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Modeling and optimization of mixed use areas. This article deals with the development of a planning tool for mixed use areas within the project 'En-Eff:Stadt - Bottrop, Welheimer Mark'. The city district Welheimer Mark is a mixed use area within the InnovationCity Ruhr of Bottrop, Germany. The main aim was the energetic optimization of the city district to reduce greenhouse gas emissions. Thus, methods for complex city district modeling has been developed and used within Welheimer Mark district. Simulated thermal and electrical demands only showed a difference of 6.4 % related to energy consumption values. While a sufficient method for generation of electrical load profiles of non-residential buildings could not be identified, a method for generation of thermal load profiles shows a good fit between generated and measured loads. An optimization model has been used to identify an optimized energy system distribution. A greenhouse gas emission reduction up to 50 % is possible at cost increase of 6 % to 11 %. [ABSTRACT FROM AUTHOR]

Recent revisions to API standards have identified offline partial discharge analysis as an appropriate factory acceptance test (FAT) to evaluate the quality of certain new medium voltage motor and generator stator windings. As a starting point, API 541 recommends a partial discharge (PD) level of 100 pC (pico-Coulombs) for sacrificial stator coils measured with an IEC 60270 compliant instrument. The recommended PD level is subject to discussion between the purchaser and the manufacturer until more data are available. This paper provides PD data from sacrificial coils and stator windings to support discussions on PD acceptance criteria. Three instruments were used for PD tests on resin-rich, press-cured sacrificial coils in laboratory conditions controlled to minimize noise, and in the typical environment of a FAT. These coils were then tested daily for a week, with identical measurement conditions of noise, time of day, operator, and instrument. Also, included are examples of offline PD data from two complete stator windings. The first was a new winding, tested in the factory on two different days. The second was an aged winding tested using the same methods but in different locations. Instrument selection, testing location, elapsed time between measurements and insulation system construction can influence offline PD measurements. The current evidence suggests that statistical distributions drawn from similar test conditions are more applicable than specific PD limits. [ABSTRACT FROM AUTHOR]

The study examines the effects of production facilities maintenance practices on competitive advantage of some selected firms in the paint manufacturing industry, Benin City, Nigeria. The study adopted a survey research, through the administration of structured questionnaires to three hundred and ninety-five (395) staff of eight selected paint companies. The data were analysed using regression and Pearson matrix correlation techniques through Ordinary Least Squares (OLS) estimation technique. The result showed a weak positive significant relationship between reactive maintenance and the competitive advantage of the firms and a strong positive significant relationship between preventive maintenance and the competitive advantage of the firms. The study establishes that the integration of production facilities maintenance practices can strategically contribute towards realization of significant competitive advantage of the firms. [ABSTRACT FROM AUTHOR]

The FGIR calls for reliable, flexible, and green industrial networks to support intelligent manufacturing. This article proposes a new architecture of SDIN and then presents the related key technologies. The proposed architecture adopts an all-IP-based flat structure, and can adaptively reconfigure networks according to the dynamic change of product orders. The crucial technologies can guarantee real-time data transmission, enable plug-and-play, and support wireless access with seamless handover capability. Energy can be reduced significantly by optimizing routes and shutting down redundant production lines. Experimental results show that our solution can achieve highly reliable and deterministic packet delivery. With the adaptive configurations in the testbed, SDIN can consume 18 percent-less energy than traditional industrial networks. [ABSTRACT FROM PUBLISHER]

