The Journal of Engineering最新文献_第3页

A novel joint depth sensor calibration method without fixture for mobile robots’ navigation 用于移动机器人导航的新型无夹具联合深度传感器校准方法

The Journal of Engineering Pub Date : 2024-05-01 DOI: 10.1049/tje2.12384

Yiming Lu, Rupeng Yuan, Tiegang Xue

{"title":"A novel joint depth sensor calibration method without fixture for mobile robots’ navigation","authors":"Yiming Lu, Rupeng Yuan, Tiegang Xue","doi":"10.1049/tje2.12384","DOIUrl":"https://doi.org/10.1049/tje2.12384","url":null,"abstract":"Commercial mobile robots are usually equipped with multiple depth sensors that can measure the point cloud information around the robot's environment. The installation process of these sensors contains assembly error and sensor measurement error, so it is necessary to calibrate each sensor to align the point cloud. In order to obtain the sensor calibration results of commercial robots under normal working conditions, this study proposes a fixture free multi depth sensor joint calibration method that can be deployed on low‐cost embedded computing units, which efficiently aligns the point clouds of each sensor. During the calibration process, the robot is placed in the center of three upright thin plates perpendicular to the ground. 2D LIDAR depicts high‐precision contours of the upright thin plates. In the calibration process of each depth sensor, the roll angle and pitch angle of the sensor point cloud are first calibrated to make it perpendicular to the ground, and then the yaw angle and position of the point cloud are calibrated to fit the high‐precision contour of the upright thin plate. The results show that this method can be deployed on low‐cost embedded computing units, with real‐time and accurate calibration results. The convergence of calibration results can be achieved through up to 5 iterations, and the average running time is less than 120 ms. This research achievement provides a reference for multi‐sensor calibration of commercial robots.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"87 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141034933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phase‐reconstruction from magnitude‐only data of electrically small antennas for body‐centric wireless communication 从用于以人体为中心的无线通信的小型天线的纯幅度数据中重建相位

The Journal of Engineering Pub Date : 2024-03-26 DOI: 10.1049/tje2.12361

J. Ø. Nielsen, S. Kvist, K. Jakobsen

引用次数: 0

Two‐stage demand‐side management in energy flexible residential buildings 能源灵活住宅建筑的两阶段需求方管理

The Journal of Engineering Pub Date : 2024-03-25 DOI: 10.1049/tje2.12372

Mohammad Ali Fotouhi Ghazvini, K. Antoniadou-Plytaria, David Steen, Le Anh Tuan

引用次数: 1

Short‐term load and spinning reserve prediction based on LSTM and ANFIS with PSO algorithm 基于 LSTM 和 ANFIS 与 PSO 算法的短期负荷和旋转储备预测

The Journal of Engineering Pub Date : 2024-02-27 DOI: 10.1049/tje2.12356

M. Ferdosian, H. Abdi, Shahram Karimi, Saeed Kharrati

{"title":"Short‐term load and spinning reserve prediction based on LSTM and ANFIS with PSO algorithm","authors":"M. Ferdosian, H. Abdi, Shahram Karimi, Saeed Kharrati","doi":"10.1049/tje2.12356","DOIUrl":"https://doi.org/10.1049/tje2.12356","url":null,"abstract":"With the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short‐term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short‐term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short‐term predicting of load and spinning reserve is performed using a combination of adaptive network‐based fuzzy inference system (ANFIS) and meta‐heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS‐PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short‐term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS‐PSO and LSTM is used to reduce the average error and error variance.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"28 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rheological characteristics and behaviour prediction of lubricating grease for RV reducer across a wide temperature range 用于 RV 减速器的润滑脂在宽温度范围内的流变特性和性能预测

The Journal of Engineering Pub Date : 2024-02-01 DOI: 10.1049/tje2.12362

Benchi Jiang, Yansheng Zhou, Zhijian Tu, Jiabao Pan

{"title":"Rheological characteristics and behaviour prediction of lubricating grease for RV reducer across a wide temperature range","authors":"Benchi Jiang, Yansheng Zhou, Zhijian Tu, Jiabao Pan","doi":"10.1049/tje2.12362","DOIUrl":"https://doi.org/10.1049/tje2.12362","url":null,"abstract":"Grease in the normal operation of the rotate vector (RV) reducer has a role that cannot be ignored, for the variable working conditions of the RV reducer, the performance of the lubricant changes directly affect its reliable operation. Therefore, the study of the rheological properties of the grease has become the focus of the study of RV reducer performance. Here, SK‐1A grease is taken as the research object, and its rheological characteristics under wide temperature range working conditions (−20–40°C) are investigated through rheological experiments to analyze the potential influence of the performance of RV reducer. However, the ordinary way of research is too complicated to better research the rheological properties of grease for a variety of working conditions. The Elman neural network (ENN) model was used to predict the rheological properties, and the results were compared with those of back propagation (BP) and radial basis function (RBF) neural networks. The results demonstrate that the ENN model demonstrates high prediction accuracy for grease rheological property prediction by comparing three types of predictions. This method can provide a theoretical reference for the accurate prediction of the rheological properties of lubricating grease affected by complex multifactors.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"292 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal dispatching of clean energy heating considering customer satisfaction 考虑客户满意度，优化清洁能源供热调度

