Preface. Automatic Train Operation System by Predictive Fuzzy Control (S. Yasunobu, S. Miyamoto). Application of Fuzzy Reasoning to the Water Purification Process (O. Yagashita, O. Itoh, M. Sugeno). The Application of a Fuzzy Controller to the Control of a Multi-Degree-of-Freedom Robot Arm (E.M. Scharf, N.J. Mandic). Optimizing Control of a Diesel Engine (Y. Murayama et al.). Development of Performance Adaptive Fuzzy Controllers with Application to Continuous Casting Plants (G. Bartolini et al.). A Fuzzy Logic Controller for Aircraft Flight Control (L.I. Larkin). Automobile Speed Control System Using a Fuzzy Logic Controller (S. Murakami, M. Maeda). An Experimental Study on Fuzzy Parking Control Using a Model Car (M. Sugeno, K. Murakami). A Fuzzy Controller in Turning Process Automation (Y. Sakai, K. Ohkusa). Design of Fuzzy Control Algorithms with the Aid of Fuzzy Models (W. Pedrycz). Human Operator's Fuzzy Model in Man-Machine System with a Nonlinear Controlled Object (K. Matsushima, H. Sugiyama). The Influence of Some Parameters on the Accuracy of a Fuzzy Model (J.B. Kiszka, M.E. Kochanska, D.S. Sliwinska). A Microprocessor Based Fuzzy Controller for Industrial Purposes (T. Yamazaki, M. Sugeno). The Application of Fuzzy and Artificial Intelligence Methods in the Building of a Blast Furnace Smelting Process Model (H. Zhao, M. Ma). An Annotated Bibliography of Fuzzy Control (R.M. Tong).

Industrial applications of fuzzy control

This chapter explains the meaning of basic terms and concepts related to expert systems, describes the architecture and functions of an expert system, and the conditions under which the building or purchase of an expert system is feasible. The benefits and disadvantages attached to the use of expert systems will be analysed. Also included are a description of the tools and methods used for building expert systems plus the criteria used to evaluate and compare these tools. Finally the reader will gain an understanding of the difficult process of knowledge acquisition and an awareness of the legal implications of the use of expert systems.

Expert systems

A Review of Smart Parking System and its Technology

本发明公开了一种包括：电力线通信系统、可见光通信系统和再生式中继控制器，电力线通信系统包括：电力线，与电力线相连的耦合器，与耦合器相连的电力线通信接收端电路；可见光通信系统包括：发光器件，与发光器件相连的可见光通信发射端电路；再生式中继控制器的输入端与电力线通信接收端电路相连，输出端与可见光通信发射端电路相连，用于对电力线通信接收端电路输出的信号进行解调、调制后，输出给可见光通信发射端电路，以实现可见光通信技术与电力线通信的结合，从而大幅降低了投资成本，实现了资源的综合利用。

A communication system

Network devices have always been considered as configurable black boxes until the emergence of software-defined networking (SDN). SDN enables the networks to be programmed according to the user requirements; furthermore, it allows the network to be easily modified to suit transient demands. However, how do we program the network? SDN-compliant switches offer a low-level interface that makes programming error-prone. The controllers provide application programming interfaces, but still low-level, limited, and inflexible. High-level languages have the potential to be a better alternative to program the network. There exist several SDN programming languages implementing different sets of functionalities, and focusing on solving various issues. In face of all this diversity, no published paper outlines a pragmatic view allowing to compare the SDN languages. This paper presents a systematic survey of up-to-date OpenFlow-based SDN programming languages. Our approach relies on a taxonomy comprising all prominent features found in those languages. A detailed review of the existing works is carried out investigating the foundational parts of the languages with their contributions. Examples are discussed to illustrate the fundamental abstractions. Last, all gathered information is summarized, discussing the main ongoing research efforts and challenges. Future abstractions and features to be incorporated into the next generations of SDN programming languages are also considered.

A Survey on SDN Programming Languages: Toward a Taxonomy

Linear Component node orchestration (LCNO) is a novel paradigm that focuses on the use of smart routing procedures to derive random connectivity among nodes in a Smart grid communication network (SGCN). It eliminates the limitations in distributed communication involving Machine to Machine interactions among edge nodes in the substations. To achieve the aims; first, the edge layer for data stream parameter collection (from both homogenous and heterogeneous sources) is automated. Second, homogeneous linear-like Internet of Things (IoT) clusters are introduced for modular deployment at the distribution network. Third, interface protection monitoring for linear-chain topology in SGCN is deployed. To study the effect of the LCNO for system resilience, a simple routing protocol (SRP) is compared with Power-Efficient Gathering in Sensor Information System (PEGASIS) and Low Energy Adaptive Clustering Hierarchy (LEACH). Quality of service (QoS) results show that SRP is more efficient for monitoring the frequency and voltage parameters.

Evaluation of Linear Component Node Orchestration for IoT Clusters in Smart Grid Communication Networks

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

Energy demand in most African countries is insufficient thereby hindering economic development. By leveraging renewable technologies, the control and management of renewable micro-grids requires a robust/reliable design that supports smart integration via Cloud for dynamic service delivery. Motivated by this concern, this paper developed smart green energy management system (SGEMS) for optimizing demand side management (DSM) in renewable micro-grids. The architecture employs solar photovoltaic (PV) to generate energy and meters user consumption pattern. SGEMS global model for solar PV metering platform is introduced including structured OpenStack Cloud application with active load-balancers, Trove/Hadoop Bigdata application program interface (API), OpenFlow firewall, and dynamic network scaling. These features manage user access with little computational overhead. An experimental demonstration of OpenStack/Amazon EC2 instance scenarios for transactional workload is briefly highlighted. The work discussed the usefulness of OpenStack engine as a distributed API middleware for SGEMS-energy application running on AWS EC2.

Kennedy Chinedu Okafor

Papers

Evaluation of Linear Component Node Orchestration for IoT Clusters in Smart Grid Communication Networks

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

Towards Smart Green Energy Metering Design for OpenStack/Amazon Elastic Cloud Integration