bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smooth Clip Advantage PPO in Reinforcement Learning
Authors: Wang Junwei , Zeng Zilin , Shang Peng 
Published: June 2023

Abstract
Deep reinforcement learning outperforms traditional methods in some domains. In this paper, we propose a novel reinforcement learning on policy (RL) algorithm, the Smoothing Clip Advantage Proximal Policy optimization Algorithm (SCAPPO), which extends the classical PPO algorithm where we exploit the smoothing properties of the sigmoid function to make full use of useful gradients. In addition, we provide more efficient gradients for policy networks effective gradients, aiming to solve the overfitting problem caused by the coupling of strategy and value functions. SCAPPO outperforms currently popular reinforcement learning algorithms in performance tasks in the Open AI Gym.
Multi-objective Optimal Motion Planning of Heavy-duty Assembly Robot for Large Spacecraft Entering Cabin
Authors: Zhang Chengli , Meng Shaohua , Liu Tonghui , Li Haiyue , Zhao Jinlong , Wang Pengfei , Xing Shuai , Hu Ruiqin 
Published: June 2023

Abstract
The space inside the space capsule such as the space station sky and the core module is narrow, the size of the depth is large, and the bearing points are scattered and weak, there are some difficulties in loading, large size, different shape, complicated interface, scattered position, difficult position and pose adjustment, high assembly risk, etc.. To solve these problems, a six-DOF heavy-duty assembly robot is proposed in this paper. The robot adopts the combination of linear drive and rotating joint to increase the working space and bearing capacity of the robot. The kinematics and dynamics analysis model of the robot is established, and the path planning is carried out by means of quintic non-uniform B-spline interpolation method, considering fully the space constraints of the assembled equipment, a multi-objective trajectory optimization model based on NSGA2 algorithm is established to obtain the Pareto optimal solution set with time, impact and energy consumption as optimization objectives, prove that the equipment can be efficient and smooth. Reliable installation, with less impact and less power consumption under multi-objective optimization analysis.
An anchor-free detector designed for dense small water object detection
Authors: Wei Tao , Li Zi-Xin , Wang Yu-Long 
Published: June 2023

Abstract
The detection of dense small objects on the water surface is one of the hot topics in object detection. In this paper, a one-to-many label assignment strategy based on the OTA algorithm, which is applied to anchor-free detector, is proposed to improve the detection accuracy of dense small objects on water surface. To be specific, one-to-many label assignment means that a ground truth (GT) corresponding to multiple prediction boxes is conducted globally. The cost function used by OTA algorithm is improved to make the distribution of positive and negative samples more reasonable. Meanwhile, an efficient training strategy is designed to accelerate network convergence by adding L1 loss in the last stage of training. The results show that the proposed strategies achieve 87.9% average precision (AP). Compared to the original algorithm, we achieve 3.3% relative improvement for the detection precision of dense small objects.
Moving Path Planning and Motion Coordination for Carrier-Borne Aircraft on Deck
Authors: Zhao H D , Liu G , Zhou L H 
Published: June 2023

Abstract
This paper mainly discusses the planning method of shipboard transportation scheduling process on aircraft carrier from two aspects of deck moving path planning and motion coordination method for carrier-borne aircraft in deck. Two kinds of moving path planning algorithms between deck carrier aircraft stations based on standard motion pattern were proposed firstly. And the evaluation criteria of the planned moving paths were discussed. Then, basing on the network of deck moving paths generated by previous path planning algorithm, we developed a motion coordination method of deck carrier-borne aircraft basing on mixed integer programming formulations which can be used to solve the problems of coordination and collision in the transportation scheduling process of multiple carrier-borne aircraft effectively.
Displacement reconstruction based on Kalman smoothing in multi-rate suspension health monitoring systems
Authors: Wang Ying , Zheng Xueke , Wang Le , Lu Gavin , Jia Yixing , Li Mian 
Published: June 2023

Abstract
Suspension displacements and wheel center accelerations are important signals for suspension health monitoring systems to improve vehicle reliability and safety. The current way to obtain these signals is to install sensors on vehicles to conduct direct measurements. Usually, displacements are sampled at a slower rate than accelerations due to technical or economic limitations in real scenarios. This paper introduces a method for displacement reconstruction with low-sampling-rate displacement and high-sampling-rate acceleration measurements by formulating the reconstruction problem as a state estimation problem. A state-space model is established by identifying two data-driven models: a time-series Auto-Regressive model and a Finite Impulse Response model. Then, Kalman smoothing is used to estimate the displacement. A series of experiments have been done to show that the estimates from Kalman smoother coincide with the measurements.
Research on the Evaluation Technology of UAV Pilot Training Action Quality based on Entropy Weight-TOPSIS Method
Authors: An Ran , Yin Wenqiang 
Published: June 2023

