2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)最新文献_第2页

Rainfall Prediction Using Gated Recurrent Unit Based on DMI and Nino3.4 Index 基于DMI和Nino3.4指数的门控循环单元降水预报

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887474

Huda Febrianto Nurrohman, D. C. R. Novitasari, F. Setiawan, Rochimah, Amal Taufiq, Abdulloh Hamid

引用次数: 1

Hypersonic Flight Control With Vector Field Orbital Path 矢量场轨道的高超声速飞行控制

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887503

Tri Kurnia Mansi, E. Susanto, M. R. Rosa

引用次数: 0

A Review of Neural Networks for Buildings Occupancy Measurement 神经网络在建筑物占用率测量中的应用综述

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887508

Oumayma Dalhoumi, Manar Amayri, N. Bouguila

引用次数: 0

A DenseNet Model for Joint Activity Recognition and Indoor Localization 关节活动识别与室内定位的DenseNet模型

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887407

Ade Irawan, Adam Marsono Putra, Hani Ramadhan

{"title":"A DenseNet Model for Joint Activity Recognition and Indoor Localization","authors":"Ade Irawan, Adam Marsono Putra, Hani Ramadhan","doi":"10.1109/IAICT55358.2022.9887407","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887407","url":null,"abstract":"Activity recognition and indoor positioning (ARIL) tasks have benefited society in various areas, such as surveillance, healthcare, and entertainment. The emerging development of ARIL employs the usage of Wi-Fi Channel State Information (CSI) as input instead of Received Signal Strength Indicator (RSSI), which is often missing and disturbed. ResNet, as one of the Deep Learning models, can perform the joint task of ARIL with high accuracy. However, due to the rapid development in Deep Learning, other newer models have the potential to improve the quality of ARIL rather than ResNet, which has a large number of training parameters. We propose applying a DenseNet model as a new feature extractor and Deep Learning architecture for the joint task of ARIL with CSI data. The architecture of DenseNet can improve the quality of ARIL thanks to the dense block, which can extract more relevant features from CSI data efficiently. We demonstrate that our proposed DenseNet model for joint ARIL improved the overall accuracy and the efficiency of the Deep Learning model using a real-world CSI dataset. Using a real-world CSI dataset, our proposed model outperforms the baseline by 4.16% on activity recognition and 1.04% on indoor localization. With hyperparameter tuning, we further reduce the trainable parameters by 64.29%, also 27.88% less than the baseline, with the cost of slightly decreasing the performance on activity recognition but increasing the performance on indoor localization.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115484566","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

Study of Stress Relief Service by Watching Personalized Videos for Elderly People at Home 居家老人个性化视频减压服务研究

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887490

Hiroto Horie, Sinan Chen, Masahide Nakamura, K. Yasuda

{"title":"Study of Stress Relief Service by Watching Personalized Videos for Elderly People at Home","authors":"Hiroto Horie, Sinan Chen, Masahide Nakamura, K. Yasuda","doi":"10.1109/IAICT55358.2022.9887490","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887490","url":null,"abstract":"In recent years, Japan has been facing a super-aging society, and the number of elderly people living at home (including the elderly requiring nursing care, living alone) has been increasing. For each older person who spends much time at home, how to deal with their individual stresses (e.g., deepening loneliness, worries about life, etc.) is a big challenge. Our research group is currently researching and developing a system to support watching over elderly people at home. In our previous study, we developed and proposed a YouTube video playback service and conducted an evaluation experiment with elderly people. However, the search and specification of videos to be viewed by the elderly depend on family members and caregivers. The videos may differ from the wishes of the elderly themselves. The purpose of this paper is to develop a prototype of a personalized video recommendation and viewing web service to promote stress reduction among the elderly at home. As an approach, we first obtain information on the hobbies and preferences of the elderly at home through a questionnaire survey (e.g., paper-based, Google form, etc.). Next, based on the questionnaire results, the service automatically recommends a list of relevant videos (by relevance and number of views) from the YouTube site. By registering the list of recommended videos to the service, the elderly can watch personalized videos at any time. This way will enable the elderly to watch videos of interest to them easily and expect to help them relieve stress.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177542","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

