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Nonlinear internal wave run-up on impermeable steep slopes

Journal of Offshore Mechanics and Arctic engineering 2009年第4期131卷 1-5页

作者： Tseng, I-Fan Chen, Chen-Yuan Kuo, Hui-Ming Department of Marine Environment and Engineering National Sun Yat-Sen University Kaohsiung 80424 Taiwan Department of Computer Science National Pingtung University of Education No. 4-18 Ming Shen Rd. Pingtung 90003 Taiwan Department of Management Information System Yung-Ta Institute of Technology and Commerce Lin-Luoh Pingtung 90941 Taiwan Doctoral Program in Management National Kaohsiung First University of Science and Technology Kaohsiung Taiwan Department of Logistics Management Shu-Te University Yen Chau Kaohsiung 82445 Taiwan

Laboratory experiments were conducted to investigate the run-up of internal solitary waves (ISWs) on steep uniform slopes in a two-layered fluid system with a free surface. A 12 m long wave flume, which incorporated a movable vertical gate for generating ISWs, was used in the experiments. A steep uniform slope was modeled at one end of the flume. In the present study we looked at internal waves with small and large amplitudes using a steep uniform slope (from one of Θ=30, 50, 60, 90, 120, and 130 deg) much longer than those previously published in the literature. Results collected from a wide range of experimental runs show that the run-up height depends on the planar slope, while the breaking depth is only antecedent to the amplitude of an ISW. The overall quantitative agreement with the linear feature aspects of the incident wave amplitude is encouraging. Copyright © 2009 by ASME.

关键词： Arctic engineering

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Convolutional Neural Network (CNN) Algorithm Development for Quality Measurement in Tire Product Classification: A systematic Literature Review

Convolutional Neural Network (CNN) Algorithm Development for...

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International Conference on Informatics and Computing (ICIC)

作者： Armando Tirta Dwilaga Lintang Yuniar Banowosari Sudaryanto Aini Suri Talita Department of Information Technology Doctoral Program Gunadarma Information Technology University West Java Indonesia Department of Informatics Gunadarma University West Java Indonesia Department of Industrial Engineering and Management Gunadarma University West Java Indonesia Department of Information System Management Gunadarma University West Java Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Convolutional Neural Networks (CNN) have shown potential in tire defect classification, but challenges like image quality variations and model limitations persist. This systematic Literature Review (SLR) employs the Preferred Reporting Items for systematic Reviews and Meta-Analyses (PRISMA) methodology and a qualitative approach. It uses the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework to analyze 30 articles from Google Scholar, published between 2018 and 2024 in Q1-Q4 journals. The review examines article distribution by publisher, evaluates the impact of key pre-training parameters (such as data split ratio, dropout rate, optimization algorithm, learning rate, and pre-trained models on CNN performance), and identifies challenges in defect classification. MDPI (Multidisciplinary Digital Publishing Institute) is the most frequent publisher, with the highest number of publications occurring in 2021. AlexNet and VGG16 are the most common models, and an 80:20 data split ratio, 0.5 dropout rate, and a 0.001 learning rate are frequently used. The Adam (Adaptive Momentum) optimization algorithm is widely adopted, but challenges remain in detecting small defects. Further improvements in CNN models, lighting techniques, and larger datasets are needed to enhance defect detection. This review highlights current trends and future research opportunities in tire quality measurement.

关键词： Image quality Adaptation models Current measurement Lighting Tires Data models Classification algorithms Convolutional neural networks Optimization systematic literature review

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Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods

Improved Chest X-Ray Image Quality Using Median and Gaussian...

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International Conference on Informatics, Multimedia, Cyber and information system (ICIMCIS)

作者： Lucky Indra Kesuma Ermatita Ermatita Erwin Erwin Purwita Sari Rudhy Ho Purabaya Doctoral Program in Engineering Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Universitas Sjakhyakirti Information System Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Departement of Computer System Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Informatic Management Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Information System Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

ISBN: (纸本)9781665473286

The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well.

关键词： Image quality PSNR Multimedia systems Noise reduction Morphology Lung Medical services

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Comparison Analysis of Intrusion Detection using Deep Learning in IoT Networks

Comparison Analysis of Intrusion Detection using Deep Learni...

