2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)最新文献_第2页

Classification System Vocal Cords Disease Using Digital Image Processing 基于数字图像处理的声带疾病分类系统

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784832

Hertiana Bethanigtyas, Suwandi, C. Anggraini

{"title":"Classification System Vocal Cords Disease Using Digital Image Processing","authors":"Hertiana Bethanigtyas, Suwandi, C. Anggraini","doi":"10.1109/ICIAICT.2019.8784832","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784832","url":null,"abstract":"This paper presents the study of vocal cords disease classification using digital image processing. Stroboscopy and laryngoscopy are common tools used by doctors to observe the state of the vocal cords directly. The vocal cords disease can be characterized by characters change the shape of glottis contour on the vocal cords. There are six classifications of vocal cords, normal, paralysis, nodule, papilloma, cyst, and granuloma which present in this paper. Before the classification process, extraction image vocal cords to get the characteristics or information of objects in the image. In this study used feature descriptions with the Speeded Up Robust Features (SURF) algorithm and shape measurement to extract the glottis contour of the vocal cords that can be analyzed and classified. The process of measuring the glottis contour of vocal cords requires the vocal image in the binary image. To get binary image this study used a method to automatically obtain the glottis area segmentation without user initialization. The segmentation is mainly based on active contour, Chan-Vese algorithm. The results of this study can optimize glottis contour extraction and results of the classification training process using K-Nearest Neighbor get an accuracy of 96.7%.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121818685","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: 4

Optimizer Comparison with Dropout for Neural Sequence Labeling in Myanmar Stemmer 缅甸茎秆神经序列标记优化器与Dropout的比较

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784850

Oo Yadanar, K. Soe

{"title":"Optimizer Comparison with Dropout for Neural Sequence Labeling in Myanmar Stemmer","authors":"Oo Yadanar, K. Soe","doi":"10.1109/ICIAICT.2019.8784850","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784850","url":null,"abstract":"In Myanmar language, texts typically contain many different forms of a basic word. Morphological variants are generally the most common problem in mis-spellings, wrong translation and irrelevant retrieval query. The effectiveness of searching is obviously related to the stemming process. Moreover, there is no space separation in Myanmar language. Therefore, the tasks of segmenting the initial texts to words sequence is fully related to the stemming process. In present-day, deep learning approaches have become very good performance in variety of tasks, such as natural language processing, speech recognition, image recognizing. Among different types of neural networks, CNN networks have been most extensively used in text processing to extract morphological information (prefix and suffix of a word). This paper proposes the optimization process in Neural Architecture, how loss functions fit into the equation and finding the best optimizer. This paper also classified the efficiency of dropout under each optimizer to improve CNN-based model which jointly learns stemming and segmentation boundaries in parallel. It has obtained significant improvements on model performance after using dropout and the highest F-score is dropout probability 0.2. According to the experimental results, the SGD and Adam optimizer have a vast effect on the performance. And then, RMSProp optimizer performs better than other optimizers even though there is less dropout nodes.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133188038","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

Disaster Victims Detection System Using Convolutional Neural Network (CNN) Method 使用卷积神经网络(CNN)方法的灾害受害者检测系统

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784782

Dean Rizki Hartawan, T. Purboyo, C. Setianingsih

{"title":"Disaster Victims Detection System Using Convolutional Neural Network (CNN) Method","authors":"Dean Rizki Hartawan, T. Purboyo, C. Setianingsih","doi":"10.1109/ICIAICT.2019.8784782","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784782","url":null,"abstract":"Natural disasters are one of the things that cannot be predicted. Natural disasters can cause losses, both assets and objects can even take lives. To reduce the number of losses, rapid evacuation handling from the Search and Rescue (SAR) team is needed to help victims of natural disasters. But in fact, there are often obstacles in the evacuation process. Such obstacles are such as bad weather conditions, disconnection of telecommunications networks, difficulty access to the victims of natural disasters and the spread of SAR teams that are not evenly distributed throughout the disaster area. Convolutional Neural Network is one of the developments of Artificial Neural Networks for image classification, image segmentation, and object recognition with high accuracy and high performance. CNN can learn to detect various images according to images from the dataset studied. So, this paper designed a system for detecting victims of natural disasters using the CNN method and implemented it on a raspberry pi which can detect victims of natural disasters through streaming cameras placed on UAVs. In this paper, the Convolutional Neural Network (CNN) method with 100% accuracy with distance object 1–4 m uses the Mobile-net SSD model.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125123569","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: 29

Automatic Counting of Chili Ripeness on Computer Vision for Industri 4.0 面向工业4.0的计算机视觉辣椒成熟度自动计数

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784858

Indrabayu, Mar'atuttahirah, I. Areni

引用次数: 3

Performance Analysis of Audio Data Transmission on FBMC - Offset QAM System FBMC -偏移QAM系统音频数据传输性能分析

