发布求助
文献互助 智能选刊 最新文献

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)最新文献

筛选
英文 中文
Prediction of Seismic Performance Level of Reinforced Concrete Building Based on Structural and Seismic Parameters Combination Using Artificial Neural Network 基于结构与地震参数组合的人工神经网络钢筋混凝土建筑抗震性能水平预测
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217356
Warren Keith J. Nietes, D. Silva
{"title":"Prediction of Seismic Performance Level of Reinforced Concrete Building Based on Structural and Seismic Parameters Combination Using Artificial Neural Network","authors":"Warren Keith J. Nietes, D. Silva","doi":"10.1109/SEAI59139.2023.10217356","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217356","url":null,"abstract":"This study presents an analytical strategy based on Artificial Neural Networks (ANN) for analyzing the seismic performance (SP) of low-to-mid-rise reinforced concrete (RC) buildings. The performance analysis was conducted using eleven (11) three-to-twelve story RC buildings subjected to a non-linear pushover analysis (PA) based on the buildings' existing materials, loadings, and geometrical characteristics. The chosen parameters included fourteen (14) properties, eight (8) of which were geometrical and structural, and six (6) of which were seismic. The performance analysis was conducted under the guidance of the Federal Emergency Management Agency (FEMA 356) and the National Structural Code of the Philippines (NSCP 2015). The selected parameters were based on their potential influence on the performance of RC buildings during a seismic event. As the training algorithm (TA), the ANN-based Levenberg-Marquardt algorithm was used, and the hyperbolic tangent sigmoid (HTS) function was employed as the activation function (AF). The assessment demonstrated that the model attained R values of 0.9959 and 0.9887 for seismic performance at points X and Y, respectively. The aforementioned findings demonstrate the feasibility of the proposed ANN-based technique for predicting the SP of RC structures.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"6 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130259131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Maneuvering Target Tracking Based on LSTM for Radar Application 基于LSTM的机动目标跟踪在雷达中的应用
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217223
Shixiang Cai, Shengli Wang, Mingjie Qiu
{"title":"Maneuvering Target Tracking Based on LSTM for Radar Application","authors":"Shixiang Cai, Shengli Wang, Mingjie Qiu","doi":"10.1109/SEAI59139.2023.10217223","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217223","url":null,"abstract":"Aiming at the problem of model mismatch in traditional model-based target tracking methods when the target undergoes maneuvers, an adaptive tracking method based on Long Short-Term Memory (LSTM) is proposed. The algorithm replaces traditional filtering algorithms with LSTM and introduces the error between the measurement and the predicted value during training. This enables the network to simultaneously acquire information about the target position and measurement error, and during prediction, use the measurement error to directly modify the network's prediction results to obtain the final tracking results. Simulation results demonstrate that this method effectively solves the problem of describing the motion state of maneuvering targets, and has higher tracking accuracy and stronger adaptability compared to traditional filtering algorithms.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114180071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fitness Function Using Postconditions for Automated Program Repair 应用后置条件的适应度函数用于自动程序修复
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217541
Yusaku Ito, H. Washizaki, Y. Fukazawa
{"title":"Fitness Function Using Postconditions for Automated Program Repair","authors":"Yusaku Ito, H. Washizaki, Y. Fukazawa","doi":"10.1109/SEAI59139.2023.10217541","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217541","url":null,"abstract":"Genetic algorithm-based automated program repair has the challenge that execution time tends to be extremely long. One of the causes is the low adequacy of the fitness, which represents the evaluation of program variants. To improve the efficiency of automated repair, we propose a new fitness function that uses the state of internal variables of the program in addition to the results of test runs. First, candidate specifications are randomly generated using variables in the program. Next, the correct specification is automatically estimated from the candidates, thus making the granularity of the fitness function more fine-grained. Experimental results with small programs show that the proposed method extracts the postcondition of the program appropriately in most cases. On the other hand, experiments with the Defects4J Math dataset suggest that the proposed method does not necessarily improve the efficiency of automated repair and may reduce the diversity of generated variants.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115950639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparative Study of Seq2Seq and Transformer Model for Chat Bot 聊天机器人Seq2Seq与Transformer模型的比较研究
