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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Feature Learning using Deep Variational Autoencoder for Prediction of Defects in Car Engine

Feature Learning using Deep Variational Autoencoder for Pred...

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2024 International Conference on information technology Research and Innovation, ICITRI 2024

作者： Susanto, Nanang Pardede, Hilman Ferdinandus Universitas Nusa Mandiri Computer Science Study Program Jakarta Indonesia National Research and Innovation Agency Indonesia Universitas Nusa Mandiri Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350376210

Machine learning has been employed to automatically detect the defects on car engines in several studies. One of crucial challenges on applying machine learning is on the amount of defect data collected is often large with high dimensional data, making manual detection inefficient and inaccurate. The other problem is on the missing data as oftentimes the collected data are incomplete. In this paper, we employ machine learning frameworks for engine defect detection. It comprises the data pre-processing stage which includes imputing missing value data, then performing feature correlation using the Pearson method, and selecting the features to use. After that, the label encoder and standard scaler are carried out. The experimental process begins with creating a baseline, then continues with improving imbalance data using SMOTE, and feature reconstruction using variational autoencoder (VAE). Afterwards, for classification, we employ convolutional neural networks (CNN). The proposed method achieved precision 99.63%. We collect engine quality dataset of 224,239 data with 90 features from major automobile manufacturing in Indonesia. This showed that SMOTE and Variational Autoencoder dimensional reconstruction method can overcome defect predictions in car engine defect data with data imbalance conditions. This novel methodology distinguished our study from prior methods and shows considerable increases in precision and recall matrix. © 2024 IEEE.

关键词： Convolutional neural networks

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An Augmented Reality Game's User Experience and Its Effect on Immersion Leading to Intention 11

An Augmented Reality Game's User Experience and Its Effect o...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Oktavia, Tanty Utomo, Kawang Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University Binus Graduate Program - Information System Management Information Systems Management Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9798350389654

Since the emergence of Pokémon Go in 2016, the world has been introduced to Augmented Reality (AR) games. Many IT companies have begun developing augmented reality (AR) games due to the great commercial potential in this industry. Certain products may have managed to remain viable in the market, while others were forced to close due to one or two factors. Since the emergence of Pokémon Go, no other game has managed to surpass its unprecedented success. The company responsible for developing Pokémon Go has decided to shut down one of their games due to a lack of consumer interest. The technology of augmented reality (AR) is consistently associated with the concept of immersion. Immersion is a key aspect of the game that allows users to deeply engage and feel fully involved in the experience. This study will employ a quantitative methodology, utilising a questionnaire that will be distributed to and completed by those who have engaged in augmented reality (AR) games. The data will be analysed using Smart PLS to examine the impact of user experience on immersion, which in turn influences intention. After gathering over 200 participants by spreading google form questionnaires and doing data analysis, the findings indicate that only a few aspects of user experience, namely Brand Experience and User Need Experience, have an impact on reasons. Niantic, the developer of Pokémon Go, was unable to replicate their success with a similar augmented reality game called Harry Potter: Wizards Unite. The initial release occurred on June 21, 2019, and the shutdown took place on December 21, 2021. In the beginning, they shown a profound enthusiasm for the game but were unable to sustain it, much as Pokémon Go. According to Niantic CEO John Hanke, the primary reason for the game's shutdown was its immersion. Immersion has a notable impact on User Confirmation, which in turn has a notable impact on Satisfaction, and Satisfaction has a notable impact on Intention. Moreover, there have be

关键词： Plant shutdowns

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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Optimation Cavacity Vehicle Routing Problem with K-Nearest Neighbor in Classification of Goods Ditribution Route 7

Optimation Cavacity Vehicle Routing Problem with K-Nearest N...

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7th IEEE International Conference of computer Science and information technology, ICOSNIKOM 2023

作者： Novelan, Muhammad Syahputra Efendi, Syahril Sihombing, Poltak Mawengkang, Herman Universitas Sumatera Utara Doctoral Program of Computer Science Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia

ISBN: (纸本)9798350360752

Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of 'nodes' on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. © 2023 IEEE.

关键词： Machine learning

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Camera-Only Perception System for Traffic Jam Assistance

Camera-Only Perception System for Traffic Jam Assistance

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2023 International Automatic Control Conference, CACS 2023

作者： Huang, Xiao-Wei Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Master Program in Artificial Intelligence Technology Taipei Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350306354

Traditional perception systems for TJA (Traffic Jam Assistance) are mostly implemented by fusing images with radar or lidar. As computer vision techniques become more powerful, cameras can almost replace the need for radar and lidar in perception tasks, which reduces the hardware cost of the system. In this research, we propose a camera-only perception system for TJA, which is able to provide the information of the vehicles ahead and the drivable area. The proposed system has been evaluated through real-world scenario sequences, and proved that it achieves high robustness, which is highly possible to be adopted for TJA development. © 2023 IEEE.