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784810

A. F. Isnawati, Viona Octaviani Citra, J. Hendry

{"title":"Performance Analysis of Audio Data Transmission on FBMC - Offset QAM System","authors":"A. F. Isnawati, Viona Octaviani Citra, J. Hendry","doi":"10.1109/ICIAICT.2019.8784810","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784810","url":null,"abstract":"Text and voice messaging is common in our daily life. Messaging technology nowadays uses wireless communication technology. In wireless communication, electromagnetic waves are propagated through air as a transmission media that brings information or messages. In order to optimize wireless communication performance, FBMC-Offset QAM has become new method to improve OFDM. FBMC is one of superior method for modulation because of its orthogonality that splits bandwidth for only contiguous sub-line. Using Offset QAM modulation which is more stable than ordinary QAM such as robust against dispersion effects, able to improve decision process, and has higher bit rate. Symbols detection algorithm is used to get transmitted information signal. In this research, Zero Forcing method is used to detect original signal sent by transmitter antenna. The performance of the system in this study is measured based on SNR against BER, and channel capacity. The result shows that FBMC-Offset QAM system equipped with Zero Forcing equalizer has better BER value than same system without equalizer. Simulation shows that on SNR 20 dB, BER value of FBMC-Offset QAM equipped with Zero Forcing is about 2.056E-2, whereas system without Zero Forcing has BER value around 0.4445. Furthermore, channel capacity analysis shows that the increased of SNR values proportional to channel capacity.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122248594","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: 11

Floods Prediction Using Radial Basis Function (RBF) Based on Internet of Things (IoT) 基于物联网的径向基函数洪水预测

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784839

Ni Komang Ega Kartika, M. A. Murti, C. Setianingsih

引用次数: 8

Development of Hyperplane-based Adaptive T-S Fuzzy Controller for Micro Aerial Robots 微型航空机器人超平面自适应T-S模糊控制器的研制

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784726

Meftahul Ferdaus, S. Anavatti, Ahmad Jobran Al-Mahasneh, Mahardhika Pratama, M. Garratt

{"title":"Development of Hyperplane-based Adaptive T-S Fuzzy Controller for Micro Aerial Robots","authors":"Meftahul Ferdaus, S. Anavatti, Ahmad Jobran Al-Mahasneh, Mahardhika Pratama, M. Garratt","doi":"10.1109/ICIAICT.2019.8784726","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784726","url":null,"abstract":"In recent times, numerous applications of autonomous systems are witnessed vividly. Efficient control of autonomous systems like micro aerial robots (MARs) is challenging since their dynamics is highly nonlinear and associated with uncertainties. Therefore, an increasing interest is noticed in developing adaptive and computationally effective intelligent controllers. In this work, a hyperplane-based adaptive Takagi-Sugeno (TS) fuzzy controller namely hyperplane-based adaptive fuzzy (HPAF) controller is developed. Unlike the existing adaptive fuzzy controller, HPAF is characterized by fewer system parameters since it has no antecedent parameters. Such feature yields a fast response to follow the desired control commands in a challenging environment. To observe a sharp convergence of tracking error to zero, the consequent parameters of the HPAF are tuned through adaptation laws derived from a radial basis function neural network (RBFNN). Our HPAF controller's closed-loop stability has also been proved using Lyapunov theorem. Finally, the proposed controller's performance has been evaluated by employing it to control the altitude of a bioinspired flapping wing MAR and compared with a proportional integral derivative (PID) and static TS-Fuzzy controller, where better tracking performances are perceived than the benchmark controllers.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"103 11S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131984240","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

Monitoring and Classification System of River Water Pollution Conditions with Fuzzy Logic 基于模糊逻辑的河流水污染状况监测与分类系统

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784857

A.S. Khalid Waleed, P. Kusuma, C. Setianingsih

{"title":"Monitoring and Classification System of River Water Pollution Conditions with Fuzzy Logic","authors":"A.S. Khalid Waleed, P. Kusuma, C. Setianingsih","doi":"10.1109/ICIAICT.2019.8784857","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784857","url":null,"abstract":"The development of the current era, and the rapid development of technology and the need for a significant increase in demand, as well as pollution, the water sector, especially the river has experienced a decline in water quality even to the occurrence of pollution, resulting in water can no longer be consumed either by human body also for other needs. Some of the systems that were developed began to be able to process existing data, be it conditions from water, chemical observations or physically. This is done because water is a necessity that cannot be tolerated, so this research is done to help fulfill or even provide a calm warning of water quality. With the development of Intemet of Things (IoT) the monitoring system will develop, because with the existence of technology such as low-power wide-area network (LPWAN) as specific as possible, short data can be sent using lower power. In this research, it was proven that the author could make a monitoring system and classification of river water pollution. By using an artificial intelligence, using the fuzzy logic method. The results of system testing show that the average accuracy of the monitoring system results is 99.7% and the results of the appropriate classification values are based on the results of system testing.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131927160","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: 11

Design and Implementation of Multi-Protocol Gateway for Internet of Things 物联网多协议网关的设计与实现

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784849

Delphi Hanggoro, Lukman Rosyidi, R. F. Sari

引用次数: 4

Adaptive Neural Altitude Control and Attitude Stabilization of a Hexacopter with Uncertain Dynamics 具有不确定动力学的六旋翼机自适应神经高度控制与姿态稳定

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784844

Ahmad Jobran Al-Mahasneh, S. Anavatti, Meftahul Ferdaus, M. Garratt

引用次数: 6