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

The paper reports our study of image stabilization in a camera emulation system. In the study, a way of dealing with optical image stabilization is proposed to cope with blurring images caused by hand shake. The idea of the proposed image stabilization is to determine possible hand-shake situations based on the proposed rules and then to correct blurring image through position fuzzy control compensation. This method directly detects motion signals and employs a rule based mechanism to distinguish possible hand shake situations from normal camera movements. If a hand-shake situation is determined, the corresponding correction signal is generated to correct the image in a real-time fashion. In order to demonstrate the effectiveness of the proposed approach, in our implementation, the system directly moves the camera mounted on an X-Y platform to compensate the hand shake effects. Some statistics of the phenomena of the actual system are observed to form the scheme proposed in this study. From our experiments, the average percentage of shake reduction is 54.64%. It is clearly evident that this method indeed can have better image quality.

Fuzzy Based Compensation for Image Stabilization in a Camera Hand-Shake Emulation System

A fuzzy-based magnetic suspension vibrator (FMSV) is designed and embedded into electrical wheelchair to promote its shock absorbing ability. Magnetic levitation system (MLS), using the electromagnetic force to float, rotate or move a suspended object, is a non-contact operation mode. This mode can effectively reduce the mechanical vibrations, friction and wearing loss caused by contact operation. Furthermore, it can also prolong the equipment's life, reduce maintenance frequency and noise. However, the MLSs possess the characteristics of nonlinear and dynamic properties, so a robust adaptive fuzzy controller (AFC) for magnetic suspension vibrator is proposed in this study. With the help of magnetic suspension vibrator, the electric power wheelchair can steer in a bumpy road and provide more comfortable riding environment for disabled people or patients.

Design of fuzzy-based magnetic suspension vibrator for electric wheelchair

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. The rough set theory is successes to deal with imprecise, incomplete or uncertain for information system. In this paper, a novel regression approach, termed as the Rough Margin Support Vector Regression (RMSVR) network, is proposed to enhance the robust capability of SVR. The basic idea of the approach is to adopt the concept of rough sets to construct the model obtained by SVR and fine tune it with a robust learning algorithm. Simulation results of the proposed approach have shown the effectiveness of the approximated function in discriminating against outliers.

A rough-based robust support vector regression network for function approximation

Good action recognition relies on correct interpretation of two critical attributes related to action: the spatial attribute on the detected person's posture, and the temporal attribute on the detected person's body movement. Whereas deep learning has greatly improved image recognition, we have not found a similar progress for action recognition. One of the main reasons is due to the complexity caused by the additional temporal dimension; another, to the fact that there are less annotated training data samples for action recognition than that for image recognition. In this regard, this paper proposes a handcrafted cued LSTM model for human action recognition based on RGB-D data, as a collection of 25 skeleton joints in 3D coordinates, found in NTU-RGB-D, currently the most comprehensive dataset for action recognition. As opposed to the raw data of skeleton joints, handcrafted cues, pre-processed results geared to facilitate focused learning, are proposed as input to the LSTM structure. In particular, pertaining to the spatial cue, the SVIT cue derived by Skeleton View-invariant Transformation is adopted; pertaining to the temporal cue, the Diff cue computed by taking the displacements of all joint across down-sampled raw data is utilized. Based on the train/test protocol, the experiment we conducted on NTU-RGB-D shows that the recognition result based on either of the proposed handcrafted cues is better than that based on the raw data. In addition, by our proposed techniques of feature fusion and/or decision fusion of these two handcrafted cues, the recognition performance is better than that of the state-of-the-art approaches conducting on the same dataset by same train/test protocol.

Two-Stream LSTM for Action Recognition with RGB-D-Based Hand-Crafted Features and Feature Combination

In this paper, a novel approach is introduced for tracking human targets in cases of high influence from complexity of environment. In real world applications, the need of interaction in real time between a tracking system and human plays an important role. In the proposed approach, a RGB-D camera is utilized to acquire the depth information which is considered to define the Depth Of Interest (DOI). This DOI is used to combine with the CAM-shift algorithm in human tracking. The Kalman filter is also implemented to help in predicting the direction of target. Comparing with the original CAM-shift algorithm, our approach performs more accurate and more effective results. The experiment results also show that our system can be implemented for real-time applications as well.

Shun-Feng Su

Papers

Fuzzy Based Compensation for Image Stabilization in a Camera Hand-Shake Emulation System

Design of fuzzy-based magnetic suspension vibrator for electric wheelchair

A rough-based robust support vector regression network for function approximation

Two-Stream LSTM for Action Recognition with RGB-D-Based Hand-Crafted Features and Feature Combination

Real time human tracking using improved CAM-shift