Abstract
The system composition and usage of UAVs are different from those of manned aircraft, and a large number of flight training sessions for UAV pilots can gradually make them understand and be familiar with the characteristics of UAV. In this paper, the quality evaluation indexes of UAV pilot training actions are sorted out and an evaluation model is constructed based on the Entropy Weight-TOPSIS method. Through application verification, the evaluation results of the model are consistent with the subjective evaluation results of flight instructors. The model can be applied to the evaluation of UAV pilot training actions quality, and at the same time has high evaluation efficiency.
Short-term Metallurgical Load Forecasting Based on Adaptive Ensemble Learning
Authors: Pengyu Ma , Weijian Kong , Zhiyong Su 
Published: June 2023

Abstract
Accuracy and rapidity are the primary objectives of load forecasting, and also the necessary conditions for ensuring power supply and production schedule. However, in actual production, due to the variability of operating modes and the interference of production environment, the difficulties such as non-stable and high fluctuation are in the power load prediction. In light of this, we propose an adaptive hybrid prediction model based on Discrete Wavelet decomposition(DWT). It is well known that DWT can show local features such as mutation and fluctuation in the sequence, and has excellent multi-scale analysis ability. Adopt energy entropy evaluate the aggregation of wavelet coefficients in order to obtain the subsequence with the highest degree of preserving the main frequency of the original signal. Random Forest (RF) and Long Short-Term Memory (LSTM) were used to track low-frequency profile and high-frequency detail fluctuations respectively. Further, parameters of the heterogeneou models are optimized using the Particle Swarm Optimization(PSO) to improve the adaptability of hybrid models to different frequency components. Compared with other excellent metallurgical load forecasting techniques, the effectiveness and superiority of the proposed model are verified by experiments on the actual industrial data set of an electrical smelting furnaces for magnesia.
Fast response flight multi-service nodes prediction based on one hidden layer neural network
Authors: Li Baiqiang , yang Xiaoming , Tang Zhengjiang , Peng Wentao , Zhou Yang , Liao Fangmin , Tan Jing , Lin Xuan 
Published: June 2023

Abstract
Airport scene multi-service process(ASMP) state prediction is one of the important links to optimize airport ground resource allocation, improve collaborative release scheduling, and improve airport operation efficiency. In order to solve the problem of the slow response of existing prediction models, a Gaussian kernel probability density model of multi-service nodes in airport scenes is established. Then the one hidden layer neural network(OHLNN) is used to predict the nodes of ASMP. A study on the data of more than 20,000 flights at a large airport in China shows that: The mean absolute error (MAE) of the mixed flight prediction result is only 2.62911min, the mean root mean square error (RMSE) is only 3.24564min, and the response speed of the prediction model in the verification set is about 0.48s. When the results of MAE and RMSE are good, the response speed of the prediction model is greatly improved.
Research on Image Fusion in UAV Visual Navigation
Authors: Li Dongtao , Shao Yuanzheng , Yu Yao , Feng Zhifang , Wen Jiaolong 
Published: June 2023

Abstract
The accuracy of multi-sensor image fusion is the key to improve the accuracy of UAV visual navigation. In this paper, a two-step registration method combining region registration and feature registration is proposed. First, rough image registration is carried out based on improved Hough transform and Fourier-Mellin transform, and image precise registration is implemented based on Harris corner feature and cross-correlation function. Finally, wavelet transform is applied to image fusion and image edge enhancement to realize the coarse to accurate registration of SAR images.
Research on Automatic Design of Substation Secondary Circuit Based on Improved SPCD Model and Knowledge Graph
Authors: Xia Guoqiang , Zhu Xiaotong , Jun Xie , Xinghua Li 
Published: June 2023

Abstract
In view of the lack of automatic design research of the physical circuit of the substation secondary system, and the digital design and handover level that the substation urgently needs to improve, this paper proposes the following solutions. First of all, an improvement plan is proposed for the existing SPCD model, adding all design related information such as external function grouping and logical interface of the panel cabinet. Secondly, extract the function group elements and secondary cabinet elements of the improved SPCD model as knowledge graph nodes, and abstract the relationship between function group’s interfaces as knowledge graph relationships. Based on the Neo4j graph database, a typical bay knowledge graph for secondary design is conFstructed. Furthermore, the electric power secondary field data set is mined, the Word2vec model is trained by the secondary field data set, and the text similarity algorithm model in the secondary design field is established. Finally, based on the above research, a method for automatic design of substation secondary physical circuits and automatic generation of SPCD model file based on word2vec text similarity is proposed.
A Personalized Alarm Distribution Approach Based on Collaborative Filtering in New-type Power System
Authors: Zhang Jiafa , Wang Yiyun , Wu Hao , Lai Yuyang , Wang Xiaoyi 
Published: June 2023

