IET Intelligent Transport Systems最新文献_第7页

Enhancing freight train delay prediction with simulation-assisted machine learning 利用仿真辅助机器学习加强货运列车延误预测

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-18 DOI: 10.1049/itr2.12573

Niloofar Minbashi, Jiaxi Zhao, C. Tyler Dick, Markus Bohlin

{"title":"Enhancing freight train delay prediction with simulation-assisted machine learning","authors":"Niloofar Minbashi, Jiaxi Zhao, C. Tyler Dick, Markus Bohlin","doi":"10.1049/itr2.12573","DOIUrl":"https://doi.org/10.1049/itr2.12573","url":null,"abstract":"Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation-assisted machine learning model with two concepts: general (adding all predictors at once) and step-wise (adding predictors as they become available in sub-yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10-fold cross-validation. The model's performance on three data sets—a real-world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective <math>\u0000 <semantics>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 <annotation>$R^2$</annotation>\u0000 </semantics></math> of 0.90, 0.87, and 0.70. Step-wise inclusion of the predictors results differently for the real-world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real-world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2359-2374"},"PeriodicalIF":2.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multispectral pedestrian detection based on feature complementation and enhancement 基于特征互补和增强的多光谱行人检测

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-17 DOI: 10.1049/itr2.12562

Linzhen Nie, Meihe Lu, Zhiwei He, Jiachen Hu, Zhishuai Yin

{"title":"Multispectral pedestrian detection based on feature complementation and enhancement","authors":"Linzhen Nie, Meihe Lu, Zhiwei He, Jiachen Hu, Zhishuai Yin","doi":"10.1049/itr2.12562","DOIUrl":"https://doi.org/10.1049/itr2.12562","url":null,"abstract":"Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all-day environmental perception. This paper proposes a novel method named FCE-RCNN, which integrates saliency detection as a sub-task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw-data level before feature extraction. Utilizing a dual-stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high-quality global semantic information for lower-level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross-spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE-RCNN significantly improves nighttime detection, achieving a log-average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE-RCNN, and the method also maintains competitive inference speed, making it suitable for real-time applications.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 11","pages":"2166-2177"},"PeriodicalIF":2.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A spatiotemporal learning approach to safety-oriented individualized driving risk assessment in a vehicle-to-everything (V2X) environment 在 "车到万物"（V2X）环境中以安全为导向的个性化驾驶风险评估的时空学习方法

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-17 DOI: 10.1049/itr2.12584

Jing Li, Xuantong Wang, Tong Zhang

{"title":"A spatiotemporal learning approach to safety-oriented individualized driving risk assessment in a vehicle-to-everything (V2X) environment","authors":"Jing Li, Xuantong Wang, Tong Zhang","doi":"10.1049/itr2.12584","DOIUrl":"https://doi.org/10.1049/itr2.12584","url":null,"abstract":"Advances in real-time basic safety message (BSM) data from sensor-equipped vehicles have created new opportunities for driving risk assessments. This paper presents a machine learning approach using BSM data to provide fine-grained risk assessments, focusing on safety-critical events (SCEs) related to driving profiles, vehicle states, and road conditions. This approach formulates a bi-level risk indicator: one level measures the observable frequency of SCEs, while the other estimates their likelihood. The coarse level calculates risk scores by classifying driving profiles as normal or risky based on SCE frequency. The fine level refines these scores by comparing normal and risky profiles using key features from a feature learning model. This combined system accounts for recent driving behaviours and road/weather conditions within a vehicle-to-everything (V2X) environment, addressing high data dimensionality and imbalance. A comprehensive case study using 1 year of data from pilot V2X infrastructure in Tampa, Florida, demonstrates the efficacy of this approach, showing practical applications of the SCE-based risk indicator and combinatorial feature learning while also highlighting the real-world utility of the assessment method in providing a detailed and actionable view of driving risk based on V2X information.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2459-2484"},"PeriodicalIF":2.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12584","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring changes in residents' daily activity patterns through sequence visualization analysis 通过序列可视化分析探索居民日常活动模式的变化

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-17 DOI: 10.1049/itr2.12511

Xiaoran Peng, Ruimin Hu, Xiaochen Wang, Nana Huang

{"title":"Exploring changes in residents' daily activity patterns through sequence visualization analysis","authors":"Xiaoran Peng, Ruimin Hu, Xiaochen Wang, Nana Huang","doi":"10.1049/itr2.12511","DOIUrl":"https://doi.org/10.1049/itr2.12511","url":null,"abstract":"The analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents’ activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra-similarities, inter-differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 10","pages":"1879-1894"},"PeriodicalIF":2.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Organizing planning knowledge for automated vehicles and intelligent transportation systems 组织自动化车辆和智能交通系统的规划知识

