Showing papers in "IEEE Transactions on Industrial Informatics in 2011"

Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

Abstract: Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.

Smart Grid Technologies: Communication Technologies and Standards

Abstract: For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.

Enterprise Systems: State-of-the-Art and Future Trends

Abstract: Rapid advances in industrial information integration methods have spurred tremendous growth in the use of enterprise systems. Consequently, a variety of techniques have been used for probing enterprise systems. These techniques include business process management, workflow management, Enterprise Application Integration (EAI), Service-Oriented Architecture (SOA), grid computing, and others. Many applications require a combination of these techniques, which is giving rise to the emergence of enterprise systems. Development of the techniques has originated from different disciplines and has the potential to significantly improve the performance of enterprise systems. However, the lack of powerful tools still poses a major hindrance to exploiting the full potential of enterprise systems. In particular, formal methods and systems methods are crucial for modeling complex enterprise systems, which poses unique challenges. In this paper, we briefly survey the state of the art in the area of enterprise systems as they relate to industrial informatics.

FPGAs in Industrial Control Applications

Abstract: The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.

Comprehensive Study of Single-Phase AC-DC Power Factor Corrected Converters With High-Frequency Isolation

Abstract: Solid-state switch mode AC-DC converters having high-frequency transformer isolation are developed in buck, boost, and buck-boost configurations with improved power quality in terms of reduced total harmonic distortion (THD) of input current, power-factor correction (PFC) at AC mains and precisely regulated and isolated DC output voltage feeding to loads from few Watts to several kW. This paper presents a comprehensive study on state of art of power factor corrected single-phase AC-DC converters configurations, control strategies, selection of components and design considerations, performance evaluation, power quality considerations, selection criteria and potential applications, latest trends, and future developments. Simulation results as well as comparative performance are presented and discussed for most of the proposed topologies.

IEC 61499 as Enabler of Distributed and Intelligent Automation: State-of-the-Art Review

Abstract: This review paper discusses the industrial and research activities around the IEC 61499 architecture for distributed automation systems. IEC 61499 has been developed to enable intelligent automation where the intelligence is genuinely decentralized and embedded into software components, which can be freely distributed across networked devices. With the recent emergence of professionally made software tools and dozens of hardware platforms, IEC 61499 is getting recognition in industry. This paper reviews research results related to the design of distributed automation systems with IEC 61499, the supporting tools and the aspects related to the execution of IEC 61499 on embedded devices. The promising application areas of IEC 61499 include flexible material handling systems, in particular airport baggage handling, flexible reconfigurable manufacturing automation, intelligent power distribution networks and SmartGrid, as well as the wide range of embedded networked systems.

Variable Structure Systems With Sliding Modes in Motion Control—A Survey

Abstract: This paper presents a comprehensive overview of the application of Variable Structure Systems (VSSs) with Sliding Mode (SM) methods in motion control systems. Our aim is to give implementable sliding mode design solutions for complex motion systems, actuators and supply converters. This paper provides a frame for further study of sliding mode applications in motion control systems.

Communication Infrastructures for Distributed Control of Power Distribution Networks

Abstract: Power distribution networks with distributed generators (DGs) can exhibit complex operational regimes which makes conventional management approaches no longer adequate. This paper looks into key communication infrastructure design aspects, and analyzes two representative evolution cases of Active Network Management (ANM) for distributed control. Relevant standard initiatives, communication protocols and technologies are introduced and underlying engineering challenges are highlighted. By analyzing two representative case networks (meshed and radial topologies) at different voltage levels (33 and 11 kV), this paper discusses the design considerations and presents performance results based on numerical simulations. This study focuses on the key role of the telecommunications provision when upgrading and deploying distributed control solutions, as part of future ANM systems.

A Multidimensional Critical State Analysis for Detecting Intrusions in SCADA Systems

Abstract: A relatively new trend in Critical Infrastructures (e.g., power plants, nuclear plants, energy grids, etc.) is the massive migration from the classic model of isolated systems, to a system-of-systems model, where these infrastructures are intensifying their interconnections through Information and Communications Technology (ICT) means. The ICT core of these industrial installations is known as Supervisory Control And Data Acquisition Systems (SCADA). Traditional ICT security countermeasures (e.g., classic firewalls, anti-viruses and IDSs) fail in providing a complete protection to these systems since their needs are different from those of traditional ICT. This paper presents an innovative approach to Intrusion Detection in SCADA systems based on the concept of Critical State Analysis and State Proximity. The theoretical framework is supported by tests conducted with an Intrusion Detection System prototype implementing the proposed detection approach.

