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Furfural content in oil is a valuable indicator of cellulose paper degradation and is widely utilized in aging assessment for power transformers. However, with the same aging degree of insulation paper, the actual furfural content in oil is influenced by temperature because furfural partitioning behavior in the oil-paper insulation system of power transformers changes with temperature. Thus, correction of the temperature effect on furfural partitioning is crucial for accurate assessment of the aging of power transformers. In this research, oil-paper samples with different furfural contents were prepared through an accelerated thermal aging test. A furfural equilibrium experiment was conducted at different temperatures to construct a furfural partitioning diagram. The mass fraction of furfural in oil and paper was calculated. Results showed that the mass fraction of furfural in oil increases with temperature rising and aging deepening, which means that furfural tends to migrate from paper to oil at high temperatures and deep aging conditions. The change in furfural partitioning between oil and paper at different temperatures causes a deviation in paper aging assessment. To correct the temperature effect on furfural partitioning, a furfural correction factor was developed to normalize furfural contents in oil at different temperatures to a reference temperature. The furfural correction factor was verified by experimental data and then introduced into the relation between degree of polymerization of paper and furfural content of oil. Verification results showed that the corrected equation can enhance the accuracy of paper aging estimation at different temperatures. The findings of this work indicated temperature effect on furfural partitioning should be considered when determining the furfural thresholds and evaluating the aging condition of power transformers. [ABSTRACT FROM PUBLISHER]

The article concerns the presence of water in insulating systems consisting of cellulose materials impregnated with different electro-insulting liquids. This is a particularly important issue regarding power transformers with their strategic importance for an electrical power system. Water is a serious threat and can be the reason of a dangerous breakdown. Thus there is a need to have insulating system diagnostics in terms of water content. The article discusses the issue of water solubility in different electro-insulating liquids. The author determined the water saturation limits for these liquids. Moreover, the author determined water sorption isotherms for cellulose materials of different aging degree. On the basis of the obtained results, equilibrium curves were constructed for some selected kinds of insulation. The research results presented here allow to improve the reliability of determining moisture in cellulose insulation of various polymerization degree on the basis of water content measurement in an electro-insulating liquid. [ABSTRACT FROM PUBLISHER]

In this paper, the influence of partial discharges (PD) on the subsequent dielectric properties of oil-impregnated paper (OIP) is investigated. Dielectric frequency response (DFR) of OIP is investigated at varied levels of moisture, thermal aging, temperature, oil conductivity and electrode material, to allow comparison of these influences. It is shown that PD activity inside OIP can significantly change the real and imaginary parts of permittivity of the paper layers, especially in the frequency range below 100 Hz. It is shown that even if the PD activity is concentrated on a few percent of the test sample, the real part of permittivity measured on the entire sample after a few tens of hours of PD activity can increase up to 10 times at very low frequencies (∼ 1 mHz) and the imaginary part of the permittivity can increase up to 100 times at low frequencies (∼ 1 Hz). The change of the real and imaginary parts of permittivity due to PD activity is permanent. It is very similar to the change caused by thermal aging, and somewhat similar to the change caused by moisture and higher oil conductivity. It is also shown that the dissipation factor of OIP in contact with copper and brass increases, even in the absence of PD, but contact with aluminum and stainless steel has no influence on DFR of OIP. The results suggest that the influence of PD can be significant when estimating the moisture content or the level of thermal aging of OIP with DFR method. [ABSTRACT FROM PUBLISHER]

This paper presents an experimental work investigating the failure data of oil-impregnated paper under pulsating DC voltage. The failure data of the oil-impregnated paper specimens were obtained through constant voltage stress tests; the Weibull distribution was used to estimate the statistical distribution of failure data. The exponential function (EF) and the inverse power function (IPF) were used to modify the Weibull distribution of failure data, so as to obtain two dual-variable Weibull failure distributions (WFDs), which were used to analyze failure data of oil-paper specimens obtained under pulsating DC and individual DC and AC voltages. Their adequacies were evaluated by using failure data obtained through the constant stress tests under various voltages. Both EF-modified and IPF-modified WFDs show more adequacy for failure data of oil-paper insulation under DC and pulsating DC voltages than AC voltages. The EF-modified WFD was more adequate than IPF-modified WFD in estimating the statistical distribution of failure data. [ABSTRACT FROM PUBLISHER]

