2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)最新文献_第5页

Elastic Fusion Dual-stage Spectrum Sensing for Random PU Accessing 随机PU接入的弹性融合双级频谱感知

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836291

Tinnaphob Dindam, Wilaiporn Lee, K. Srisomboon

{"title":"Elastic Fusion Dual-stage Spectrum Sensing for Random PU Accessing","authors":"Tinnaphob Dindam, Wilaiporn Lee, K. Srisomboon","doi":"10.1109/jcsse54890.2022.9836291","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836291","url":null,"abstract":"According to the channel usage right, licensed users (PU) can leave or access the license channel randomly. Then, the sensing slot may not contain the PU signal in all data samples which affect the predetermined threshold of traditional spectrum sensing techniques which is generated according to the available channel condition. Then, the traditional techniques present poor detection performance when the PU leaves or accesses the channel during the sensing slot. Once the late access behavior is concerned, the multi-slot solution is adopted with the spectrum sensing algorithm. Then, the data fusion rule becomes the major function to declare the channel status. Once the multi-slots spectrum sensing methods did not take, their fusion rules present poor detection performance when PU leaves the channel early. In this paper, we propose a new spectrum sensing - elastic fusion dual-stage spectrum sensing (EFDS) - with an elastic fusion rule to address the random access behavior issue. The simulation results show that EFDS presents reliable detection performance under normal usage and noticeably outperforms the detection performance of existing techniques under random usage behaviors.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114459047","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: 3

Convolution Neural Networks Backbone model for Citrus Leaf Disease Detection 柑橘叶病检测的卷积神经网络主干模型

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836298

Saran Khotsathian, Taninnuch Lamjiak, S. Donnua, Jumpol Polvichai

{"title":"Convolution Neural Networks Backbone model for Citrus Leaf Disease Detection","authors":"Saran Khotsathian, Taninnuch Lamjiak, S. Donnua, Jumpol Polvichai","doi":"10.1109/jcsse54890.2022.9836298","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836298","url":null,"abstract":"In agriculture, Leaf disease inferred that the plant lacks elements, gets infected, or even the environment is not suitable and needs special treatment. Specific knowledge and experience were needed to classify the leaf disease. As a result, the Artificial Intelligence system to classify plant diseases was developed to help reduce the time needed and precision. The backbone model or the base model is the model that proved to be efficient in extracting the feature from the input images. This research aimed to find the backbone model that is suitable for citrus disease classification with localization. In this paper, Four backbone models chosen as a candidate were VGG16 [1], ResNet50V2 [2], DenseNet169 [3], and MobileNetV3 [4]. Both trainings from the scratch and transfer learning were used [5]–[8] to compare the model's compatibility and to detect Citrus leaf disease. The dataset [9] contains 596 images of diseased(canker, black spot, and greening) and healthy Citrus leaves with data augmentation. In this research, the model with transfer learning could achieve the best results in the most selected model. The models that have the best performance were VGG16, ResNet50V2, and DenseNet169, respectively. For the evaluation result of local collected data, The best model was VGG16 however the improvement was needed in the planed future work with the diseases detection with localization.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129069670","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: 2

Wet Gas Pipeline Maintenance Process Using Reinforcement Learning 使用强化学习的湿气管道维修过程

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836258

Peeranat Kongkijpipat, Chayanant Sandee, Supakorn Vachirapaneegul, Kanes Sumetpipat, Pat Vatiwutipong

{"title":"Wet Gas Pipeline Maintenance Process Using Reinforcement Learning","authors":"Peeranat Kongkijpipat, Chayanant Sandee, Supakorn Vachirapaneegul, Kanes Sumetpipat, Pat Vatiwutipong","doi":"10.1109/jcsse54890.2022.9836258","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836258","url":null,"abstract":"Oil and gas extraction is one of the essential businesses globally, since petroleum and natural gases, including wet gas, a liquid-based natural gas are necessary for people's lives. Wet gas pipeline systems often face internal corrosion problems leading to gas leakage, environmental pollution, and human fatalities. In addition, the wet gas pipeline system is usually installed underground or under the sea, making it difficult to maintain and resulting in high costs. In this project, the pipeline maintenance scheduling system has been developed by applying Reinforcement Learning, a type of Machine Learning widely and efficiently used in condition-based maintenance problems with a stochastic environment. A combination of Q-learning technique and epsilon-greedy policy had been utilized as the algorithm for the learning process. According to the results, the pipeline maintenance scheduling process from our developed system could prevent leakage and rupture during the experimental period, which was 40 years. It had significantly reduced the cost of periodic maintenance process, from 19,455.28 USD to 8,463.60 USD per month. Furthermore, our pipeline maintenance schedule system can be developed to a greater extent, to be more ecologically friendly with environmental impact in mind.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"8 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134529283","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

The Distance - Based Selection Technique for Crossover in Genetic Algorithm 遗传算法中基于距离的交叉选择技术

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836306

Nitima Lukkananuruk, Kata Praditwong, S. Hengpraprohm

{"title":"The Distance - Based Selection Technique for Crossover in Genetic Algorithm","authors":"Nitima Lukkananuruk, Kata Praditwong, S. Hengpraprohm","doi":"10.1109/jcsse54890.2022.9836306","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836306","url":null,"abstract":"The aim of this research is to study and develop the natural inspired parent selections for the crossover operator in genetic algorithms. There are three distance-based methods of mating selection: the hamming distance-based selection (HS), the cosine coefficient distance-based selection (CS), and the Pearson coefficient distance-based selection (PS). The experiment conducts the comparison of the distance-based selection methods with two traditional selections: the roulette wheel selection (RWS) and the tournament selection (TS). In the experiment, all selection methods are evaluated based on four binary testing problems: one-max, zero-max, random-max, and two trap problems. [1] The measurement criterion is the number of generations when the answer is found and the fitness values when the correct answer is not found. From the experimental results, the suitable approaches are divided into two groups according to the characteristics of the benchmark problems. For the trap problem with many local optima [2], the distance-based selection methods outperformed the traditional selection. However, for the other benchmark problems, the tournament selection is the better method than others.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133440865","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