The Journal of Engineering Pub Date : 2024-01-31 DOI: 10.1049/tje2.12355

Haifeng Cheng, Houjing Guo, Huang Minli, Zhixuan Pan, Cheng Jin, Wu Dabala, Jieren Tan

引用次数: 0

Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment 跨层环境下基于蜻蜓优化的 LTE-A 网络联合延迟和能量感知上行链路资源分配方案

The Journal of Engineering Pub Date : 2024-01-31 DOI: 10.1049/tje2.12353

Leeban Moses, Perarasi T. Sambantham, Muhammad Faheem, Shoukath Ali K, A. Khan

{"title":"Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment","authors":"Leeban Moses, Perarasi T. Sambantham, Muhammad Faheem, Shoukath Ali K, A. Khan","doi":"10.1049/tje2.12353","DOIUrl":"https://doi.org/10.1049/tje2.12353","url":null,"abstract":"The exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE‐A network offers high‐speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human‐to‐human (H2H) communications. LTE‐A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement‐based dragonfly optimization (DELFBDO)‐based uplink resource allocation scheme for LTE‐A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140478104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental demonstration of an indoor visible light positioning system using RGB LEDs for multi‐cell networks 使用 RGB LED 的多蜂窝网络室内可见光定位系统的实验演示

The Journal of Engineering Pub Date : 2024-01-30 DOI: 10.1049/tje2.12349

R. A. Martínez-Ciro, F. López-Giraldo, J. M. Luna-Rivera, A. M. Ramirez-Aguilera

引用次数: 0

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer 储能电池 PACK 的 SOC 估算和故障识别策略：基于自适应滑模观测器

The Journal of Engineering Pub Date : 2024-01-30 DOI: 10.1049/tje2.12352

Huang Xueyi, Tinglong Pan

{"title":"SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer","authors":"Huang Xueyi, Tinglong Pan","doi":"10.1049/tje2.12352","DOIUrl":"https://doi.org/10.1049/tje2.12352","url":null,"abstract":"Accurate state of charge (SOC) estimation and fault identification and localization are crucial in the field of battery system management. This article proposes an innovative method based on sliding mode observation theory for SOC estimation and short‐circuit fault location. The core of this new method is the design of an adaptive sliding mode observer, which reduces jitter by introducing adaptive switching gain, establishes an internal loop of gain and error, and improves the performance of SOC estimation. In addition, recursive least squares method was used to identify the key parameters of the model. Secondly, based on obtaining the SOC of each battery cell in series with the energy storage PACK, the specificity of the faulty battery cell in SOC change trend is utilized to identify and locate the short‐circuit fault of the energy storage PACK. The simulation and test results show that the designed adaptive sliding mode observer can significantly improve the estimation accuracy of SOC and has better stability. Compared to the commonly used Kalman estimation and BP neural network estimation methods, the designed method has improved accuracy by 5.53% and 3.42%, respectively. In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short‐circuit fault identification and positioning, and has broad application prospects.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"167 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140482061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SWISS rectifier structure sector boundary current distortion suppression based on multi‐step predictive control 基于多步预测控制的 SWISS 整流器结构扇形边界电流畸变抑制技术

The Journal of Engineering Pub Date : 2024-01-30 DOI: 10.1049/tje2.12354

Zhun Cheng, Jiangwei Deng, Bing Luo, Yang Zhang

{"title":"SWISS rectifier structure sector boundary current distortion suppression based on multi‐step predictive control","authors":"Zhun Cheng, Jiangwei Deng, Bing Luo, Yang Zhang","doi":"10.1049/tje2.12354","DOIUrl":"https://doi.org/10.1049/tje2.12354","url":null,"abstract":"The SWISS rectifier is a three‐phase BUCK type Power Factor Correction rectifier with the advantages of adjustable output full voltage range, low voltage stress in the back stage devices, and high efficiency. However, because the voltage ripple of the input filter capacitor will cause the input current deviation, the input current of the SWISS rectifier will produce distortion at the sector boundary, which will affect the system performance. To this end, a multi‐stage predictive model method based on the spherical algorithm is presented. By predicting the grid‐side capacitor voltage and the input current state of the rectifier, the harmonic injection network switching tube is controlled in advance to supplement the grid‐side capacitor voltage, so that the grid‐side capacitor voltage approximately tracks the input voltage. Meanwhile, considering the current step that may be generated after the current distortion returns to normal, which leads to the resonance problem with the pre‐stage filter, this problem is incorporated into the value function and damping is optimized according to the feedback value. Finally, a 10‐kW SWISS rectifier on the SIMULINK platform is used to verify the feasibility of the new control method.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"300 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140480808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0