The oil-paper insulation of valve-side windings in a high voltage direct current (HVDC) converter transformer is subjected to both complicated voltage and severe thermal stresses. Partial discharge (PD) occurring in oil-paper bounded cavities is influenced by voltage stress type as well as temperature. This paper presents PD behaviors in an oil-paper bounded gas cavity under pulsating DC voltages. The voltage across the gas cavity and PD inception voltages at different test conditions are analyzed. Influences of temperature on statistical PD distributions and parameters are also presented. A numerical model is used to interpret PD behaviors under pulsating DC voltages and various temperatures. Experimental results and theoretical statements show that the temperature exhibits a great influence on the behaviors of cavity discharge under pulsating DC voltages. Volume resistivity, gas pressure, charge decay time constant, and electron generation rate are the primary parameters influencing PD behaviors at different temperatures. [ABSTRACT FROM PUBLISHER]

This paper discusses the effect of temperature on polarization and depolarization current measurements on paper oil insulation system. The influence of copper sulphide formed due to corrosive sulphur in mineral oil, on the changes in polarization and depolarization current at different temperatures are considered. A significant increase in the magnitude of polarization and depolarization currents due to deposits of copper sulphide on paper layers is observed. The conductivity of paper insulation increases significantly with increase in temperature. The increase in current is attributed to thermally active conducting particles of copper sulphide. The Debye model parameters of equivalent circuit under copper corrosion have been computed and a model for failure due to copper sulphide is explained using the data on polarization and depolarization currents. [ABSTRACT FROM PUBLISHER]

Supercapacitors have drawn numerous attentions in recent years since they can deliver relatively high power and energy densities. This article introduces an all-printed paper based surface mountable supercapacitor (SMS), which could withstand the reflow soldering process when integrated into microelectronic systems. The device was fabricated by screen printing and stencil printing processes, contributing to low cost and large-scale fabrication. Furthermore, building electronic devices on ubiquitous paper substrates had advantages of light weight, low cost, and eco-friendliness, etc. Based on activated carbon electrode material and ionic liquid electrolyte, the capacity of the supercapacitor could reach 14 mF at the device volume of 3.2 mm ? 2.5 mm ? 0.175 mm, besides, the specific volumetric capacity was superior than most of the commercial SMS devices. [ABSTRACT FROM PUBLISHER]

Aging of transformer paper insulation is commonly investigated through indirect measurements conducted in the oil, e.g. furanic compounds. In addition to the conventional indicators, recent investigations showed that alcohols like methanol and ethanol in oil could be used as paper aging indicators. This paper focuses on the applicability of these new paper aging indicators in a novel, gas-to-liquid (GTL) technology based transformer oil through an accelerated laboratory aging experiment. The experiment was conducted at 120 °C for up to 280 days. A conventional mineral oil was also tested as a reference. Kraft paper aged in both oil types showed similar reductions in degree of polymerization (DP) and tensile strength (TS). Amount of methanol in oil is higher than that of 2-FAL in oil when DP is over 375, confirming the promise of using methanol as an early paper aging indicator. Both the new paper aging indicator, methanol and the conventional paper aging indicator, 2-FAL are applicable for the GTL oil without any modifications. In addition, oil only aging experiments showed that oil does produce methanol but it is negligible compared with oil paper aging experiment; however the amount of ethanol measured in the present oil paper aging experiment is believed to be originated from oil oxidation rather than from paper aging. [ABSTRACT FROM PUBLISHER]

To improve the thermal stability of the cellulose insulation paper used in power transformers, Al2O3 nanoparticles were used to modify traditional cellulose insulation paper. Molecular simulations and experimental methods were used to analyze the enhancement and the microscopic mechanism of Al2O3 nanoparticles in the thermal aging process of insulation paper. Molecular simulations were performed for an unmodified and a Al2O3 nanoparticle-modified cellulose model to explore the changes in mechanical properties, cohesive energy densities and the glass transition temperatures of the two models in a temperature range of 70?150 ?C. The results of these simulations showed that the elastic modulus of the cellulose model modified with Al2O3 nanoparticles was larger than that of the unmodified model. Further, the addition of Al2O3 nanoparticles increased the glass transition temperature, signifying that the number of hydrogen bond connections was increased and the thermal stability of the modified model was improved. In addition, physical samples of modified insulation papers mixed with Al2O3 nanoparticles were made, and accelerated thermal aging experiments on these samples showed that the decrease in the degree of polymerization for the modified insulation paper was slower than that of the unmodified insulation paper. Further, breakdown tests suggested that the addition of Al2O3 nanoparticles has a certain positive effect on the AC breakdown voltage. Both the simulated and experimental results indicate that the addition of Al2O3 nanoparticles improves the thermal aging performance of cellulose insulation paper. [ABSTRACT FROM PUBLISHER]

