2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)最新文献_第4页

Research on Acquisition and Processing Method for Extravehicular Panoramic Images of Manned Spacecraft 载人飞船舱外全景图像采集与处理方法研究

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217549

Yue Yu, Bin Li, Lei Huang, Chunliu Zhang, M. Guo, Tao Feng

{"title":"Research on Acquisition and Processing Method for Extravehicular Panoramic Images of Manned Spacecraft","authors":"Yue Yu, Bin Li, Lei Huang, Chunliu Zhang, M. Guo, Tao Feng","doi":"10.1109/SEAI59139.2023.10217549","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217549","url":null,"abstract":"Compared to the discrete image recording method during the assembly process of manned spacecraft, panoramic imaging technology has the advantages of high resolution and wide viewing angle. However, the application of traditional panoramic image acquisition and processing methods in the image recording during the assembly process of manned spacecraft can lead to issues such as low coverage of extravehicular panoramic images and insufficient image registration accuracy. According to the structural characteristics of manned spacecraft, this paper proposes a method for acquiring and processing extravehicular panoramic images of manned spacecraft, designs a layout scheme of extravehicular panoramic images, develops a control-by-wire panoramic pan-tilt-zoom(PTZ) system, and proposes an image registration algorithm based on perceptual hash matching point purification. The perceptual hash matching point purification method proposed in this paper calculates the similarity of small area images around two matching points based on hash fingerprints, and removes matching point pairs that are less than the set threshold. The method proposed in this paper meets the remote acquisition requirements of extravehicular manned spacecraft above 15 meters, ensuring complete coverage of extravehicular panoramic images, and improving the quality of panoramic images.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"117 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":"127289767","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

AyatDroid: A Lightweight Code Cloning Technique Using Different Static Features AyatDroid:使用不同静态特性的轻量级代码克隆技术

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217577

Yasir Glani, Luo Ping, Ke Lin, Syed Asad Shah

{"title":"AyatDroid: A Lightweight Code Cloning Technique Using Different Static Features","authors":"Yasir Glani, Luo Ping, Ke Lin, Syed Asad Shah","doi":"10.1109/SEAI59139.2023.10217577","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217577","url":null,"abstract":"In recent decades, malicious code reuse has surged in numbers and sophistication, it is a common practice among adversaries to reuse malicious code, which significantly threatens user privacy and security. Several signature-based code clone detection techniques have been proposed to detect malicious clones in Android applications that use the MD5 hash function to generate signatures. Meanwhile, these techniques only retrieve signatures from Java files. Due to the 128-bit signature size of the MD5 hash function, these techniques take longer to generate signatures. In this article, we propose the AyatDroid technique, which efficiently identifies malicious chunks by retrieving signatures from Java and manifest files . AyatDroid technique is tested on reliable CiCMalDroid 2020 dataset. We have evaluated the AyatDroid technique with other cutting-edge code clone detection techniques. Our experimental results demonstrated that AyatDroid outperformed regarding detection time and accuracy. AyatDroid is not only lightweight but also efficient, allowing it to be implemented on the large scale.","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":"125501162","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 in Blockchain-Based Manufacturing Supply Chain 基于区块链的制造业供应链研究

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217397

Alex Kibet Langat, Yu Guo

{"title":"Research in Blockchain-Based Manufacturing Supply Chain","authors":"Alex Kibet Langat, Yu Guo","doi":"10.1109/SEAI59139.2023.10217397","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217397","url":null,"abstract":"This paper proposes an innovative approach for enhancing the efficiency and security of manufacturing supply chains by integrating the Internet of Things, blockchain technology, and genetic algorithm-based consensus mechanism. The IoT devices are employed to monitor and collect real-time data from various stages of the supply chain, ensuring transparency and traceability. The blockchain technology is utilized to store and secure the collected data, enabling immutable and decentralized data management. To achieve consensus in the Blockchain network, a GA-based consensus algorithm is introduced, which optimizes the routing and allocation of resources in the supply chain. The GA considers factors such as distance, capacity, and demand to minimize transportation costs and maximize resource utilization. Experimental results demonstrate that the proposed approach significantly improves the efficiency and reliability of the manufacturing supply chain, reducing operational costs and enhancing customer satisfaction. This research contributes to the development of intelligent and trustworthy supply chain systems by leveraging the synergies among IoT, blockchain, and GA-based consensus mechanisms.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"3 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":"133412318","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

