Dispersed Wind Power Planning Method Considering Network Loss Correction with Cold Weather

In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.

A method for power suppliers’optimal cooperative bidding strategies considering network losses

The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.

A new network losses allocation method in deregulated environment

Network losses allocation is one of the major problems in the market environment. The quadric function of the injected nodal power is used in this paper as a representation for network losses, which are allocated fairly using the called market equilibrium principle while the bidding curves are corrected. The power market equilibrium is simulated as three different models that can be solved simply by the optimal power flow algorithm combining the generation scheduling problem with network losses allocation. The case study is made at an IEEE-30 nodes system and a perfect result is proved in this paper.

Water Supply Network Losses in Jordan

Water supply network losses are an international problem especially in countries suffering from water scarcity like Jordan. Jordan is one of the poorest countries in its water resources and it is estimated to be below the water poverty line. Jordan is located in the Middle East and has a surface area of approximately 90,000 km2. Its population is around 6.3 million and it is estimated that the population will be 7.8 million in 2022. The gap between water supply and demand is widening due to development and a relatively high population growth rate. In addition, global climate change is expected to intensify the water shortage problem in Jordan. Thirteen years of complete records obtained from the Ministry of Water and Irrigation were analyzed. According to these records, water losses in Jordan reach about 50%. In view of the evaluation of the data and the case study conducted in this research, it is believed that Jordan can overcome the water shortage problem by adopting a water demand management strategy. In this context, efforts should be focused on reducing water losses. If this is achieved, it will save huge quantities of water and revenue.

The Actuarial Data Intelligent Based Artificial Neural Network (ANN) Automobile Insurance Inflation Adjusted Frequency Severity Loss Reserving Model

This study proposes a novel approach for estimating automobile insurance loss reserves utilizing Artificial Neural Network (ANN) techniques integrated with actuarial data intelligence. The model aims to address the challenges of accurately predicting insurance claim frequencies, severities, and overall loss reserves while accounting for inflation adjustments. Through comprehensive data analysis and model development, this research explores the effectiveness of ANN methodologies in capturing complex nonlinear relationships within insurance data. The study leverages a data set comprising automobile insurance policyholder information, claim history, and economic indicators to train and validate the ANN-based reserving model. Key aspects of the methodology include data preprocessing techniques such as one-hot encoding and scaling, followed by the construction of frequency, severity, and overall loss reserving models using ANN architectures. Moreover, the model incorporates inflation adjustment factors to ensure the accurate estimation of future loss reserves in real terms. Results from the study demonstrate the superior predictive performance of the ANN-based reserving model compared to traditional actuarial methods, with substantial improvements in accuracy and robustness. Furthermore, the model’s ability to adapt to changing market conditions and regulatory requirements, such as IFRS17, highlights its practical relevance in the insurance industry. The findings of this research contribute to the advancement of actuarial science and provide valuable insights for insurance companies seeking more accurate and efficient loss reserving techniques. The proposed ANN-based approach offers a promising avenue for enhancing risk management practices and optimizing financial decision-making processes in the automobile insurance sector.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Traffic dynamics considering packet loss in finite buffer networks

In real complex systems, the limited storage capacity of physical devices often results in the loss of data. We study the effect of buffer size on packet loss threshold in scale-free networks. A new order parameter is proposed to characterize the packet loss threshold. Our results show that the packet loss threshold can be optimized with a relative small buffer size. Meanwhile, a large buffer size will increase the travel time. Furthermore, we propose a Buffered-Shortest-Path-First(BSPF) queuing strategy. Compared to the traditional First-In-First-Out(FIFO) strategy, BSPF can not only increase the packet loss threshold but can also significantly decrease the travel length and travel time in both identical and heterogeneous node capacity cases. Our study will help to improve the traffic performance in finite buffer networks.

Fault Tolerant Control for Networked Control Systems with Packet Loss and Time Delay

In this paper,a fault tolerant control with the consideration of actuator fault for a networked control system （NCS） with packet loss is addressed.The NCS with data packet loss can be described as a switched system model.Packet loss dependent Lyapunov function is used and a fault tolerant controller is proposed respectively for arbitrary packet loss process and Markovian packet loss process.Considering a controlled plant with external energy-bounded disturbance,a robust H ∞ fault tolerant controller is designed for the NCS.These results are also expanded to the NCS with packet loss and networked-induced delay.Numerical examples are given to illustrate the effectiveness of the proposed design method.