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International Conference of information and Communication Technology (ICoICT)

作者： Syariful Ikhwan Purwanto Purwanto Adian Fatchur Rochim Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

This study focuses on network security for Internet of Things (IoT) devices, which are particularly vulnerable to attack due to limited security measures. To protect IoT networks from attackers, the research implemented intrusion detection on the networks. The study compared several Deep Learning algorithms, including DNN, CNN, LSTM, and AE, to identify the most effective algorithm for solving network security problems using intrusion detection. The research used the UNSW-NB15 dataset for testing and employed binary classification for evaluation. The results showed that the DNN algorithm achieved an accuracy value of 99.76% and a loss value of 0.006%, outperforming the other algorithms. This study highlights the importance of implementing intrusion detection in protecting IoT devices and networks and demonstrates the efficacy of the DNN algorithm in detecting and preventing network security breaches.

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Height Estimation via Stereo Vision system for Unmanned Helicopter Autonomous Landing

Height Estimation via Stereo Vision System for Unmanned Heli...

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2010 International Symposium on Computer,Communication, Control and Automation

作者： Kuo-Hsien Hsia Shao-Fan Lien Juhng-Perng Su Department of Management Information System Far East University Graduate School of Engr.Sci.&Tech.(Doctoral Program) National Yunlin University of Science&Technology Department of Electrical Engineering National Yunlin University of Science&Technology Department of Information Technology Overseas Chinese University

ISBN: (纸本)9781424455652;9781424455669

Auto landing is a very important function of control in the development of autonomous unmanned *** this paper,the stereo vision system is proposed to provide the height information on landing control instead of global positioning system(GPS).GPS provided 3-axis information,but the height error of GPS is usually in meters,which is not accurate enough for *** stereo vision system provided a more accurate height information to correct the error of *** this paper,the stereo vision system is composed of two webcams and the variable baseline method is selected to increase the measurement *** results show that variable baseline method did increase the accuracy of the stereo vision system with the webcams,and that not only provided an authentic piece of height information but also provided another option of sensor for autonomous unmanned helicopter's auto landing control.

关键词： stereo vision system height estimation helicopter landing

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Impact of Filter-Based Feature Selection on Prediction Model Performance for Early Detection of Stunting

Impact of Filter-Based Feature Selection on Prediction Model...

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International Conference on Informatics and Computing (ICIC)

作者： Sri Handani Widiastuti Purwanto Adi Wibowo Ratih Wirapuspita Wisnuwardani Informatics Engineering Study Program Sekolah Tinggi Teknologi Bontang Indonesia Doctoral Program of Information System School of Postgraduate Studies Diponegoro Univercity Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro Univercity Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro Univercity Semarang Indonesia Faculty of Public Health Mulawarman Univercity Samarinda Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study investigates the impact of feature selection results using the filter method on the performance of predictive models for the nutritional status of children aged 0-23 months. This study aimed to understand how correlation-based feature selection methods affect the efficiency and accuracy of predictive models. The dataset comes from the Basic Health Research survey, which consists of 84 variables and around 130,000 data, including anthropometric, family, socioeconomic, and environmental information. In this study, various filter-based feature selection methods were applied to evaluate and identify features that have significant relationships with children’s nutritional status, both linearly and non-linearly. The mutual information method was used to identify complex relationships between features and target variables, while the Pearson Correlation Coefficient and information gain were used to assess the relevance of numerical and categorical features. Of the 84 variables in the dataset, 37 features were selected after the selection process. The model performance evaluation was carried out using the Random Forest (RF) and Gradient Boosting (GB) algorithms, by comparing the results between the dataset that had been selected and without feature selection. The RF model without feature selection produces accuracy: 0,84600, precision: 0,83958, recall: 0,84600, F1-score: 0,78962, and a computation time of 9 seconds. The GB model without feature selection produces accuracy: 0,86330, precision: 0,84722, recall: 0,86330, F1-score: 0,84925, and a computation time of 2 minutes 50 seconds. After feature selection and cross-validation, the RF model shows accuracy: 0,85701, precision: 0,83599, recall: 0,85701, and F1 score: 0,83484. The GB model shows accuracy at 0,86124, precision at 0,84226, recall at 0,86130, and F1 score at 0,84120.

关键词： Radio frequency Pediatrics Accuracy Correlation Computational modeling Predictive models Aging Feature extraction Prediction algorithms Numerical models

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Multi Task Classification Using Deep Learning Approaches for Big Five Personality Traits Prediction: A Review

Multi Task Classification Using Deep Learning Approaches for...

