Jinpeng Dai, Zhijie Zhang, Xiaoyuan Yang, Qicai Wang, Jie He
This study explores nine machine learning (ML) methods, including linear, non-linear and ensemble learning models, using nine concrete parameters as characteristic variables. Including the dosage of cement (C), fly ash (FA), Ground granulated blast furnace slag (GGBS), coarse aggregate (G), fine aggregate (S), water reducing agent (WRA) and water (W), initial gas content (GC) and number of freeze-thaw cycles (NFTC), To predict relative dynamic elastic modulus (RDEM) and mass loss rate (MLR). Based on the linear correlation analysis and the evaluation of four performance indicators of R2, MSE, MAE and RMSE, it is found that the nonlinear model has better performance. In the prediction of RDEM, the integrated learning GBDT model has the best prediction ability. The evaluation indexes were R2 = 0.78, MSE = 0.0041, MAE = 0.0345, RMSE = 0.0157, SI = 0.0177, BIAS = 0.0294. In the prediction of MLR, ensemble learning Catboost algorithm model has the best prediction ability, and the evaluation indexes are R2 = 0.84, MSE = 0.0036, RMSE = 0.0597, MAE = 0.0312, SI = 5.5298, BIAS = 0.1772. Then, Monte Carlo fine-tuning method is used to optimize the concrete mix ratio, so as to obtain the best mix ratio.
{"title":"Machine learning prediction of concrete frost resistance and optimization design of mix proportions","authors":"Jinpeng Dai, Zhijie Zhang, Xiaoyuan Yang, Qicai Wang, Jie He","doi":"10.3233/jifs-236703","DOIUrl":"https://doi.org/10.3233/jifs-236703","url":null,"abstract":"This study explores nine machine learning (ML) methods, including linear, non-linear and ensemble learning models, using nine concrete parameters as characteristic variables. Including the dosage of cement (C), fly ash (FA), Ground granulated blast furnace slag (GGBS), coarse aggregate (G), fine aggregate (S), water reducing agent (WRA) and water (W), initial gas content (GC) and number of freeze-thaw cycles (NFTC), To predict relative dynamic elastic modulus (RDEM) and mass loss rate (MLR). Based on the linear correlation analysis and the evaluation of four performance indicators of R2, MSE, MAE and RMSE, it is found that the nonlinear model has better performance. In the prediction of RDEM, the integrated learning GBDT model has the best prediction ability. The evaluation indexes were R2 = 0.78, MSE = 0.0041, MAE = 0.0345, RMSE = 0.0157, SI = 0.0177, BIAS = 0.0294. In the prediction of MLR, ensemble learning Catboost algorithm model has the best prediction ability, and the evaluation indexes are R2 = 0.84, MSE = 0.0036, RMSE = 0.0597, MAE = 0.0312, SI = 5.5298, BIAS = 0.1772. Then, Monte Carlo fine-tuning method is used to optimize the concrete mix ratio, so as to obtain the best mix ratio.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140212415","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}
José E. Valdez-Rodríguez, Nahum Rangel, M. Moreno-Armendáriz
Visual detection of fingering on the trumpet is an increasingly interesting topic in music research. The ability to recognize and track the movements of the trumpet player’s fingers during the performance of a musical piece can provide valuable information for analyzing and improving instrument technique. However, this is a largely unexplored task, as most works focus on audio quality rather than instrument fingering techniques. Developing techniques for identifying essential finger positions on a musical instrument is crucial, as poor fingering techniques can harm instrument performance. In this work, we propose the visual detection of this fingering using convolutional neural networks with a proprietary dataset created for this purpose. Additionally, to improve the results and focus on the essential parts of the instrument, we use self-attention mechanisms by extracting these features automatically.
