Alexander Soudaei, Jianhua Zhang, Mohamed Elmi, Mikael Tsechoev, Zishan Khan, Ahmed Osman
Accurate energy consumption prediction can provide insights to make better informed decisions on energy purchase and generation. It also can prevent overloading and make it possible to store energy more efficiently. In this work, we propose a new deep learning model to predict the household energy consumption. In the new model, we employ differential evolution (DE) algorithm to automatically determine the optimal architecture of the deep neural network. The energy prediction results are presented and analyzed to show the effectiveness of the deep neuroevolution model constructed.
{"title":"Household Energy Consumption Prediction: A Deep Neuroevolution Approach","authors":"Alexander Soudaei, Jianhua Zhang, Mohamed Elmi, Mikael Tsechoev, Zishan Khan, Ahmed Osman","doi":"10.1145/3611450.3611474","DOIUrl":"https://doi.org/10.1145/3611450.3611474","url":null,"abstract":"Accurate energy consumption prediction can provide insights to make better informed decisions on energy purchase and generation. It also can prevent overloading and make it possible to store energy more efficiently. In this work, we propose a new deep learning model to predict the household energy consumption. In the new model, we employ differential evolution (DE) algorithm to automatically determine the optimal architecture of the deep neural network. The energy prediction results are presented and analyzed to show the effectiveness of the deep neuroevolution model constructed.","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127487692","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}
Lin Zhang, Hongtu Xie, Shifei Li, Chengsheng Zhang, Di Zhang, Wenhui Tang, Zhaojian Zhang
At present, the direct current (DC) micro-grid based on the solid oxide fuel cell (SOFC) can supply the power to the external load independently. Despite an adequate and steady supply of the electricity to the external load, the high efficiency and avoiding fuel starvation is other points for the attention. In this paper, a control method of the SOFC-based DC micro-grid has been proposed, which can avoid the fuel starvation when the external load power increases This method adopts the optimal operating points (OOPs) to obtain the maximum efficiency, and then a novel time-delay control algorithm based on the system electric current is designed to avoid the fuel starvation. All simulation results demonstrate that the proposed method is feasible, which can effectively solve the fuel starvation problem. What's more, the output efficiency can be up to 40%, which can get the high efficiency of the power supply. The works in this paper can provide the reference for other similar systems to solve the fuel starvation problem.
{"title":"A Control Method of SOFC-based DC Micro-grid to Avoid Fuel Starvation when External Load Power Increases","authors":"Lin Zhang, Hongtu Xie, Shifei Li, Chengsheng Zhang, Di Zhang, Wenhui Tang, Zhaojian Zhang","doi":"10.1145/3611450.3611455","DOIUrl":"https://doi.org/10.1145/3611450.3611455","url":null,"abstract":"At present, the direct current (DC) micro-grid based on the solid oxide fuel cell (SOFC) can supply the power to the external load independently. Despite an adequate and steady supply of the electricity to the external load, the high efficiency and avoiding fuel starvation is other points for the attention. In this paper, a control method of the SOFC-based DC micro-grid has been proposed, which can avoid the fuel starvation when the external load power increases This method adopts the optimal operating points (OOPs) to obtain the maximum efficiency, and then a novel time-delay control algorithm based on the system electric current is designed to avoid the fuel starvation. All simulation results demonstrate that the proposed method is feasible, which can effectively solve the fuel starvation problem. What's more, the output efficiency can be up to 40%, which can get the high efficiency of the power supply. The works in this paper can provide the reference for other similar systems to solve the fuel starvation problem.","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125451322","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}
In recent years, self-driving delivery vehicles have been used more and more widely. The route planning of courier vehicles can be abstracted as the traveling salesman problem (TSP). For the courier vehicle path optimization problem, an improved population-based ant colony optimization algorithm (IPACO) is proposed. The ant colony optimization algorithm (ACO) is a swarm intelligent bionic algorithm with the advantages of positive feedback, robustness, and easy combination with other algorithms, but it also has the problems of low solution accuracy and easy to fall into local optimality. In order to avoid these problems, the 2-opt local optimization operator is combined in the algorithm search process to improve the diversity of the population. In addition, the property that the simulated annealing algorithm probabilistically accepts relatively poor solutions is used to optimize the optimal ants during the iterative process. Finally, some TSPLIB examples are selected to verify the performance of the algorithm, and the fast adaptation capability of the algorithm under the change of path node weights is verified by simulation.
