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 study, we continue our analysis on the use of RLS in neural fuzzy systems. The recursive least square (RLS) algorithms can have great learning performance for neural fuzzy networks. From our previous work, it can be observed that the advantages of using RLS instead of using BP are not so obvious. For the use of forgetting factor in RLS, the idea is to account for the effects of the change in the premise part. In this study, we have observed that the use of a forgetting factor can still have some advantages when the premise part is fixed. The idea is similar to the used of Widrow-Hoff learning concept in the backpropagation learning algorithm. From our experiments, a strong forgetting factor (smaller value) can let the consequent part trace the error in the learning phase. But the testing error becomes very large. When the system capacity is sufficient, a forgetting factor will improve both in the learning phase and in the testing phase. Finally, the initial value of the covariance matrix is considered. The learning capacity will rise when the initial value increases. But it will increase the error tracing phenomenon in the consequent part too. But it is opposite in a system with less learning capacity.

Analysis of using RLS in neural fuzzy systems

This paper proposes an antilock braking system (ABS), in which unknown road characteristics are resolved by a road estimator. This estimator is based on the LuGre friction model with a road condition parameter, and can transmit a reference slip ratio to a slip ratio controller through a mapping function considering the effect of wheel angular velocity. In the controller design, a direct adaptive fuzzy-neural controller (DAFC) for an ABS is developed. Finally, this paper gives simulation results of an ABS with the road estimator and the DAFC, and shows good effectiveness under varying road conditions.

Dynamic slip ratio estimation and control of antilock braking systems considering wheel angular velocity

The design target of a supervisory controller (SC) is to stabilize the system states within a designate region. In this paper, we propose two improved SCs designed for SISO nonlinear system based on the Lyapunov stable theory. These improved SCs have the original stabilize function and moreover they can have acceptable control ability even if the system does not have a main controller. Since these SCs design is independent to the main controller, they can be applied to many different main controllers with the constraint that the bounds information of plant model must be known. In addition, we design a bounded lay to improve the chattering problem based on the improved SC form. Finally, we present numerical simulations to illustrate the correctness and practicability of the improved SCs.

Supervisory Controller Design Based On Lyapunov Stable Theory

The rapid development of artificial intelligence significantly promotes collision-avoidance navigation of maritime autonomous surface ships (MASS), which in turn provide prominent services in maritime environments and enlarge the opportunity for coordinated and interconnected operations. Clearly, full autonomy of the collision-avoidance navigation for the MASS in complex environments still faces huge challenges and highly requires persistent innovations. In this chapter, major advances in state-of-the-art autonomous maritime vessels and systems, and collision avoidance technologies, are thoroughly addressed in various maritime scenarios, from academic to industrial sides. Moreover, compositions of autonomous navigation and E-navigation technologies are analyzed to efficiently systematically clarify the mechanism and principles in typical maritime environments, whereby trade-off between autonomy and navigation action are highlighted. Finally, in light of overview of promising collision avoidance and action planning technologies, it is pointed out that collision-free navigation would significantly benefits the integration of MASS autonomy in various maritime scenarios.

Survey on Collision-Avoidance Navigation of Maritime Autonomous Surface Ships

Microarray analysis had been became a widely used tool for disease detection. It used tens of thousands of genes that would be a huge computational problem. The proposed approach applied feature genes selection and classification with support vector machine (SVM) for the microarray data of lung tissue. Based on the proposed approach, feature genes could be finding out according to the epsilon-support vector regression (epsilon-SVR) and selection ranked genes from each class. Moreover, applied multi-class support vector classification (multi-class SVC), cross-validation and parameter search methods to acquire great prediction classification accuracy and less computing time. That is, the effective dimension reduction for finding out feature genes is an important process in the proposed approach. The results shows the feature genes which our purposed approach finding out could acquire great prediction classification accuracy.

Shun-Feng Su

Papers

Analysis of using RLS in neural fuzzy systems

Dynamic slip ratio estimation and control of antilock braking systems considering wheel angular velocity

Supervisory Controller Design Based On Lyapunov Stable Theory

Survey on Collision-Avoidance Navigation of Maritime Autonomous Surface Ships

Feature genes selection and classification with SVM for microarray data of lung tissue