Artificial Situation Awareness for an Intelligent Agent 智能Agent的人工态势感知

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836282

Rinta Kridalukmana, D. Eridani, Risma Septiana, A. F. Rochim, Charisma T. Setyobudhi

{"title":"Artificial Situation Awareness for an Intelligent Agent","authors":"Rinta Kridalukmana, D. Eridani, Risma Septiana, A. F. Rochim, Charisma T. Setyobudhi","doi":"10.1109/jcsse54890.2022.9836282","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836282","url":null,"abstract":"A behavioural representation of an intelligent agent (IA) is considered an important part to generate explanations on its behaviours to understand what it is thinking. Previous studies have introduced various behavioural representations, such as decision tree, goal hierarchy, belief-desire-intention (BDI) hierarchy, and physical system network. However, they cannot optimally disclose IA's comprehension on given situations which is needed in certain cases of human-autonomy teaming like collaborative driving. To address this gap, this paper proposes a new behavioural representation based on artificial situational awareness to reveal situations encountered by the IA behind its executed action. The experimental implementation was conducted in collaborative driving context using the Carla simulator. The results show that the proposed behavioural representation has better performance in extracting IA's situational awareness compared to the baseline method. This work is significant to enhance human comprehension on IA so their trust in IA can be calibrated.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133674866","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

Tokenization-based data augmentation for text classification 基于标记的文本分类数据增强

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836268

Patawee Prakrankamanant, E. Chuangsuwanich

引用次数: 3

Improved Generative Adversarial Networks for Intersection of Two Domains 两域交集的改进生成对抗网络

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836273

Monthol Charattrakool, Jittat Fakcharoenphol

引用次数: 0

A component recommendation model for issues in software projects 软件项目中问题的组件推荐模型

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836311

Pacawat Kangwanwisit, Morakot Choetkiertikul, Chaiyong Ragkhitwetsagul, T. Sunetnanta, Rungroj Maipradit, Hideki Hata, Kenichi Matsumoto

{"title":"A component recommendation model for issues in software projects","authors":"Pacawat Kangwanwisit, Morakot Choetkiertikul, Chaiyong Ragkhitwetsagul, T. Sunetnanta, Rungroj Maipradit, Hideki Hata, Kenichi Matsumoto","doi":"10.1109/jcsse54890.2022.9836311","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836311","url":null,"abstract":"In modern software development projects, developer teams usually adopt an issue-driven approach to increase their productivity. The component of an issue report implicitly or-ganize issues in a software project (e.g, defects, new feature requests, and tasks) into a group of issues that have similar characteristics. A component of an issue report is an important attribute needed to be identified in an issue triaging process. Thus, assigning the correct component(s) to an issue is crucial in issue resolution. However, it is a challenging task since large-scale projects contain a considerable amount of components (e.g. almost one-hundred components in the Bamboo project) and it can increase significantly as the project evolves over time. In this paper, we propose an approach that uses textual feature extraction and machine learning techniques with Binary Relevance (BR) to develop a component recommendation model to support the task of assigning component(s) to an issue. The empirical evaluation over 60,000 issue reports shows that our proposed models outperform the baseline benchmarks and other techniques by achieving on average 0.480 Precision@1, 0.616 Recall@3, 0.432 MAP, and 0.596 MRR.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121479150","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: 1

Multi-Label Classification for Articles in Thai Journal Database from Article's Abstract 基于文章摘要的泰国期刊数据库文章多标签分类

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836270

Chintrai Puttipornchai, Chanyachatchawan Sapa, Nuengwong Tuaycharoen

引用次数: 1

Breath sound classification by using the smart phone 使用智能手机进行呼吸音分类

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836304

Thanapat Sangkharat

{"title":"Breath sound classification by using the smart phone","authors":"Thanapat Sangkharat","doi":"10.1109/jcsse54890.2022.9836304","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836304","url":null,"abstract":"Respiratory sounds are non-expensive, non-invasive, and give more information, so respiratory sound analysis is important for clinical testing. However, the accuracy of respiratory sound analysis depends on the clinician's expertise. Many studies try to develop an automation system for the classification of breath sounds. The system is the cooperation of sound processing, image processing, and neural networks. However, the systems are based on computers and the computer based systems are not easy to use in the remote area. Thus, this study proposed to develop the breath sound classify that easy to use in the remote area. Recently, the smart phone has become more powerful and more flexible than the PC, and there is a possibility of developing the breath sound classification on the smart phone. This study proposes to develop a smart phone-based respiratory sound classification. The advantage of the smart phone base system is that it is more flexible and patients can easily use it. In this study, the Android phone cooperates with the TarsosDSP sound library and Tensorflow lite. Some samples of breath sounds from the ICHBI database and an online learning website for respiratory sounds were used. The samples included normal breath sounds (136 samples), crackling sounds (111 samples) and wheeze sounds (111 samples). The experimental method, the samples of breath sounds were played with audio player software on a computer, and the electronic stethoscope was used to record the sounds. Then the breath sound classification software was used for filtering noise, recording sound, computing the spectrogram, and processing the neural network. The result found the smart phone's base respiratory sound classification system can diagnose breath sound. The accuracy for normal breath sounds was 80%, crackle sounds were 87%, and wheeze sounds were 85%. Finally, the characteristics of the breath sound spectrogram were discussed.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121045753","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: 2