{"title":"A solution of TSP based on the improved ant colony optimization","authors":"Hengyu Nie, Meijuan Li, X. Chen, Zaihui Cui","doi":"10.1145/3611450.3611465","DOIUrl":"https://doi.org/10.1145/3611450.3611465","url":null,"abstract":"In recent years, self-driving delivery vehicles have been used more and more widely. The route planning of courier vehicles can be abstracted as the traveling salesman problem (TSP). For the courier vehicle path optimization problem, an improved population-based ant colony optimization algorithm (IPACO) is proposed. The ant colony optimization algorithm (ACO) is a swarm intelligent bionic algorithm with the advantages of positive feedback, robustness, and easy combination with other algorithms, but it also has the problems of low solution accuracy and easy to fall into local optimality. In order to avoid these problems, the 2-opt local optimization operator is combined in the algorithm search process to improve the diversity of the population. In addition, the property that the simulated annealing algorithm probabilistically accepts relatively poor solutions is used to optimize the optimal ants during the iterative process. Finally, some TSPLIB examples are selected to verify the performance of the algorithm, and the fast adaptation capability of the algorithm under the change of path node weights is verified by simulation.","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126278946","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}
Nested named entity recognition (NER) is an important and challenging task in information extraction. One effective approach is to detect regions in sentences that are later classified by neural networks. Since pre-trained language models (PLMs) were proposed, nested NER models have benefited a lot from them. However, it is common that only one PLM is utilized for a given model, and the performance varies with different PLMs. We note that there exist some conflicting predictions which lead to the final variation. Thus, there is still room for investigation as to whether a model could achieve even better performance by conducting a comprehensive analysis of results from various PLMs. In this paper, we propose an evidential classifier with multiple PLMs for nested NER. First, the well-known deep exhaustive model is trained separately with different PLMs, whose predictions are then treated as pieces of evidence that can be represented in the framework of Dempster-Shafer theory. Finally, the pooled evidence is obtained using a combination rule, based on which the inference is performed. Experiments are conducted on the GENIA dataset, and detailed analysis demonstrates the merits of our model.
{"title":"An Evidential Classifier with Multiple Pre-trained Language Models for Nested Named Entity Recognition","authors":"Haitao Liu, Jihua Song, Weiming Peng","doi":"10.1145/3611450.3611477","DOIUrl":"https://doi.org/10.1145/3611450.3611477","url":null,"abstract":"Nested named entity recognition (NER) is an important and challenging task in information extraction. One effective approach is to detect regions in sentences that are later classified by neural networks. Since pre-trained language models (PLMs) were proposed, nested NER models have benefited a lot from them. However, it is common that only one PLM is utilized for a given model, and the performance varies with different PLMs. We note that there exist some conflicting predictions which lead to the final variation. Thus, there is still room for investigation as to whether a model could achieve even better performance by conducting a comprehensive analysis of results from various PLMs. In this paper, we propose an evidential classifier with multiple PLMs for nested NER. First, the well-known deep exhaustive model is trained separately with different PLMs, whose predictions are then treated as pieces of evidence that can be represented in the framework of Dempster-Shafer theory. Finally, the pooled evidence is obtained using a combination rule, based on which the inference is performed. Experiments are conducted on the GENIA dataset, and detailed analysis demonstrates the merits of our model.","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130346736","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}
Jingbo Sun, T. Song, Haitao Liu, Weiming Peng, Jihua Song
Dialogue systems are a valuable technology in the field of natural language processing to improve work, learning, and daily life. Currently, dialogue systems are employed as an educational technology for mentoring, evaluation, and personalized learning. To make dialogue teaching achieve the purpose of training vocabulary at the primary level of international Chinese learning education, we first collect the entire dialogue corpus from textbooks to create the dataset, and then we propose a dialogue response generation model, Seq2BF-Attention, containing a specific word based on the sequence to backward and forward sequences framework by adding an attention to enhance the modeling of dialogue posts. We also provide two decoder connection strategies, backward hidden connection and backward attention, to handle the problems of not sharing parameters and incoherent generation separately. It has been experimentally proven that our suggested models perform well in both the ICLE and Weibo datasets across all metrics.
