2023 15th International Conference on Computer and Automation Engineering (ICCAE)最新文献_第6页

Simultaneous Localization and Mapping (SLAM) in Swarm Robots for Map-Merging and Uniform Map Generation Using ROS 基于ROS的群体机器人同步定位与地图生成

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111365

Abhishek Salunke, Chinmay Patil, Ram Mude, Radhika D. Joshi

{"title":"Simultaneous Localization and Mapping (SLAM) in Swarm Robots for Map-Merging and Uniform Map Generation Using ROS","authors":"Abhishek Salunke, Chinmay Patil, Ram Mude, Radhika D. Joshi","doi":"10.1109/ICCAE56788.2023.10111365","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111365","url":null,"abstract":"Swarm robotics is a sort of multi-robotics where many robots work together to coordinate in a distributed and decentralized way. It is based on the application of local rules and basic robots relative to the difficulty of the task, and it is motivated by social insects. A collection of basic robots with greater resilience and flexibility than a single robot may be able to do challenging tasks more effectively. In this paper, a multi-robot system is proposed– Swarm Robots for Simultaneous Localization And Mapping (SLAM). Individual robots in the swarm interact with each other and contribute to a single map as it localizes itself in the environment. The robots are equipped with RPLIDAR which is utilised for the hardware implementation of SLAM. The map constructed by each robot in the swarm is merged to make a uniform map. This map can be used by any other robot to effectively traverse an unknown environment and perform tasks that include search and rescue, cleanup of toxic spills, collective transportation, and unknown environment exploration like areas where it is dangerous for humans to work.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115208870","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

A Selection Technique for Effective Utilization of Post-Quantum Cryptography in 5G Application and Beyond 5G及以后应用中有效利用后量子密码的选择技术

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111350

B. Ojetunde, Takuya Kurihara, K. Yano, Toshikazu Sakano, Hiroyuki Yokoyama

{"title":"A Selection Technique for Effective Utilization of Post-Quantum Cryptography in 5G Application and Beyond","authors":"B. Ojetunde, Takuya Kurihara, K. Yano, Toshikazu Sakano, Hiroyuki Yokoyama","doi":"10.1109/ICCAE56788.2023.10111350","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111350","url":null,"abstract":"In the quantum computing era, classical cryptography algorithms are expected to be unsafe. To provide a solution to the problem that may occur to this effect, Post-Quantum Cryptography (PQC) algorithms are being considered as replacements to classical algorithms. It is essential to consider the effect of using PQC algorithms on applications and services as many applications come with strict Quality of Service (QoS) requirements, especially in 5G and beyond 5G applications. Therefore, this paper proposes a technique for selecting a suitable PQC algorithm to achieve an effective utilization of PQC algorithms in applications/protocols. In this study, a rule-based selection technique is adopted. In particular, a PQC selection rule is generated based on an application/protocol traffic pattern/characteristics and the security requirements such as key/signature size, the strength of the PQC algorithm, time taken to share secret keys and so on. Thereafter, the process of selecting a PQC algorithm is presented. To select a suitable PQC algorithm, the use case in which an application traffic belongs is identified, and based on the traffic flow tag, a selection rule is applied. The performance of the proposed method is evaluated by experimenting with the PQC algorithms using the Transport Layer Security (TLS) handshake. The evaluation results show that the proposed PQC selection method is useful in selecting a suitable algorithm based on real application traffic and the QoS requirements.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130888396","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

Medium- and Long-Term Orbit Prediction of Satellite Based on LSTNet 基于LSTNet的卫星中长期轨道预测