AbstractDue to the complexity and heterogeneous characteristics of the information system architecture of the new-type power system, once a fault occurs during operation, a large number of alarm information will be generated. In order to improve the efficiency of operation and maintenance (O&M) personnel and minimize the loss caused by failures, a collaborative filtering algorithm is introduced to provide personalized alarm information distribution. Each alarm is preferentially assigned to O&M personnel with high processing probability, so that O&M personnel can receive alarm information they can handle. In addition, the minimum distribution number is limited to ensure that each alarm can be received by enough O&M personnel. Finally, experiments show that the proposed method can effectively improve the O&M efficiency and improve the fault recovery capability of the new-type power system.
Multivariate Time Series Clustering based on Graph Convolutional Network
Authors: Zhu Chuanze , Zhong Xu , Lin Zhenjie , Wang Liming , Li Wenzhong , Lu Sanglu 
Published: June 2023

Abstract
Multivariable time series (MTS) clustering is an important topic in time series data mining. The major challenge of MTS clustering is to capture the temporal correlations and the dependencies between multiple variables. In this paper, we propose a novel MTS clustering approach based on graph convolutional network (GCN), which is a powerful feature extractor for graph structure data. We regard each variable in MTS as a node in the graph and construct edges through the correlation between variables. Furthermore, GCN and deep learning back-ropagation technology are used to continuously learn the relationship between multiple variables. Combining the learned variables with the characteristics of the time dimensions, the comprehensive features can be fused to form effective representation for MTS clustering task. We carry out extensive experimental analysis on four open time series data sets and six benchmark algorithms, which shows the superiority of the proposed method.
Reliability Analysis for Programs with Redundancy Computation for Soft Errors
Authors: Meng Xiankai , Zhang Zhuo , Xue Jianxin , Chen Fangshu , Wang Jiahui 
Published: June 2023

Abstract
Soft error is one of the factors which may affect the reliability of computer programs. A common method to alleviate the impact of soft errors is redundancy computation, a classical data flow error detection mechanism. However, a program with redundancy computation may still have some vulnerable spots, which might be caused by the flaw during implementation or the instruction reordering given by compiler optimization. Finding the vulnerable spots of a program with redundancy computation is of great significance to evaluate the capability of the error detection mechanism. There are some conventional methods to analyze the reliability of a program under soft errors, such as the irradiation experiment, fault injection, and modeling analysis. However, the irradiation experiment is expensive, fault injection is very time-consuming, and the existing modeling analysis methods have not considered the error detection mechanism. This paper proposes a novel method of reliability analysis for programs with redundancy computation by analyzing the dynamic instruction sequence. Experimental results show that our approach has fairly high accuracy and a false negative rate of about 0.0545.
A Multi-channel System Architecture Design for Myocardial Infarction Prediction
Authors: Li Qiuping , Li Xueqing , Wang Xin’an , Zhang Xing , Yang Jie , Wu Fangke 
Published: June 2023

Abstract
Cardiac and cerebral infarctions represent the most perilous pathologies within the spectrum of cardiovascular and cerebrovascular diseases, with grave consequences following their onset. However, prompt diagnosis and treatment can substantially enhance the chances of successful rescue and avert severe outcomes. Therefore, the development of a non-invasive, low-cost, and easy-to-use cardiac and cerebral infarction prediction system for home and personal use bears considerable social and economic significance.
The electrocardiogram (ECG), acupoint electrical signals, and pulse, as physiological signals, contain abundant information on human health status. Integrating the information from these three signals can offer a more comprehensive and accurate risk prediction of cardiovascular and cerebrovascular diseases in patients. This approach represents a promising avenue for advancing the field of healthcare and disease management.
Firstly, we analyzed the myocardial infarction and cerebral infarction prediction system requirements. The research determines four pulse collection points, two ECG, and ten acupoint electricity collection points. Then, we completed the cardiovascular and cerebrovascular disease risk warning system’s overall architecture design and front-end scheme design of ECG, acupoint electricit and pulse wave acquisition. The system successfully collects data from the test subjects by connecting to the computer.
A Research on Operator Design Method for Health Detection SoC
Authors: Li Qiuping , Wu Fangke , Zhang Xing , Wang Xin’an , Ma Jieru , Li Xueqing 
Published: June 2023

Abstract
Improving the efficiency of System-on-Chip(SoC) design has always been a hot topic in SoC design methods. The quality of SoC architecture design directly affects the core elements of SoC performance and cost. However, architecture design usually requires highly experienced and skilled personnel, which are scarce resources. Operators in SoC, like instructions in a CPU, are extracted from algorithms and are both universal and flexible. SoC can be designed based on operators, and algorithmic functions can be implemented through operators’ interconnection. In this paper, we propose an operator design method to improve the efficiency of SoC design. Additionally, we design algorithmic operators using pulse signal processing as an example to enhance the efficiency of designing health detection SoCs.
Knowledge graph based AR content adaptive generation for 5G communication network maintenance services
Authors: Ming Wei , wang Li , xiangyu Hu , jiwei Hu , junfeng Wang , tao Peng , chong Zhang , jian Zhang 
Published: June 2023

