Optical and electronic properties of some semiconductors from energy gaps

II-VI and III-V tetrahedral semiconductors have significant potential for novel optoelectronic applications. In the present work, some of the optical and electronic properties of these groups of semiconductors have been studied using a recently proposed empirical relationship for refractive index from energy gap. The calculated values of these properties are also compared with those calculated from some well known relationships. From an analysis of the calculated electronic polarisability of these tetrahedral binary semiconductors from different formulations, we have proposed an empirical relation for its calculation. The predicted values of electronic polarisability of these semiconductors agree fairly well with the known values over a wide range of energy gap. The proposed empirical relation has also been used to calculate the electronic polarisability of some ternary compounds.     

Understanding the galvanic corrosion of the Q-phase/Al couple using SVET and SIET

The galvanic corrosion of the Q-phase/Al couple in 0.1 M NaCl solutions has been studied using the scanning vibrating electrode technique (SVET), the scanning ion-selective electrode technique (SIET) and energy dispersive X-ray spectroscopy (EDX). The galvanic corrosion of the Q-phase/Al couple was found to be dependent on pH and immersion time. Current density maps obtained by SVET shows that the anodic oxidation processes emerge from Al in a localized manner in pH 2 and 6 solutions but is initiated in a uniform manner in pH 13 solution, whereas, the cathodic processes are more homogeneously distributed over the Q-phase at pH 2, 6 and 13. It is seen that the Q-phase remains cathodic in the Q-phase/Al couple in acidic, neutral and alkaline solutions indicating that the galvanic polarity of the Q-phase is independent of pH. The effect of the galvanic corrosion was largest at pH 2 and 13 compared to pH 6. The pH map obtained by SIET indicates that the galvanic activity of the Q-phase/Al couple proceeds via heavy alkalization of the Q-phase surface with the generation of appreciable amounts of OH⁻ ions. The enrichment of Cu indicated by EDX is responsible for the observed cathodic activity of the Q-phase in the Q-phase/Al couple.     

Unpowered Knee Exoskeleton Reduces Quadriceps Activity during Cycling

Cycling is an eco-friendly method of transport and recreation. With the intent of reducing the energy cost of cycling without providing an additional energy source, we have proposed the use of a torsion spring for knee-extension support. We developed an exoskeleton prototype using a crossing four-bar mechanism as a knee joint with an embedded torsion spring. This study evaluates the passive knee exoskeleton using constant-power cycling tests performed by eight healthy male participants. We recorded the surface electromyography over the rectus femoris muscles of both legs, while the participants cycled at 200 and 225 W on a trainer with the developed wheel-accelerating system. We then analyzed these data in time–frequency via a continuous wavelet transform. At the same cycling speed and leg cadence, the median power spectral frequency of the electromyography increases with cycling load. At the same cycling load, the median power spectral frequency decreases when cycling with the exoskeleton. Quadriceps activity can be relieved despite the exoskeleton consuming no electrical energy and not delivering net-positive mechanical work. This fundamental can be applied to the further development of wearable devices for cycling assistance.     

Optimizing Decision-Making of A Smart Prosumer Microgrid Using Simulation

Distributed renewable energy sources offer significant alternatives for Qatar and the Arab Gulf region’s future fuel supply and demand. Microgrids are essential for providing dependable power in difficult-to-reach areas while incorporating significant amounts of renewable energy sources. In energy-efficient data centers, distributed generation can be used to meet the facility’s overall power needs. This study primarily focuses on the best energy management practices for a smart microgrid in Qatar while taking demand-side load management into account. This article looked into a university microgrid in Qatar that primarily aimed to get all of its energy from the grid. While diesel generators are categorized as a dispatchable distributed generation with energy storage added to handle solar radiation from the sun and high grid power operating costs in the suggested scenario, wind turbines and solar Photovoltaic (PV) are classified as non-dispatchable distributed generators. The resulting linear math issues are assessed and displayed in MATLAB optimization software using a mixed-integer linear programming (MILP) strategy. According to the simulation results, the suggested energy management strategy reduced the university microgrid’s grid power costs by 38.8%, making it an affordable solution which is somehow greater than the prior case scenario’s 23% savings. The installed solar system capacity’s effects on the economy, society, and finances were also assessed, and it became clear that the best option for the smart microgrid was determined that would be 325 kW of solar PV, 25 kW of wind turbine, and 600 kW of diesel generators, respectively. Given the current situation, university administrators are urged to participate in distributed generators and adopt cutting-edge designs for energy storage technologies due to the significant environmental and financial benefits.     

CHV190系列变频器在永磁同步电机驱动矿井提升机中的应用

本文分析了矿井提升机的运行特点,详细阐述了英威腾CHV190系列起重提升专用变频器用于永磁同步电动机驱动的节能型矿井提升机的选型配置原则,最后介绍了应用现场的调试步骤及注意事项。     

Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure

Clustering analysis of temporal gene expression data is widely used to study dynamic biological systems, such as identifying sets of genes that are regulated by the same mechanism. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which imposes challenges for traditional clustering methods of extracting meaningful information. In this paper, we introduce an improved clustering approach based on the regularized spline regression and an energy based similarity measure. The proposed approach models each gene expression profile as a B-spline expansion, for which the spline coefficients are estimated by regularized least squares scheme on the observed data. To compensate the inadequate information from noisy and short gene expression data, we use its correlated genes as the test set to choose the optimal number of basis and the regularization parameter. We show that this treatment can help to avoid over-fitting. After fitting the continuous representations of gene expression profiles, we use an energy based similarity measure for clustering. The energy based measure can include the temporal information and relative changes of the time series using the first and second derivatives of the time series. We demonstrate that our method is robust to noise and can produce meaningful clustering results.     

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.     

基于UC-7420的电能量管理终端的设计与实现

在智能电网的建设中,电能量管理系统是必备的系统,电能量管理终端又是此系统的重要组成部分。本文介绍一种基于MOXA公司的UC-7420平台的电能量管理终端,UC-7420的CPU是精简指令集IntelXscale架构的IXP422处理器,预装了嵌入式Linux操作系统。用户软件采用模块化设计,各个模块由单独的线程实现,整体采用多线程技术。终端可以启用多个抄表线程,极大地提高了抄表的效率和电力数据的实时性;添加了shell脚本,提高了终端的稳定性和可靠性。     

Energy constrained resource allocation optimization for mobile grids

A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted.     

Energy-efficient cooperative data aggregation for wireless sensor networks

Recently, cooperative communication mechanism is shown to be a promising technology to improve the transmit diversity only by a single transceiver antenna. Using this communication paradigm, multiple source nodes are able to coordinate their transmissions so as to obtain energy savings. As data aggregation is one of the most important operations in wireless sensor networks, this paper studies the energy-efficient data aggregation problem through cooperative communication. We first define the cooperative data aggregation (CDA) problem, and formally prove that this problem is NP-Hard. Due to the difficult nature of this problem, we propose a heuristic algorithm MCT for cooperative data aggregation. The theoretical analysis shows that this algorithm can reach the approximate performance ratio of 2. Moreover, the distributed implementation DMCT of the algorithm is also described. We prove that both centralized and distributed algorithms can construct the same topology for cooperative data aggregation. The experimental simulations show that the proposed algorithms will decrease the power consumption by about 12.5% and 66.3% compared with PEDAP and PEGASIS algorithms respectively.