Fractional Order Systems in Industrial Automation—A Survey

Abstract: This paper describes an emerging tool for industry: fractional order systems. Conventional understanding of the notion of derivative and integral uses integer orders and our sense is mature in their physical interpretations. Derivatives or integrals of fractional orders are generalizations of the concept containing the classical cases and solutions based on fractional order operators utilize the full flexibility offered by the mathematical definitions. The interest of the industry to fractional order systems lie in the fact that complicated modules can be simplified significantly and practical applications can be diverse. This paper describes linear and nonlinear cases with necessary stability and performance considerations for the benefit of a practicing engineer exploiting informatics in industry.

A Comprehensive Analysis of the MAC Unreliability Problem in IEEE 802.15.4 Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) represent a very promising solution in the field of wireless technologies for industrial applications. However, for a credible deployment of WSNs in an industrial environment, four main properties need to be fulfilled, i.e., energy efficiency, scalability, reliability, and timeliness. In this paper, we focus on IEEE 802.15.4 WSNs and show that they can suffer from a serious unreliability problem. This problem arises whenever the power management mechanism is enabled for energy efficiency, and results in a very low packet delivery ratio, also when the number of sensor nodes in the network is very low (e.g., 5). We carried out an extensive analysis-based on both simulation and experiments on a real WSN-to investigate the fundamental reasons of this problem, and we found that it is caused by the contention-based Medium Access Control (MAC) protocol used for channel access and its default parameter values. We also found that, with a more appropriate MAC parameters setting, it is possible to mitigate the problem and achieve a delivery ratio up to 100%, at least in the scenarios considered in this paper. However, this improvement in communication reliability is achieved at the cost of an increased latency, which may not be acceptable for industrial applications with stringent timing requirements. In addition, in some cases this is possible only by choosing MAC parameter values formally not allowed by the standard.

DSP-Based Control of Grid-Connected Power Converters Operating Under Grid Distortions

Abstract: Power electronic Grid-Connected Converters (GCCs) are widely applied as grid interface in renewable energy sources. This paper proposes an extended Direct Power Control with Space Vector Modulation (DPC-SVM) scheme with improved operation performance under grid distortions. The real-time operated DPC-SVM scheme has to execute several important tasks as: space vector pulse width modulation, active and reactive power feedback control, grid current harmonics and voltage dips compensation. Thus, development and implementation of the DPC-SVM algorithm using single chip floating-point microcontroller TMS320F28335 is described. It combines large peripheral equipment, typical for microcontrollers, with high computation capacity characteristic for Digital Signal Processors (DSPs). The novelty of the proposed system lies in extension of the generic DPC-SVM scheme by additional higher harmonic and voltage dips compensation modules and implementation of the whole algorithm in a single chip floating point microcontroller. Overview of the laboratory setup, description of basic algorithm subtasks sequence, software optimization as well as execution time of specific program modules on fixed-point and floating-point processors are discussed. Selected oscillograms illustrating operation and robustness of the developed algorithm used in 5 kVA laboratory model of the GCC are presented.

Neural Network Assisted Computationally Simple PI $^\lambda$ D $^\mu$ Control of a Quadrotor UAV

Abstract: The applications of Unmanned Aerial Vehicles (UAVs) require robust control schemes that can alleviate disturbances such as model mismatch, wind disturbances, measurement noise, and the effects of changing electrical variables, e.g., the loss in the battery voltage. Proportional Integral and Derivative (PID) type controller with noninteger order derivative and integration is proposed as a remedy. This paper demonstrates that a neural network can be trained to provide the coefficients of a Finite Impulse Response (FIR) type approximator, that approximates to the response of a given analog PIλDμ controller having time varying action coefficients and differintegration orders. The results obtained show that the neural network aided FIR type controller is very successful in driving the vehicle to prescribed trajectories accurately. The response of the proposed scheme is highly similar to the response of the target PIλDμ controller and the computational burden of the proposed scheme is very low.