The oil-impregnated paper, which is the major section in converter transformer, plays a considerable role in the reliability and safety of power grid. However, the insulation degradation will be accelerated by the accumulation of charges that are on the surface of oil-impregnated paper. Therefore, the operating overvoltage occurs and irremediable damage will appear. Direct fluorination is an effective method of modifying the chemical components of polymer matrix so as to have effects on the surface characteristic of the insulating material without reforming the bulk properties. In this research, for studying effects of fluorination on charge behavior on the surface of oil-impregnated paper under the application of DC and impulse voltage, the F2/N2 compound, which the volume fraction for the F2 gas occupied 10 % and the pressure was 0.05 MPa in a reaction kettle for 5, 30 and 60 min respectively, was applied. Meanwhile, the chemical bond groups and the surface morphology of the fluorinated specimen were tested. Besides, the DC voltage amplitude was 4 kV and the applied time was 4 min. The impulse frequency was 500 Hz and the impulse voltage amplitude was 1, 2, 3 and 4 kV, respectively. The charge properties under various situations were investigated. From the research under the application of different fluorination time, impulse voltage amplitudes and polarities, conclusions can be drawn that, fluorination has significant effects on the chemical structure properties and surface characteristic of oil-impregnated paper. Furthermore, the initial potential can be reduced and the decay process of surface charge can be accelerated with different fluorination time increasing from 0 to 30 min. Nevertheless, the tendency is the opposite when the fluorination time continuously extends from 30 to 60 min. The trap distribution also exhibits alternation through the fluorination. [ABSTRACT FROM PUBLISHER]

Oil-paper insulation system is an extraordinarily essential system of converter transformer as well as HVDC bushing. This paper studies the interface charge accumulation and dissipation characteristics of oil-paper insulation system under DC voltage. In this paper, an induced electrification was used to charge the oil-paper samples. The amplitudes of the DC voltage varied from 1 to 4 kV and the paper was 0.13 mm in thickness. From the research of different voltage amplitudes and charging times of the paper, conclusions can be drawn that the decay is fast initially, and then becomes slower with the lapse of time. At last the dissipation trend tends to be flat. With the increase of the voltage amplitudes, the surface potential rises more rapidly, and higher voltage would also cause the charge to dissipate more quickly. Longer charging times would cause the charge to dissipate a bit more slowly. The decay rate of the negative voltage is a little smaller than that of the positive charge. Moreover, when it comes to the effect on the interface charge of the paper layers, the surface potential is more proportional to the increase of paper layers. When the amplitude of the voltage applied in the test is small or the charging time is short enough, the interface charge can be hardly accumulated on the bottom layer. In addition, the tdV/dt and the decay time curves under the positive voltage usually have two peak values, while there is only one peak value in most of the curves under the negative condition. [ABSTRACT FROM PUBLISHER]

Aging sensitive information related to cellulosic insulation can be obtained from the monotonically decaying dielectric relaxation function ?(?). Several circuits comprising of energy storing and dissipating elements are reported for modeling the dielectric relaxation function. Maxwell's ladder structure is one of the most popular approaches for modeling the dielectric response. For better modeling of dielectric response, a modified Maxwell's ladder structure is also reported that considers the effect of the aging due to the presence of a radial temperature gradient. In this paper it is proposed that for more accurate modeling of the dielectric response, the axial temperature gradient present in an operating transformer must also be taken into consideration. A further improved Maxwell two parameter model has been proposed that incorporates the effect of such axial temperature distribution. In the proposed technique each series branch of the two parameter model has been further divided into a number of parallel branches representing the effect of aging due to axial temperature gradient. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to facilitate the improvement of the control and operation of industrial facilities, by providing decision support tools. More specifically, this paper has three main contributions. First, this paper presents the definition of operation metrics that provide insight into the behavior of: 1) annunciated alarms, 2) alarm floods, and 3) operator actions in industrial facilities. Second, this paper presents visualization plots named multilayered radar plots that can present information in an elegant, dense, and comprehensive fashion. Three types of plots are proposed, which collectively compare the behavior of metrics, variables, and operation times in industrial facilities. Third, this paper presents a ranking method and a reordering design procedure of displayed alarms during an alarm flood to reorder the alarms based on the proposed alarm-flood criticality index. The purpose is to provide additional assistance to operators to focus on more critical issues. As the operation metrics, visualization plots, and the ranking in alarm floods heavily utilize historized data and given the industrial-oriented application of these decision support tools, this paper also addresses the extraction of information and the integration of the tools into industrial automation platforms. Note to Practitioners—In a control system used for an industrial facility, a large amount of data is collected and historized. The data include sensor measurements, status of actuators, alarms, and operator actions, and it therefore contains valuable information. The information can be extracted and utilized to assist in the improvement of the control and operation of the industrial facility. The objective of this paper is to provide decision support tools by: 1) defining operation metrics that can characterize the information extracted from the historized data; 2) presenting visualization plots that allow for a clear presentation and comparison of the metrics, where three types of plots are proposed for different purposes of comparison; and 3) a ranking method and a reordering design procedure of displayed alarms during an alarm flood. Furthermore, this paper discusses how the information is extracted from the historized data and how to integrate the proposed decision support tools into existing industrial automation platforms. [ABSTRACT FROM AUTHOR]