Loss forecasting of earthquake fire based on radial basis function network

According to complexity and multiplicity of the post-earthquake fire, the loss forecasting model of earthquake fire is established by using radial basis function neural network with adaptability, self-learning and fault-tolerant based on the historical information. The applicability and validity of the model is manifested through testing and discussion. A simple and available method is provided for the prediction of losses of other natural disaster.

Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Cooperative Loss Recovery for Reliable Multicast in Ad Hoc Networks

Providing reliable multicast service is very challenging in Ad Hoc networks. In this paper, we propose an efficient loss recovery scheme for reliable multicast (CoreRM). Our basic idea is to apply the notion of cooperative communications to support local loss recovery in multicast. A receiver node experiencing a packet loss tries to recover the lost packet through progressively cooperating with neighboring nodes, upstream nodes or even source node. In order to reduce recovery latency and retransmission overhead, CoreRM caches not only data packets but also the path which could be used for future possible use to expedite the loss recovery process. Both analytical and simulation results reveal that CoreRM significantly improves the reliable multicast performance in terms of delivery ratio, throughput and recovery latency compared with UDP and PGM.

Packet-loss-dependent stabilization of NCSs with network-induced delay and packet dropout

This paper is concerned with controller design of net- worked control systems （NCSs） with both network-induced delay and arbitrary packet dropout. By using a packet-loss-dependent Lyapunov function, sufficient conditions for state/output feedback stabilization and corresponding control laws are derived via a switched system approach. Different from the existing results, the proposed stabilizing controllers design is dependent on the packet loss occurring in the last two transmission intervals due to the network-induced delay. The cone complementary lineara- tion （CCL） methodology is used to solve the non-convex feasibility problem by formulating it into an optimization problem subject to linear matrix inequality （LMI） constraints. Numerical examples and simulations are worked out to demonstrate the effectiveness and validity of the proposed techniques.

Prediction of Pitting Corrosion Mass Loss for 304 Stainless Steel by Image Processing and BP Neural Network

Image processing technique was employed to analyze pitting corrosion morphologies of 304 stainless steel exposed to FeCl3 environments. BP neural network models were developed for the prediction of pitting corrosion mass loss using the obtained data of the total and the average pit areas which were extracted from pitting binary image. The results showed that the predicted results obtained by the 2-5-1 type BP neural network model are in good agreement with the experimental data of pitting corrosion mass loss. The maximum relative error of prediction is 6.78%.

基于YOLOv3的金属表面缺陷检测研究

为了解决金属表面缺陷检测的漏检、误检等问题,提出了一种改进YOLOv3算法。首先,使用动态激活函数替换主干特征提取网络中所有残差块的激活函数,并加入了混合注意力机制,强化其对复杂缺陷目标的特征提取能力。然后,在特征金字塔网络部分新增一个104×104的特征层,并将浅层网络与深层网络进行逐层特征融合,增强算法对小缺陷目标检测的敏感性。最后,利用K-Means++聚类算法替换K-Means聚类算法,筛选出适用于金属表面缺陷检测的最优先验框尺寸,使目标定位更加准确。实验结果表明,改进YOLOv3算法的每秒检测帧数(frames per second,FPS)可达到32.3,平均精度均值(mean average precision,mAP)可达到78.69%,检测性能得到了明显提升。

基于改进YOLOv5s的无人机图像识别

无人机在军事情报、航拍检测等领域能够提供目标相关的图像信息,为处理任务提供目标信息。针对无人机图像背景复杂、检测目标小、可提取特征少等问题,提出基于YOLOv5s的改进无人机图像识别算法。首先,结合CotNet模块对网络结构进行优化,提升模型自学习能力并增强识别精度;其次,对颈部网络进行改进,通过跨层链接和提高特征图分辨率更好地利用浅层特征图中包含的丰富信息来定位目标,并且在检测头部分采用解耦检测头,减少预测过程中定位与分类任务对于特征信息的冲突;最后,为了提高收敛速度和模型精度,在CIoU和EIoU损失函数的基础上对损失函数的宽高纵横比进行优化。在公开数据集VisDrone测试集上进行测试,所提算法相比原始YOLOv5s算法的mAP_(50)与mAP_(50∶95)分别提升了6.1与2.9个百分点,实验结果表明,所提模型能够有效提升无人机图像识别的准确率。