Comparison of Embedded System Design for Industrial Applications

Abstract: This paper presents a survey on embedded systems design and applications. Several platforms for embedded systems, including microcontrollers, microprocessors, field-programmable gate arrays, digital signal processors, and application-specific integrated circuits are discussed and compared. A survey of embedded system-based industrial applications is presented. Examples of real-life design decisions specific to development of such systems are also presented. The carefully selected three design case study examples include industrial control of wind tunnel with emphasis on actuator control, a mobile robot navigation system with emphasis on integration and synchronization of several subsystems, and optimized implementation of computationally intensive control system on a small microcontroller system.

FPGA Implementation of the Multilayer Neural Network for the Speed Estimation of the Two-Mass Drive System

Abstract: This paper presents a practical realization of a neural network (NN)-based estimator of the load machine speed for a drive system with elastic coupling, using a reconfigurable field-programmable gate array (FPGA). The system presented is unique because the multilayer NN is implemented in the FPGA placed inside the NI CompactRIO controller. The neural network used as a state estimator was trained with the Levenberg-Marquardt algorithm. Special algorithm for implementation of the multilayer neural networks in such hardware platform is presented, focused on the minimization of the used programmable blocks of the FPGA matrix. The algorithm code for the neural estimator implemented in C-RIO was realized using the LabVIEW software. The neural estimators are tested: offline (based on the measured testing database) and online (in the closed-loop control structure). These estimators are tested also for changeable inertia moment of the load machine of the drive system with elastic joint. Presented results of the experimental tests confirm that the multilayer NN, implemented in the FPGA with the use of the higher level programming language, ensures a high-quality state variable estimation of the two-mass drive system.

Service-Oriented Infrastructure to Support the Deployment of Evolvable Production Systems

Abstract: Nowadays, Service-oriented Architecture (SOA) paradigm is becoming a broadly deployed standard for business and enterprise integration. It continuously spreads across the diverse layers of the enterprise organization and disparate domains of application envisioning a unified communication solution. In the industrial domain, Evolvable Production System (EPS) paradigm focus on the identification of guidelines and solutions to support the design, operation, maintenance, and evolution of complete industrial infrastructures. Similarly to several other domains, the crescent ubiquity of smart devices is raising important lifecycle concerns such as device setup, control, management, supervision and diagnosis. From initial setup and deployment to system lifecycle monitoring and evolution, each device needs to be taken into account and easily reachable. The present work exploits the association of EPS and SOA paradigms in the pursuit of a common architectural solution to support the different phases of the device lifecycle. The result is a modular, adaptive and open infrastructure forming a complete SOA ecosystem that will make use of the embedded capabilities supported by the proposed device model. The infrastructure components are specified and it is shown how they can interact and be combined to adapt to current system specificity and requirements. Finally, a proof-of-concept prototype deployed in a real industrial production scenario is also detailed and results are presented.

Wireless Model-Based Predictive Networked Control System Over Cooperative Wireless Network

Abstract: Owing to their distributed architecture, networked control systems (NCSs) are proven to be feasible in scenarios where a spatially distributed feedback control system is required. Traditionally, such NCSs operate over real-time wired networks. Recently, in order to achieve the utmost flexibility, scalability, ease of deployment, and maintainability, wireless networks such as IEEE 802.11 wireless local area networks (LANs) are being preferred over dedicated wired networks. However, conventional NCSs with event-triggered controllers and actuators cannot operate over such general purpose wireless networks since the stability of the system is compromised due to unbounded delays and unpredictable packet losses that are typical in the wireless medium. Approaching the wireless networked control problem from two perspectives, this work introduces a practical wireless NCS and an implementation of a cooperative medium access control protocol that work jointly to achieve decent control under severe impairments, such as unbounded delay, bursts of packet loss and ambient wireless traffic. The proposed system is evaluated on a dedicated test platform under numerous scenarios and significant performance gains are observed, making cooperative communications a strong candidate for improving the reliability of industrial wireless networks.