Practitioners of multi-objective optimization currently lack open tools that provide decision support through knowledge discovery. There exist many software platforms for multi-objective optimization, but they often fall short of implementing methods for rigorous post-optimality analysis and knowledge discovery from the generated solutions. This paper presents Mimer, a multi-criteria decision support tool for solution exploration, preference elicitation, knowledge discovery, and knowledge visualization. Mimer is openly available as a web-based tool and uses state-of-the-art web-technologies based on WebAssembly to perform heavy computations on the client-side. Its features include multiple linked visualizations and input methods that enable the decision maker to interact with the solutions, knowledge discovery through interactive data mining and graph-based knowledge visualization. It also includes a complete Python programming interface for advanced data manipulation tasks that may be too specific for the graphical interface. Mimer is evaluated through a user study in which the participants are asked to perform representative tasks simulating practical analysis and decision making. The participants also complete a questionnaire about their experience and the features available in Mimer. The survey indicates that participants find Mimer useful for decision support. The participants also offered suggestions for enhancing some features and implementing new features to extend the capabilities of the tool. [ABSTRACT FROM AUTHOR]

The presence of condensate inside the dryer cylinders of a paper machine produces a significant increase in the power required to accelerate the section, risking a drive overload and/or section shutdown. In previous works, the authors developed a robust estimation algorithm for the condensate power and a generalized correlation for the estimation of condensate loads in industrial dryer cylinders up to 55 mm of condensate rim. This paper presents and evaluates a rule-based expert system (ES) capable of detecting the presence and amount of condensate in up to three groups of cylinders with different condensate loads. The ES can give an early warning message to the operators and determine the condensate load and number of cylinders within each group. Evaluation is done using a combined condensate load for a fictitious 1500-mm eight-cylinder dryer section. Results show the capacity of the developed ES to give an early warning as well as a correct estimation of the condensate loads and number of cylinders involved. [ABSTRACT FROM PUBLISHER]

The operation of variable-speed ac drives with loss-minimizing flux strategies can produce important energy savings without the need of any investment. This paper proposes algorithms for the evaluation of loss motor components and the flux level that produces the minimum-loss operating condition, covering the whole constant-torque operating region. Both scalar and vector drives are considered, although it is found that the operation of scalar- and vector-controlled drives with optimum flux (or $d$ -axis current) gives very similar results for the same operating points. Evaluation is done using four induction motors of 4, 11, 55, and 110 kW. Results confirm that important savings can be achieved, especially in applications where motors operate with high speed and low to moderate load torque. Results will be used in a follow-up paper to evaluate the expected savings of the use of these strategies in a pulp mill and in a paper mill. [ABSTRACT FROM PUBLISHER]

Quality inspection, a widely adopted practice in supply chains, measures whether delivered products conform to prespecified quality requirements. Due to potential economic benefits, suppliers may deliberately manipulate products to falsify test results. However, unqualified products could cause severe problems in supply chains or even tragedies, such as China’s tainted milk scandal. In this paper, we propose a belief-desire-intention modeling approach to predict suppliers’ behavior and provide inspection suggestions to buyers to overcome the problem. Assuming access to the production process, our approach can represent suppliers’ knowledge of production and deception to mimic their reasoning processes and predict their deception intentions. This flexible approach can also adapt to environmental changes and deliver effective results. In this paper, we build a prototype system for supply chain quality inspections based on the proposed method. We conduct laboratory experiments to collect data for computational assessments of the performance of the prototype. It is shown that our proposed approach is more accurate than classic machine learning methods in detecting suppliers’ deceptions. [ABSTRACT FROM AUTHOR]

The integration of condition monitoring with queueing systems to support decision making is not well explored. This paper addresses the impact of condition monitoring of the server on the system-level performance experienced by entities in a queueing system. The system consists of a queue with a single-server subject to Markovian degradation. The model assumes a Poisson arrival process with service times and repair times according to general distributions. We develop stability conditions and perform steady-state analysis to obtain performance measures (average queue length, average degradation, and so on). We propose minimizing an objective function involving four types of costs: repair, catastrophic failure, quality, and holding. The queue performance measures derived from steady-state analysis are benchmarked and compared to those from a discrete event simulation model. After verifying the queuing model, a sensitivity analysis is performed to determine the relationships between system performance and model parameters. Results indicate that the total cost function is convex and, thus, subject to an optimal repair policy. The model is sensitive to service time, quality costs, and failure costs for late-stage policy repairs decisions and sensitive to expected repair times and repair costs for early stage policy repair decisions. Note to Practitioners—We propose condition-based maintenance for queues with degrading servers. The proposed model has been verified in simulation using degradation data of tools in a manufacturing facility. In order to implement, one must first model the underlying degradation process using the data from condition monitoring. We can get service and repair time distributions using data from the plant floor. Then, we need reasonable estimates of costs. These include costs of holding, quality, repair, and failure. They can be relative, prioritized or zero. The long-run average cost per unit time can then be evaluated. Then, the model provides the degradation state of the server at which maintenance is the most cost efficient. The degradation data can be used to continually obtain the optimal state of degradation at which the server can be taken offline with minimal costs. [ABSTRACT FROM AUTHOR]

