Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

METrust: A Mutual Evaluation-based Trust Model for P2P Networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer （P2P） network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers＇ evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers＇ malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers＇ own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

Evaluation of the Functionality of the Networking of Sexual and Reproductive Health Rights Services in the Event of Gender-Based Violence in the Commune of Kpomasse in 2022

Introduction: In developing countries, gender-based violence (GBV) is a real public health problem. In Benin, GBV affects the majority of women and girls (69%). Benin has implemented strategies and set up integrated centers for the management of violence in order to reduce cases of violence and ensure the holistic management of victims. The objective of our study was to assess the functionality of the network of sexual and reproductive health rights (SRHR) in case of GBV in the commune of Kpomasse in 2022. Method: This descriptive and evaluative study was conducted from March 21 to April 11, 2022. The sampling method used was non-probabilistic. Reasoned choice and convenience were the techniques used for the different targets of the study. The functionality of the SRHR service network was assessed first by calculating scores at the structure, process and outcome levels, and then by analysis using the human rights-based approach. Results: Out of the 34 GBV victims identified, only one had received a full response and 54% of the victims had no knowledge of SRHR. The lack of knowledge about health care facilities was 41% for victims and 80% for non-victims in the community who participated in the study. In the case of gender-based violence, the community preferred to settle out of court rather than report it. The functionality of the networking of sexual and reproductive health rights services in the event of the occurrence of gender-based violence in the commune of Kpomassè is insufficient. Lack of knowledge of the roles of rights holders (DD) and duty bearers (DO) explains the insufficient functionality of networking. Conclusion: Training of SRHR service agents and community sensitization are essential to improve the functionality of SRHR service networking in the commune of Kpomasse.

User satisfaction-based quality evaluation model and survey analysis of network information service

On the basis of user satisfaction,authors made research hypotheses by learning from relevant e-service quality evaluation models.A questionnaire survey was then conducted on some content-based websites in terms of their convenience,information quality,personalization and site aesthetics,which may affect the overall satisfaction of users.Statistical analysis was also made to build a user satisfaction-based quality evaluation system of network information service.

Evaluation of hydraulic fracturing of horizontal wells in tight reservoirs based on the deep neural network with physical constraints

Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs.

Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

An evaluation method of contribution rate based on fuzzy Bayesian networks for equipment system-of-systems architecture

The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems(ESoS),and the Bayesian network is an effective tool to solve the uncertain information,a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network(FBN)is proposed.Firstly,based on the operation loop theory,an ESoSA is constructed considering three aspects:reconnaissance equipment,decision equipment,and strike equipment.Next,the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information.Furthermore,the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA,and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established.Finally,the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA.Compared with traditional methods,the evaluation method based on FBN takes various failure states of equipment into consideration,is free of acquiring accurate probability of traditional equipment failure,and models the uncertainty of the relationship between equipment.The proposed method not only supplements and improves the ESoSA contribution rate assessment method,but also broadens the application scope of the Bayesian network.

A Performance Evaluation Platform for Mega Satellite Constellation Network

With the rapid development of satellite technology, mega satellite constellations have become a research hotspot. A large number of related techniques have been developed on orbit topology, network routing, energy balance and resource control. However, it is difficult to accurately compare the performance of similar studies due to differences in the means of validation. Especially for invulnerability studies in many military applications, a unified evaluation system is essential. This paper proposes a network evaluation system for mega satellite constellations. Evaluation parameters include orbit topology, communication network, energy balance and invulnerability. Different application algorithms and traffic models were used to validate the specific system. .

Systematic Evaluation of Deep Neural Network based Dynamic Modeling Method for AC Power Electronic System

Since the high penetration of renewable energy complicates the dynamic characteristics of the AC power electronic system(ACPES),it is essential to establish an accurate dynamic model to obtain its dynamic behavior for ensure the safe and stable operation of the system.However,due to the no or limited internal control details,the state-space modeling method cannot be realized.It leads to the ACPES system becoming a black-box dynamic system.The dynamic modeling method based on deep neural network can simulate the dynamic behavior using port data without obtaining internal control details.However,deep neural network modeling methods are rarely systematically evaluated.In practice,the construction of neural network faces the selection of massive data and various network structure parameters.However,different sample distributions make the trained network performance quite different.Different network structure hyperparameters also mean different convergence time.Due to the lack of systematic evaluation and targeted suggestions,neural network modeling with high precision and high training speed cannot be realized quickly and conveniently in practical engineering applications.To fill this gap,this paper systematically evaluates the deep neural network from sample distribution and structural hyperparameter selection.The influence on modeling accuracy is analyzed in detail,then some modeling suggestions are presented.Simulation results under multiple operating points verify the effectiveness of the proposed method.