Energy-Efficient Coverage of Wireless Sensor Networks Using Ant Colony Optimization With Three Types of Pheromones

Abstract: The Efficient-Energy Coverage (EEC) problem is an important issue when implementing Wireless Sensor Networks (WSNs) because of the need to limit energy use. In this paper, we propose a new approach to solving the EEC problem using a novel Ant Colony Optimization (ACO) algorithm. The proposed ACO algorithm has a unique characteristic that conventional ACO algorithms do not have. The proposed ACO algorithm (Three Pheromones ACO, TPACO) uses three types of pheromones to find the solution efficiently, whereas conventional ACO algorithms use only one type of pheromone. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI), and the other is used to form a sensor set that has as many sensors as an ant has selected the number of active sensors by using the former pheromone. The TPACO algorithm has another advantage in that the two user parameters of ACO algorithms are not used. We also introduce some techniques that lead to a more realistic approach to solving the EEC problem. The first technique is to utilize the probabilistic sensor detection model. The second method is to use different kinds of sensors, i.e., heterogeneous sensors in continuous space, not a grid-based discrete space. Simulation results show the effectiveness of our algorithm over other algorithms, in terms of the whole network lifetime.

Development and Implementation of Parameterized FPGA-Based General Purpose Neural Networks for Online Applications

Abstract: This paper presents the development and implementation of a generalized backpropagation multilayer perceptron (MLP) architecture described in VLSI hardware description language (VHDL). The development of hardware platforms has been complicated by the high hardware cost and quantity of the arithmetic operations required in online artificial neural networks (ANNs), i.e., general purpose ANNs with learning capability. Besides, there remains a dearth of hardware platforms for design space exploration, fast prototyping, and testing of these networks. Our general purpose architecture seeks to fill that gap and at the same time serve as a tool to gain a better understanding of issues unique to ANNs implemented in hardware, particularly using field programmable gate array (FPGA). The challenge is thus to find an architecture that minimizes hardware costs, while maximizing performance, accuracy, and parameterization. This work describes a platform that offers a high degree of parameterization, while maintaining generalized network design with performance comparable to other hardware-based MLP implementations. Application of the hardware implementation of ANN with backpropagation learning algorithm for a realistic application is also presented.

Uncertainty-Robust Design of Interval Type-2 Fuzzy Logic Controller for Delta Parallel Robot

Abstract: Type-2 Fuzzy Logic Controllers (T2 FLCs) have been recently applied in many engineering areas. While understanding the control potentials of T2 FLCs can still be considered an open question researchers, commonly claim superiority of T2 FLCs based on a limited exploration of the space of design parameters. The contribution of this work is based on a problem-driven design of uncertainty-robust Interval T2 (IT2) FLCs. The presented methodology starts with a baseline optimized T1 FLC. Next, a group of IT2 FLCs is designed using partially dependent approach by symmetrically blurring the membership functions around the original T1 fuzzy sets. This constrained design space allows for its systematic exploration and analysis. The performance of the designed controllers was evaluated on delta parallel robot hardware under two kinds of commonly encountered uncertainties: i) sensory noise and ii) uncertain system parameters. The experimental results showed that IT2 FLCs provide improved control performance against T1 FLCs when appropriate design of IT2 fuzzy sets is performed. In addition, it was demonstrated that excessive amount of “type-2 fuzziness” in the IT2 FLC design leads to rapid performance degradation.

A Survey on Applications of Agent Technology in Industrial Process Control

Abstract: The agents and multiagent systems technology is actively researched by the academia and industrial community. However, the technology is particularly popular in the manufacturing domain, while the applications in other domains of industrial control are scarce. This survey focuses exclusively on the technology applications in the automation of continuous industrial processes, as the differences in the technology adoption between the process automation and manufacturing are significant. A large part of the literature on the subject is reviewed. The analysis of the literature is provided from several points of view, the main trends of research are described, including the shift of the researchers' interest from the agent-based supervisory control to the low-level agent-based control algorithms. Conclusions are provided regarding the lack of the technology support on the part of control instrumentation vendors; probable directions of the development are indicated, which turn out to be especially promising in the domain of biotechnological processes.

