2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)最新文献_第2页

Using the MQTT Broker as a Speech-Activated Medium to Control the Operation of Devices in the Smart Office 利用MQTT代理作为语音激活介质控制智能办公设备的运行

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960287

K. Namee, Rudsada Kaewsaeng-On, J. Polpinij, G. Albadrani, Kavin Rueagraklikhit, A. Meny

{"title":"Using the MQTT Broker as a Speech-Activated Medium to Control the Operation of Devices in the Smart Office","authors":"K. Namee, Rudsada Kaewsaeng-On, J. Polpinij, G. Albadrani, Kavin Rueagraklikhit, A. Meny","doi":"10.1109/iSAI-NLP56921.2022.9960287","DOIUrl":"https://doi.org/10.1109/iSAI-NLP56921.2022.9960287","url":null,"abstract":"This research is applying the MQTT broker protocol as a medium for various work orders in smart office management. It is an experiment and development of all functions of MQTT broker whether publishing, chatting and subscribing both globally and locally. The results are able to perform all commands correctly. In addition, in this research, the command procedure was added. This is a human speech command to operate all MQTT Brokers functions. However, there are still some weaknesses in the matter of voice commands are delayed response. It might not be a very good user experience. In this experiment, many functions were woven into the smart office. Regardless of whether the bulb acts as an IoT bulb internally connected to the MQTT broker, the camera performs the function of recognizing a person's face which is internally connected to MQTT broker. Speech also serves voice commands, lamp and feedback are connected to MQTT broker. Air conditioner acts as IoT air conditioner switch externally connected to cloud server. In addition, dashboard It also acts as an IoT visual light switch that connects externally to the cloud.","PeriodicalId":399019,"journal":{"name":"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116752841","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

Development of Internet of Things System for Environment Control in Niam Hom (Strobilanthesnivea Craib) House Niam Hom (Strobilanthesnivea crab) House环境控制物联网系统的开发

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960279

Sancha Panpaeng, Natawut Payakkhin, Pipop Maneejamnong

引用次数: 0

Source Code Plagiarism Detection Based on Abstract Syntax Tree Fingerprintings 基于抽象语法树指纹的源代码抄袭检测

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960266

V. Suttichaya, Niracha Eakvorachai, Tunchanok Lurkraisit

引用次数: 0

Simulation of Homogenous Fish Schools in the Presence of Food and Predators using Reinforcement Learning 利用强化学习模拟食物和捕食者存在下的同质鱼群

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960278

Ravipas Wangananont, Norapat Buppodom, Sanpat Chanthanuraks, Vishnu Kotrajaras

引用次数: 1

A Comparative Study of Noise Augmentation and Deep Learning Methods on Raman Spectral Classification of Contamination in Hard Disk Drive 噪声增强与深度学习方法在硬盘污染拉曼光谱分类中的比较研究

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960277

S. Gulyanon, Somrudee Deepaisam, Chayud Srisumarnk, Nattapol Chiewnawintawat, Angkoon Anzkoonsawaenasuk, Seksan Laitrakun, Pakorn Ooaorakasit, P. Rakpongsiri, Thawanpat Meechamnan, D. Sompongse

{"title":"A Comparative Study of Noise Augmentation and Deep Learning Methods on Raman Spectral Classification of Contamination in Hard Disk Drive","authors":"S. Gulyanon, Somrudee Deepaisam, Chayud Srisumarnk, Nattapol Chiewnawintawat, Angkoon Anzkoonsawaenasuk, Seksan Laitrakun, Pakorn Ooaorakasit, P. Rakpongsiri, Thawanpat Meechamnan, D. Sompongse","doi":"10.1109/iSAI-NLP56921.2022.9960277","DOIUrl":"https://doi.org/10.1109/iSAI-NLP56921.2022.9960277","url":null,"abstract":"Deep neural networks have become state-of-the-art for many tasks in the past decade, especially Raman spectral classification. However, these networks heavily rely on a large collection of labeled data to avoid overfitting. Although labeled data is scarce in many application domains, there are techniques to help alleviate the problem, such as data augmentation. In this paper, we investigate one particular kind of data augmentation, noise augmentation that simply adds noise to input samples, for the Raman spectra classification task. Raman spectra yield fingerprint-like information about all chemical components but are prone to noise when the material's particles are small. We study the effectiveness of three noise models for noise augmen-tation in building a robust classification model, including noise from the background chemicals, extended multiplicative signal augmentation (EMSA), and statistical noises. In the experiments, we compared the performance of 11 popular deep learning models with the three noise augmentation techniques. The results suggest that RNN-based models perform relatively well with the increase in augmented data size compared to CNN-based models and that robust noise augmentation methods require characteristics of random variations. However, hyperparameter optimization is crucial for taking optimal advantage of noise augmentation.","PeriodicalId":399019,"journal":{"name":"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121353976","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

ThEconSum: an Economics-domained Dataset for Thai Text Summarization and Baseline Models consum:一个经济学领域的数据集，用于泰国文本摘要和基线模型

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960271

Sawittree Jumpathong, Akkharawoot Takhom, P. Boonkwan, Vipas Sutantayawalee, Peerachet Porkaew, Sitthaa Phaholphinyo, Charun Phrombut, T. Supnithi, Khemarath Choke-Mangmi, Saran Yamasathien, Nattachai Tretasayuth, Kasidis Kanwatchara, Atiwat Aiemleuk