In the Industry 4.0 framework based on IoT and smart manufacturing, it is essential to support factory automation and flexibility in harsh or dynamic industrial environments. State-of-the-art technology suggests building a controlled workspace using large-scale deployment of wireless sensors. To overcome the technological challenges in scalability and heterogeneity for large-scale industrial deployment, group-based industrial wireless sensor networks (GIWSNs) are suggested, in which wireless sensors are divided into multiple groups for multiple monitoring tasks, and each group of sensors is deployed densely within a subarea in a large plant or along a long production/assembly line, while connectivity between groups is required. As wireless sensors are equipped with batteries with limited power, it has been challenging to plan sleep schedules of sensors, which are influenced significantly by deployment of such a large-scale GIWSN. However, most previous works on wireless sensor networks independently investigated deployment and sleep scheduling problems, both of which have been shown to be NP-hard. Therefore, this work jointly considers deployment and sleep scheduling of sensors in a GIWSN along a production line. Via the theory of symmetries, we alleviate the computational concerns from multiple groups to one group and another medium-size group. Then we propose a hybrid harmony search and genetic algorithm, which incorporates deployment and sleep schedules to reduce energy consumption. Simulations verify this joint methodology to effectively achieve energy efficiency. [ABSTRACT FROM PUBLISHER]

The paper presents the steps required to undertake an energy audit in oil treatment facilities and presents key energy performance indicators that can be applied to benchmarking and monitoring of this type of system. The proposed indicators are classified into the five following categories: production, consumption, efficiency, cost and reliability indicators. The paper also presents some general initiatives, short, medium or long term, that are classified according to their maturity and their economic benefits in order to increase energy efficiency. [ABSTRACT FROM AUTHOR]

Due to increasing global competition, effective resource utilization is critical for maintaining competitive advantages. However, little research has been done to address space utilization of wafer fabrication facility. This paper aims to design a novel index, overall space effectiveness (OSE), to diagnose the fab layouts and identify directions for improving space usage effectiveness to enhance the overall space productivity as partial effort for total resources management. Furthermore, the proposed OSE can be extended to incorporate other critical performance indices such as cycle time, throughput, and revenue for effective management. Focusing on real settings of a leading semiconductor manufacturing company, a number of case studies were conducted and compared. The results have shown OSE gaps among various fabs and the proposed OSE can effectively derive improvement directions. This paper concludes with discussions on value propositions and future research directions. [ABSTRACT FROM PUBLISHER]

The reliability and throughput in an industrial wireless sensor network can be improved by incorporating the predictions of channel gains when forming routing tables. Necessary conditions for such predictions to be useful are that statistical dependences exist between the channel gains and that those dependences extend over a long enough time to accomplish a rerouting. In this paper, we have studied such long-term dependences in channel gains for fixed wireless links in three factories. Long-term fading properties were modeled using a switched regime model, and Bayesian change point detection was used to split the channel gain measurements into segments. In this way, we translated the study of long-term dependences in channel gains into the study of dependences between fading distribution parameters describing the segments. We measured the strengths of the dependences using mutual information and found that the dependences exist in a majority of the examined links. The strongest dependence appeared between mean received power in adjacent segments, but we also found significant dependences between segment lengths. In addition to the study of statistical dependences, we present the summaries of the distribution of the fading parameters extracted from the segments, as well as the lengths of these segments. [ABSTRACT FROM AUTHOR]

This paper presents a multirobot pairwised transportation (MRPWT) approach for factory automated material and product deliveries. We consider MRPWT from the viewpoint of robotics and incorporate practical factory application constraints in the transportation method design. The proposed MRPWT approach is a two-level hybrid planning method, consisting of an incidental delivery based single robot level planner and a simulated annealing based robot group level planner. Each robot resolves its individual transportation plan incidentally to reduce the transportation cost, whereas the group level planner utilizes predefined random actions to search the task assignment solution space and then incorporates the simulated annealing algorithm to resolve the MRPWT problem as a combinatorial optimization problem. By implementing a distributed auction mechanism, the proposed MRPWT approach can be further extended to resolve the online task allocation or reallocation problem in dynamic environments. Experiments performed on a group of mobile robots successfully demonstrate the effectiveness and the practical applicability of the proposed MRPWT approach for factory automated material and product deliveries. [ABSTRACT FROM PUBLISHER]

Recent analysis of the popular Android platform highlights common security misconfigurations as well as the respective roles that users, manufacturers, and researchers play in protecting devices against cyberattacks. [ABSTRACT FROM AUTHOR]

