | Year: 2015

Human error identification and subsequent prioritization are the foremost tasks involved in HRA. In this study a methodology is developed for performing these tasks with an application to overhead crane operations. The application of the present methodology will help to understand how the risk associated with the human errors propagates through different hierarchy levels. The methodology provides a framework for quantifying the risk of different human errors using the experts' subjective opinions only. The incorporation of fuzzy VIKOR technique enables us develop a ranking mechanism for the failure modes where the individual constituent components are non-commensurable in nature. The developed ranking mechanism helps the decision makers in optimal allocation of safety critical resources, used for risk mitigation purposes. © 2015 Elsevier Ltd. All rights reserved.


News Article | September 19, 2016
Site: phys.org

Preparing for the take off of faster production, Lockheed Martin and the Department of Industrial and Systems Engineering at Texas A&M University are investigating the use of advanced industrial engineering tools and procedures to study F-35 rate production.

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