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International Conference on information Technology, Computer and Electrical engineering (ICITACEE)

作者： Salamun Rohman Nudin Rahmat Gernowo Maman Somantri Adi Wibowo Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98 % ), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67 % ), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36 % ) and lowest (59 % ). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG.

关键词： Deep learning Accuracy Reviews Psychology Predictive models Brain modeling Multitasking Reliability information technology Standards

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Comparative Study of Deep Learning Methods through Improvement of Skin Image Quality of Basal Cell Carcinoma

Comparative Study of Deep Learning Methods through Improveme...

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International Seminar on Research of information Technology and Intelligent systems (ISRITI)

作者： Ade Miftahul Jannah Kahlil Muchtar Maulisa Oktiana Aulia Rahman Yagus Cahyadi Novi Maulina Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Aceh Indonesia Dept. of Information System Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Doctorate Student of Doctoral Program in Medical Sciences Faculty of Medicine Universitas Syiah Kuala Indonesia Aceh Indonesia

Basal cell carcinoma (BCC) or commonly known as basal cell cancers is the most common non-melanoma skin disease in the world. BCC is the result of an interaction between a combination of genetic and environmental factors that are directly related to prolonged exposure to ultraviolet (UV) rays, such as the neck and head area. This study proposes to use the CLAHE (Contrast Limited Adaptive Histogram Equalization) method on skin images to improve image quality. CLAHE is an image quality improvement method that aims to obtain images with better contrast than the original image without reducing the quality of the image itself. The experimental results show that the CLAHE image are more accurate than the original image. For quantitative measurements, the performance is seen from the value of accuracy, precision, recall, specificity, and F-score. To be more specific, the experiments utilized the well-known public dataset called ISIC 2018 Dataset (for BCC image), while the normal skin used our own USK-Normal Skin dataset. The results for original image using VGG-16 architecture is 98,3%, ResNet-34 is 97,5%, EfficientNetB0 is 98,2%, and EfficientNet-B7 is 99,7%, respectively. Meanwhile, in the CLAHE image, the accuracy values obtained are higher, for the VGG-16 is 100%, ResNet-34 99.5%, EfficientNetB0 100% and EfficientNet-B7 100%, respectively. The normal skin dataset is available through this link: https://***/USK-NORMAL-SKIN-DATASETI.

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Monitoring system for Elderly Health Care Using Smart Band, Raspberry Pi, and Node-Red

Monitoring System for Elderly Health Care Using Smart Band, ...

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Electronic and Electrical engineering and Intelligent system (ICE3IS), International Conference on

作者： Yudhi Ardiyanto Kusworo Adi Kurnianingsih Karisma Trinanda Putra Pradityo Utomo Doctoral Program of Information System School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Physics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Politeknik Negeri Semarang Semarang Indonesia Department of Informatics Management Faculty of Engineering Universitas Merdeka Madiun Madiun Indonesia

ISBN: (纸本)9781665465427

The decline in physical, cognitive, and sensory functions for most elderly people leads to the development of several health issues such as memory loss, a tendency to fall, dizziness, and reduced ability to care for oneself independently. These can be mitigated using devices designed to monitor vital signs and human activity such as a smart band. However, the application of a smart band is characterized by a disadvantage which involves storing user data in the vendor's cloud and only one device can access the client applications. Therefore, nurses, doctors, and family members cannot simultaneously analyze data and receive alerts when abnormal conditions occur in elderly patients. This study aims to develop a system capable of monitoring the health conditions of multiple elderly individuals and sending alerts during abnormal conditions based on the heart rate value read by the smart band sensor. The process involved installing a database on the Raspberry Pi to store data while a monitoring system was built on the Node-RED platform to display elderly health data and made it accessible locally. The detection of an abnormal heart rate condition in the elderly patient leads the system to send notifications via telegram to multiple receivers registered in the system using their telegram identity.

关键词： Heart rate Electrical engineering Cloud computing Databases Medical services Receivers Older adults

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things

Intrusion Detection using Deep Neural Network Algorithm on t...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Syariful Ikhwan Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Computer Science Informatics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia Statistics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small.

关键词： Deep learning Training Satellites Neural networks Intrusion detection Training data Feature extraction

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