Digital Object Identifier 10.1109/JBHI.2021.3058864 IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (J-BHI) https://jbhi.embs.org Statement of Editorial Policy IEEE Journal of Biomedical and Health Informatics (J-BHI) publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Papers published by J-BHI are typically organized under the section headings of Bioinformatics, Imaging Informatics, Sensor Informatics, Medical Informatics, and Public Health Informatics. [Extracted from the article]

A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Entities within the system show presence or absence in these events by entering or exiting the locations of the events. Given radio frequency identification (RFID) data from a schedule-based system, what can we learn about the system (the events and entities) through data mining? Which data mining methods can be applied so that one can obtain rich actionable insights regarding the system and the domain? The research goal of this paper is to answer these posed research questions, through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data mining methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under certain basic assumptions. The types of insights are also general, and are formulated in this paper in the most abstract way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive network fuzzy inference system (ANFIS) based decision support system that can provide European policy makers with systematic guidance in allocating and prioritizing scant public resources, whilst accomplishing the best growth performance at a regional level. We do so by taking the stance of the Smart Specialization Strategies, which aim at consolidating the regional strengths and make effective and efficient use of public investment in R&D. By applying the ANFIS method, we were able to understand how—and to what extent—the competitiveness drivers promoted technological development and how the latter contributes to the economic growth of European regions. We used socio–economic, spatial, and patent-based data to train, test, and validate the models. What emerges is that an increase of R&D investments enhances the regional employment rate and the number of patents per capita; in turn, by taking into account the several combinations of specialization and diversification indicators, this leads to an increase of the regional gross domestic product (GDP). [ABSTRACT FROM AUTHOR]

This paper deals with the routing problem of pick-up and delivery services considering time windows and capacitated vehicles. In order to consider large real problems, this paper proposes a distributed vehicle routing problem (VRP) with time windows, based on cluster first, route second methods. First, by using a graph partitioning solved by local integer linear programing problems, the vehicles autonomously generate a number of clusters equal to the number of available vehicles. Second, each vehicle solves a traveling salesman problem with time windows to compute its route in the assigned cluster. The method can be applied by the vehicles for both planning the workday of the pick-up services and adapting the routing plan to manage the ongoing requests. Some benchmark problems are generated and solved by the distributed algorithm and compared with the solution obtained by the exact approach. Moreover, a real case study involving a courier service shows the efficiency of the solution method. Note to Practitioners—The motivation of this paper is to propose a distributed heuristic method for solving real pick-up service problems in static and dynamic settings. The proposed approach provides solutions with twofold key features. First, the distributed and iterative algorithm of the proposed method enables vehicles to compute routes that are characterized by balanced workloads for the drivers. Second, the method is applicable to plan the daily workload of vehicles and to dynamically adapt the routing plan for servicing the ongoing requests. Future research aims at extending the method to other variants of VRPs and at including the solver into a decision support system, with a suitable use of the modern information and communication technologies. [ABSTRACT FROM AUTHOR]

This paper discusses the design and simulation of an integrated load forecasting and state estimation tool for distribution system operations. A predictive database is created and applied to forecast future network states in order to allow short-term (e.g., hours/days ahead) planning to be carried out. The predictive database is based on adaptive nonlinear auto-regressive exogenous (NARX) load estimation and forecasting models, which are continuously updated using feedback from the state estimator. This creates a closed-loop information flow designed to continuously monitor and improve the system state estimation performance by updating and retraining models where appropriate. The aim of this methodology is to improve situational awareness and help to provide network operators with early warning of potential issues, in medium voltage (MV) networks where the number of on-line measurements is limited, and state estimation relies heavily on estimates of power injections. The applicability of the approach is demonstrated through simulation using supervisory control and data acquisition (SCADA) and smart meter measurements recorded from an actual MV distribution network. [ABSTRACT FROM PUBLISHER]

