Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation methods still suffer from insufficiently exploiting sparse information and the mismatch between personalized interests and general knowledge. This paper proposes a model named Adaptive Knowledge Contrastive Learning with Dynamic Attention (AKCL-DA) to address the above challenges. Specifically, instead of building contrastive views by randomly discarding information, in this study, an adaptive data augmentation method was designed to leverage sparse information effectively. Furthermore, a personalized dynamic attention network was proposed to capture knowledge-aware personalized behaviors by dynamically adjusting user attention, therefore alleviating the mismatch between personalized behavior and general knowledge. Extensive experiments on Yelp2018, LastFM, and MovieLens datasets show that AKCL-DA achieves a strong performance, improving the NDCG by 4.82%, 13.66%, and 4.41% compared to state-of-the-art models, respectively.
{"title":"Adaptive Knowledge Contrastive Learning with Dynamic Attention for Recommender Systems","authors":"Hongchan Li, Jinming Zheng, Baohua Jin, Haodong Zhu","doi":"10.3390/electronics13183594","DOIUrl":"https://doi.org/10.3390/electronics13183594","url":null,"abstract":"Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation methods still suffer from insufficiently exploiting sparse information and the mismatch between personalized interests and general knowledge. This paper proposes a model named Adaptive Knowledge Contrastive Learning with Dynamic Attention (AKCL-DA) to address the above challenges. Specifically, instead of building contrastive views by randomly discarding information, in this study, an adaptive data augmentation method was designed to leverage sparse information effectively. Furthermore, a personalized dynamic attention network was proposed to capture knowledge-aware personalized behaviors by dynamically adjusting user attention, therefore alleviating the mismatch between personalized behavior and general knowledge. Extensive experiments on Yelp2018, LastFM, and MovieLens datasets show that AKCL-DA achieves a strong performance, improving the NDCG by 4.82%, 13.66%, and 4.41% compared to state-of-the-art models, respectively.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"3 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.3390/electronics13183599
Suyao Liu, Chunmei Xu, Yifei Zhang, Haoying Pei, Kan Dong, Ning Yang, Yingtao Ma
Conventional methods of parameterizing fuel cell hybrid power systems (FCHPS) often rely on engineering experience, which leads to problems such as increased economic costs and excessive weight of the system. These shortcomings limit the performance of FCHPS in real-world applications. To address these issues, this paper proposes a novel method for optimizing the parameter configuration of FCHPS. First, the power and energy requirements of the vehicle are determined through traction calculations, and a real-time energy management strategy is used to ensure efficient power distribution. On this basis, a multi-objective parameter configuration optimization model is developed, which comprehensively considers economic cost and system weight, and uses a particle swarm optimization (PSO) algorithm to determine the optimal configuration of each power source. The optimization results show that the system economic cost is reduced by 8.76% and 18.05% and the weight is reduced by 11.47% and 9.13%, respectively, compared with the initial configuration. These results verify the effectiveness of the proposed optimization strategy and demonstrate its potential to improve the overall performance of the FCHPS.
{"title":"Multi-Objective Parameter Configuration Optimization of Hydrogen Fuel Cell Hybrid Power System for Locomotives","authors":"Suyao Liu, Chunmei Xu, Yifei Zhang, Haoying Pei, Kan Dong, Ning Yang, Yingtao Ma","doi":"10.3390/electronics13183599","DOIUrl":"https://doi.org/10.3390/electronics13183599","url":null,"abstract":"Conventional methods of parameterizing fuel cell hybrid power systems (FCHPS) often rely on engineering experience, which leads to problems such as increased economic costs and excessive weight of the system. These shortcomings limit the performance of FCHPS in real-world applications. To address these issues, this paper proposes a novel method for optimizing the parameter configuration of FCHPS. First, the power and energy requirements of the vehicle are determined through traction calculations, and a real-time energy management strategy is used to ensure efficient power distribution. On this basis, a multi-objective parameter configuration optimization model is developed, which comprehensively considers economic cost and system weight, and uses a particle swarm optimization (PSO) algorithm to determine the optimal configuration of each power source. The optimization results show that the system economic cost is reduced by 8.76% and 18.05% and the weight is reduced by 11.47% and 9.13%, respectively, compared with the initial configuration. These results verify the effectiveness of the proposed optimization strategy and demonstrate its potential to improve the overall performance of the FCHPS.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.3390/electronics13173573
Seungjin Jo, Dong-Hee Kim, Jung-Hoon Ahn
This paper presents an integrated coil design method for inductive power-transfer (IPT) systems. Because a medium-voltage direct current (MVDC) distribution network transmits power at relatively high voltages (typically in the tens of kV), accurate fault diagnosis using high-performance sensors is crucial to improve the safety of MVDC distribution networks. With the increasing power consumption of high-performance sensors, conventional power supplies using optical converters with 5 W-class output characteristics face limitations in achieving the rated output power. Therefore, this paper proposes a safe and reliable power supply method using the principle of IPT to securely maintain the insulation distance between the distribution network and the current sensor-supply line. A 100 W prototype IPT system is investigated, and its feasibility is validated by comparing its performance with conventional optical converters.