Managing Process Model Complexity via Concrete Syntax Modifications

Abstract: While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with the ever-growing complexity of business process models. Mechanisms for dealing with this complexity can be classified into two categories: 1) those that are solely concerned with the visual representation of the model and 2) those that change its inner structure. While significant attention is paid to the latter category in the BPM literature, this paper focuses on the former category. It presents a collection of patterns that generalize and conceptualize various existing mechanisms to change the visual representation of a process model. Next, it provides a detailed analysis of the degree of support for these patterns in a number of state-of-the-art languages and tools. This paper concludes with the results of a usability evaluation of the patterns conducted with BPM practitioners.

Schedule Optimization of Time-Triggered Systems Communicating Over the FlexRay Static Segment

Abstract: FlexRay is a new high-bandwidth communication protocol for the automotive domain, providing support for the transmission of time-critical periodic frames in a static segment and priority-based scheduling of event-triggered frames in a dynamic segment. The design of a system scheduling with communication over the FlexRay static segment is not an easy task because of protocol constraints and the demand for extensibility and flexibility. We study the problem of the ECU and FlexRay bus scheduling synthesis from the perspective of the application designer, interested in optimizing the scheduling subject to timing constraints with respect to latency- or extensibility-related metric functions. We provide solutions for a task and signal scheduling problem, including different task scheduling policies based on existing industry standards. The solutions are based on the Mixed-Integer Linear Programming optimization framework. We show the results of the application of the method to case studies consisting of an X-by-wire system on actual prototype vehicles.

Managing Process Model Complexity Via Abstract Syntax Modifications

Abstract: As a result of the growing adoption of Business Process Management (BPM) technology, different stakeholders need to understand and agree upon the process models that are used to configure BPM systems. However, BPM users have problems dealing with the complexity of such models. Therefore, the challenge is to improve the comprehension of process models. While a substantial amount of literature is devoted to this topic, there is no overview of the various mechanisms that exist to deal with managing complexity in (large) process models. As a result, it is hard to obtain an insight into the degree of support offered for complexity reducing mechanisms by state-of-the-art languages and tools. This paper focuses on complexity reduction mechanisms that affect the abstract syntax of a process model, i.e., the formal structure of process model elements and their interrelationships. These mechanisms are captured as patterns so that they can be described in their most general form, in a language- and tool-independent manner. The paper concludes with a comparative overview of the degree of support for these patterns offered by state-of-the-art languages and tools, and with an evaluation of the patterns from a usability perspective, as perceived by BPM practitioners.

Multi-DOF Micro-Macro Bilateral Controller Using Oblique Coordinate Control

Abstract: In this study, we show that tasks can be realized by appropriate coordinate transformation. This approach, oblique coordinate control, decouples tasks. Even though a system is large, they can be designed by a combination of small tasks. Therefore, tasks can be regarded as reusable components. In this study, micro-macro bilateral control is achieved as a complicated task. Small objects can be manipulated as large objects in micro-macro bilateral control. However, ideal micro-macro bilateral controllers are usually derived only for single-degree-of-freedom (DOF) systems. Then, we propose a micro-macro bilateral controller for multi-DOF on the basis of oblique coordinate control. To obtain multi-DOF bilateral control, we first derive control goals as kinematic relations. This study also considers the case where scaling gains of position and force are selected in a different manner. The goals of micro-macro bilateral control are then controlled by oblique coordinate control. The validity of the proposed method is experimentally verified.

Real-Time Modeling for Direct Load Control in Cyber-Physical Power Systems

Abstract: This paper presents an innovative approach to use real-time scheduling techniques for the automation of electric loads in Cyber-Physical Power Systems. The goal is to balance the electric power usage to achieve an optimized upper bound on the power peak load, while guaranteeing specific constraints on the physical process controlled by the electric loads. Timing parameters derived from the scheduling discipline of real-time computing systems are used to model electric devices. Real-time scheduling algorithms can be exploited to achieve the upper bound by predictably and timely switching on/off the devices composing the electrical system. The paper shows the relevance of electric load balancing in power systems to motivate the use of real-time techniques to achieve predictability of electric loads scheduling. Real-Time Physical Systems (RTPS) are introduced as a novel modeling methodology of a physical system based on real-time parameters. They enable the use of traditional real-time system models and scheduling algorithms, with adequate adaptations, to manage loads activation/deactivation. The model of the physical process considered in this work is characterized by uncertainties that are compensated by a suitable feedback control policy, based on the dynamic adaptation of real-time parameter values. A number of relevant relationships between real-time and physical parameters are derived.

