Optimized structure and electrochemical properties of sulfonated carbon nanotubes/Co–Ni bimetallic layered hydroxide composites for high-performance supercapacitors

Technical development in electronic devices is frequently stifled by their insufficient capacity and cyclic stability of energy-storage devices. The nano-structured materials have sensational importance for providing novel and optimized combination to overcome exiting boundaries and provide efficient energy storage systems. Metal hydroxide materials with high capacity for pseudo-capacitance properties have grabbed special attention. Lately, the blend of nickel and cobalt hydroxides has been considered as a favorable class of metallic hydroxide materials owing to their comparatively high capacitance and exceptional redox reversibility. The sulfonated carbon nanotube fluid (SCNTF) was prepared by the ion exchange method to be utilized as the exceptional templates due to astonishing specific surface area, ensuring the maximum utilization of the active material. The CoNi-layered double hydroxides (LDHs)/SCNTF core-shell nanocomposite was prepared by the simple solvothermal method. Structural analysis showed that the composite material had the high conductance of carbon materials, the pseudo-capacitance characteristics of metal hydroxides, and porous structure, which facilitates the ion shuttle when the electrolyte reacts with the active material. Electrochemical analysis results showed that CoNi-LDHs/SCNTF had excellent rate performance, reversible charge-discharge properties and cycle stability. It exhibited an extreme specific capacity of 1190.5 F g^?1 at a current density of 1 A g^?1; whereas specific capacity remained 953.7 F g^?1 at the current density was 10 A g^?1. In addition, the capacity retention rate after 5000 charge-discharge cycles at a current density of 20 A g^?1 was 81.0%. The results indicated that the CoNi-LDHs/SCNTF core-shell nanocomposite material is cost efficient and an effective substitute in energy storage applications.     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

Boosting discharge capability of α-Ni(OH)2 by cobalt doping based on robust spherical structure

α-Ni(OH)₂ is a promising candidate of the currently commercialized β-Ni(OH)₂ due to its higher theoretical discharge capacity in alkaline solution; however, its instability and poor conductivity plague the practical application. Herein, we propose α-Ni(OH)₂ with Co doping and spherical structure to strengthen the stability and enhance the conductivity and use it as the cathode for nickel-metal hydride batteries. Studies show that proper Co doping promotes the electrochemical reaction between the active materials and the electrolyte due to the spherical α-Ni(OH)₂ with enlarged interlayer distance and abundant hole channels, as well as high conductivity of Co, therefore, the obtained spherical α-Ni(OH)₂ with 7 mol% Co doping delivers significantly improved discharge capability, which is 384.6 mAh g^?1 at 70 mA g^?1 (0.2 C), increased by 54.3 mAh g^?1 compared with pure α-Ni(OH)₂, and at a high current of 5 C, it still gives 269.4 mAh g^?1, in contrast 218.5 mA g^?1 for the pure α-Ni(OH)₂. Besides, the cycling stability of the α-Ni(OH)₂ with 7 mol% Co doping maintains 340 cycles at a capacity retention of 80% (1C), which is extended 110 cycles in contrast to the pure α-Ni(OH)₂. These results provide the underpinning platform of α-Ni(OH)₂ for battery applications with high discharge ability and cycle life.     

基于双时间点检测的毫米波雷达人流量监测方法

针对现有基于视频监控的人流量统计方案成本高、算法复杂且不利于个人隐私保护的局限性，利用毫米波雷达体积小、成本低、分辨率高的特点，提出了一种基于双时间点检测的人流量监测方法。该方法先获取人体目标散射点位置和多普勒频移信息来构成点云数据，然后根据多普勒频移正负来判断人体的运动方向，并筛选具有高多普勒频移值的点云数据以降低干扰点对聚类结果的影响；在双时间点对特定区域内人员数量进行统计，并根据双时间点之间所获取的点云数据聚类结果对所统计人员数据进行修正。实验结果表明，该方法能够用匿名的方式以较高的正确率统计人员进出。     

Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one.Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions.     

Pattern design and optimization of yarn-dyed plaid fabric using isolation niche genetic algorithm and rough set theory

Under the circumstance of perceptual consumption, it is still challenging to grasp consumer's emotions and demands due to the large search space, diversified preferences, and easy fatigue of consumers. To reduce user fatigue and enlarge search space, a novel method was presented to design and optimize the pattern of yarn-dyed plaid fabric using the isolation niche genetic algorithm and rough set theory. Each pattern was encoded as a chromosome based on the real number code. The population was initialized and evolved using INGA to maintain the diversity. The rough set theory was adopted as the fitness function of isolation niche genetic algorithm to extract the consumer's demands. After multiple evolutions, a large set of practical patterns of the yarn-dyed plaid fabric are obtained. Experiments were carried out by 24 testers of different ages and genders. The results prove that the proposed method based on the isolation niche genetic algorithm and rough set theory is feasible and effective, supplying references to the designer.