本文介绍了电感式功率传输 (IPT) 系统的集成线圈设计方法。由于中压直流（MVDC）配电网络以相对较高的电压（通常为几十千伏）传输电力，因此使用高性能传感器进行准确的故障诊断对于提高中压直流配电网络的安全性至关重要。随着高性能传感器功耗的增加，使用具有 5 W 级输出特性的光转换器的传统电源在实现额定输出功率方面面临着限制。因此，本文利用 IPT 原理提出了一种安全可靠的供电方法，以确保配电网络与电流传感器供电线路之间的绝缘距离。本文研究了一个 100 W 的原型 IPT 系统，并通过与传统光电转换器的性能比较验证了其可行性。
{"title":"Enhanced Coil Design for Inductive Power-Transfer-Based Power Supply in Medium-Voltage Direct Current Sensors","authors":"Seungjin Jo, Dong-Hee Kim, Jung-Hoon Ahn","doi":"10.3390/electronics13173573","DOIUrl":"https://doi.org/10.3390/electronics13173573","url":null,"abstract":"This paper presents an integrated coil design method for inductive power-transfer (IPT) systems. Because a medium-voltage direct current (MVDC) distribution network transmits power at relatively high voltages (typically in the tens of kV), accurate fault diagnosis using high-performance sensors is crucial to improve the safety of MVDC distribution networks. With the increasing power consumption of high-performance sensors, conventional power supplies using optical converters with 5 W-class output characteristics face limitations in achieving the rated output power. Therefore, this paper proposes a safe and reliable power supply method using the principle of IPT to securely maintain the insulation distance between the distribution network and the current sensor-supply line. A 100 W prototype IPT system is investigated, and its feasibility is validated by comparing its performance with conventional optical converters.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"12 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.3390/electronics13173577
Ye Jin Kim, Jung Ho Song, Ki Hwan Cho, Jong Hyeon Shin, Jong Sik Kim, Jung Sik Yoon, Sang Jeen Hong
Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data using long short-term memory (LSTM), a kind of recurrent neural network (RNN). The proposed method is based on the time series data collected through optical emission spectroscopy (OES) data during the SiO2 etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endpoint more accurately than existing methods by considering the entire time series. The LSTM model achieved an accuracy of 97.1% in a given condition, which shows that considering the flow and context of time series data can significantly reduce the false detection rate. To improve the performance of the proposed LSTM model, we created an attention-based LSTM model and confirmed that the model accuracy is 98.2%, and the performance is improved compared to that of the existing LSTM model.
