一种直升机避障电力线检测方法

电力线是一类形状细长、特征稀疏、随着视角的变化容易混淆在大量背景信息中的特殊障碍物，常规电力线检测识别算法得到的目标框对电力线所在位置的估计不够准确。为此，提出了一种相对角度估计方法，基于常规电力线目标检测与识别算法，并结合电力线相对角度估计，从而提高电力线的检测识别过程中所在位置的精度。相比电力线绝对角度回归的方法，提出的相对角度估计方法容易训练易收敛，计算量小，适用于实时性要求较高的应用场合。     

Stable power supply of an independent power source for a remote island using a Hybrid Energy Storage System composed of electric and hydrogen energy storage systems

We propose a self-sustaining power supply system consisting of a “Hybrid Energy Storage System (HESS)” and renewable energy sources to ensure a stable supply of high-quality power in remote islands. The configuration of the self-sustaining power supply system that can utilize renewable energy sources effectively on remote islands where the installation area is limited is investigated. It is found that it is important to select renewable energy sources whose output power curve is close to the load curve to improve the efficiency of the system. The operation methods that can increase the cost-effectiveness of the self-sustaining power supply system are also investigated. It is clarified that it is important for increasing the cost effectiveness of the self-sustaining power supply system to operate the HESS with a smaller capacity of its components by setting upper limits on the output power of the renewable energy sources and cutting the infrequent generated power.     

IRS辅助的SWIPT系统中基于能效优先的波束成形设计与优化

以智能反射面(intelligent reflecting surface,IRS)辅助的无线携能通信(simultaneous wireless information and power transfer,SWIPT)系统为背景,研究了该系统中基于能效优先的多天线发送端有源波束成形与IRS无源波束成形联合设计与优化方法。以最大化接收端的最小能效为优化目标,构造在发送端功率、接收端能量阈值、IRS相移等多约束下的非线性优化问题,用交替方向乘子法(alternating direction method of multipliers,ADMM)求解。采用Dinkelbach算法转化目标函数,通过奇异值分解(singular value decomposition,SVD)和半定松弛(semi-definite relaxation,SDR)得到发送端有源波束成形向量。采用SDR得到IRS相移矩阵与反射波束成形向量。结果表明,该系统显著降低了系统能量收集(energy harvesting,EH)接收端的能量阈值。当系统总电路功耗为?15 dBm时,所提方案的用户能效为300 KB/J。当IRS反射阵源数与发送天线数均为最大值时,系统可达最大能效。     

Systematic analysis and multi-objective optimization of an integrated power and freshwater production cycle

This study represents the results of the analysis and optimization of an integrated system for cogenerating electricity and freshwater. This setup consists of a Solid Oxide Fuel cell (SOFC) for producing electricity. Unburned fuel of the SOFC is burned in the afterburner to increase the temperature of the SOFC's outlet gasses and operate a Gas turbine (GT) to produce additional power and operate the air compressor. At the bottom of this cycle, a combined setup of a Multi-Effect Desalination (MED) and Reverse Osmosis (RO) is considered to produce freshwater from the unused heat capacity of the GT's exhaust gasses. Also, a Stirling engine is used in the fuel supply line to increase the fuel's temperature. Using LNG and the Stirling engine will replace the fuel compressor with a pump which increases the system performance and eliminates the need for the expansion valve. To study the system performance a mathematical model is developed in Engineering Equation Solver (EES) program. Then, the system's simulated data from the EES has been sent to MATLAB to promote the best operating condition based on the optimization criteria. An energetic, exergetic, economic, and environmental analysis has been performed and a Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to achieve the goal. The two-objective optimization is performed to maximize the exergetic efficiency of the proposed system while minimizing the system's total cost of production. This cost is a weighted distribution of the Levelized Cost of Electricity (LCOE) and Levelized Cost of freshwater (LCOW). The results showed that the exergetic and energetic efficiencies of the system can reach 73.5% and 69.06% at the optimum point. The total electricity production of the system is 99 MW. The production cost is 11.71 Cents/kWh, of which 1.04 Cents/kWh is emission-related and environmental taxes. The freshwater production rate is 42.44 kg/s which costs 4.38 USD/m³.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