This paper presents an urban-decision support system (U-DSS) devoted to manage, in a unified framework, the logistic services of the smart cities, such as postal delivery (PD) and waste collection (WC) services. The U-DSS architecture is proposed by describing its main components. In particular, this paper focuses on the core of the U-DSS, i.e., the model component that provides the solutions of a general vehicle assignment and routing optimization problem with the aim of minimizing the length of the routes and satisfying time and capacity constraints. In order to solve the vehicle routing problems in reasonable time, a two-phase heuristic algorithm is proposed based on a clustering strategy and a farthest insertion heuristic for the solution of a traveling salesman problem. The applicability of the proposed U-DSS is enlightened by comparing the proposed heuristic algorithm solutions with the mixed integer linear programming problem solutions of the PD and WC services. Moreover, the discussion of the real case studies of the city of Bari (Italy) assesses the proposed approach. [ABSTRACT FROM PUBLISHER]

In this paper, the closed-loop supply chain (CLSC) models are extended to consider the case that the manufacturer can improve the product quality and the retailer can have effective marketing activities to promote the product. One centralized and three decentralized CLSC models are mathematically formulated in this paper. For the centralized CLSC model, the optimal solutions are derived in closed-form format analytically. For three decentralized CLSC models, by assuming the manufacturer is the leader, the equilibrium solutions are also obtained in closed-form format under the Stackelberg game settings. Based on the optimal/equilibrium solutions and the corresponding objective function values for the proposed models, several interesting managerial insights, and economic implications are obtained. Moreover, the proposed models with product quality improvement and marketing efforts are compared with the models with only product quality improvement effort and the models with only marketing effort. The results show that the performance of the proposed models with both product quality improvement and marketing efforts is better in the decentralized CLSC models. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for overcoming Mamdani-type inference failures on a fuzzy-logic-based decision-making process applied to collision avoidance in ship navigation. The fuzzy inference failures are observed in three distinct situations: 1) intersected contradictory decision boundaries; 2) an improper transition region between the inference boundaries of nonintersected contradictory decisions; and 3) contradictory decision accumulation under multiple obstacle scenarios. The solutions for overcoming these fuzzy inference failures and their limitations are also discussed in this paper. The proposed solutions consist of insertion of smooth transition regions, determination of the proper size of the smooth transition regions, and use of multilevel decision/action formulations. Furthermore, this paper analyzes a decision-making process for ship navigation, derives input and output fuzzy membership functions (FMFs), formulates an if–then-rule-based fuzzy inference system (FIS), and presents simulation results that support recovery from rule inference failures in several contradictory decision boundary conditions. [ABSTRACT FROM PUBLISHER]

Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach. [ABSTRACT FROM AUTHOR]

This paper presents and analyzes a novel concatenated coding scheme for enabling error resilience in two distributed storage settings: one being storage using existing regenerating codes and the second being storage using locally repairable codes. The concatenated coding scheme brings together a maximum rank distance code as an outer code and either a globally regenerating or a locally repairable code as an inner code. In addition, error resilience for combination of locally repairable codes with regenerating codes is considered. This concatenated coding system is designed to handle two different types of adversarial errors: the first type includes an adversary that can replace the content of an affected node only once; while the second type studies an adversary that is capable of polluting data an unbounded number of times. The paper establishes an upper bound on the resilience capacity for a locally repairable code. This paper also proves that the proposed concatenated coding approach attains the upper bound on the resilience capacity in the presence of the first type of adversary for both minimum storage regenerating codes and locally repairable codes. Further, this paper presents mechanisms that combine the presented concatenated coding scheme with subspace signatures to achieve error resilience for the second type of errors. [ABSTRACT FROM PUBLISHER]

Car sharing (CS) services are promising solutions complementary to the classic public transport forms. In order to make CS effectively competitive, suitable planning and management strategies are required. This paper presents a decision support system (DSS) for handling the user-based vehicle relocation problem by applying economic incentives ruled by a threshold policy. Unlike the existing approaches, a methodology is proposed for determining the optimal threshold, which considers explicitly the stochastic reactions of the customers to the incentives. To this aim, the CS system is described in detail by unified modeling language diagrams and is modeled in a discrete event system framework. Moreover, a closed-loop control strategy is introduced to implement the vehicle relocation policy on the basis of the system state and the best threshold values, evaluated by discrete event simulation and particle swarm optimization. A case study simulation analysis shows that the proposed DSS management strategy can significantly improve the system performance. [ABSTRACT FROM AUTHOR]