{"title":"TrumpetNet: A Convolutional Neural Network with Self-Attention Mechanisms for visual detection of trumpet fingering","authors":"José E. Valdez-Rodríguez, Nahum Rangel, M. Moreno-Armendáriz","doi":"10.3233/jifs-219342","DOIUrl":"https://doi.org/10.3233/jifs-219342","url":null,"abstract":"Visual detection of fingering on the trumpet is an increasingly interesting topic in music research. The ability to recognize and track the movements of the trumpet player’s fingers during the performance of a musical piece can provide valuable information for analyzing and improving instrument technique. However, this is a largely unexplored task, as most works focus on audio quality rather than instrument fingering techniques. Developing techniques for identifying essential finger positions on a musical instrument is crucial, as poor fingering techniques can harm instrument performance. In this work, we propose the visual detection of this fingering using convolutional neural networks with a proprietary dataset created for this purpose. Additionally, to improve the results and focus on the essential parts of the instrument, we use self-attention mechanisms by extracting these features automatically.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140212720","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}
V. Lomas-Barrie, Michelle Reyes-Camacho, Antonio Neme
Horizontal gene transference is a biological process that involves the donation of DNA or RNA from an organism to a second, unrelated organism. This process is different from the more common one, vertical transference, which is present whenever an organism or pair of organisms reproduce and transmit their genetic material to the descendants. The identification of segments of genetic material that are the result of horizontal transference is relevant to construct accurate phylogenetic trees, on one hand, and to detect possible drug-resistance mechanisms, on the other, since this movement of genetic material is the main cause behind antibiotic resistance in bacteria. Here, we describe a novel algorithm able to detect sequences of foreign origin, and thus, possible acquired via horizontal transference. The general idea of our method is that within the genome of an organism, there might be sequences that are different from the vast majority of the remaining sequences from the same organism. The former are candidate anomalies, and thus, their origin may be explained by horizontal transference. This approach is equivalent to a particular instance of the authorship attribution problem, that in which from a set of texts or paragraphs, almost all of them were written by the same author, whereas a minority has a different authorship. The constraint is that the author of each text is not known, so the algorithm has to attribute the authorship of each one of the texts. The texts detected to be written by a different author are the equivalent of the sequences of foreign origin for the case of genetic material. We describe here a novel method to detect anomalous sequences, based on interpretable embeddings derived from a common attention mechanism in humans, that of identifying novel tokens within a given sequence. Our proposal achieves novel and consistent results over the genome of a well known organism.
横向基因转移是一个生物过程，涉及从一个生物体向另一个不相关的生物体捐赠 DNA 或 RNA。这一过程与更常见的垂直转移不同，后者是指当一种生物或一对生物进行繁殖并将其遗传物质传给后代时，就会发生基因水平转移。识别水平转移过程中的遗传物质片段，一方面有助于构建准确的系统发生树，另一方面也有助于检测可能的耐药性机制，因为这种遗传物质的移动是细菌产生抗生素耐药性的主要原因。在此，我们介绍一种新颖的算法，该算法能够检测外源序列，从而检测可能通过水平转移获得的序列。我们的方法的总体思路是，在一个生物体的基因组中，可能有一些序列与同一生物体的绝大多数其余序列不同。前者是候选异常，因此，它们的起源可以用水平转移来解释。这种方法等同于作者归属问题的一个特殊实例，即在一组文本或段落中，几乎所有文本或段落都是由同一作者所写，而少数文本或段落的作者则不同。问题的限制条件是不知道每篇文本的作者，因此算法必须确定每篇文本的作者归属。被检测出作者不同的文本就相当于遗传物质中的异源序列。我们在此介绍一种检测异常序列的新方法，该方法基于人类常见的注意力机制（即识别给定序列中的新标记）衍生出的可解释嵌入。我们的建议在已知生物的基因组上取得了新颖而一致的结果。
{"title":"Identification of horizontal gene transference by means of anomaly detection and natural language-inspired interpretable embeddings","authors":"V. Lomas-Barrie, Michelle Reyes-Camacho, Antonio Neme","doi":"10.3233/jifs-219337","DOIUrl":"https://doi.org/10.3233/jifs-219337","url":null,"abstract":"Horizontal gene transference is a biological process that involves the donation of DNA or RNA from an organism to a second, unrelated organism. This process is different from the more common one, vertical transference, which is present whenever an organism or pair of organisms reproduce and transmit their genetic material to the descendants. The identification of segments of genetic material that are the result of horizontal transference is relevant to construct accurate phylogenetic trees, on one hand, and to detect possible drug-resistance mechanisms, on the other, since this movement of genetic material is the main cause behind antibiotic resistance in bacteria. Here, we describe a novel algorithm able to detect sequences of foreign origin, and thus, possible acquired via horizontal transference. The general idea of our method is that within the genome of an organism, there might be sequences that are different from the vast majority of the remaining sequences from the same organism. The former are candidate anomalies, and thus, their origin may be explained by horizontal transference. This approach is equivalent to a particular instance of the authorship attribution problem, that in which from a set of texts or paragraphs, almost all of them were written by the same author, whereas a minority has a different authorship. The constraint is that the author of each text is not known, so the algorithm has to attribute the authorship of each one of the texts. The texts detected to be written by a different author are the equivalent of the sequences of foreign origin for the case of genetic material. We describe here a novel method to detect anomalous sequences, based on interpretable embeddings derived from a common attention mechanism in humans, that of identifying novel tokens within a given sequence. Our proposal achieves novel and consistent results over the genome of a well known organism.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140215899","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}
This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found.
