On the design of secure primitives for real world applications

This editorial paper provides an overview of new advances on hardware security challenges and promising solutions for real world applications. It first presents the increasing hardware vulnerabilities with the emergence of diverse computing platforms. Then, it details the technical challenges, research opportunities, and potential solutions to cyber-attacks on -physical- devices.     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

Simulation of dance performance analysis with microprocessor and machine vision interaction

Dance is the expression of artists' favourite art forms such as express emotions, their body language, and the combination of dance art and stage effects in dance performance. In almost all genres of the art form, the effectiveness of the dance performance is largely due to the quality of the individual dancer's and group dance performance. Performing arts, particularly dance, it is one of the most important of intangible cultural heritage. However, due to the preservation, documentation, analysis, and visualization understanding of dance mode, it is difficult because of technical difficulties relations. The Proposed Machine Learning Support Decision Vector Machine (SDVM) algorithm and Field Programmable Gate Array (FPGA) is a dance expert watching dance due to the recognition task, the task knowledge of professional forecasters, gestures, and facial expressions and face-to-face conditions led to better synchronization of timing. In the proposed Machine learning SDVM algorithm, the results show that positive and dancers in the audience increased negative emotions; acceleration rate and body movement also increased. SDVM is classified as dancer performance based on the artist's facial expressions, stage performance, emotions. The simulation results show good results compared to other methods.     

基于ZigBee的温室监测系统的低功耗设计

针对传统温室大棚有线监测系统存在施工复杂、线路多和维护难等缺陷,提出了一种基于ZigBee无线传感网络技术的低功耗温室监测系统的设计方案;通过对系统各部分的能耗进行分析,结合实际情况对传感器节点采用低功耗设计策略;硬件设计上采用低功耗射频芯片和智能开关芯片,软件编写上采用事件驱动方法延长节点休眠时间;该系统能够准确采集温室内光照度、空气温湿度、土壤水分和二氧化碳浓度等环境信息,并具有低功耗、低成本和易扩展等特点;测试结果验证了该方案的可行性。     

HH神经元的自抗扰同步研究

因外部电磁环境变化导致生物神经元放电节律不同步是生物控制中需要解决的一个难点问题。HH神经元模型是描述神经元动力学特性的第一个数学模型,以HH神经元为研究对象,利用各种现代控制方法获得HH神经元的同步,方法的不足在于对外部干扰的敏感性以及控制律复杂不易实现。为增强神经元同步的鲁棒性和可行性,利用自抗扰控制算法,在三种不同情况下研究HH神经元的同步,并且依次加入不同干扰验证其同步效果。仿真结果表明,自抗扰控制能够对神经元系统的总扰动进行实时估计和补偿,消除扰动对神经元系统同步的影响,从而获得良好的同步效果。     

Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach

In this paper, we investigate the problem of time series forecasting using single hidden layer feedforward neural networks (SLFNs), which is optimized via multiobjective evolutionary algorithms. By utilizing the adaptive differential evolution (JADE) and the knee point strategy, a nondominated sorting adaptive differential evolution (NSJADE) and its improved version knee point-based NSJADE (KP-NSJADE) are developed for optimizing SLFNs. JADE aiming at refining the search area is introduced in nondominated sorting genetic algorithm II (NSGA-II). The presented NSJADE shows superiority on multimodal problems when compared with NSGA-II. Then NSJADE is applied to train SLFNs for time series forecasting. It is revealed that individuals with better forecasting performance in the whole population gather around the knee point. Therefore, KP-NSJADE is proposed to explore the neighborhood of the knee point in the objective space. And the simulation results of eight popular time series databases illustrate the effectiveness of our proposed algorithm in comparison with several popular algorithms.     

Novel associative classifier based on dynamic adaptive PSO: Application to determining candidates for thoracic surgery

Association rule mining is a data mining technique for discovering useful and novel patterns or relationships from databases. These rules are simple to infer and intuitive and can be easily used for classification in any domain that requires explanation for and investigation into how the classification works. Examples of such areas are medicine, agriculture, education, etc. For such a system to find wide adoptability, it should give output that is correct and comprehensible. The amount of data has been growing very fast and so has the search space of these problems. So we need to change traditional methods. This paper discusses a rule mining classifier called DA-AC (dynamic adaptive-associative classifier) which is based on a Dynamic Particle Swarm Optimizer. Due to its seeding method, exemplar selection, adaptive parameters, dynamic reconstruction of regions and velocity update, it avoids premature convergence and provides a better value in every dimension. Quality evaluation is done both for individual rules as well as entire rulesets. Experiments were conducted over fifteen benchmark datasets to evaluate performance of proposed algorithm in comparison with six other state-of-the-art non associative classifiers and eight associative classifiers. Results demonstrate competitive performance of proposed DA-AC while considering predictive accuracy and number of mined patterns as parameters. The method was then applied to predict life expectancy of post operative thoracic surgery patients.