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-16 DOI: 10.1049/itr2.12583

David Yagüe-Cuevas, María Paz-Sesmero, Pablo Marín-Plaza, Araceli Sanchis

{"title":"Organizing planning knowledge for automated vehicles and intelligent transportation systems","authors":"David Yagüe-Cuevas, María Paz-Sesmero, Pablo Marín-Plaza, Araceli Sanchis","doi":"10.1049/itr2.12583","DOIUrl":"https://doi.org/10.1049/itr2.12583","url":null,"abstract":"Intelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision-making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high-level road plan. To this end, this research compares cutting-edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real-time results have been obtained for fast KNN algorithms, leading to a robust tree index-based methodology for decision making in self-driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 S1","pages":"2977-2994"},"PeriodicalIF":2.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12583","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly detection and confidence interval-based replacement in decay state coefficient of ship power system 船舶电力系统衰减状态系数的异常检测与置信区间替换

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-14 DOI: 10.1049/itr2.12581

Xingshan Chang, Xinping Yan, Bohua Qiu, Muheng Wei, Jie Liu, Hanhua Zhu

{"title":"Anomaly detection and confidence interval-based replacement in decay state coefficient of ship power system","authors":"Xingshan Chang, Xinping Yan, Bohua Qiu, Muheng Wei, Jie Liu, Hanhua Zhu","doi":"10.1049/itr2.12581","DOIUrl":"https://doi.org/10.1049/itr2.12581","url":null,"abstract":"The anomaly detection and predictive replacement of the degradation decay state coefficient (Desc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first-order, and second-order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z-score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2409-2439"},"PeriodicalIF":2.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps AVP动态室内地图：没有预先地图的众包地图

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-14 DOI: 10.1049/itr2.12578

ZhiHong Jiang, Haobin Jiang, ShiDian Ma

{"title":"Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps","authors":"ZhiHong Jiang, Haobin Jiang, ShiDian Ma","doi":"10.1049/itr2.12578","DOIUrl":"https://doi.org/10.1049/itr2.12578","url":null,"abstract":"High-definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer-grade sensors in mass-produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high-definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing-based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high-precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud-based map.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2397-2408"},"PeriodicalIF":2.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised vessel trajectory segmentation via learning spatio-temporal semantics 通过学习时空语义进行自监督血管轨迹分割

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-14 DOI: 10.1049/itr2.12570

Rui Zhang, Haitao Ren, Zhipei Yu, Zhu Xiao, Kezhong Liu, Hongbo Jiang

{"title":"Self-supervised vessel trajectory segmentation via learning spatio-temporal semantics","authors":"Rui Zhang, Haitao Ren, Zhipei Yu, Zhu Xiao, Kezhong Liu, Hongbo Jiang","doi":"10.1049/itr2.12570","DOIUrl":"https://doi.org/10.1049/itr2.12570","url":null,"abstract":"The study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self-supervised vessel trajectory segmentation method (SS-VTS), which segments VTs based on their inherent spatio-temporal semantics. SS-VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi-dimensional feature sequence of the VTs using self-supervised learning. Finally, spatio-temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS-VTS achieves state-of-the-art segmentation results compared to seven baseline methods.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 11","pages":"2242-2254"},"PeriodicalIF":2.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Validity of GPS data in driving cycles 驾驶周期中 GPS 数据的有效性

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-13 DOI: 10.1049/itr2.12574

Harry Smith II, Suhail Akhtar, Brian Caulfield, Margaret O'Mahony

{"title":"Validity of GPS data in driving cycles","authors":"Harry Smith II, Suhail Akhtar, Brian Caulfield, Margaret O'Mahony","doi":"10.1049/itr2.12574","DOIUrl":"https://doi.org/10.1049/itr2.12574","url":null,"abstract":"There is continuous research into driving cycles (DCs) as researchers across the globe seek to define driving characteristics, energy consumption, and emissions in a local context. For decades, data collection for the development of DCs has been conducted in three ways: chase car, instrumented vehicle, or a combination of both. Many studies have moved on to cheap and easily available global positioning system (GPS) technology, while others record vehicle data directly through the on-board diagnostics (OBD) port. However, there are major limitations to GPS data collection such as frequent inaccuracies and loss of coverage in urban environments. For this reason, both OBD and GPS vehicle speed data have been collected. Then, the recorded data has been analysed to capture any differences in sampling rates and dropping data. Finally, basic DCs were created from smoothed GPS and OBD data and compared. DCs were developed with a microtrip-based method, and a relative error term was used to compare candidate DCs to the collected data. DCs were compared based on kinematic characteristic parameters that are most used in the field. The results of this study could be used to assess the validity of GPS-based DCs compared to OBD cycles using low-cost devices.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 S1","pages":"3034-3040"},"PeriodicalIF":2.3,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing real-time traffic volume prediction: A two-step approach of object detection and time series modelling 加强实时交通流量预测：物体检测和时间序列建模两步法

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-10-11 DOI: 10.1049/itr2.12576

Junwoo Lim, Juyeob Lee, Chaehee An, Eunil Park

{"title":"Enhancing real-time traffic volume prediction: A two-step approach of object detection and time series modelling","authors":"Junwoo Lim, Juyeob Lee, Chaehee An, Eunil Park","doi":"10.1049/itr2.12576","DOIUrl":"https://doi.org/10.1049/itr2.12576","url":null,"abstract":"A two-step framework that integrates real-time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO-v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO-v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO-v7's detection speed of 7.8 ms per frame further validates the feasibility of real-time data construction. The findings indicate that the combination of YOLO-v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2744-2758"},"PeriodicalIF":2.3,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0