本文介绍了一种将模糊逻辑技术融入卷积神经网络的方法，以管理模型在训练时处理的多种数据源中存在的不确定性。实施过程中考虑了模糊频谱中信息和滤波器的使用，以及创建一个新层，用模糊卷积层取代传统卷积层。其目的是设计人工智能算法，结合深度卷积神经网络和模糊逻辑的潜力，创建稳健的系统，以模拟数据源中存在的不确定性，并应用于分类问题。模糊化过程使用了三种成员函数，包括三角函数、高斯函数和 S 函数。由于发现交通标志的不同环境和因素的复杂性，这项工作在面向交通标志的数据库中进行了测试。
{"title":"Fuzzy convolutional neural network model applied to classification problems","authors":"Claudia I. Gonzalez, Cesar Torres","doi":"10.3233/jifs-219369","DOIUrl":"https://doi.org/10.3233/jifs-219369","url":null,"abstract":"This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140219185","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 ability of deep learning based bearing fault diagnosis methods is developing rapidly. However, it is difficult to obtain sufficient and comprehensive fault data in industrial applications, and changes in vibration signals caused by machine operating conditions can also hinder the accuracy of the model. The problem of limited data and frequent changes in operating conditions can seriously affect the effectiveness of deep learning methods. To tackle these challenges, a novel transformer model named the Differential Window Transformer (Dwin Transformer), which employs a new differential window self-attention mechanism, is presented in this paper. Meanwhile, the model introduces a hierarchical structure and a new patch merging to further improve performance. Furthermore, a new fault diagnosis model dealing with limited training data is proposed, which combines the Auxiliary Classifier Generative Adversarial Network with the Dwin Transformer(DT-ACGAN). The DT-ACGAN model can generate high-quality fake samples to facilitate training with limited data, significantly improving diagnostic capabilities. The proposed model can achieve excellent results under the dual challenges of limited data and variable working conditions by combining Dwin Transformer with GAN. The DT-ACGAN owns superior diagnostic accuracy and generalization performance under limited sample data and varying working environments when compared with other existing models. A comparative test about cross-domain ability is conducted on the Case Western Reserve University dataset and Jiang Nan University dataset. The results show that the proposed method achieves an average accuracy of 11.3% and 3.76% higher than other existing methods with limited data respectively.
{"title":"A novel rolling bearing fault diagnosis method for limited data","authors":"Haibin Sun, Wenbo Zhang","doi":"10.3233/jifs-236787","DOIUrl":"https://doi.org/10.3233/jifs-236787","url":null,"abstract":"The ability of deep learning based bearing fault diagnosis methods is developing rapidly. However, it is difficult to obtain sufficient and comprehensive fault data in industrial applications, and changes in vibration signals caused by machine operating conditions can also hinder the accuracy of the model. The problem of limited data and frequent changes in operating conditions can seriously affect the effectiveness of deep learning methods. To tackle these challenges, a novel transformer model named the Differential Window Transformer (Dwin Transformer), which employs a new differential window self-attention mechanism, is presented in this paper. Meanwhile, the model introduces a hierarchical structure and a new patch merging to further improve performance. Furthermore, a new fault diagnosis model dealing with limited training data is proposed, which combines the Auxiliary Classifier Generative Adversarial Network with the Dwin Transformer(DT-ACGAN). The DT-ACGAN model can generate high-quality fake samples to facilitate training with limited data, significantly improving diagnostic capabilities. The proposed model can achieve excellent results under the dual challenges of limited data and variable working conditions by combining Dwin Transformer with GAN. The DT-ACGAN owns superior diagnostic accuracy and generalization performance under limited sample data and varying working environments when compared with other existing models. A comparative test about cross-domain ability is conducted on the Case Western Reserve University dataset and Jiang Nan University dataset. The results show that the proposed method achieves an average accuracy of 11.3% and 3.76% higher than other existing methods with limited data respectively.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223811","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}
Cattle detection and counting is one of the most important topics in the development of modern agriculture and animal husbandry. The traditional manual monitoring methods are inefficient and constrained by factors such as site. To solve the above problems, a SCS-YOLOv5 cattle detection and counting model for complex breeding scenarios is proposed. The original SPPF module is replaced in the YOLOv5 backbone network with a CSP structured SPPFCSPC. A CA (Coordinate Attention) mechanism is added to the neck network, as well as the SC (Standard Convolution) of the Neck network is replaced with a light convolution GSConv and Slim Neck is introduced, and training strategies such as multi-scale training are also employed. The experimental results show that the proposed method enhances the feature extraction ability and feature fusion ability, balances the localization accuracy and detection speed, and improves the use effect in real farming scenarios. The Precision of the improved network model is improved from 93.2% to 95.5%, mAP@0.5 is improved from 94.5% to 95.2%, the RMSE is reduced by about 0.03, and the FPS reaches 88. Compared with other mainstream algorithms, the comprehensive performance of SCS-YOLOv5 s is in a leading position, with fewer missed and false detections, and the strong robustness and generalization ability of this model are proved on multi-category public datasets. Applying the improvement ideas in this paper to YOLOv8 s also yields an increase in accuracy. The improved method in this study can greatly improve the accuracy of cattle detection and counting in complex environments, and has good real-time performance, so as to provide technical support for large-scale cattle breeding.