{"title":"Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Memory Machine Learning","authors":"Ye Jin Kim, Jung Ho Song, Ki Hwan Cho, Jong Hyeon Shin, Jong Sik Kim, Jung Sik Yoon, Sang Jeen Hong","doi":"10.3390/electronics13173577","DOIUrl":"https://doi.org/10.3390/electronics13173577","url":null,"abstract":"Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data using long short-term memory (LSTM), a kind of recurrent neural network (RNN). The proposed method is based on the time series data collected through optical emission spectroscopy (OES) data during the SiO2 etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endpoint more accurately than existing methods by considering the entire time series. The LSTM model achieved an accuracy of 97.1% in a given condition, which shows that considering the flow and context of time series data can significantly reduce the false detection rate. To improve the performance of the proposed LSTM model, we created an attention-based LSTM model and confirmed that the model accuracy is 98.2%, and the performance is improved compared to that of the existing LSTM model.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"43 11 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.3390/electronics13173580
Yizhou Mao, Shubin Zheng, Liming Li, Renjie Shi, Xiaoxue An
Rail surface defect detection is vital for railway safety. Traditional methods falter with varying defect sizes and complex backgrounds, while two-stage deep learning models, though accurate, lack real-time capabilities. To overcome these challenges, we propose an enhanced one-stage detection model based on CenterNet. We replace ResNet with ResNeXt and implement a multi-branch structure for better low-level feature extraction. Additionally, we integrate SKNet attention mechanism with the C2f structure from YOLOv8, improving the model’s focus on critical image regions and enhancing the detection of minor defects. We also introduce an elliptical Gaussian kernel for size regression loss, better representing the aspect ratio of rail defects. This approach enhances detection accuracy and speeds up training. Our model achieves a mean accuracy (mAP) of 0.952 on the rail defects dataset, outperforming other models with a 6.6% improvement over the original and a 35.5% increase in training speed. These results demonstrate the efficiency and reliability of our method for rail defect detection.
{"title":"Research on Rail Surface Defect Detection Based on Improved CenterNet","authors":"Yizhou Mao, Shubin Zheng, Liming Li, Renjie Shi, Xiaoxue An","doi":"10.3390/electronics13173580","DOIUrl":"https://doi.org/10.3390/electronics13173580","url":null,"abstract":"Rail surface defect detection is vital for railway safety. Traditional methods falter with varying defect sizes and complex backgrounds, while two-stage deep learning models, though accurate, lack real-time capabilities. To overcome these challenges, we propose an enhanced one-stage detection model based on CenterNet. We replace ResNet with ResNeXt and implement a multi-branch structure for better low-level feature extraction. Additionally, we integrate SKNet attention mechanism with the C2f structure from YOLOv8, improving the model’s focus on critical image regions and enhancing the detection of minor defects. We also introduce an elliptical Gaussian kernel for size regression loss, better representing the aspect ratio of rail defects. This approach enhances detection accuracy and speeds up training. Our model achieves a mean accuracy (mAP) of 0.952 on the rail defects dataset, outperforming other models with a 6.6% improvement over the original and a 35.5% increase in training speed. These results demonstrate the efficiency and reliability of our method for rail defect detection.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"42 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we propose a smart transfer planer equipped with multiple antenna arrays to improve ground links for low Earth orbit (LEO) satellite communication. The STP features a symmetrical structure and is strategically placed on both ends of a window, serving both indoor and outdoor environments. Using the window glass as a medium, energy transmission occurs through a coupling mechanism between the planers. The design focuses on large array antenna design, beamforming networks, and coupler design on both sides of the glass. Beamforming networks enable the indoor and outdoor antenna arrays to switch beams in various directions, optimizing high-gain antennas with narrow beamwidths. Through electromagnetic induction and filter couplers, a robust signal transmission channel is established between indoor and outdoor environments. This setup significantly enhances communication efficiency, particularly in non-line-of-sight environments.
