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

In this paper, a two-loop PID (proportional integral derivative) controller is presented for a class of solar water-source heat pump systems (SWSHPS), which provide warm water with desired temperature for a semiconductor factory. A combination of the well-known particle swarm optimization (PSO) and real-coded genetic algorithm (RGA) is proposed to off-line optimally find the controller parameters of the proposed control law. Through simulations, the two-loop PID control strategy is shown to be effective and robust against parameter variations. By comparing with two existing two-loop PID control strategies, the proposed two-loop PID controller, tuned by the new PSO-RGA algorithm, is shown to give superior transient performance, smaller steady-state errors and better robustness against parameter variations.

Two-loop PID control using PSO-RGA algorithm for solar heat pumps

This work proposed a methodology of designing controllers for affine TSK fuzzy models. For hybrid compensation control (HCC) design technique in which fuzzy controllers share the same premise parts with the considered fuzzy systems and that can design controllers directly for affine fuzzy systems. On the other hand, the closed loop performance can also be theoretically anticipated. The modeling error always exists between real model and TSK fuzzy model. Hence, the HCC fuzzy controller can not achieve the performance in the real application. We added the third term to compensate the modeling error by sliding concept. Thus, the performance can be achieved with the real model by means of this three terms compensation fuzzy controller.

http://ieeexplore.ieee.org/iel5/9459/30161/01384606.pdf

A sliding manner compensation control for affine TSK fuzzy control systems

Prescription dispensing accuracy is of paramount importance for all hospitals. However, human errors are inevitable due to multiple reasons, such as fatigue, stress, heavy workload, lack of effective verification measures, mismanagement. Such human errors pose serious safety and health concerns on the part of patients and may as well lead to a series of medical disputes. Based on induced deep learning, this paper proposes a real-time Blister Package Identification System (BPIS) to assist pharmacists’ drug verification and dispensing. Under the guidance of the induction strategy, image preprocessing is introduced to form a standardized image containing the front and back side of the blister package, which is subsequently sent to CNN-based object identification network for feature extraction and identification. This preprocessing method allows the identification system to promote the deep learning system to focus on feature learning to obtain more information about the appearance of the package ruling out confounding factors such as background noise, size, shape or positioning. In addition, this article collects and establishes an image dataset of adult lozenges. Under this dataset, this paper verifies the enhancement of Induced Deep Learning (IDL) on YOLO v2, ResNet, and SENet. By optimizing the deep learning identification network with the help of the embedded technology and a two-side extraction mechanism, a real-time BPIS is built. Long-term tests in hospitals prove the effectiveness of the proposed system.

/pdf/pharmaceutical-blister-package-identification-based-on-4ci1r0jr8b.pdf

Pharmaceutical Blister Package Identification Based on Induced Deep Learning

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 (GAFSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GAFSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.

A dynamic hierarchical fuzzy neural network for a general continuous function

In this paper, a novel algorithm describing ant colonies, with cooperation, is proposed to solve the resource allocation problem. The resource allocation problem is to allocate resources to activities, with the objectiveof optimizing the cost function. In our study, we viewed the search in ant colonies as a mechanism providing a main portion of diversity in search space. The cooperative process conducts fine-tuning for the solution provided by ant colonies, and it has the ability to escape from poor local optima. In this paper, several examples are tested to prove the superiority of our proposed algorithm. From simulation results, the proposed algorithm indeed has remarkable performance.

Shun-Feng Su

Papers

Two-loop PID control using PSO-RGA algorithm for solar heat pumps

A sliding manner compensation control for affine TSK fuzzy control systems

Pharmaceutical Blister Package Identification Based on Induced Deep Learning

A dynamic hierarchical fuzzy neural network for a general continuous function

Ant colonies with cooperative process applied to the resource allocation problem