Industrial automation systems continuously get more complex and growing over time. This leads to an ever growing demand for efficient integration task bridging the gap between technologies, tools used across the enterprise, and alongside the value chain. With the new challenges imposed by introducing cyber–physical systems (CPSs) into industrial applications and while addressing Industry 4.0 concepts, the matter of integration is becoming even more crucial for the introduction of new technologies and their acceptance to customers. This paper introduces integration tasks to be tackled and describes up-to-date technologies as they are used in today’s automation industry. This is the basis for concepts addressing the integration issue when introducing CPSs and establishing mixed systems. Investigations are made regarding exploiting integration strategies for future systems. [ABSTRACT FROM AUTHOR]

Industrie 4.0 (English translation: Industry 4.0) stands for functional integration, dynamic reorganization, and resource efficiency. Technical advances in control and communication create infrastructures that handle more and more tasks automatically. As a result, the complexity of today's and future technical systems is hidden from the user. These advances, however, come with distinct challenges for user interface design. A central question is: how to empower users to understand, monitor, and control the automated processes of Industrie 4.0? Addressing these design challenges requires a full integration of user-centered design (UCD) processes into the development process. This paper discusses flexible but powerful methods for usability and user experience engineering in the context of Industrie 4.0. [ABSTRACT FROM AUTHOR]

Future industrial systems can be realized using the cyber–physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system’s functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges. [ABSTRACT FROM AUTHOR]

Much has been published about potential benefits of the adoption of cyber–physical systems (CPSs) in manufacturing industry. However, less has been said about how such automation systems might be effectively configured and supported through their lifecycles and how application modeling, visualization, and reuse of such systems might be best achieved. It is vitally important to be able to incorporate support for engineering best practice while at the same time exploiting the potential that CPS has to offer in an automation systems setting. This paper considers the industrial context for the engineering of CPS. It reviews engineering approaches that have been proposed or adopted to date including Industry 4.0 and provides examples of engineering methods and tools that are currently available. The paper then focuses on the CPS engineering toolset being developed by the Automation Systems Group (ASG) in the Warwick Manufacturing Group (WMG), University of Warwick, Coventry, U.K. and explains via an industrial case study how such a component-based engineering toolset can support an integrated approach to the virtual and physical engineering of automation systems through their lifecycle via a method that enables multiple vendors' equipment to be effectively integrated and provides support for the specification, validation, and use of such systems across the supply chain, e.g., between end users and system integrators. [ABSTRACT FROM AUTHOR]

Industrial control systems (ICSs) are transitioning from legacy-electromechanical-based systems to modern information and communication technology (ICT)-based systems creating a close coupling between cyber and physical components. In this paper, we explore the ICS cybersecurity landscape including: 1) the key principles and unique aspects of ICS operation; 2) a brief history of cyberattacks on ICS; 3) an overview of ICS security assessment; 4) a survey of “uniquely-ICS” testbeds that capture the interactions between the various layers of an ICS; and 5) current trends in ICS attacks and defenses. [ABSTRACT FROM AUTHOR]

Industrial control systems have traditionally been built around dedicated wired solutions. The requirements of flexibility, mobility, and cost have created a strong push toward wireless solutions, preferably solutions requiring low power. Simultaneously, the increased need for interoperability and integration with the wider Internet made a transition to IP-based communication unavoidable. Following these trends, we survey 6TiSCH, the emerging family of standards for IP-based industrial communication over low-power and lossy networks. We describe the state of the standardization work, the major issues being discussed, and open questions recently identified. Based on extensive first-hand experience, we discuss challenges in implementation of this new wave of standards. Lessons learned are highlighted from four popular open-source implementations of these standards: OpenWSN, Contiki, RIOT, and TinyOS. We outline major requirements, present insights from early interoperability testing and performance evaluations, and provide guidelines for chip manufacturers and implementers. [ABSTRACT FROM AUTHOR]

Traditional models unable to model adaptive software systems since they deal with fixed requirements only, but cannot handle the behaviors that change at runtime in response to environmental changes. In this paper, an adaptive Petri net (APN) is proposed to model a self-adaptive software system. It is an extension of hybrid Petri nets by embedding a neural network algorithm into them at some special transitions. The proposed net has the following advantages: 1) it can model a runtime environment; 2) the components in the model can collaborate to make adaption decisions while the system is running; and 3) the computation is done at the local component, while the adaption is for the whole system. We illustrate the proposed APN by modeling a manufacturing system. [ABSTRACT FROM PUBLISHER]