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111400

Wenxing Hong, Mingtao Chen, Peng Gao, Duanqin Hong

{"title":"Medium- and Long-Term Orbit Prediction of Satellite Based on LSTNet","authors":"Wenxing Hong, Mingtao Chen, Peng Gao, Duanqin Hong","doi":"10.1109/ICCAE56788.2023.10111400","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111400","url":null,"abstract":"Establishing high-accuracy satellite orbit prediction models is of great importance for completing space missions. Traditional satellite orbit prediction methods are mainly based on physical modeling. However, due to the complex perturbation forces of satellites in orbit, it is difficult to establish an accurate dynamic model and obtain high prediction accuracy. In this research, we propose a medium- and long-term satellite orbit prediction method based on long- and short-term time-series network (LSTNet). LSTNet is used to extract the long- and short-term dependencies and ultra-long-term repetitive patterns in satellite orbit sequences, and Huber Loss is introduced to enhance the robustness of the model to orbit outliers, so as to conduct high-precision orbit prediction. BEIDOU IGSO 1 satellite orbit data is selected for simulation validation. The experimental results show that the proposed method outperforms the traditional dynamic orbit prediction model and other deep learning models in medium-and long-term orbit prediction. The prediction accuracy of the LSTNet model is also improved by the introduction of the Huber loss function.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130658903","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

Plant Seedling Classification Using Preprocessed Deep CNN 利用预处理深度CNN进行植物幼苗分类

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111357

Ghazanfar Latif, Nazeeruddin Mohammad, J. Alghazo

{"title":"Plant Seedling Classification Using Preprocessed Deep CNN","authors":"Ghazanfar Latif, Nazeeruddin Mohammad, J. Alghazo","doi":"10.1109/ICCAE56788.2023.10111357","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111357","url":null,"abstract":"In developing and developed countries, farmers are struggling to reduce costs and provide organic produce. Farming large areas of land requires equipment, workers, and other material that burden farmers with increased costs to compete in the local, regional, and global markets. With the advent of new technologies in the field of Artificial Intelligence, Internet of Things (IoT), cloud computing, and others, there is a glimpse of hope for inventing new techniques in farming that will eventually reduce the cost of farming large areas of land. In this paper, a method is proposed that can automatically classify plant seedlings with great accuracy thus making it possible for automatic farming processes. We propose a Deep CNN architecture for the automatic classification of plant seedlings using whole images and using segmented images as input. The test accuracies on a dataset of 4722 images of 12 different species outperform similar methods reported in previous studies. The experiments showed that the proposed method achieved an average test accuracy of 91.58% when whole images are used as input and an average accuracy of 95.02% when segmented images are used as input to the proposed Deep CNN architecture. The segmented images increased the accuracy by 3.44%.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126843957","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

A GPU-Accelerated 3D Visual Simulation Platform of X-Ray Digital Imaging Based on Unity 基于Unity的gpu加速x射线数字成像三维可视化仿真平台

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111203

Zhiyu Gao, J. Fu, Junyi Lu, Xianliang Gao

引用次数: 0

Motion Intention Estimation of Finger Motions with Spatial Variations of HD EMG Signals 基于高清肌电信号空间变化的手指运动意图估计

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111336

D. Bandara, He Chongzaijiao, J. Arata

引用次数: 0

Disease Classification of Oranda Goldfish Using YOLO Object Detection Algorithm 基于YOLO目标检测算法的桔黄色金鱼疾病分类

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111494

Jeanne Katherine Medina, Pete Jasper P. Tribiana, J. Villaverde

{"title":"Disease Classification of Oranda Goldfish Using YOLO Object Detection Algorithm","authors":"Jeanne Katherine Medina, Pete Jasper P. Tribiana, J. Villaverde","doi":"10.1109/ICCAE56788.2023.10111494","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111494","url":null,"abstract":"Owning an ornamental fish aquarium at home is not easy. It requires much attention to maintain its healthy state. However, if the keeper is a beginner and unable to take care of it properly, a problem may arise. One example is Oranda Goldfish which can easily acquire fish lice, fungi, and white spots. If not recognized at a possible early time, the diseases may spread to the entire aquarium or, worse, the loss of the fish. This study aims to create a system that can diagnose fish disease accurately earlier than the conventional method. The system acquires goldfish images or live feeds using a camera module and undergoes pre-processing to emphasize the essential features. Once the features are segmented, the YOLO algorithm will extract them. The system will then classify any detected disease. The results of the data sampling were able to detect and classify the goldfish samples accurately, with an overall accuracy of 91.4286%. This study utilizing CNN and YOLO helped resolve this problem by diagnosing the common disease of the goldfish. It helps beginner fish farmers, veterinarians, aquarium owners to detect the disease at the earliest time.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129767565","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

