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%.

结合改进VGGNet和Focal Loss的人脸表情识别

针对目前表情识别准确率偏低,表情数据集中类别样本类间差异小、类内差异大以及误标注样本产生的误分类等问题,提出了一种结合改进VGGNet和Focal Loss的人脸表情识别算法。在迁移学习的基础上,通过设计新的输出模块对VGGNet模型进行改进,提升了模型的特征提取能力,能够较好地避免过拟合现象;通过设置概率阈值对Focal Loss进行改进,避免误标注样本对模型分类性能产生影响。实验结果表明,该模型在CK+、JAFFE以及FER2013数据集上的识别准确率分别达到了99.68%、97.61%和72.49%,在实际应用中泛化能力突出。

Predictive Tracking Control of Network-Based Agents With Communication Delays

This paper investigates the remote tracking control problem of Network-based Agents with communication delays existing in both forward and feedback communication channels.A networked predictive tracking controller is proposed to compensate the negative effects caused by bilateral time-delays in a wireless network. Furthermore, the problem of consecutive data loss in the feedback channel is solved using aforementioned controller, where lateral movement perturbations are introduced.Simulations and experiments are provided for several cases,which verify the realizability and effectiveness of the proposed controller.

基于双流卷积与双中心loss的行为识别研究

针对行为视频中相似动作类内差异大、类间差异小,识别准确率不高的问题,提出了一种基于双流卷积网络与双中心loss的行为识别方法.该方法首先构建双流卷积网络结构,以C3Dnet模型作为双流结构的基础模型,分别提取多尺度RGB视频帧中的表观短时运动信息和堆叠光流图中的长时运动信息;然后将双流结构提取的深度信息经长短时记忆(LSTM)网络解析后进行特征融合;最后,利用基于双中心loss的2C-softmax目标函数,来最大化类间距离和最小化类内距离,从而实现相似动作的分类与识别.在数据集KTH上的实验结果表明,该方法能够准确识别相似动作,识别准确率可达98.2%,具有很好的识别效果.

基于Class Balanced Loss修正交叉熵的非均衡样本信用风险评价模型

针对传统信用风险预测模型存在对非违约样本识别过度、对违约样本识别不足的问题,将平衡损失Class Balanced Loss函数引入信用风险评价,构建Class Balanced Loss修正交叉熵的非均衡样本信用风险评价模型。利用所建模型与交叉熵神经网络、支持向量机、决策树、随机森林和K最近邻5种分类模型进行对比,验证BPNN-CBCE对中国某金融机构1 534笔农户贷款数据信用风险预测的有效性;在此基础上,利用UCI公开的德国信贷数据验证BPNN-CBCE模型的稳健性。研究表明:对于农户数据,BPNN-CBCE模型在AUC、违约召回率Default recall方面普遍优于BPNN-CE、SVM、DT、RF和KNN模型,其中,BPNN-CBCE的Default recall相比5种对比模型提升了41.3个百分点,AUC相比5种对比模型提升了15.6个百分点;对于德国数据集,BPNN-CBCE评级模型在AUC、违约召回率Default recall方面也均优于5种对比模型。因此,BPNN-CBCE信用评价模型对农户不均衡信贷数据中的违约样本具有较好的识别能力,可有效降低金融机构客户误判带来的损失。创新与特色:①利用Class Balanced Loss中的平衡因子ω,增大违约样本在目标损失中的权重、降低非违约样本在目标损失中的权重,客观调节正负样本损失在目标损失中权重,弥补交叉熵函数无法调节两类样本损失权重的缺陷,克服由样本不均衡带来的评价模型对非违约样本识别过度、对违约样本识别不足。②通过考虑数据重叠,利用随机覆盖方法,分别对贷款数据中违约、非违约样本进行不放回采样,以对全样本空间X_(违约)、X_(非违约)进行不重叠覆盖,计算两类贷款客户的有效样本数量。既反映由于真实数据之间的内在相似性,随着样本数量的增加,新添加样本很可能是现有样本近似重复的客观事实,也保证基于有效样本对两类样本损失进行重新加权的客观性。将图像识别领域中的Class Balanced Loss函数引入信用评价领域,既拓展了Class Balanced Loss的使用边界,也为解决不均衡样本的信用风险评价提供了新的研究思路。

Survey on the stability of networked control systems

The insertion of the communication network in the feedback control loop makes the analysis and design of a network control system more complex, and induces some issues that degrade the control system＇s performance and even cause system instability. The main aspects are focused on the stability analysis of Network Control Systems （NCSs） with network-induced delays, data packet dropouts, and multiple-packet transmission. These issues must be considered in the design of an NCS. This work summarizes the main research results, and remarks on some related handling approaches and techniques. The main purpose of the survey is to present the new research state of NCSs and to point out some fields of future work.