Using Generative Adversarial Networks for Conditional Creation of Anime Posters 使用生成对抗网络进行动画海报的条件创作

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887491

Donthi Sankalpa, Jayroop Ramesh, I. Zualkernan

{"title":"Using Generative Adversarial Networks for Conditional Creation of Anime Posters","authors":"Donthi Sankalpa, Jayroop Ramesh, I. Zualkernan","doi":"10.1109/IAICT55358.2022.9887491","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887491","url":null,"abstract":"Japanese animation, known as anime, has become one of the most accessible forms of entertainment across globe. Recent advances in generative adversarial networks (GAN) and deep learning have contributed greatly to multiple interesting applications in the domain of anime, particularly in face generation, style transfer, and colorization. However, there are no existing implementations for generating composite anime posters with a genre accompaniment prompt. This work proposes a novel application of genre to anime poster generation conditioned on BERT-tokenized binary genre-tags of light-hearted or heavy-hearted categorized based on the thematic subject content of the medium. A dataset of 9,840 image with genre tags and synopses was constructed by scraping MyAnimeList. The conditional Deep Convolution GAN with Spectral Normalization produced the best posters, achieving the quantitative scores of FID: 90.17, average IS: 3.505, 1KNN with PSNR: 0.445 across inter-label discernability, and FID: 166.4, across genuine versus generated poster distinguishability. The primary contribution of this work is to present results outlining the feasibility of various GAN architectures in synthesizing controllable and complex composite anime posters. The larger implication of this project is to provide an introductory approach showing the promise of a creativity assistant for authors, artists, and animators, where they can simply enter a key phrase representing a concept they have in mind, to generate a baseline idea as an initial phase.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125565547","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

Optimization of Approach Time Waiting at The Port of Tanjung Perak Surabaya using Genetic Algorithm 用遗传算法优化丹绒霹雳泗水港进港等待时间

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887473

Nur Hidayah, D. C. R. Novitasari, I. A. Wijaya, M. Faizin, Umi Hanifah, Abdulloh Hamid

引用次数: 0

Prediction of Sea Surface Current Velocity and Direction using Gated Recurrent Unit (GRU) 基于门控循环单元(GRU)的海面流速度和方向预测

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887516

Elen Riswana Safila Putri, D. C. R. Novitasari, F. Setiawan, Abdulloh Hamid, Dwi Susanto, Muhammad Fahmi

{"title":"Prediction of Sea Surface Current Velocity and Direction using Gated Recurrent Unit (GRU)","authors":"Elen Riswana Safila Putri, D. C. R. Novitasari, F. Setiawan, Abdulloh Hamid, Dwi Susanto, Muhammad Fahmi","doi":"10.1109/IAICT55358.2022.9887516","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887516","url":null,"abstract":"The development of tourism activities in Labuan Bajo, especially in marine tourism, which is increasing, shows the need for knowledge regarding the conditions of the water area. One of the components in these waters is the condition of the ocean currents in Labuan Bajo. Therefore, this study aims to predict the sea surface's current velocity and direction. This study uses the Gated Recurrent Unit (GRU) algorithm. The data used in this study is the u component, namely the current velocity from east to west, and the component v is the current velocity from north to south. The data is processed through two gates, namely the update gate and the reset gate. The results of the output are used as input in the next stage. Based on trials with several parameters, 50 hidden layers, 32 batch size, and 150 learning rate drop with 70:30 data division, the smallest MAPE value for u component is 9.32% and the v component is 27.94%. he calculation of the sea surface currents direction at u and v component points is towards the southwest with a range between 207° and 213°.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124955524","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

Human Inspired Memory Module for Memory Augmented Neural Networks 用于记忆增强神经网络的人类启发记忆模块

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887485

Amir Bidokhti, S. Ghaemmaghami

引用次数: 0

Synthetic Occluded Masked Face Recognition using Convolutional Neural Networks 卷积神经网络合成遮挡人脸识别

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887517

I. Recto, M. Devaraj

引用次数: 0