Machine Learning Model for Sentiment Analysis on Mental Health Issues 心理健康问题情感分析的机器学习模型

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111148

B. Kaushik, Akshita Sharma, Akshma Chadha, Reya Sharma

{"title":"Machine Learning Model for Sentiment Analysis on Mental Health Issues","authors":"B. Kaushik, Akshita Sharma, Akshma Chadha, Reya Sharma","doi":"10.1109/ICCAE56788.2023.10111148","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111148","url":null,"abstract":"Social media study concerning mental health has increasingly piqued researchers' interest. One of the most well-known sites where users can express their ideas, feelings, and opinions is Reddit. Research statistics on important topics are available on social media. People's mental health is one of the main issues because it is a new area of interest. Different issues, including anxiety, tension, anger, and despair, can be brought on by mental disorders. In recent years, people appear to be busy and have less time to contact one another. Instead, they seem to prefer to engage in online discussion forums. Data has been collected from social networking sites like Reddit, and 10,000 posts were aggregated for investigating the posts among suicidal and non-suicidal. Machine learning algorithms such as Support Vector Machine, Logistic Regression, and Multinomial Navïe Bayes segregated the posts into two classes. Pre-processing of data is done which is the vital step in text analysis, which includes tokenization, removal of stop words and special characters, etc. The performance in terms of accuracy and precision Logistic Regression outperforms the other algorithms. Multinomial Naive Bayes yields great recall. The study also signifies the current research trends and proffers an overview of the researcher’s accomplishments in the related fields.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129819130","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

CLDA-Net: A Novel Citrus Leaf Disease Attention Network for Early Identification of Leaf Diseases CLDA-Net:柑橘叶片病害早期识别的新型关注网络

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111244

Vivek Sharma, A. Tripathi, Himanshu Mittal

{"title":"CLDA-Net: A Novel Citrus Leaf Disease Attention Network for Early Identification of Leaf Diseases","authors":"Vivek Sharma, A. Tripathi, Himanshu Mittal","doi":"10.1109/ICCAE56788.2023.10111244","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111244","url":null,"abstract":"Efficient and precise identification of plant disease is crucial for disease prevention. Deep learning models have been the main stream methods for plant disease identification. However, the performance has been compromised in extracting the tiny lesion feature of a plant leaf, resulting in low accuracy. Moreover, the leaves of the citrus crop are flimsy and highly susceptible to various diseases, such as canker, blackspot, and greening. To mitigate the same, this paper presents a novel citrus leaf disease attention (CLDA)-Net. To enhance the learning ability of tiny lesion features, the network embeds the convolutional block attention module (CBAM) into the passage layer for extracting the channel and spatial features, which results in avoiding feature redundancy. The performance of the proposed CLDA-Net has been compared on four citrus plant diseases against seven state-of-the-art deep learning models, namely, XceptionNet, DenseNet-121, ResNet-50, VGGNet16, AlexNet, EfficientNet B2, and SoyNet. Eight performance parameters have been considered for performance validation i.e. accuracy, error, precision, recall, sensitivity, specificity, F1-score, and MCC (Matthews correlation coefficient). From experimental results, the proposed model outperforms the compared models with a classification accuracy of 94.74% without augmenting the dataset, and 97.91% on augmenting the dataset.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126698414","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

Proximity-Based Reward System and Reinforcement Learning for Path Planning 基于邻近度的奖励系统和路径规划的强化学习

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111485

Marc-Andrė Blais, M. Akhloufi

引用次数: 0