{"title":"SCS-YOLOv5s: A cattle detection and counting method for complex breeding environment","authors":"Zhi Weng, Rongfei Bai, Zhiqiang Zheng","doi":"10.3233/jifs-237231","DOIUrl":"https://doi.org/10.3233/jifs-237231","url":null,"abstract":"Cattle detection and counting is one of the most important topics in the development of modern agriculture and animal husbandry. The traditional manual monitoring methods are inefficient and constrained by factors such as site. To solve the above problems, a SCS-YOLOv5 cattle detection and counting model for complex breeding scenarios is proposed. The original SPPF module is replaced in the YOLOv5 backbone network with a CSP structured SPPFCSPC. A CA (Coordinate Attention) mechanism is added to the neck network, as well as the SC (Standard Convolution) of the Neck network is replaced with a light convolution GSConv and Slim Neck is introduced, and training strategies such as multi-scale training are also employed. The experimental results show that the proposed method enhances the feature extraction ability and feature fusion ability, balances the localization accuracy and detection speed, and improves the use effect in real farming scenarios. The Precision of the improved network model is improved from 93.2% to 95.5%, mAP@0.5 is improved from 94.5% to 95.2%, the RMSE is reduced by about 0.03, and the FPS reaches 88. Compared with other mainstream algorithms, the comprehensive performance of SCS-YOLOv5 s is in a leading position, with fewer missed and false detections, and the strong robustness and generalization ability of this model are proved on multi-category public datasets. Applying the improvement ideas in this paper to YOLOv8 s also yields an increase in accuracy. The improved method in this study can greatly improve the accuracy of cattle detection and counting in complex environments, and has good real-time performance, so as to provide technical support for large-scale cattle breeding.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140220760","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 large-scale scenes, how to quickly obtain paths while ensuring the shortest possible path length is a key issue. Rapidly-exploring Random Tree (RRT) have the characteristic of quickly exploring the state space, but it is often difficult to obtain a short path. To overcome this problem, this paper proposes an improved RRT algorithm based on equidistance retention strategy and A* local search(ERRRT-A*). First, RRT is used for large-step global fast exploration to obtain approximate paths. Then, an equidistance retention strategy is used to discard most of the points and retain a small number of key points. Finally, A* is used to search between each segment to obtain a new path. The ERRRT-A* algorithm is compared with other commonly used algorithms on maps of different size in terms of path length and planning time. Simulation results indicate that compared with other algorithms, this algorithm achieves fast planning in large-scale scenes while obtaining short path length, which can effectively balance the path length and planning time.
{"title":"ERRRT-A*: A fast path planning algorithm suitable for large-scale scenes","authors":"Lixin Zhang, Hongtao Yin, Ang Li, Longbiao Hu","doi":"10.3233/jifs-238695","DOIUrl":"https://doi.org/10.3233/jifs-238695","url":null,"abstract":"In large-scale scenes, how to quickly obtain paths while ensuring the shortest possible path length is a key issue. Rapidly-exploring Random Tree (RRT) have the characteristic of quickly exploring the state space, but it is often difficult to obtain a short path. To overcome this problem, this paper proposes an improved RRT algorithm based on equidistance retention strategy and A* local search(ERRRT-A*). First, RRT is used for large-step global fast exploration to obtain approximate paths. Then, an equidistance retention strategy is used to discard most of the points and retain a small number of key points. Finally, A* is used to search between each segment to obtain a new path. The ERRRT-A* algorithm is compared with other commonly used algorithms on maps of different size in terms of path length and planning time. Simulation results indicate that compared with other algorithms, this algorithm achieves fast planning in large-scale scenes while obtaining short path length, which can effectively balance the path length and planning time.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140222963","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 response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper.