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217667
Nipun Bansal, A. Singh, Abhishek Kumar, Abhishek Tyagi
{"title":"Comparative Study of Seq2Seq and Transformer Model for Chat Bot","authors":"Nipun Bansal, A. Singh, Abhishek Kumar, Abhishek Tyagi","doi":"10.1109/SEAI59139.2023.10217667","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217667","url":null,"abstract":"Today, every company relies on technology for the effective delivery of services and the optimal use of technical resources. With the increasing adoption of AI based technologies in company operations worldwide, including India, the global Chatbot market will grow rapidly in the coming years. Chatbot technology has gained widespread acceptance in recent years, especially in the food delivery, finance, and e-commerce industries. Chatbots are computer programs that can answer questions posed by humans, and they are accessible anytime, providing quick and efficient communication with thousands of people simultaneously. Chatbots are becoming increasingly popular in human-machine interactions due to their ability to provide information without requiring time-consuming searches. In this paper, we will compare the methodologies, underlying algorithms, accuracy, and constraints of the various models for chat bots. Models compared in this study are Seq2seq Model with Attention Mechanism and Transformer. The result shows that the transformer model requires less number of iterations (epochs) to train than seq2seq model. Compared to LSTM networks, training transformer networks is easier due to the smaller number of parameters involved and Transformer network is faster than RNN based models.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bifurcation Analysis and Control of a Four-Wing Chaotic System 四翼混沌系统的分岔分析与控制
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217400
Qin Han, Liang Zhang
{"title":"Bifurcation Analysis and Control of a Four-Wing Chaotic System","authors":"Qin Han, Liang Zhang","doi":"10.1109/SEAI59139.2023.10217400","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217400","url":null,"abstract":"This paper presents a new chaotic system that exhibits a four-wing chaotic attractor. The Lyapunov exponent dimension and chaotic attractor of the system are get at first. Then, according to the high-dimensional Hopf bifurcation theory, the Hopf bifurcation characteristics of the system are analyzed. A nonlinear feedback controller with an equilibrium point is set to the system. By adjusting the control parameters, the Hopf bifurcation of the system occurs at the specified position and controls the amplitude of the limit cycle. Hopf bifurcation control of the system is realized.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134042530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sea-Surface Target Detection Based on Neural Network Prediction 基于神经网络预测的海面目标检测
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217388
Jingtao Zeng, Lin Jin, Yujun Zhou
{"title":"Sea-Surface Target Detection Based on Neural Network Prediction","authors":"Jingtao Zeng, Lin Jin, Yujun Zhou","doi":"10.1109/SEAI59139.2023.10217388","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217388","url":null,"abstract":"In this paper, we use the theory of phase space reconstruction to calculates the parameters required for phase space reconstruction, and employ the powerful nonlinear fitting ability of neural network to approximate the nonlinear dynamical equation of reconstructed chaotic systems, achieving nonlinear prediction of sea clutter. We introduce the genetic optimization algorithm to initialize the network weights of BP and WNN network, and utilize multiple sets of IPIX radar data sets to experimentally verify the network model. Comparing prediction errors and target unit detection of the two, the results show that sea clutter prediction model based on neural network method can effectively detect sea surface targets in different sea conditions, with good prediction ability and generalizability. Meanwhile, the improved WNN network improves the detection performance by at least 6% compared to the BP network.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134098179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Based on the Improved LSTM Model Aircraft Long Track Prediction Research 基于改进LSTM模型的飞机长航迹预测研究
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217708
Ting-ting Bi, Jiangtao Lyu, Tao He
{"title":"Based on the Improved LSTM Model Aircraft Long Track Prediction Research","authors":"Ting-ting Bi, Jiangtao Lyu, Tao He","doi":"10.1109/SEAI59139.2023.10217708","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217708","url":null,"abstract":"The plane long track forecast for half an hour or more forecast for a long period of time, is a military action to intentions and cracking down on the key technology of auxiliary decision, also it can be used in the field of civil aviation traffic control, strengthen the management of the aircraft. This article adopts data mining methods to improve the LSTM network structure. It proposes an LSTM network that combines convolutional neural networks and attention mechanisms and uses radar-measured aircraft trajectory data for long-term prediction. The predicted results show that the improved LSTM network structure has certain performance improvements in prediction. Based on this, the article analyzes the impact of prediction steps on the algorithm and presents the analysis results.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130355676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Inverse Kinematics Analysis of 5-DOF Cooperative Robot Based on Long Short-Term Memory Network 基于长短期记忆网络的五自由度协作机器人运动学逆分析
