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

Human action recognition from video sequences is one of the most challenging computer vision applications, primarily owing to intrinsic variations in lighting, pose, occlusions, and other factors. The human skeleton joints extracted by the depth camera Kinect have the advantages of simplified structures and rich contents, and are therefore widely used for capturing human actions. However, at present, most of the skeletal joint and Deep learning based action recognition methods treat all skeletal joints equally in both spatial and temporal dimensions. Logically, this is not in accordance with the fact that for different human actions the contributions from skeletal joints could significantly vary spatially and temporally. Incorporating information pertaining to such natural variations will certainly aid in designing a robust human action recognitions system. Hence, in this work, we endeavor to propose a global spatial attention (GSA) model to suitably express the different skeletal joints with different weights so as to provide precise spatial information for human action recognition. Further, we will introduce the notion of accumulative learning curve (ALC) model that can highlight which frames contribute most to the final decision by giving varying temporal weights to each intermediate accumulated learning results provided by an LSTM upon input frames. The proposed GSA (for spatial information) and ALC (for temporal processing) models are integrated into the LSTM framework to construct a robust action recognition framework that takes the human skeletal joints as input and predicts the human action using the enhanced spatial-temporal attention model. Rigorous experiments on NTU datasets (by-far the largest benchmark RGB-D dataset) show that the proposed framework offers the best performance accuracy, least algorithmic complexity and training overheads, when compared with other state-of-the-art human action recognition models.

Robust Human Action Recognition Using Global Spatial-Temporal Attention for Human Skeleton Data

Dynamic Programming (DP) is widely used in Multiple Sequence Alignment (MSA) problems. However, when the number of the considered sequences is more than two, multiple dimensional DP may suffer from large storage and computational complexities. Often, progressive pairwise DP is employed for MSA. However, such an approach also suffers from local optimum problems. In this paper, we present a hybrid algorithm for MSA. The algorithm combines the pairwise DP and the particle swarm optimization (PSO) techniques to overcome the above drawbacks. In the algorithm, pairwise DP is used to align sequences progressively and PSO is employed to avoid the result of alignment being trapped into local optima. Several existing MSA tools are employed for comparison. The experimental results show excellent performance of the proposed algorithm.

Multiple sequence alignment using modified dynamic programming and particle swarm optimization

In this paper, a novel scheme of incorporating a learning mechanism into previous step supervisory controllers for adaptive fuzzy control is proposed to relax bounds required in the control process. In traditional supervisory adaptive fuzzy control approaches, the use of fuzzy estimators for approximating system functions and a robust supervisory control law are necessary to deal with any possible uncertainties caused in the system. This kind of supervisory controller depends on the robust bounds of system functions so that it can ensure the Lyapunov stability of controlled systems. However, in those approaches, the output may not be able to follow the reference trajectory well if the robust bounds are predicted improperly. In our implementation, CMAC (Cerebellar Model Articulation Controllers) is used as the learning mechanism because of its quick learning capability. Under the Lyapunov stable criterion, the proposed CMAC learning mechanism can improve the output performance and can relax the robust bound limitation so that practical systems can easily be realized. In summary, the proposed approach not only can relax bounds for previous step supervisory controllers in adaptive fuzzy control, but also can significantly improve the control performance of the system.

CMAC-based previous step supervisory control schemes for relaxing bound in adaptive fuzzy control

In this paper, a novel control scheme adopted from moment control is proposed. In the proposed approach, an adaptive fuzzy system is employed to learn the effective moment. It is easy to see that such an approach can avoid wild guessing for the effective moment, and as shown in our simulation, can have nice control performance. In traditional adaptive fuzzy control approaches, bounds of system functions are required to facilitate supervisory control so as to have the robust control property. It can be expected that when those bounds used in the supervisory controller are not proper, the output may not be able to follow the reference trajectory satisfactorily. With the proposed moment adaptive fuzzy control, the bound needed is only the supremum of the control variance between two consecutive steps. It is much easier to predict. In our study, in order to further relax this requirement, another adaptive system is employed to estimate the residue of the moment adaptive fuzzy control system. It is called residue compensation in this paper. It can be found that with residue compensation, the approach does not need a supervisory controller, but still can quickly track the reference in a satisfactory fashion. Various simulations are conducted to demonstrate the effectiveness of the proposed approaches.

Moment Adaptive Fuzzy Control and Residue Compensation

Statechart has been utilized as a visual formalism for the modeling of complex systems It illuminates the features on describing properties of causality and concurrency This paper focuses on the use of statecharts to model the preemption of emergency vehicles system The advantage of the proposed approach is the clear presentation of traffic lights' behaviors in terms of conditions and events that cause the preemption phase Moreover, the paper also proposes a new emergency vehicle preemption policy that provides the emergency vehicles can pass through the intersections with minimal delay The analysis of the control models is performed to demonstrate how the models enforce the lights' transitions by reachability tree method And then the reachability and reversibility properties of the control statecharts will be obtained

Shun-Feng Su

Papers

Robust Human Action Recognition Using Global Spatial-Temporal Attention for Human Skeleton Data

Multiple sequence alignment using modified dynamic programming and particle swarm optimization

CMAC-based previous step supervisory control schemes for relaxing bound in adaptive fuzzy control

Moment Adaptive Fuzzy Control and Residue Compensation

Modelling of emergency vehicle preemption systems using statecharts