{"title":"Multi-objective optimization scheduling method considering flexible load integration for distribution network","authors":"Yingjie Li, Rongrong Sun, Guangrong Huang, Yuanbin Deng, Haixuan Zhang, Delong Zhang","doi":"10.3233/jifs-238367","DOIUrl":"https://doi.org/10.3233/jifs-238367","url":null,"abstract":"In response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223683","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}
Andrés Antonio Arenas Muñiz, Dante Mújica-Vargas, Arturo Rendón Castro, Antonio Luna-Álvarez, Virna V. Vela-Rincón
The selection of an appropriate trajectory for self-driving vehicles involves the analysis of several criteria that describe the generated trajectories. This problem evolves into an optimization problem when it is desired to increase or decrease the values for a specific criterion. The contribution of this thesis is to explore the use and optimization of another technique for decision-making, such as TOPSIS, with a sufficiently robust method that allows the inclusion of multiple parameters and their proper optimization, incorporating human experience. The proposed approach showed significantly higher safety and comfort performance, with about 20% better efficiency and 80% fewer safety violations compared to other state-of-the-art methods, and in some cases outperforming in comfort by about 30.43%.
{"title":"Optimized TOPSIS technique for trajectory selection of self-driving vehicles on highways","authors":"Andrés Antonio Arenas Muñiz, Dante Mújica-Vargas, Arturo Rendón Castro, Antonio Luna-Álvarez, Virna V. Vela-Rincón","doi":"10.3233/jifs-219365","DOIUrl":"https://doi.org/10.3233/jifs-219365","url":null,"abstract":" The selection of an appropriate trajectory for self-driving vehicles involves the analysis of several criteria that describe the generated trajectories. This problem evolves into an optimization problem when it is desired to increase or decrease the values for a specific criterion. The contribution of this thesis is to explore the use and optimization of another technique for decision-making, such as TOPSIS, with a sufficiently robust method that allows the inclusion of multiple parameters and their proper optimization, incorporating human experience. The proposed approach showed significantly higher safety and comfort performance, with about 20% better efficiency and 80% fewer safety violations compared to other state-of-the-art methods, and in some cases outperforming in comfort by about 30.43%.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223946","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}
Changshun Shao, Zhenglin Yu, Jianyin Tang, Zheng Li, Bin Zhou, Di Wu, Jingsong Duan
The main focus of this paper is to solve the optimization problem of minimizing the maximum completion time in the flexible job-shop scheduling problem. In order to optimize this objective, random sampling is employed to extract a subset of states, and the mutation operator of the genetic algorithm is used to increase the diversity of sample chromosomes. Additionally, 5-tuple are defined as the state space, and a 4-tuple is designed as the action space. A suitable reward function is also developed. To solve the problem, four reinforcement learning algorithms (Double-Q-learning algorithm, Q-learning algorithm, SARS algorithm, and SARSA(λ) algorithm) are utilized. This approach effectively extracts states and avoids the curse of dimensionality problem that occurs when using reinforcement learning algorithms. Finally, experimental results using an international benchmark demonstrate the effectiveness of the proposed solution model.
{"title":"Research on flexible job-shop scheduling problem based on variation-reinforcement learning","authors":"Changshun Shao, Zhenglin Yu, Jianyin Tang, Zheng Li, Bin Zhou, Di Wu, Jingsong Duan","doi":"10.3233/jifs-236981","DOIUrl":"https://doi.org/10.3233/jifs-236981","url":null,"abstract":"The main focus of this paper is to solve the optimization problem of minimizing the maximum completion time in the flexible job-shop scheduling problem. In order to optimize this objective, random sampling is employed to extract a subset of states, and the mutation operator of the genetic algorithm is used to increase the diversity of sample chromosomes. Additionally, 5-tuple are defined as the state space, and a 4-tuple is designed as the action space. A suitable reward function is also developed. To solve the problem, four reinforcement learning algorithms (Double-Q-learning algorithm, Q-learning algorithm, SARS algorithm, and SARSA(λ) algorithm) are utilized. This approach effectively extracts states and avoids the curse of dimensionality problem that occurs when using reinforcement learning algorithms. Finally, experimental results using an international benchmark demonstrate the effectiveness of the proposed solution model.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140224543","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：