{"title":"Word-Constrained Response Generation for International Chinese Language Education based on Decoder Backward Attention","authors":"Jingbo Sun, T. Song, Haitao Liu, Weiming Peng, Jihua Song","doi":"10.1145/3611450.3611478","DOIUrl":"https://doi.org/10.1145/3611450.3611478","url":null,"abstract":"Dialogue systems are a valuable technology in the field of natural language processing to improve work, learning, and daily life. Currently, dialogue systems are employed as an educational technology for mentoring, evaluation, and personalized learning. To make dialogue teaching achieve the purpose of training vocabulary at the primary level of international Chinese learning education, we first collect the entire dialogue corpus from textbooks to create the dataset, and then we propose a dialogue response generation model, Seq2BF-Attention, containing a specific word based on the sequence to backward and forward sequences framework by adding an attention to enhance the modeling of dialogue posts. We also provide two decoder connection strategies, backward hidden connection and backward attention, to handle the problems of not sharing parameters and incoherent generation separately. It has been experimentally proven that our suggested models perform well in both the ICLE and Weibo datasets across all metrics.","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121287095","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}
The Deep Q_Network (DQN) algorithm in reinforcement learning is introduced to main engine fault diagnosis to improve the accuracy and efficiency of fault diagnosis by using the optimized DQN network algorithm to compensate for the lack of data imbalance for unbalanced fault data that are close to the real situation. The optimization of the DQN network algorithm is reflected in three aspects: firstly, the ɛ-greedy algorithm is optimized using the Upper Confidence Bound (UCB) algorithm, which makes the algorithm achieve a better balance between experience and exploratory in the selection of fault types; secondly, the fully connected network of the basic DQN is optimized using the triple-formed layer CNN network layer is optimized to improve the algorithm operation efficiency; meanwhile, the reward function for unbalanced data is set according to the balance rate, and the problem of reward value bias and local optimum for small amount of data is considered, so that the optimized DQN network algorithm gets improved accuracy in fault diagnosis of unbalanced data. Finally, the optimized DQN network, the base DQN network, the DCNN, and the ResNet18 are run for diagnosis on the unbalanced data set. Compared with other algorithmic networks, the optimized DQN improved 5.18%∼18.58% in accuracy. The results show that the DQN algorithm model can be applied with main engine unbalanced data fault diagnosis, and the improved DQN algorithm achieves good results in the efficiency and stability of diagnosis.
{"title":"Study on the fault diagnosis method of ship main engine unbalanced data based on improved DQN","authors":"Meiwen Wang, Hui Cao, Guozhong Li","doi":"10.1145/3611450.3611453","DOIUrl":"https://doi.org/10.1145/3611450.3611453","url":null,"abstract":"The Deep Q_Network (DQN) algorithm in reinforcement learning is introduced to main engine fault diagnosis to improve the accuracy and efficiency of fault diagnosis by using the optimized DQN network algorithm to compensate for the lack of data imbalance for unbalanced fault data that are close to the real situation. The optimization of the DQN network algorithm is reflected in three aspects: firstly, the ɛ-greedy algorithm is optimized using the Upper Confidence Bound (UCB) algorithm, which makes the algorithm achieve a better balance between experience and exploratory in the selection of fault types; secondly, the fully connected network of the basic DQN is optimized using the triple-formed layer CNN network layer is optimized to improve the algorithm operation efficiency; meanwhile, the reward function for unbalanced data is set according to the balance rate, and the problem of reward value bias and local optimum for small amount of data is considered, so that the optimized DQN network algorithm gets improved accuracy in fault diagnosis of unbalanced data. Finally, the optimized DQN network, the base DQN network, the DCNN, and the ResNet18 are run for diagnosis on the unbalanced data set. Compared with other algorithmic networks, the optimized DQN improved 5.18%∼18.58% in accuracy. The results show that the DQN algorithm model can be applied with main engine unbalanced data fault diagnosis, and the improved DQN algorithm achieves good results in the efficiency and stability of diagnosis.","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117081856","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}
The courier industry in China has grown quickly due to the rise of online shopping. However, courier notes can unavoidably become smudged or damaged during the delivery process, making it difficult to read the printed Chinese address information or recognize the barcodes. To solve this problem, this paper proposes an end-to-end solution to recognize damaged Chinese addresses: the CRNN model is trained for address recognition for damaged Chinese courier orders using a large Chinese address dataset generated via data augmentation and manual collection. And an address association algorithm is proposed to reduce the recognition errors at the provincial and municipal levels of the addresses. By applying this algorithm, the final accuracy is increased by 2% to 98.7%.