{"title":"Smart Transfer Planer with Multiple Antenna Arrays to Enhance Low Earth Orbit Satellite Communication Ground Links","authors":"Mon-Li Chang, Ding-Bing Lin, Hui-Tzu Rao, Hsuan-Yu Lin, Hsi-Tseng Chou","doi":"10.3390/electronics13173581","DOIUrl":"https://doi.org/10.3390/electronics13173581","url":null,"abstract":"In this study, we propose a smart transfer planer equipped with multiple antenna arrays to improve ground links for low Earth orbit (LEO) satellite communication. The STP features a symmetrical structure and is strategically placed on both ends of a window, serving both indoor and outdoor environments. Using the window glass as a medium, energy transmission occurs through a coupling mechanism between the planers. The design focuses on large array antenna design, beamforming networks, and coupler design on both sides of the glass. Beamforming networks enable the indoor and outdoor antenna arrays to switch beams in various directions, optimizing high-gain antennas with narrow beamwidths. Through electromagnetic induction and filter couplers, a robust signal transmission channel is established between indoor and outdoor environments. This setup significantly enhances communication efficiency, particularly in non-line-of-sight environments.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"58 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents an improved collaborative control to resist grid voltage unbalance for brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). The mathematical model of grid-connected BDFG including machine side converter (MSC) and grid side converter (GSC) in the αβ reference frame during unbalanced grid voltage condition is established. On this base, the improved collaborative control between MSC and GSC is presented. Under the control, the control objective of the whole BDFGWT system, including canceling the pulsations of electromagnetic torque and the unbalance of BDFGWT’s total currents, pulsations of BDFGWT’s total powers are capable of being realized; therefore, the control capability of BDFGWT to resist unbalanced grid voltage is greatly improved. Moreover, improved single-loop current controllers adopting PR regulators are proposed for both MSC and GSC where the sequence extractions for both MSC and GSC currents are not needed any more, and hence the proposed control is much simpler. In addition, the transient characteristics are also improved. Moreover, in order to achieve the decoupling control of current and average power, current controller also adopts the feedforward control approach. Case studies for a two MW BDFGWT system are implemented, and the results verify that the presented control is capable of effectively improving the control capability for BDFGWT to resist grid voltage unbalance and exhibit good stable and dynamic control performances.
{"title":"An Improved Collaborative Control Scheme to Resist Grid Voltage Unbalance for BDFG-Based Wind Turbine","authors":"Defu Cai, Rusi Chen, Sheng Hu, Guanqun Sun, Erxi Wang, Jinrui Tang","doi":"10.3390/electronics13173582","DOIUrl":"https://doi.org/10.3390/electronics13173582","url":null,"abstract":"This article presents an improved collaborative control to resist grid voltage unbalance for brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). The mathematical model of grid-connected BDFG including machine side converter (MSC) and grid side converter (GSC) in the αβ reference frame during unbalanced grid voltage condition is established. On this base, the improved collaborative control between MSC and GSC is presented. Under the control, the control objective of the whole BDFGWT system, including canceling the pulsations of electromagnetic torque and the unbalance of BDFGWT’s total currents, pulsations of BDFGWT’s total powers are capable of being realized; therefore, the control capability of BDFGWT to resist unbalanced grid voltage is greatly improved. Moreover, improved single-loop current controllers adopting PR regulators are proposed for both MSC and GSC where the sequence extractions for both MSC and GSC currents are not needed any more, and hence the proposed control is much simpler. In addition, the transient characteristics are also improved. Moreover, in order to achieve the decoupling control of current and average power, current controller also adopts the feedforward control approach. Case studies for a two MW BDFGWT system are implemented, and the results verify that the presented control is capable of effectively improving the control capability for BDFGWT to resist grid voltage unbalance and exhibit good stable and dynamic control performances.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"37 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.3390/electronics13173576
Emi Yuda, Aoi Otani, Atsushi Yamada, Yutaka Yoshida
In this study, we investigated the effects of the smell environment in the work booth on autonomic nervous activity (ANS) and psychomotor vigilance levels (PVLs) using linalool (LNL) and trans-2-nonenal (T2N). The subjects were six healthy males (31 ± 6 years old) and six healthy females (24 ± 5 years old). They sat in the work booth filled with the smells of LNL and T2N for 10 min, and their electrocardiograms (ECGs), skin conductance levels, pulse wave variabilities, skin temperatures, and seat pressure distributions were measured. In addition, the orthostatic load test (OLT) and psychomotor vigilance test (PVT) were performed before and after entering the work booth, and a subjective evaluation of the smell was also performed after the experiment. This paper focused on ECG and PVT data and analyzed changes in heart rate variability indices and PVT scores. Males felt slightly comfortable with the LNL smell and showed promoted sympathetic nerve activity in the OLT after the smell presentation. Females felt slightly uncomfortable with the T2N smell and showed promoted sympathetic nerve activity and a decrease in PVT scores in the OLT after the smell presentation. Gender differences were observed in ANS and PVLs, and it is possible that the comfort of LNL increased sympathetic nervous activity in males, while the uncomfortableness of T2N may have reduced work performance in females.
