Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

Optimisation of a Bus Network Configuration and Frequency Considering the Common Lines Problem

Public transportation network reorganisation can be a key measure in designing more efficient networks and increasing the number of passengers. To date, several authors have proposed models for the “transit route network design problem” (TRNDP), and many of them use a transit assignment model as one component. However, not all models have considered the “common lines problem,” which is an essential feature in transit network assignment and is based on the concept that the fastest way to get to a destination is to take the first vehicle arriving among an “attractive” set of lines. Thus, we sought to reveal the features of considering the common lines problem by comparing results with and without considering the problem in a transit assignment model. For comparison, a model similar to a previous one was used, formulated as a bi-level optimisation problem, the upper problem of which is described as a multi-objective problem. As a result, although the solutions with and without considering the common lines showed almost the same Pareto front, we confirmed that a more direct service is provided if the common lines problem is considered whereas a less direct service is provided if it is not. With a small network case study, we found that considering the common lines problem in the TRNDP is important as it allows operators to provide more direct services.

Communication-Censored Distributed Learning for Stochastic Configuration Networks

This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a trigger time. For this purpose, we propose the communication-censored distributed learning algorithm for SCN, namely ADMMM-SCN-ET, by introducing the event-triggered communication mechanism to the alternating direction method of multipliers (ADMM). To avoid unnecessary information transmissions, each learning agent is equipped with a trigger function. Only if the event-trigger error exceeds a specified threshold and meets the trigger condition, the agent will transmit the variable information to its neighbors and update its state in time. The simulation results show that the proposed algorithm can effectively reduce the communication cost for training decentralized SCNs and save communication resources.

Double Pruning Structure Design for Deep Stochastic Configuration Networks Based on Mutual Information and Relevance

Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model structures.Aiming at the above problems,this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance.During the training process,the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner,the node pruning rate of each layer is set according to the depth of the DSCN at the current time,the nodes that contribute little to the model are deleted,and the network-related parameters are updated.When the model completes the configuration procedure,the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections;then,the network parameters are updated after pruning is completed.The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed;the model accuracy loss is small,and fine-tuning for accuracy restoration is not needed.The obtained DSCN model has certain application value in the field of regression analysis.

基于混沌反馈乌燕鸥优化算法的随机配置网络参数优化

为了解决随机配置网络(stochastic configuration network,SCN)隐含层参数的选择与分配会影响其预测精度的问题,提出一种基于混沌反馈乌燕鸥优化算法(chaotic feedback sooty tern optimization algorithm,CFSTOA)的SCN参数优化方法。首先,利用Tent映射、线性因子调节策略、劣势种群反馈原则来改进乌燕鸥优化算法(sooty tern optimization algorithm,STOA),以增强算法的局部搜索能力,得到一种具备更快收敛速度和更高收敛精度的CFSTOA;然后,将CFSTOA用于优化SCN的正则化参数和权重偏差的尺度因子,从而得到最优的隐含层参数;最后,利用10个基准函数和4个标准回归数据集分别对CFSTOA的性能进行了测试。结果表明,CFSTOA具有更快的收敛速度且不易陷入局部最优,可以提高SCN算法的预测精度和训练速度。

Impact of the Objective Function on the Construction of Internal Grids of Wind Farms Using Genetic Algorithm

The macro complex of the construction industry is energy intensive. Solutions that enable the supply of this demand while meeting the principles of sustainability are needed. The construction of wind farms has been a strategy employed by many countries to produce clean energy. An increase in the construction of wind farms has also been witnessed in Brazil. This calls for different activities, such as the design and construction of infrastructure. This article focuses on the design of internal medium voltage distribution grids for wind farms. The purpose is to find a radial configuration that connects a set of wind generators to the substation, in an optimum way, minimizing operational and construction costs, reducing loss and therefore contributing to sustainability. In large farms, the project design consists of a large combinatorial optimization problem, given the large number of possible configurations to be deployed. Finding the best solution for the internal grid depends on the criterion adopted for the objectives pursued. This article analyzes the different criteria that can be adopted in the design of the wind farm’s internal grid using a methodology based on genetic algorithm (GA). Its aim is to identify their influence on the solution of the problem and help decision-making by finding the most adequate criterion for the objectives pursued. The results show that the design of the internal grid is sensitive to the criteria adopted for the objective function. In addition, the degree of sensitivity is analyzed, showing that, in some cases, the solutions are not economically attractive and do not contribute to the reduction of losses.

A Heuristic Algorithm for P-Cycles Configuration in WDM Optical Networks

Aiming at minimizing spare capacity for optical WDM networks, we propose a new heuristic algorithm for preconfigured protection cycle (p-cycle) design. Numerical results show that the spare capacity obtained by our new algorithm is very close to the optimal solution.

Model Prediction and Optimal Control of Gas Oxygen Content for A Municipal Solid Waste Incineration Process

In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an optimization control method of gas oxygen content based on model predictive control.First,a stochastic configuration network is utilized to establish a prediction model of gas oxygen content.Second,an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow.Finally,model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions.The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value,which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.

Optimal blockchain network construction methodology based on analysis of configurable components for enhancing Hyperledger Fabric performance

Presently,blockchain technology has been widely applied in various application domains such as the Internet of Things(IoT),supply chain management,healthcare,etc.So far,there has been much confusion about whether blockchain performs with scale,and admittedly,a lack of information about best practices that can improve the performance and scale.This paper proposes a novel blockchain network construction methodology to improve the performance of Hyperledger Fabric.As a highly scalable permissioned blockchain platform,Hyperledger Fabric supports a wide range of enterprise use cases from finance to governance.A comprehensive evaluation is performed by observing various configurable network components that can affect the blockchain performance.To demonstrate the significance of the proposed methodology,we set up the experiment environment for the baseline and the test network using optimized parameters,respectively.The experimental results indicate that the test network's performance is enhanced effectively compared to the baseline in transaction throughput and transaction latency.