{"title":"Deep Learning-based End-to-End Address Recognition Solution on Chinese Courier Order Forms","authors":"Jiayi Zhang, Yue Liu","doi":"10.1145/3611450.3611473","DOIUrl":"https://doi.org/10.1145/3611450.3611473","url":null,"abstract":"The courier industry in China has grown quickly due to the rise of online shopping. However, courier notes can unavoidably become smudged or damaged during the delivery process, making it difficult to read the printed Chinese address information or recognize the barcodes. To solve this problem, this paper proposes an end-to-end solution to recognize damaged Chinese addresses: the CRNN model is trained for address recognition for damaged Chinese courier orders using a large Chinese address dataset generated via data augmentation and manual collection. And an address association algorithm is proposed to reduce the recognition errors at the provincial and municipal levels of the addresses. By applying this algorithm, the final accuracy is increased by 2% to 98.7%.","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125815138","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}
Abstract—Based on neural network end-to-end speech synthesis systems, high-quality speech can be synthesized when there is sufficient training data. However, it is difficult for languages with small datasets to synthesize speech with high quality and naturalness. Vietnamese is a tonal language, belonging to the Vietic branch of the Austroasiatic language family, which uses a spelling system. To improve the quality and naturalness of speech synthesis with limited dataset resources, we first use transfer learning to improve the acoustic model of Vietnamese by leveraging the similarities in pronunciation and grammar between Mandarin Chinese and Vietnamese. Secondly, based on the prosodic characteristics of Vietnamese, we use a "speech-text" alignment tool to extract prosodic boundary information and supplement it to the training text sequence. Using FastSpeech2 as the experimental baseline system, we designed and added a prosody embedding layer. The experimental results show that the model trained with prosodic markers has better prosody expression compared to the original text. Furthermore, compared to the baseline system, adding the prosody embedding layer improved the prosody expression of the synthesized speech and eliminated the need for marked text during speech synthesis.
{"title":"Multi-Feature Cross-Lingual Transfer Learning Approach for Low-Resource Vietnamese Speech Synthesis","authors":"Zhi Qiao, Jian Yang, Zhan Wang","doi":"10.1145/3611450.3611476","DOIUrl":"https://doi.org/10.1145/3611450.3611476","url":null,"abstract":"Abstract—Based on neural network end-to-end speech synthesis systems, high-quality speech can be synthesized when there is sufficient training data. However, it is difficult for languages with small datasets to synthesize speech with high quality and naturalness. Vietnamese is a tonal language, belonging to the Vietic branch of the Austroasiatic language family, which uses a spelling system. To improve the quality and naturalness of speech synthesis with limited dataset resources, we first use transfer learning to improve the acoustic model of Vietnamese by leveraging the similarities in pronunciation and grammar between Mandarin Chinese and Vietnamese. Secondly, based on the prosodic characteristics of Vietnamese, we use a \"speech-text\" alignment tool to extract prosodic boundary information and supplement it to the training text sequence. Using FastSpeech2 as the experimental baseline system, we designed and added a prosody embedding layer. The experimental results show that the model trained with prosodic markers has better prosody expression compared to the original text. Furthermore, compared to the baseline system, adding the prosody embedding layer improved the prosody expression of the synthesized speech and eliminated the need for marked text during speech synthesis.","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129931659","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}
{"title":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","authors":"","doi":"10.1145/3611450","DOIUrl":"https://doi.org/10.1145/3611450","url":null,"abstract":"","PeriodicalId":289906,"journal":{"name":"Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms","volume":"76 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126020383","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：