{"title":"An Evaluation of the Autonomic Nervous Activity and Psychomotor Vigilance Level for Smells in the Work Booth","authors":"Emi Yuda, Aoi Otani, Atsushi Yamada, Yutaka Yoshida","doi":"10.3390/electronics13173576","DOIUrl":"https://doi.org/10.3390/electronics13173576","url":null,"abstract":"In this study, we investigated the effects of the smell environment in the work booth on autonomic nervous activity (ANS) and psychomotor vigilance levels (PVLs) using linalool (LNL) and trans-2-nonenal (T2N). The subjects were six healthy males (31 ± 6 years old) and six healthy females (24 ± 5 years old). They sat in the work booth filled with the smells of LNL and T2N for 10 min, and their electrocardiograms (ECGs), skin conductance levels, pulse wave variabilities, skin temperatures, and seat pressure distributions were measured. In addition, the orthostatic load test (OLT) and psychomotor vigilance test (PVT) were performed before and after entering the work booth, and a subjective evaluation of the smell was also performed after the experiment. This paper focused on ECG and PVT data and analyzed changes in heart rate variability indices and PVT scores. Males felt slightly comfortable with the LNL smell and showed promoted sympathetic nerve activity in the OLT after the smell presentation. Females felt slightly uncomfortable with the T2N smell and showed promoted sympathetic nerve activity and a decrease in PVT scores in the OLT after the smell presentation. Gender differences were observed in ANS and PVLs, and it is possible that the comfort of LNL increased sympathetic nervous activity in males, while the uncomfortableness of T2N may have reduced work performance in females.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.3390/electronics13173579
Dongbao Jia, Ming Cao, Jing Sun, Feimeng Wang, Wei Xu, Yichen Wang
Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty.
{"title":"Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization","authors":"Dongbao Jia, Ming Cao, Jing Sun, Feimeng Wang, Wei Xu, Yichen Wang","doi":"10.3390/electronics13173579","DOIUrl":"https://doi.org/10.3390/electronics13173579","url":null,"abstract":"Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"8 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multilingual large language models (MLLMs) have demonstrated remarkable performance across a wide range of cross-lingual Natural Language Processing (NLP) tasks. The emergence of MLLMs made it possible to achieve knowledge transfer from high-resource to low-resource languages. Several MLLMs have been released for cross-lingual transfer tasks. However, no systematic evaluation comparing all models for Arabic cross-lingual Named-Entity Recognition (NER) is available. This paper presents a benchmark evaluation to empirically investigate the performance of the state-of-the-art multilingual large language models for Arabic cross-lingual NER. Furthermore, we investigated the performance of different MLLMs adaptation methods to better model the Arabic language. An error analysis of the different adaptation methods is presented. Our experimental results indicate that GigaBERT outperforms other models for Arabic cross-lingual NER, while language-adaptive pre-training (LAPT) proves to be the most effective adaptation method across all datasets. Our findings highlight the importance of incorporating language-specific knowledge to enhance the performance in distant language pairs like English and Arabic.
多语言大型语言模型（MLLMs）在广泛的跨语言自然语言处理（NLP）任务中表现出卓越的性能。多语言大型语言模型的出现使知识从高资源语言向低资源语言转移成为可能。目前已经发布了几种用于跨语言转移任务的 MLLM。但是，目前还没有针对阿拉伯语跨语言命名-实体识别（NER）的所有模型进行比较的系统评估。本文提出了一个基准评估，以实证研究阿拉伯语跨语言 NER 中最先进的多语言大型语言模型的性能。此外，我们还研究了不同 MLLMs 适应方法的性能，以更好地模拟阿拉伯语。我们对不同的适应方法进行了误差分析。实验结果表明，在阿拉伯语跨语言 NER 中，GigaBERT 的表现优于其他模型，而在所有数据集中，语言自适应预训练 (LAPT) 被证明是最有效的自适应方法。我们的研究结果凸显了结合特定语言知识以提高英语和阿拉伯语等遥远语言对的性能的重要性。
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