Current manufacturing facilities lack proper performance indicators that can accurately pinpoint areas of energy inefficiency on the manufacturing line. This paper establishes new indices that utilize readily available, real sensor information from the production line, such as buffer levels, machine throughput, etc., to find the machine that is the least energy efficient. This is the machine that causes the line to waste the most energy without producing parts. The energy structure of the production line is analyzed to better understand the complex system dynamics and to find the root cause of the energy inefficiencies. A baseline energy consumption is established, which is the least amount of energy that is needed to produce a certain number of parts on the line. Using this knowledge, the new sustainable manufacturing performance indicators are defined to properly monitor the performance of the line. These indices utilize the energy structure to illustrate the static (energy used when there are no downtime events) and dynamic (energy lost due to downtime) portions of the energy consumption. The concepts of the downtime energy bottleneck and the rated power bottleneck are introduced and proven analytically. These methods are verified using simulation studies in Simulink/MATLAB. [ABSTRACT FROM AUTHOR]

The wireless channel in an industrial environment behaves much differently when compared with wave propagation in home and office environments. This is due to the presence of significant noise and interference caused by large machinery and heavy multipath propagation effects induced by highly reflective structures. In this article, a comprehensive review of radio wave propagation in an industrial environment is presented. The reported channel models and measurement results, at different frequencies and link configurations, of path loss, noise, interference, multipath propagation, and time-varying channel conditions are discussed, and the factors that influence them are highlighted. In addition, open research issues on the propagation of radio waves in an industrial environment are identified. [ABSTRACT FROM AUTHOR]

In this paper, a predicate transition model for discrete-event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model, a hybrid Petri net including explicit differential equations and shared variables is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30% reduction in energy consumption. Moreover, a flexible online and event-based information architecture called the Tweeting Factory is proposed. Simple messages (tweets) from all kinds of equipment are combined into high-level knowledge, and it is demonstrated how this information architecture can be used to support optimization of robot stations. [ABSTRACT FROM AUTHOR]

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“ Cloud of things (CoT)” is a concept that provides smart things’ functions as a service and allows them to be used by multiple applications. In the CoT, a single smart thing instance should efficiently host multiple applications, called multi-tenancy. Therefore, simultaneous accesses to shared smart things may lead to resource conflicts. Moreover, smart things inherently form complex dependencies on real-world. Since handling resource conflicts with complex dependencies at an application level is typically ad-hoc and error-prone, it increases the application development burden. To address these issues, we propose a middleware for Evolvable ClOut of things (ECO). The ECO middleware manages organizations to handle dependency among/between smart things and virtualizes physical smart things to enable isolation between/among multiple applications yet internally controls smart things’ sharing to resolve resource conflicts and provides a consolidation framework for efficient utilization of the shared smart things. A lease-based sharing control is employed with two tenant switch schemes which are analyzed. All these features are hidden from application context, thus reducing complexities in developing applications. From implementation and evaluations with workload applications, we show that the ECO middleware provides efficient sharing controls and access controls with negligible virtualization overhead. [ABSTRACT FROM PUBLISHER]

Tuning game difficulty prior to release requires careful consideration. Players can quickly lose interest in a game if it is too hard or too easy. Assessing how players will cope prior to release is often inaccurate. However, modern games can now collect sufficient data to perform large scale analysis postdeployment and update the product based on these insights. AI Factory Spades is currently the top rated Spades game in the Google Play store. In collaboration with the developers, we have collected gameplay data from 27 592 games and statistics regarding wins/losses for 99 866 games using Google Analytics. Using the data collected, this study analyses the difficulty and behavior of an Information Set Monte Carlo Tree Search player we developed and deployed in the game previously. The methods of data collection and analysis presented in this study are generally applicable. The same workflow could be used to analyze the difficulty and typical player or opponent behavior in any game. Furthermore, addressing issues of difficulty or nonhuman-like opponents postdeployment can positively affect player retention. [ABSTRACT FROM PUBLISHER]