关键词： Cameras

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Robust Object Detection Model for UAV Application

Robust Object Detection Model for UAV Application

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2023 International Automatic Control Conference, CACS 2023

作者： Ku, Chun Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Master Program in Artificial Intelligence Technology Taipei Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350306354

Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network architecture that provides high efficiency and accuracy object detection results. This paper adopts YOLOv7 as an object detection model for two different kinds of targets, one is vehicle, and the other is ocean flotsam. By training the model with open datasets and fine-tuning the model with self-collected datasets, we prove through sequences collected from real-world scenarios that YOLOv7 is able to provide robust and accurate object detection results, including vehicles and ocean flotsam, with real-time efficiency. Based on such experimental result, we confirmed that YOLOv7 can be the baseline for object detection model development. © 2023 IEEE.

关键词： Object detection

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Automatic Identification and Reconstruction 3D Image of the Rhinosinusitis Mucus 12

Automatic Identification and Reconstruction 3D Image of the ...

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12th International Conference on information and Communication technology, ICoICT 2024

作者： Eka Putra, Ondra Sumijan Tajuddin, Muhammad Universitas Putra Indonesia YPTK Padang Faculty of Computer Science Department of Informatics Technology Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia Bumigora University Department of Computer Science Mataram Indonesia

ISBN: (纸本)9798350363432

Rhinosinusitis Mucus is mucus produced by mucus glands found in the nose in the paranasal sinus area which indicates inflammation. Segmentation of rhinosinusitis mucus is very difficult to do because of the many characteristics of different objects in the image of the paranasal Sinus CT-Scan in separating the mucus object with its background before the extraction process. The purpose of this study was to accurately identify and measure the mucus area of rhinosinusitis from CT-scan images of rhinosinusitis patients. The test data used in this study is secondary data from CT-Scan of paranasal sinus sagittal view in radiology RSUP M. Djamil Padang, Padang City, West Sumatra, Indonesia, using the Philips Ingenuity CT CT-Scan model type MRC880 numbered Tube series 163889. The segmentation process is carried out to obtain the optimal multi threshold value from each slice of the test image using the Improved Adaptive Multi Threshold (IAMT) segmentation method which produces a binary image to separate the mucus object with its background before the extraction process. Furthermore, the results of IAMT are applied to the identification and extraction model automatically to obtain the area of the rhinosinusitis mucus object. The extraction results were used for 2D to 3D image reconstruction in calculating the volume of rhinosinusitis mucus objects. The test results showed that the smallest area of rhinosinusitis mucus was 0.516 cm2 and the largest was 1.807 cm2, and the average volume was 0.95 cm3 with Accuracy is 96,5%. The identified areas and volumes indicate inflammation in the area of the paranasal sinuses and require treatment. Thus, this study can help doctors in making the right decision to take further medical action. © 2024 IEEE.

关键词： computerized tomography

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Using Real-Time Integrated computer Vision and Deep Learning for Advanced Factory Safety 4

Using Real-Time Integrated Computer Vision and Deep Learning...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Chang, Yu-Cheng Huang, Chao-Yi Yang, Fang-Yuan Chien, Wei Wu, Zong-Rong School Art and Design Sanming University Fujian China National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Yunlin University of Science and Technology Department of Information Management Yunlin Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan

ISBN: (纸本)9798350360721

Innovative safety in the workplace is vital as the high safety risks associated with electrical engineering construction can lead to injuries or even fatalities. Using computer vision technology, we experimented with scenarios such as 'normal operations' and 'unexpected incidents' to enhance safety measures. We integrated an Internet of Things system into the setup, enabling the system to quickly detect and alert to unexpected events in real-time, thereby improving workplace safety. © 2024 IEEE.

关键词： computer vision

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Analysis of Several Quantum Encoding Methods Implemented on A Quantum Circuit Architecture to Improve Classification Accuracy 6

Analysis of Several Quantum Encoding Methods Implemented on ...

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6th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2022

作者： Fauzi, Rahmad Zarlis, Muhammad Mawengkang, Herman Sihombing, Poltak Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9798350320718

Quantum encoding is a process to transform classical information into quantum states. It plays a crucial role in using quantum algorithms to solve classical problems, especially in quantum machine learning tasks. There are many QE methods. It is very difficult to determine which QE method to choose to improve classification accuracy. Therefore, this paper will analyze several QE methods. Training and testing on Iris flower datasets were performed in a architecture quantum circuit and some performances parameters were evaluated. The expected result is that we can compare the classification accuracy of some of these Quantum encodings. © 2022 IEEE.

关键词： Machine learning

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