Automated Tobacco Leaf Grading System Based on Deep Learning 基于深度学习的烟叶自动分级系统

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217575

Qi Li, Hualing Lin, Jianxin Hu, Hui Wang

{"title":"Automated Tobacco Leaf Grading System Based on Deep Learning","authors":"Qi Li, Hualing Lin, Jianxin Hu, Hui Wang","doi":"10.1109/SEAI59139.2023.10217575","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217575","url":null,"abstract":"To ensure the quality of tobacco products, tobacco leaves need to be graded to prevent mixing different quality tobacco leaves. The considerable intra-class variation and slight inter-class variation of different tobacco leaves may cause grading problems. Traditional methods of tobacco leaf grading rely on manual labor, which is inefficient and subjective. This work proposes an automated tobacco grading system based on deep learning. The reflectance image reflects information such as the texture, color, and shape of the tobacco leaf, and the transmittance image reflects the thickness of the tobacco leaf. Therefore, we capture both reflection and transmittance images of tobacco leaves and apply convolutional neural networks (CNNs) for automatic grading. During the acquisition process, the transmittance images of different areas are quickly stitched together to build a complete transmittance image of the tobacco leaf. Then the reflectance and transmittance images are aligned to improve the feature extraction of the CNN. After comparing the state-of-the-art models, we used DenseNet121 to grade the tobacco leaves and analyzed the effect of transmittance images on tobacco leaf grading.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"105 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":"121989977","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

Exploring code2vec and ASTminer for Python Code Embeddings 探索code2vec和ASTminer的Python代码嵌入

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217505

Long H. Ngo, Veeraraghavan Sekar, Étienne Leclercq, Jonathan Rivalan

{"title":"Exploring code2vec and ASTminer for Python Code Embeddings","authors":"Long H. Ngo, Veeraraghavan Sekar, Étienne Leclercq, Jonathan Rivalan","doi":"10.1109/SEAI59139.2023.10217505","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217505","url":null,"abstract":"Automated understanding of code meaning and use has become an intrinsic part of software development recently. Neural models are being used in various natural language processing tasks, as they can represent natural language using vectors that carry semantic meanings. Although code is not natural language, we believe neural models to be capable of learning semantics and syntactic properties available in code snippets. To achieve such goal, we represent a code snippet using its abstract syntax tree (AST) syntactic paths to capture regularities that reflect common code patterns. This representation lowers significantly learning effort while being scalable to multiple problems and large code bases. In our work, we adopt ASTminer with code2vec, to represent code snippets as continuously distributed code vectors called \"code embeddings\", used to predict the semantic properties of the snippets. This approach decomposes code into a collection of AST paths and learns each path's atomic representation while learning how to aggregate them. Code2vec is then paired with other neural models, which represent query, to create a hybrid model for the task of code search. While code2vec was originally developed for Java only, we present in this article our efforts to extend the method to Python language.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"15 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":"125084395","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

Simulation Study of Vector Control of Permanent Magnet Synchronous Motor with Fuzzy PI Controller 基于模糊PI控制器的永磁同步电机矢量控制仿真研究

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217659

Yun-feng Zou, Yanli Li, Xin Dai

引用次数: 0

Confrontation Game in Complex Networks Based on Intuitionistic Fuzzy Set 基于直觉模糊集的复杂网络对抗博弈

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217413

Yibo Dong, Jin Liu, Weili Li, Zhe Li, Jiaqi Ren

引用次数: 0

Moving Object Detection Based on Improved Gaussian Mixture Model and Three-Frame Difference 基于改进高斯混合模型和三帧差分的运动目标检测

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217610

Lihua Li, Yang Yang, Maohua Liu, Xiaowei Lv, Yongrong Li, Ning Yang

引用次数: 0

Learning Method to Detect Anomalies in Sewer Pipe Images Using Object Detection 学习基于目标检测的污水管道图像异常检测方法

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217734

Ryutaro Igarashi, Tomomi Ogawa

引用次数: 0

Towards Accurate Stress Classification: Combining Advanced Feature Selection and Deep Learning 迈向准确应力分类:结合高级特征选择和深度学习

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217367

Lovish Arya, Harshal Chowdhary, Ishita Agrawal, I. Sreedevi

{"title":"Towards Accurate Stress Classification: Combining Advanced Feature Selection and Deep Learning","authors":"Lovish Arya, Harshal Chowdhary, Ishita Agrawal, I. Sreedevi","doi":"10.1109/SEAI59139.2023.10217367","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217367","url":null,"abstract":"Stress is a significant health issue that affect both physical and mental health. The application of deep learning and machine learning techniques for physiological signal processing has become popular in detecting and classifying stress. In this research, we developed a fresh approach to classify stress on multiple levels using advanced machine and deep learning models. The PhysioNet SRA database was used to collect physiological signals which were pre-processed using a pipeline of filters and normalisation techniques to remove noise and artefacts. These signals were subjected to various feature extraction algorithms to determine the most pertinent characteristics for categorisation. On the acquired features, dimensionality reduction techniques were applied in order to further improve classification performance. The evaluation of Random Forest model outscored all the other classic ML models, achieving an accuracy of 93.77% and 82.42% in classifying stress on two levels and three levels respectively. Among the DL models, the LSTM model is most effective, achieving an accuracy of 95.67% and 88.70% in classifying stress on two levels and three levels respectively. The proposed approach provides a more comprehensive analysis of stress levels, which can help individuals manage stress more effectively.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"77 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":"134348308","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