Manufacturing companies are currently forced to reconsider their production processes by adopting more flexible, robust, and adaptive systems, aiming to improve their competitiveness. Multiagent systems (MASs) technology is suitable to address this challenge by providing an alternative way to design these complex systems based on the decentralization of the control functions over distributed entities. This paper describes the installation of a MAS solution in an industrial factory plant producing laundry washing machines. The installed solution focuses on the integration of quality and process control, and contributes to the maximization of the factory profitability facing changing conditions by applying self-adaptation procedures at the local and global levels. The preliminary results show improvements in the production efficiency and product quality, as well as a reduction of the scrap costs. [ABSTRACT FROM PUBLISHER]

It still remains a big challenge to accurately identify the geospatial objects with well-regulated outlines within remote sensing (RS) images such as residential buildings, factory storage buildings, highways, local roads, cars, and planes. In this paper, a novel spatial feature index, which is named regular shape similarity index (RSSI), is defined to address the challenge. It represents the ratio between the area of an object and its minimum bounding shape area. The application of RSSI in identifying objects with different shapes is discussed, and its capability is found to be a great supplement to the existing spatial feature hierarchy. An approach combining RSSI with object-based image analysis (OBIA) technology is proposed for image object extraction. A Web service for RSSI calculation is developed and integrated into a Web OBIA system. In the system, four experiments extracting factory storage buildings, residential buildings, roads, and planes, respectively, are conducted on three large-scale high-resolution RS images. In each experiment, two tests, i.e., one using traditional spatial features and the other using RSSI, are performed and compared. The results show that RSSI improves the accuracy of regular object extraction. [ABSTRACT FROM PUBLISHER]

The detection of defects in steel strips is a very important task which can improve the performance of factories by giving the possibility of early and real-time detection of defects. Defect detection methods have such a large amount of parameters that makes finding the best configuration a complex task. The search space of the value of these parameters is pretty large also, so it is necessary to use a search algorithm in order to reduce the computing time. In this article a genetic algorithm is developed for solving this search problem. The genetic algorithm looks for an optimal or sub-optimal solution without examining the whole search space. In addition, the computing time can be reduced by running the algorithm on a grid of computers. The genetic algorithm designed allows a near-optimal configuration of defect detection methods in a short time. [ABSTRACT FROM PUBLISHER]

In this paper the results from the behavior analysis of the electrical protections from a distribution grid to include distributed generation are presented. For that purpose IEEE 37 nodes test grid was modeled, and distributed resources were included by two technologies: synchronous machine and static generator. Changes produced both in load currents and short-circuit are established and cases for which the coordination of protection devices holds are classified. Optimization methodology is used to classify the cases either coordination holds or coordination lost, to involve distributed source location in a node changing its location in each of the nodes of the grid. In the same way, capacity of distributed generation was changed from 100 kW to 1200 kW. Based on the results a change in the setting time of the protections is proposed to reduce the cases for which the protections coordination is lost. [ABSTRACT FROM PUBLISHER]

We present a high-performance and real-time production scheduling algorithm for digital print production based on a dynamic incremental evolutionary algorithm. The optimization objective is to prioritize the dispatching sequence of orders and balance resource utilization. The scheduler is scalable for realistic problem instances and it provides solutions quickly for diverse print products that require complex fulfillment procedures. Furthermore, it dynamically ingests the transient state of the factory, such as process information and resource failure probability in print production; therefore, it minimizes the management-production mismatch. Discrete-event simulation results show that the production scheduler leads to a higher and more stable order on-time delivery ratio compared to a rule-based heuristic. Its beneficial attributes collectively contribute to the reduction or elimination of the shortcomings that are inherent in today's digital printing environment and help to enhance a print factory's productivity and profitability. [ABSTRACT FROM PUBLISHER]