This paper explores a dual-channel closed-loop supply chain (CLSC) system consisting of a manufacturer and a retailer. The manufacturer is the CLSC Stackelberg leader. We investigate the effect of exerting marketing effort on the optimal decisions and profits of supply chain members by considering several marketing effort supported models, namely the models when the manufacturer is the investor, the retailer is the investor, and the centralized CLSC system, respectively. We propose a two-part tariff contract for coordinating the CLSC. We analytically reveal that the two-part tariff contract can coordinate the CLSC when the manufacturer is the investor but unable to coordinate supply chain when the retailer is the investor. Finally, we present the numerical analysis to uncover insights on how the consumers’ preference toward the direct channel affects the optimal decisions in the dual-channel CLSC system. [ABSTRACT FROM AUTHOR]

The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. We demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure. [ABSTRACT FROM PUBLISHER]

In this paper, distributed storage systems with exact repair are studied. Constructions for exact-regenerating codes between the minimum storage regenerating (MSR) and the minimum bandwidth regenerating (MBR) points are given. To the best of our knowledge, no previous construction of exact-regenerating codes between MBR and MSR points is done except in the works by Tian et al. and Sasidharan et al. In contrast to their works, the methods used here are elementary. In this paper, it is shown that in the case that the parameters \(n\) , \(k\) , and \(d\) are close to each other, the given construction is close to optimal when comparing with the known functional repair capacity. This is done by showing that when the distances of the parameters \(n\) , \(k\) , and \(d\) are fixed but the actual values approach to infinity, the fraction of the performance of constructed codes with exact repair and the known capacity of codes with functional repair, approaches to one. Also, a simple variation of the constructed codes with almost the same performance is given. Also some bounds for the capacity of exact-repairing codes are given. These bounds illustrate the relationships between storage codes with different parameters. [ABSTRACT FROM PUBLISHER]

This paper specifies a cloud-based cooperative decision support system (DSS) that aims at integrating logistics management and decision support strategies for intermodal transportation systems. The proposed DSS is dedicated to synchronize different transportation means by using the modern information and communications technology tools and by taking into account environmental aspects. This paper describes the DSS cloud-based architecture and presents the procedure to be followed in order to design a DSS able to support decision makers in different logistic decision fields. The advantages of the proposed DSS are enlightened by specifying three main decision modules: cargo transport optimization, intelligent truck parking, and CO2 monitoring. Moreover, the applicability of the proposed DSS is described by specifying a DSS for the case study of the logistic network of the Trieste port (Italy), including the port, the inland terminal, and the highway connecting them. Some simulation campaigns are employed both to set the decision modules and evaluate the DSS application benefits. [ABSTRACT FROM AUTHOR]

This paper presents a fully automatic leak detection system for slow oscillation high-pressure fluid-filled (HPFF) circuits. It also presents the concept of detecting leaks in such circuits, utilizing only a limited number of sensors. It will describe algorithms built upon this concept, including two different approaches to the construction of the decision module, crucial for detection capabilities. The proposed solution utilizes adaptive digital filters for modeling the circuit behavior and a Bayesian network coupled with a software leak simulator in the decision stage. These tests were performed using data obtained from a real-life circuit, including real-leak data. This paper may enable practical application of high-precision low-cost leak detection solutions for a wide range of HPFF circuits. [ABSTRACT FROM AUTHOR]

The main purpose of structural health monitoring (SHM) is to obtain information about the state of a structure in order to guide bridge management decisions. Nevertheless, in practice, once a rigorous estimate of the structural state is available, decisions are usually made based on the decision-maker's intuition or experience. In this paper, we present the implementation of expected utility theory (EUT) in those civil engineering decision problems in which decision-makers have to act based on the output of SHM. EUT is an analytical quantitative framework that allows the identification of the financially most convenient decisions, based on the possible outcomes of each action and on the probabilities of each structural state occurring. The advantage of the presented implementation is the optimization of decision strategies in SHM. In the paper, we first formalize the solution of single-stage decision processes, in which the decision-maker has to take only one action. Then, we formalize the solution of multistage decision processes, in which multiple actions may be taken over time. Finally, using an example based on a case study, we describe the variables involved in the analysis of SHM decision problems, discuss the possible results, and address the issues that may arise in the application of EUT in real-life settings. [ABSTRACT FROM PUBLISHER]

Distributed storage systems (DSSs) in the presence of an external wiretapper are considered. A DSS is parameterized by $(n, k, d)$ , in which the data are stored across $n$ nodes (each with storage capacity $\alpha $ ), and must be recoverable by accessing the contents stored on any $k$ out of $n$ nodes. If a node fails, any $d \geq k$ out of $(n-1)$ nodes help in the repair (regeneration) of the failed node (by sending $d\beta $ units of repair data, where $\beta \leq \alpha $ ), so that the data can still be recovered from the DSS. For such a $(n, k, d)$ -DSS, security from the two types of wiretappers is investigated: 1) Type-I (node data) wiretapper, which can read the data stored on any $\ell