多堆厂址始发事件分析探讨

多堆厂址一级概率安全评价（PSA）研究中，机组数目的增加使得建模工作量剧增，给整个核电厂的风险评估带来困难。结合已有基础，本文研究了多堆厂址始发事件分析的筛选方法，提出利用堆芯损伤频率（CDF）上下限值评估方法，分析厂址内不同机组数对厂址CDF的影响。结果表明，双机组厂址适合优先进行具体分析。针对双机组核电站，对多堆厂址内各始发事件进行筛选。结果表明，丧失厂外电、丧失热阱等事件适合建模分析，并对其他筛选结果给出后续分析建议，为多堆厂址一级PSA后续事故序列建模工作提供了重要基础。     

Bioinspired Triboelectric Soft Robot Driven by Mechanical Energy

A sustainable power source is a key technical challenge for practical applications of electrically responsive soft robots, especially the required voltage is over several thousand volts. Here, a practicable new technology, triboelectric soft robot (TESR) system with the primary characteristics of power source from mechanical energy, is developed. At its heart is TESR with bioinspired architectures made of soft-deformable body and two triboelectric adhesion feet, which is driven and accurately controlled through triboelectric effect, while reaching maximum crawling speeds of 14.9 mm s⁻¹ on the acrylic surface. The characteristics of the TESR, including displacement and force, are tested and simulated under the power of a rotary freestanding triboelectric nanogenerator (RF-TENG). Crawling of TESR is successfully realized on different materials surfaces and different angle slopes under the driven of RF-TENG. Furthermore, a real-time visual monitoring platform, in which TESR carries a micro camera to transmit images in a long narrow tunnel, is also achieved successfully, indicating that it can be used for fast diagnosis in an area inaccessible to human beings in the future. This study offers a new insight into the sustainable power source technologies suitable for electrically responsive soft robots and contributes to expanding the applicability of TENGs.     

Design optimization for the hybrid power system of a green building

This paper proposes an optimal design procedure for a green building equipped with renewable energy, energy storages, and proton exchange membrane fuel cells (PEMFCs). First, we introduce the hybrid power system of the green building and construct a simulation model using Matlab/SimPowerSystem?. The model parameters are tuned so that the system responses can be estimated without extensive experiments in the optimization processes. Second, we define the cost and reliability indexes to optimize the system design using three steps: component selection, component sizing, and power management (PM) adjustment. We further define the safety index to evaluate the system's sustainability under extreme conditions when no renewable energy is available. Last, we apply the proposed procedures to the green building and demonstrate the benefits of the optimal design. The proposed method can be directly applied to develop customized hybrid power systems in the future.     

From Oxadiazole to Triazole Analogues: Optimization toward a Dual Orexin Receptor Antagonist with Improved in vivo Efficacy in Dogs

The orexin system is responsible for regulating the sleep-wake cycle. Suvorexant, a dual orexin receptor antagonist (DORA) is approved by the FDA for the treatment of insomnia disorders. Herein, we report the optimization efforts toward a DORA, where our starting point was (5-methoxy-4-methyl-2-[1,2,3]triazol-2-yl-phenyl)-{(S)-2-[5-(2-trifluoromethoxy-phenyl)-[1,2,4]oxadiazol-3-yl]-pyrrolidin-1-yl}methanone ( 6 ), a compound which emerged from our in-house research program. Compound 6 was shown to be a potent, brain-penetrating DORA with in vivo efficacy similar to suvorexant in rats. However, shortcomings from low metabolic stability, high plasma protein binding (PPB), low brain free fraction (f_u brain), and low aqueous solubility, were identified and hence, compound 6 was not an ideal candidate for further development. Our optimization efforts addressing the above-mentioned shortcomings resulted in the identification of (4-chloro-2-[1,2,3]triazol-2-yl-phenyl)-{(S)-2-methyl-2-[5-(2-trifluoromethoxy-phenyl)-4H-[1,2,4]triazol-3-yl]-pyrrolidin-1-yl}l-methanone ( 42 ), a DORA with improved in vivo efficacy compared to 6 .     

计及RSC控制的DFIG短路电流直流分量解析表达

现有文献针对计及转子变流器(RSC)控制的双馈感应风电机组(DFIG)定子短路电流解析表达,将定子磁链当作一阶直流衰减分量或忽略功率外环控制。基于DFIG电压、磁链和RSC控制方程,得到定子电流关于定子电压和定子功率的传递函数,提出定子电流的精确解析表达式。基于RSC内、外环PI参数关系,推导直流分量衰减时间常数和角频率关于PI参数的表达式。分析了RSC内外环PI参数对定子电流直流衰减分量的影响。仿真结果验证了解析表达式的准确性,为PI参数选取和保护装置测量、整定提供依据。