FANN-Based Surface Water Quality Evaluation Model and Its Application in the Shaoguan Area

一个模糊神经网络模型被建议评估水质。模型包含二部分：首先，模糊数学理论被用来标准化样品；秒， RBF 神经网络和 BP 神经网络被用来训练标准化样品。建议模型被使用在 2005 在 Shaoguan 区域在 9 条河里估计 16 节的水质。评估结果在在 2005 的 Shaoguan 区域与 RBF 神经网络方法和报导结果的相比。它显示建议模糊神经网络模型的性能在水质评价的应用程序实际上是可行的，它的操作简单。

A Web-Based Integrated Intelligent System for Sensory Fabric Hand Evaluation

This paper presents a web-based integrated system for on-line sensory fabric hand evaluation. The methods of fuzzy techniques, neural networks, classical factorial analysis and other data analysis are used in the system to analyze the objective and subjective data, and to build the relationship between them. Given the objective data of a new fabric sample, the system can provide its sensory hand data and its total hand grade. In meantime, the total hand grade can be obtained directly from the sensory fabric hand data if provided. The sensory evaluation system is developed in Internet environment using Java language and SQL server database management system.

Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation

A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of electronic countermeasures, which not only exerts the advantages of the fuzzy theory, but also has a good ability in machine learning and data analysis. The subjective value of sample versus class is computed by the fuzzy computing theory, and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer. Meanwhile, the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples. The simulation result indicates that the proposed algorithm is feasible and effective.

New Knowledge Network Evaluation Method for Design Rationale Management

Current design rationale （DR） systems have not demonstrated the value of the approach in practice since little attention is put to the evaluation method of DR knowledge. To systematize knowledge management process for future computer-aided DR applications, a prerequisite is to provide the measure for the DR knowledge. In this paper, a new knowledge network evaluation method for DR management is presented. The method characterizes the DR knowledge value from four perspectives, namely, the design rationale structure scale, association knowledge and reasoning ability, degree of design justification support and degree of knowledge representation conciseness. The DR knowledge comprehensive value is also measured by the proposed method. To validate the proposed method, different style of DR knowledge network and the performance of the proposed measure are discussed. The evaluation method has been applied in two realistic design cases and compared with the structural measures. The research proposes the DR knowledge evaluation method which can provide object metric and selection basis for the DR knowledge reuse during the product design process. In addition, the method is proved to be more effective guidance and support for the application and management of DR knowledge.

Evaluation of Rural Broadband Network Based on Broadband Universal Service Management System

Carrying out pilot project to provide broadband universal service nationwide, especially in rural impoverished areas, is a major policy decision in China. To accelerate implementation and ensure quality of the constructed network, it is of great significance to conduct comprehensive and scientific evaluation of the network status. In this paper, we present the evaluation of the broadband network constructed in rural China with several key indicators. It shows that with steppedup efforts of the telecom industry, broadband networks have been introduced into more and more villages. The average network speed reaches 60 Mbps, which is far exceeding 12 Mbps’ obligation.

Reliability Risk Evaluation Method for Complex Mechanical System Based on Optimal Bayesian Network

In order to reduce the calculation of the failure probability in the complex mechanical system reliability risk evaluation,and to implement importance analysis of system components effectively,the system fault tree was converted into five different Bayesian network models. The Bayesian network with the minimum conditional probability table specification and the highest computation efficiency was selected as the optimal network. The two heuristics were used to optimize the Bayesian network. The fault diagnosis and causal reasoning of the system were implemented by using the selected Bayesian network. The calculation methods of Fussel-Vesely( FV),risk reduction worth( RRW),Birnbaum measure( BM) and risk achievement worth( RAW) importances were presented. A certain engine was taken as an application example to illustrate the proposed method. The results show that not only the correlation of the relevant variables in the system can be accurately expressed and the calculation complexity can be reduced,but also the relatively weak link in the system can be located accurately.

End-to-End Latency Evaluation of the Sat5G Network Based on Stochastic Network Calculus

Simultaneous use of heterogeneous radio access technologies to increase the performance of real-time,reliability and capacity is an inherent feature of satellite-5G integrated network(Sat5G).However,there is still a lack of theoretical characterization of whether the network can satisfy the end-to-end transmission performance for latency-sensitive service.To this end,we build a tandem model considering the connection relationship between the various components in Sat5G network architecture,and give an end-to-end latency calculation function based on this model.By introducing stochastic network calculus,we derive the relationship between the end-to-end latency bound and the violation probability considering the traffic characteristics of multimedia.Numerical results demonstrate the impact of different burst states and different service rates on this relationship,which means the higher the burst of arrival traffic and the higher the average rate of arrival traffic,the greater the probability of end-to-end latency violation.The results will provide valuable guidelines for the traffic control and cache management in Sat5G network.

