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

One main difficulty in applying action recognition to practical applications is the need to segment beginnings and ends of actions in a continuous online monitoring process. This paper proposed a finite state machine (FSM) model for automatic action segmentation and recognition solution, based on pose streams in the form of skeleton joint data provided by Kinect. With the action recognition problem reframed as a state identification problem, the key solution to state identification hinges on detection of changing events, which signify the start of new action and the recognition of the underlying action. In that regard, a decision tree is constructed to detect these events based on the spatial positions and the changes of the skeleton data. In addition, to identify the current state or equivalently the action of a detected person, a fault observer is derived from the modeled action FSM. The fault observer does not only identify the initial state of action when the recognition process starts, but also serve the purpose of error recovery when the system loses track of ongoing events at times of intermittent sensor faults. Additionally, an AutoCorrect mechanism is presented to further enhance the accuracy of action recognition. To evaluate the proposed approach, an experiment with 300 participating subjects has been conducted for a total of 900 test sequences. The 98.63% correct identification result ensures the proposed approach a promising solution to constant action monitoring solution.

Automatic action segmentation and continuous recognition for basic indoor actions based on kinect pose streams

In this paper we present some performance comparisons of several unsupervised clustering techniques include: Self-Organizing Map (SOM), Fuzzy C-means (FCM) and hierarchical clustering, and they are employed to analyze the ovarian cancer microarray data. The data includes 15 samples with 9,600 genes and these samples include 5 benign ovarian tumors (OVT), 1 borderline ovarian malignancy (OVTT), 4 ovarian cancers at stage I (OVCAI), and 5 ovarian cancers at stage III (OVCAIII). A regression analysis is used to reduce the dimension and get 9600 residuals of genes. The genes with 100 largest and 100 smallest residual are picked to analyze using analysis of variance (ANOVA). After the ANOVA, 12 gene markers are got and can be used to distinguish OVT, OVTT, OVCAI and OVCAIII samples. The 12 gene markers are performed clustering by the SOM, FCM and hierarchical clustering techniques and to compare the results between these clustering techniques. Our experimental results show that the hierarchical clustering can get best performance of clustering and users do not need to define the number of clusters.

Performance Comparisons between Unsupervised Clustering Techniques for Microarray Data Analysis on Ovarian Cancer

In this study, a marine robotic system is designed to locate and to quantify the surface water pollution in lakes and ponds with the consideration of the influences of neighboring buildings and trees. The proposed system consists of an aerial robot and a marine robot, which are actuated by pattern recognition at the base station with safe path planning. In the designed system,on quadrotor a Forward Looking Infra Red (FLIR) thermal imaging camera is employed to spot oil spills following a neural network method, while a wireless digital camera is used to identify the apropos path by imparting edge detection techniques. The surrounding buildings and trees dysfunction the digital camera’s reflectivity, thereby disparate from optimal path planning. To dwindle the experimental impediment, the Voronoi algorithm is employed by fractionating the experiment’s domain into small sub-sections. After that, Dijkstra’s algorithm is considered to assure the optimal path planning from the starting point to the target. Fuzzy logic is introduced to direct boats towards the target. Global positioning system (GPS), sonar and navigation sensors based boat follow the trajectory defined by the aerial robot towards target point and take samples for quantification test through the spectrometer. These results then sent to the base station for pattern recognition using neural networks. Such systems can efficiently and effectively navigate the water pollution problems as disaster response.

Detection, Localization and Analysis of Oil Spills in Water Through Wireless Thermal Imaging and Spectrometer Based Intelligent System

This paper addresses the problem of designing robust static output-feedback controllers for nonlinear discrete-time interval systems with time delays both in states and in control input based on the Takagi-Sugeno fuzzy model. In the approach, we do not directly employ the Lyapunov approach, as do in most of traditional fuzzy control design approaches. Instead, sufficient conditions for guaranteeing the robust stability for the considered systems are derived in terms of the matrix spectral norm of the closed-loop fuzzy system. The sufficient conditions are further formulated into linear matrix inequalities so that the desired controller can be easily obtained by using the Matlab LMI toolbox.

Shun-Feng Su

Papers

Automatic action segmentation and continuous recognition for basic indoor actions based on kinect pose streams

Performance Comparisons between Unsupervised Clustering Techniques for Microarray Data Analysis on Ovarian Cancer

Detection, Localization and Analysis of Oil Spills in Water Through Wireless Thermal Imaging and Spectrometer Based Intelligent System

Fuzzy Control Design for Nonlinear Systems

Finite-time H∞ control for switched fuzzy systems: A dynamic adaptive event-triggered control approach