Abstract
5G technology puts forward new requirements for communication network human maintenance services. However, with the complexity of 5G maintenance knowledge and the diversification of maintenance scenarios, the ability of maintenance engineer cannot be greatly improved in a short time. This paper proposed a knowledge graph (KG) based augmented reality (AR) content adaptive generation method for 5G communication network maintenance services. The KG and AR technology are used to reduce the cognitive burden of manual maintenance and improve the efficiency of communication network maintenance.
Design Scheme of Distributed Energy Resources Interconnection Oriented the VPP System
Authors: Suo Siliang , Che Xiangbei , Xu Aidong , Huang Yingqi , Chen Liming , Cao Yang , Zhong Han 
Published: June 2023

Abstract
At present, the virtual power plant is still in the stage of theoretical research and early pilot development. It has not yet formed a systematic and comprehensive Distributed Energy Resource (DER) modeling, market participation mechanism, operational risk management, operational optimization decision-making method, and systematic operating platform. Given the massive DERs interconnection requirements of the virtual power plant system, this paper proposes a design scheme for the DER interconnection protocol, which solves the problems of the existing application layer protocol, such as high overhead, poor security, and low efficiency of massive interconnection, and supports the safe and stable operation of power grid enterprises and virtual power plant systems.
Research on Multi-objective Optimal Configuration of Distributed Energy Storage System
Authors: Cao Yuanzheng , Zhang Lei 
Published: June 2023

Abstract
The purpose is to improve the absorption capacity of new energy generation added to the power system, the distributed energy storage system (ESS) is introduced. Considering the new energy generation as the main distributed power supply is united to the power grid, the mathematical model of wind-photoelectric-energy storage system is constructed. Taking active power loss, voltage offset and comprehensive cost as objectives, a multi-objective internal and external layer optimization energy storage device configuration model is established. The outer layer uses branch and bound method to locate the location of energy storage. In the inner layer, NSGA-II algorithm is used to optimize the capacity and the situation of storing or releasing power. Eventually, the IEEE 30 node test system with distributed power supply is used for simulation, and the optimal location, capacity and charging/discharging strategy of the ESS in the grid topology are obtained. The results verify the rationality of the configured energy storage, and show that system can absorb distributed energy to a certain extent.
Optimal Dispatching Method of Urban Comprehensive Energy System Based on Multi-energy Complementarity
Authors: Liu Hengmen , Liu Zhong , Ye Zhelin , Chen Ying 
Published: June 2023

Abstract
Considering the complexity of the multi-microgrid system and the diversity of internally distributed power sources, it is challenging for the centralized control scheduling mode to deal with new microgrid systems. Hierarchical management and distributed control strategy can adapt to the power grid management system. In this paper, considering the source-load uncertainty of multi-energy complementary power grid operation, the influence of the multi-energy output distribution model on the supply-demand balance of power grid regulation is constructed, and the probability balance model of power grid regulation capacity is built. To minimize the cost of cooperative operation between multi-energy microgrids and energy storage power stations and maximize the flexibility of regulation, the multi-objective particle swarm optimization (PSO) algorithm is used to constrain the deterministic equivalence of the regulation power probability balance constraint. The effectiveness of the proposed model in describing uncertain factors, reducing operating costs, and improving the regulation capacity of multi-energy microgrids and energy storage power stations is verified through an example analysis.
A Method for Optimal Allocation of Distributed PV and Energy Storage Based on High Reliability of Distribution Network
Authors: Shao Jingwei , Zhou Weidong , Xu Jun , Sun Ran , Hua Ke 
Published: June 2023

Abstract
In the context of the current widespread application of distributed PV, the energy storage system has bi-directional power characteristics and flexible regulation capability, and the reasonable joint planning of the two can enhance the safe and reliable operation of the system. In order to maximize long-term economic benefits, this paper proposes an optimal allocation method of distributed PV and energy storage based on high reliability of distribution network. Considering both normal operation and disaster scenarios for distributed photovoltaic storage planning, firstly, the investment and operation cost of distributed energy storage system is the target, and the storage planning model under normal operation scenario is established by considering the storage access location, configuration capacity, load state and grid operation state as constraints; then, the distributed photovoltaic storage optimal configuration model under disaster scenario is established by taking grid economy as the objective function. In summary, a two-layer planning model is established, with the upper layer using genetic algorithm to optimize the location, power and capacity of energy storage configuration, and the lower layer using particle swarm algorithm combined with tide calculation tool to optimize the intra-day operation strategy of energy storage; finally, the feasibility and effectiveness of the optimal configuration method are verified.