Comprehensive evaluation of 5G+smart distribution network based on combined weighting method-cloud model

With the large-scale application of 5G technology in smart distribution networks,the operation effects of distribution networks are not clear.Herein,we propose a comprehensive evaluation model of a 5G+smart distribution network based on the combination weighting and cloud model of the improved Fuzzy Analytic Hierarchy-Entropy Weight Method(FAHP-EWM).First,we establish comprehensive evaluation indexes of a 5G+smart distribution network from five dimensions:reliable operation,economic operation,efficient interaction,technological intelligence,and green emission reduction.Second,by introducing the principle of variance minimization,we propose a combined weighting method based on the improved FAHP-EWM to calculate the comprehensive weight,so as to reduce the defects of subjective arbitrariness and promote objectivity.Finally,a comprehensive evaluation model of 5G+smart distribution network based on cloud model is proposed by considering the uncertainty of distribution network node information and equipment status information.The example analysis indicates that the overall operation of the 5G+smart distribution network project is decent,and the weight value calculated by the combined weighting method is more reasonable and accurate than that calculated by the single weighting method,which verifies the effectiveness and rationality of the proposed evaluation method.Moreover,the proposed evaluation method has a certain guiding role for the large-scale application of 5G communication technology in smart distribution networks.

SRAFE:Siamese Regression Aesthetic Fusion Evaluation for Chinese Calligraphic Copy

Evaluation of calligraphic copy is the core of Chinese calligraphy appreciation and in-heritance.However,previous aesthetic evaluation studies often focussed on photos and paintings,with few attempts on Chinese calligraphy.To solve this problem,a Siamese regression aesthetic fusion method is proposed,named SRAFE,for Chinese calligraphy based on the combination of calligraphy aesthetics and deep learning.First,a dataset termed Evaluated Chinese Calligraphy Copies(E3C)is constructed for aesthetic evalu-ation.Second,12 hand‐crafted aesthetic features based on the shape,structure,and stroke of calligraphy are designed.Then,the Siamese regression network(SRN)is designed to extract the deep aesthetic representation of calligraphy.Finally,the SRAFE method is built by fusing the deep aesthetic features with the hand‐crafted aesthetic features.Experimental results show that scores given by SRAFE are similar to the aesthetic evaluation label of E3C,proving the effectiveness of the authors’method.

The Application of Trapezoidal Fuzzy AHP on Evaluation Item Weight Calculation of Highway Network Programming Schemes

The fuzzy theory is invited in this paper to calculate weights of evaluation items, which is a very important stage in highway network evaluation. Highway network evaluation is a composite of various attributes that lack of pertinency and comparability, and their commonality is that they are more or less tainted with uncertainty, with different daily decision making problems of diverse intensity, the results can be misleading if the fuzziness of human decision-making is not taken into account. So we introduce fuzzy theory into the measurement of evaluating weights to overcome the issue and Analytic Hierarchy Process in scheme ranking. It is testified to be a more reliable way through the case study of Yangtze River Delta highway network evaluation.

Modified Adhoc On-Demand Distance Vector for Trust Evaluation And Attack Detection

Recently,Wireless Sensor Network(WSN)becomes most potential technologies for providing improved services to several data gathering and track-ing applications.Because of the wireless medium,multi-hop communication,absence of physical protectivity,and accumulated traffic,WSN is highly vulner-able to security concerns.Therefore,this study explores a specific type of DoS attack identified as a selective forwarding attack where the misbehaving node in the network drops packet on a selective basis.It is challenging to determine if packet loss is caused by a collision in the medium access path,poor channel quality,or a selective forwarding assault.Identifying misbehaving nodes at the earliest opportunity is an acceptable solution for performing secure routing in such networks.As a result,in this study effort,we present a unique Modified Ad Hoc On-Demand Distance Vector(AODV)Routing protocol depending upon the One time password(OTP)method that employs the RSA algorithm.Finally,a trust evaluation process determines which approach is the most optimal.Accord-ing to the simulationfindings of the suggested routing protocol and comparison with existing routing protocols provided in this article,the proposed work is both efficient and cost-effective.