Robust Data-Driven Modeling Approach for Real-Time Final Product Quality Prediction in Batch Process Operation

Abstract: Making on-specification products is a primary goal, and also a challenge in chemical batch process operation. Due to the uncertainty of raw materials and instability of operating conditions, it may not produce the desired on-spec final product. It would be helpful if one can predict the product quality during each operation, so that one can make adjustments to process conditions in order to make on-spec product. This paper addresses the issue of real-time prediction of final product quality during a batch operation. First, a data-driven modeling approach is presented. This multimodel approach uses available process information up to the current points to capture their time-varying relationships with the final product quality during the course of operation, so that the prognosis of product quality can be obtained in real-time. Then, due to its data-driven nature, the focus is given on how to make the models robust in order to eliminate the effect of noise, especially, outliers in the data. A model-based outlier detection method is presented. The proposed approach is applied to a generic chemical batch case study, with its prediction performance being evaluated.

A Data Mining Framework for Electricity Consumption Analysis From Meter Data

Abstract: This paper presents a novel data mining framework for the exploration and extraction of actionable knowledge from data generated by electricity meters. Although a rich source of information for energy consumption analysis, electricity meters produce a voluminous, fast-paced, transient stream of data that conventional approaches are unable to address entirely. In order to overcome these issues, it is important for a data mining framework to incorporate functionality for interim summarization and incremental analysis using intelligent techniques. The proposed Incremental Summarization and Pattern Characterization (ISPC) framework demonstrates this capability. Stream data is structured in a data warehouse based on key dimensions enabling rapid interim summarization. Independently, the IPCL algorithm incrementally characterizes patterns in stream data and correlates these across time. Eventually, characterized patterns are consolidated with interim summarization to facilitate an overall analysis and prediction of energy consumption trends. Results of experiments conducted using the actual data from electricity meters confirm applicability of the ISPC framework.

Mean Shift-Based Defect Detection in Multicrystalline Solar Wafer Surfaces

Abstract: This paper presents an automated visual inspection scheme for multicrystalline solar wafers using the mean-shift technique. The surface quality of a solar wafer critically determines the conversion efficiency of the solar cell. A multicrystalline solar wafer contains random grain structures and results in a heterogeneous texture in the sensed image, which makes the defect detection task extremely difficult. Mean-shift technique that moves each data point to the mode of the data based on a kernel density estimator is applied for detecting subtle defects in a complicated background. Since the grain edges enclosed in a small spatial window in the solar wafer show more consistent edge directions and a defect region presents a high variation of edge directions, the entropy of gradient directions in a small neighborhood window is initially calculated to convert the gray-level image into an entropy image. The mean-shift smoothing procedure is then performed on the entropy image to remove noise and defect-free grain edges. The preserved edge points in the filtered image can then be easily identified as defective ones by a simple adaptive threshold. Experimental results have shown the proposed method performs effectively for detecting fingerprint and contamination defects in solar wafer surfaces.

Coordination and Risk Analysis of VMI Supply Chains With RFID Technology

Abstract: RFID technology is an important tool in modern supply chain management. This paper analytically studies the use of RFID in a two-echelon single-manufacturer single-retailer supply chain with the vendor managed inventory (VMI) scheme. First, the supply chain models under a retail replenishment problem with and without RFID are constructed. Second, both the levels of risk and the expected profits of the supply chains are explored. Third, measures which can coordinate the supply chains with and without RFID are proposed. Fourth, comparisons between the cases with and without RFID are made. This paper analytically illustrates several important managerial insights which include: (i) when the RFID tag cost is very small, employing the RFID technology yields an improved supply chain with both larger expected profit and smaller risk; (ii) there exist multiple return policies which can coordinate the supply chain with RFID and the respective upper and lower bounds are identified; (iii) it is beneficial for the manufacturer to take the initiative to share the retailer's cost of RFID implementation, and this action not only can help coordinate the supply chain but also lower the manufacturer's risk; and (iv) compared to the case without RFID, the return rate under the coordinating return policy for the case with RFID can be lower if the RFID tag cost is appropriately shared between the retailer and the manufacturer. These insights are important for both industrialists and academicians.