On August 5, 2013, a prototype sample of cultured, or in vitro, meat was tasted at a well-publicized event in London [1]. This hamburger was not grown in an animal, but rather from bovine stem cells in Dr. Mark Post?s laboratory at Maastricht University in the Netherlands. The event may foreshadow a day when traditional livestock production has given way to large-scale growth of meat in factories, or carneries. Dr. Post has suggested that commercialization of cultured meat could be ten to twenty years away [1]. The implications are profound. By some accounts the technology could reduce the environmental impacts of meat production [2], promote human health by eliminating harmful contents such as saturated fats and pathogens [2], address global hunger issues [3], and alleviate the ethical concerns associated with industrial livestock operations [4]. However, technologies powerful enough to address such significant challenges often come with unforseen consequences and a host of costs and benefits that seldom accrue to the same actors. In extreme cases, they can even be destabilizing to social, institutional, economic, and cultural systems [5]. [ABSTRACT FROM PUBLISHER]

Because of anticipated occasional hydraulic issues in an oil sands slurry pumping application, it was expected that the existing adjustable speed drive (ASD)-driven 2500-hp motors would be overloaded at up to 3333 hp at a slightly reduced speed from time to time. Working from factory test data, estimates were made of losses and temperature rises at the expected load points. The temperature rises were verified by testing a motor on an ASD using a “back-to-back” ASD/motor arrangement in a specially set up off-site arrangement, with the base power available being an 800-kVA alternator. In the particular application, the temperature rises were not excessive, and the predicted loss of insulation life due to the higher insulation temperatures for brief times involved would not be excessive. The expected losses and temperature rises and the test setup and operation are discussed, as well as the achieved temperatures and the method of predicting loss of insulation life. [ABSTRACT FROM PUBLISHER]

Search-based approaches have been extensively applied to solve the problem of software test-data generation. Yet, test-data generation for object-oriented programming (OOP) is challenging due to the features of OOP, e.g., abstraction, encapsulation, and visibility that prevent direct access to some parts of the source code. To address this problem we present a new automated search-based software test-data generation approach that achieves high code coverage for unit-class testing. We first describe how we structure the test-data generation problem for unit-class testing to generate relevant sequences of method calls. Through a static analysis, we consider only methods or constructors changing the state of the class-under-test or that may reach a test target. Then we introduce a generator of instances of classes that is based on a family of means-of-instantiation including subclasses and external factory methods. It also uses a seeding strategy and a diversification strategy to increase the likelihood to reach a test target. Using a search heuristic to reach all test targets at the same time, we implement our approach in a tool, JTExpert, that we evaluate on more than a hundred Java classes from different open-source libraries. JTExpert gives better results in terms of search time and code coverage than the state of the art, EvoSuite, which uses traditional techniques. [ABSTRACT FROM PUBLISHER]

The stochastic economic lot scheduling problem (SELSP) considers the make-to-stock production of multiple standardized products on a single machine with limited capacity and set-up costs under random demands, random set-up times, and random production times. The SELSP is an NP-hard inventory problem. Current solutions for the SELSP can be classified as analytic or heuristic. In both approaches, however, the computation time needed to obtain an optimal solution is still unsatisfactory. In this paper, the SELSP is first formulated as a fixed-sequence base-stock (FSBS) system with quantity-limited lot-sizing policy. An algorithm combining artificial bee colony (ABC) approach and ordinal optimization (OO) theory, abbreviated as ABCOO, is then proposed to find a good enough base-stock level of the FSBS system using reasonable computation time. The proposed algorithm combines the advantage of multidirectional search in ABC with the advantage of goal softening in OO. Finally, the ABCOO algorithm is used to solve an SELSP involving 12 products and three queuing models. Test results obtained by the ABCOO algorithm are compared with four lot-sizing policies and three meta-heuristic methods. The base-stock level obtained by the ABCOO algorithm is excellent in terms of solution quality and computational efficiency. Furthermore, a time series forecasting technique is used to predict the variant demand rates needed to resolve time-lag problems of the ABCOO algorithm. Tests of the forecasting technique confirm that it considerably improves the performance and enables the proposed algorithm real-time applications. [ABSTRACT FROM PUBLISHER]