An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

/pdf/a-survey-on-analysis-and-design-of-model-based-fuzzy-control-3hrjlrel0k.pdf

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

Computational Analysis of Microarray Data

Our implementation of data mining skills for the data of the semiconductor manufacturing process is reported and the possible relationship between machines of the manufacturing process and the yield rates of wafers is discussed. In our implementation, we considered a full manufacturing process as a transactional data and viewed machines of the manufacturing process as objects of the transactional data. Therefore, the sum of transactional data were obtained from the objects constituted by machines. In our algorithm, we employed the hierarchical clustering methods to distinguish groups according to the similarity of objects. That information can be analyzed and be used to schedule the manufacturing process. Hopefully, the obtained information can provide references for improving the yield rates in the manufacturing process.

Hierarchical clustering methods for semiconductor manufacturing data

A novel detection concept, content-independent image processing system (CIIP system) is designed in this study. The regular life safety of elders who live alone is concerned. It is desired to detect possible falling behaviors and unconscious situations of elders in the case. We intend to use image processing techniques to detect elders' fall but without the concern of being watched. The idea is to put a very cheap wireless small camera on the user (elder) to recode environment images instead of users' motion images. The image process of this approach needs to find the motion information directly from the image instead of some known contents. Thus, this kind of approach is referred to as the content-independent image processing in our study. The proposed system uses several basic digital image processing skills and voting mechanisms to distinguish fall and unconscious situations from activities of daily living. The experiments conducted in our study show the effectiveness of the proposed approach.

Content-independent image processing based fall detection

The synchronization control problem for a class of nonlinear complex dynamical networks with network attacks and actuator faults is addressed in this paper. The main contributions are as follows: (1) The proposed controller can be used to compensate for nonlinearity and actuator faults simultaneously; (2) in contrast to existing network attacks results, in which an identity input matrix is considered, the input matrix considered in this paper is an arbitrary one and is more general. In particular, a new adaptive controller is developed by introducing a fuzzy logic system (FLS) to approximate the nonlinear uncertainty. By introducing an adaptive mechanism to estimate the norm of weight vector in the FLS, the proposed FLS-based controller can compensate for the effect of nonlinearity under actuator faults. By using matrix factorization technique and sliding mode control method, the developed method is shown to be resilient to network attacks. Moreover, it is proven that the proposed control method can guarantee asymptotic convergence of synchronization errors. Finally, an illustrative example is used to demonstrate the efficiency and effectiveness of the developed method.

Fuzzy Synchronization Control of Complex Dynamical Networks Under Network Attacks and Actuator Faults

This article focuses on global finite-time output feedback stabilization for uncertain nonlinear systems with unknown measurement sensitivity. The existence of the continuous measurement error resulting from limited accuracy of sensors invalidates the existing design strategies depending on the use of the precise output in the construction of an observer, which highlights the contribution of this article. Essentially, different from related works, we propose a new finite-time convergent observer by avoiding the use of the information on nonlinearities. By combining the homogeneous domination with the addition of a power integrator method, an output feedback controller composed of multiple nested sign functions is successfully developed. Finally, the effectiveness of the presented scheme is exhibited by a numerical example.

Global Finite-Time Stabilization for Uncertain Systems With Unknown Measurement Sensitivity.

A serious problem limiting the applicability of the fuzzy neural networks is the ”curse of dimensionality”, especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GA_FSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GA_FSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.

Shun-Feng Su

Papers

Hierarchical clustering methods for semiconductor manufacturing data

Content-independent image processing based fall detection

Fuzzy Synchronization Control of Complex Dynamical Networks Under Network Attacks and Actuator Faults

Global Finite-Time Stabilization for Uncertain Systems With Unknown Measurement Sensitivity.

A Dynamic Hierarchical Fuzzy Neural Network for a General Continuous Function