电气设备剩余电压测试仪校准方法

分析了电气设备剩余电压测试仪基本原理,提出一种基于标准阻容网络法对剩余电压测试仪剩余电压幅值和延迟时间参数进行校准的方法。结合交流标准电阻器和电容器等效模型,分析了标准电阻时间常数和标准电容损耗因数对校准结果的影响,推导了校准时剩余电压和延迟时间标准值的计算方法。采用1 MΩ交流标准电阻和1μF标准电容器组成的电路网络对剩余电压测试仪进行了校准,结果表明标准阻容网络法可满足该仪器的校准。对测量结果进行了不确定度分析,剩余电压幅值测量不确定度为1.1%,延迟时间测量不确定度为1.2%,主要不确定度分量为标准电容器损耗因数的影响。

基于改进FWA的配电网优化重构方法研究

针对配电网重构问题,以降低网络损耗、提高节点电压为目标,建立了配电网重构模型,提出了一种解决配电网重构问题的改进烟花算法(firework algorithm,FWA)。算法使用环路编码压缩解空间,提高求解效率。基于核心烟花给出了火花爆炸幅度的计算方法,采用高斯和均匀分布均值混合的变异方式以及精英选择策略,提高解的多样性、搜索效率和收敛速度。为了验证所提算法的适用性和正确性,在IEEE33和IEEE69节点系统上进行了算法测试,结果表明,重构后的网络损耗明显降低,节点电压得到提高,所提算法具有较好的鲁棒性。将所提算法与其他算法进行比较,结果表明,所提算法在降低网络损耗、提高节点电压、求解效率等方面均优于其他算法。

行人搜索算法综述

随着深度学习技术的快速发展,行人搜索算法的研究得到大量学者的关注。行人搜索是在行人检测和行人重识别任务的基础上在图像中寻找特定目标行人。该文对近年来行人搜索任务相关研究进展进行了详细梳理。按照模型网络结构和损失函数两方面对现有方法进行分析和总结。依据卷积神经网络和Transformer两类不同的技术路线,重点阐述各自代表性方法的主要研究工作;并按照传统损失函数、OIM损失函数及混合损失函数对行人搜索采用的训练损失函数进行详细总结。此外,总结了行人搜索任务领域常用的公开数据集,比较和分析了主要算法在相应数据集上的性能表现。最后总结了行人搜索任务的未来研究方向。

基于深度神经网络的台风中心定位方法

台风中心定位的微小误差会对台风路径预报造成较大的偏离,因此精确定位台风中心是台风路径预测和灾害预报的重要步骤。台风云系随时间不断变化且风力强弱不一,在卫星云图中呈现了多样性和复杂性,现有基于神经网络的模型由于缺少对台风特征图像多维度参数的权重合理分配,在自动提取台风图像特征上受到了限制。为此,提出一种融合通道注意力与坐标注意力的神经网络模型(TY-LOCNet),首先搭建深度卷积神经网络模型提取台风特征;其次引入通道注意力机制从台风特征中捕获通道级别的信息,提升模型对重要通道的关注度;然后将通道注意力结果输入到坐标注意力机制中全局标定台风位置信息,使模型能够在较大的区域关注到台风的形态结构;此外,均方误差损失函数未能融合计算坐标导致定位精度低,因此提出距离损失函数(DISTLoss)通过距离回归提高模型定位精度。实验结果表明,TY-LOCNet的平均位置误差(MLE)、平均定位误差(MAE)和检测速度分别为3.502像素,0.292°和17 FPS,优于其他模型。台风中心定位模型TY-LOCNet可为台风预报提供实时性台风中心定位支持。