In this paper, we propose a heterogeneous framework to deliver smart grid (SG) data cost effectively. The data generated by distributed SG loads, and generation units should be delivered to the utility control center (UCC) within the tolerated delay, which is crucial for SG applications. To this end, a heterogeneous communication framework is proposed, where the cellular network (CN) provides ubiquitous yet expensive data transmission, and vehicle-assisted device-to-device (D2D) communications are leveraged to offload the CN by delivering the delay-tolerant SG data in a store–carry–forward fashion with low cost. To improve the offloading and cost performance of the proposed framework, we put effort in the following aspects: 1) optimal forwarding schemes to optimally select vehicles to carry and forward the data; and 2) mode selection and dynamic resource allocation to maximize the amount of data delivered by D2D communications, reduce the cost of SG data delivery, and guarantee the fairness among SG users. Simulation results are given to validate proposed approaches and demonstrate that the proposed framework is efficient in saving cost for the utility and offloading the CN. [ABSTRACT FROM AUTHOR]

Fractional repetition (FR) codes are a class of regenerating codes for distributed storage systems with an exact (table-based) repair process that is also uncoded, i.e., upon failure, a node is regenerated by simply downloading packets from the surviving nodes. In this paper, we present the constructions of FR codes based on Steiner systems and resolvable combinatorial designs, such as affine geometries, Hadamard designs, and mutually orthogonal Latin squares. The failure resilience of our codes can be varied in a simple manner. We construct codes with normalized repair bandwidth ( $\beta $ ) strictly larger than one; these cannot be obtained trivially from codes with $\beta = 1$ . Furthermore, we present the Kronecker product technique for generating new codes from existing ones and elaborate on their properties. FR codes with locality are those where the repair degree is smaller than the number of nodes contacted for reconstructing the stored file. For these codes, we establish a tradeoff between the local repair property and the failure resilience and construct codes that meet this tradeoff. Much of prior work only provided lower bounds on the FR code rate. In this paper, for most of our constructions, we determine the code rate for certain parameter ranges. [ABSTRACT FROM AUTHOR]

The damping and screening system (DSS) is a key structure of the hybrid high-temperature superconducting generator. In this paper, an analytical method was introduced to analyze the performance of the DSS with different time constants on both the $d$ -axis and the $q$ -axis of the generator. First, the distribution of the eddy currents in the DSS was analyzed, and the calculation of the equivalent resistance, inductance, and time constant of the DSS was considered. Then, to analyze the performance of the nonuniform DSS (NDSS), a splitting method was introduced. Using the new method, parameters of the damping part, screening part, and supplementing part of the NDSS were calculated, respectively. At last, the screening and damping performances of the NDSS were evaluated. The finite-element method was also employed to verify the analytical results. An experimental platform was built up to test the screening ratio and verify the simulating results. [ABSTRACT FROM AUTHOR]

The influences of network reconfiguration and distributed generations (DGs) on distribution system state estimation (DSSE) and measurement placement should not be ignored in active distribution systems. In this paper, considering network reconfiguration and the output uncertainties of DGs, a robust measurement placement method for active distribution systems is proposed based on measurement saturation analysis and heuristic algorithm. First, the saturation number determined by measurement saturation characteristic is chosen as the measurement number. Then, the impacts of different network topologies are represented by various weights in the robust measurement placement model, which are computed by Markov chain and analytic hierarchy process. In addition, Gaussian mixture model is applied to approximate the power fluctuations of DGs. Uncertainties caused by measurements are considered by Monte Carlo simulations. The accuracy of DSSE in different network topologies can be guaranteed by the proposed robust measurement placement method. Simulation results based on the IEEE 33-bus and 119-bus distribution systems demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Recently, it was found that most multiobjective particle swarm optimizers (MOPSOs) perform poorly when tackling many-objective optimization problems (MaOPs). This is mainly because the loss of selection pressure that occurs when updating the swarm. The number of nondominated individuals is substantially increased and the diversity maintenance mechanisms in MOPSOs always guide the particles to explore sparse regions of the search space. This behavior results in the final solutions being distributed loosely in objective space, but far away from the true Pareto-optimal front. To avoid the above scenario, this paper presents a balanceable fitness estimation method and a novel velocity update equation, to compose a novel MOPSO (NMPSO), which is shown to be more effective to tackle MaOPs. Moreover, an evolutionary search is further run on the external archive in order to provide another search pattern for evolution. The DTLZ and WFG test suites with 4–10 objectives are used to assess the performance of NMPSO. Our experiments indicate that NMPSO has superior performance over four current MOPSOs, and over four competitive multiobjective evolutionary algorithms (SPEA2-SDE, NSGA-III, MOEA/DD, and SRA), when solving most of the test problems adopted. [ABSTRACT FROM PUBLISHER]