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217721
Songtao Wang, Yanyan Zhang, Sisi Chen, Minghan Xu, Yimeng Yu, Pingxian Liu
{"title":"Inverse Kinematics Analysis of 5-DOF Cooperative Robot Based on Long Short-Term Memory Network","authors":"Songtao Wang, Yanyan Zhang, Sisi Chen, Minghan Xu, Yimeng Yu, Pingxian Liu","doi":"10.1109/SEAI59139.2023.10217721","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217721","url":null,"abstract":"To solve the problems of lengthy computation times and slow path searches for the 5-DOF cooperative robot, an algorithm based on the long short-term memory network (LSTM) is proposed in this paper. Firstly, the forward and inverse analytical kinematics models of the 5-DOF cooperative robot are established based on the physical structure. Secondly, the forward and inverse kinematic models are verified by the Matlab robot toolbox. Then, a large amount data of the joints and the corresponding pose data are obtained by the analysis of the kinematics robot. Finally, the algorithm based on the LSTM network is established by training the data of the joints and poses. Compared with the traditional analytical method, the algorithm based on the LSTM network is more efficient and satisfies the demands of real-time computing.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114628855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DenseNet-SALD: A Lightweight Diseases and Pests Spatial Attention Classification Model DenseNet-SALD:一种轻量级病虫害空间注意分类模型
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217692
Zhanghao Qin, Longqiu Chen, Yanan Wang
{"title":"DenseNet-SALD: A Lightweight Diseases and Pests Spatial Attention Classification Model","authors":"Zhanghao Qin, Longqiu Chen, Yanan Wang","doi":"10.1109/SEAI59139.2023.10217692","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217692","url":null,"abstract":"Leaf diseases and pests significantly impact crop yields, making it crucial to rapidly and accurately identify plant foliar diseases and implement appropriate control measures. We present a novel DenseNet-SALD-based model for identifying maize diseases and pests to improve classification performance. The proposed model was trained on a balanced dataset, incorporating an attention mechanism, Leaky ReLU activation function, and Dropout regularization to enhance its classification accuracy. The experimental results demonstrate that the proposed model outperforms conventional convolutional neural network models, such as DenseNet and AlexNet, achieving an accuracy of 99.0% and 85.03% for diseases and pests testing, respectively, meeting the primary pests recognition needs. This research provides valuable insights into the application of attention mechanisms in deep learning models for image recognition tasks, which could have potential implications for advancing the classification of corn leaf diseases and pests images.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132473266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on License Plate Recognition Algorithm Based on YOLOv5 and LPRNet 基于YOLOv5和LPRNet的车牌识别算法研究
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217466
Xianjing Xiao, Haiqing Chang, Kai Tang, Jianhang Zou, Yijun Cai
{"title":"Research on License Plate Recognition Algorithm Based on YOLOv5 and LPRNet","authors":"Xianjing Xiao, Haiqing Chang, Kai Tang, Jianhang Zou, Yijun Cai","doi":"10.1109/SEAI59139.2023.10217466","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217466","url":null,"abstract":"The traditional license plate recognition (LPR) algorithm is susceptible to the photo-taking conditions such as the weather, illumination and angle. Thus, the deep learning method is introduced to solve this problem. Using YOLOv5 to locate license plates can reduce environmental interference and improve location accuracy. The obtained license plate area is corrected through perspective transformation and then as an input of LPRNet. LPRNet can be used to identify characters without character segmentation, so the disruption of the border is avoided. In this method, we use Multi-Scale Retinex with Color Restore (MSRCR) to enhance pictures. Finally, end-to-end license plate recognition is realized by connecting YOLOv5 and LPRNet models in series. The experimental results show that the accurate localization rate reaches 99.5% with the Intersection over Union (IoU) of 0.5 after image enhancement. The recognition accuracy of characters is 96%. Compared to traditional LPR methods, our method improves the accuracy of license plate location and can directly recognize characters without segmentation.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131766499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书