引用次数: 0

Visual-based Musical Data Representation for Composer Classification 基于视觉的作曲家分类音乐数据表示

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960254

S. Deepaisarn, Suphachok Buaruk, Sirawit Chokphantavee, Sorawit Chokphantavee, Phuriphan Prathipasen, Virach Sornlertlamvanich

{"title":"Visual-based Musical Data Representation for Composer Classification","authors":"S. Deepaisarn, Suphachok Buaruk, Sirawit Chokphantavee, Sorawit Chokphantavee, Phuriphan Prathipasen, Virach Sornlertlamvanich","doi":"10.1109/iSAI-NLP56921.2022.9960254","DOIUrl":"https://doi.org/10.1109/iSAI-NLP56921.2022.9960254","url":null,"abstract":"Automated classification for musical genres and composers is an artificial intelligence research challenge insofar as music lacks a rigidly defined structure and may result in varied interpretations by individuals. This research collected acoustic features from a sizable musical database to create an image dataset for formulating a classification model. Each image was constructed by combining pitch, temporal index length, and additional incorporated features of velocity, onset, duration, and a combination of the three. Incorporated features underwent Sigmoid scaling, creating a novel visual-based music representation. A deep learning framework, fast.ai, was used as the primary classification instrument for generated images. The results were that using velocity solely as an incorporated feature provides optimal performance, with an F1-score of 0.85 using the ResN$e$t34 model. These findings offer preliminary insight into composer classification for heightening understanding of music composer signature characterizations.","PeriodicalId":399019,"journal":{"name":"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125881321","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

Fault Prediction Model for Motor and Generative Adversarial Networks for Acceleration Signal Generation 电机故障预测模型及生成对抗网络加速度信号生成

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960281

Saran Deeluea, C. Jeenanunta, Apinun Tunpun

{"title":"Fault Prediction Model for Motor and Generative Adversarial Networks for Acceleration Signal Generation","authors":"Saran Deeluea, C. Jeenanunta, Apinun Tunpun","doi":"10.1109/iSAI-NLP56921.2022.9960281","DOIUrl":"https://doi.org/10.1109/iSAI-NLP56921.2022.9960281","url":null,"abstract":"The manufacturing process must continuously be improved. One of the most efficient strategies is maintenance scheduling by predictive maintenance for early fault detection and assisting with real-time decisions. The major concern of developing a predictive maintenance system is the lack of abnormal data and the cost of a high-specification sensor device for collecting data. This paper introduces the unsupervised learning model called Generative Adversarial Networks (GANs) for generating abnormal data in the form of acceleration signals to provide a dataset for developing an early fault prediction model and assisting a real-time decision on a low-frequency sensor device. The prediction model dataset is labeled on IS010816 to classify the label of data by Velocity Vibration (mm/s). The machine learning classifier model implements a hyperparameters optimization framework called OPTUNA to provide the best model performance. The proposed system aims to assist in real-time decision and maintenance schedules for the injection molding machine and offer the prediction model based on low-frequency sensor data from a drive motor.","PeriodicalId":399019,"journal":{"name":"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126488006","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

Improving Neural Machine Translation for Low-resource English-Myanmar-Thai Language Pairs with SwitchOut Data Augmentation Algorithm 基于SwitchOut数据增强算法的低资源英缅泰语对神经机器翻译

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960261

Mya Ei San, Ye Kyaw Thu, T. Supnithi, Sasiporn Usanavasin

{"title":"Improving Neural Machine Translation for Low-resource English-Myanmar-Thai Language Pairs with SwitchOut Data Augmentation Algorithm","authors":"Mya Ei San, Ye Kyaw Thu, T. Supnithi, Sasiporn Usanavasin","doi":"10.1109/iSAI-NLP56921.2022.9960261","DOIUrl":"https://doi.org/10.1109/iSAI-NLP56921.2022.9960261","url":null,"abstract":"To improve the data resource of low-resource English- Myanmar- Thai language pairs, we build the first parallel medical corpus, named as En-My- Th medical corpus which is composed of total 14,592 parallel sentences. In our paper, we make experiments on the English-Myanmar language pair of new En-My-Th medical corpus and in addition, English-Thai and Thai-Myanmar language pairs from the existing ASEAN- MT corpus. The experiments of SwitchOut data augmentation algorithm and the baseline attention-based sequence to sequence model are trained on the aforementioned language pairs in both directions. Experimental results show that combination of Switch Out algorithm with the baseline model outperforms the baseline only model in the translation of most language pairs for both corpora. Furthermore, we investigate the performance of the baseline model and baseline+SwitchOut model by adding or removing word dropout at the recurrent layers, at which baseline+SwitchOut model with the dropout increases around (+1.0) BLEU4 and GLEU scores in some of language nairs.","PeriodicalId":399019,"journal":{"name":"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121042020","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

Image Captioning for Thai Cultures 泰国文化图片说明

2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2022-11-05 DOI: 10.1109/iSAI-NLP56921.2022.9960251

S. Watcharabutsarakham, S. Marukatat, K. Kiratiratanapruk, Pitchayagan Temniranrat

引用次数: 0