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arXiv 2023年

作者： Huroon, Aamer Mohamed Huang, Yu-Chih Wang, Li-Chun Department of Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan

In this paper, we study the transmission strategy of a ground-based beyond diagonal reconfigurable intelligent surface (BD-RIS), a.k.a RIS 2.0, in a network where multiple unmanned aerial vehicles (UAVs) simultaneously transmit signals to the respective groups of users. It is assumed that each group is assigned subcarriers orthogonal to those assigned to other groups and rate splitting multiple access (RSMA) is adopted within each group. A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders. To solve the problem, we propose using generalized benders decomposition (GBD) augmented with a manifold-based algorithm. GBD splits the MINLP problem into two sub-problems, namely the primal and the relaxed master problem, which are solved alternately and iteratively. In the primal problem, we apply block coordinate descent (BCD) to manage the coupling of variables effectively. Moreover, we recognize the manifold structure in the phase rotation constraint of BD-RIS, enabling the Riemannian conjugate gradient (RCG). Simulation results demonstrate the effectiveness of the proposed approach in maximizing spectral efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm

Psychological Stress Classification Using Extreme Gradient B...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Maria Mahardini Sakanti Viacheslav Siniaev Aurelia Amaris Win-Jet Luo Suhartono C. Bambang Dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taichung Taiwan Institute of Applied Computer Science ITMO University St. Petersburg Russian Federation Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taichung Taiwan Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.

关键词： Accuracy Microcontrollers Psychology Human factors Predictive models Boosting Prediction algorithms Real-time systems Sensors Wearable devices

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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Analysis of the Cyber Attacks in Indonesia’s Health Sector in Industry 4.0 Era

Analysis of the Cyber Attacks in Indonesia’s Health Sector ...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Arif Hidayat Teddy Mantoro Armi Susandi Medical Intelligence Post-Graduate Program Indonesian State Intelligence College Bogor Indonesia Department of Computer Science Media-Tech Lab Sampoema University Jakarta Indonesia

ISBN: (纸本)9781665453905

At this time the world has undergone considerable changes, where the world has entered the era of the industrial revolution 4.0. With these changes, there are many positive impacts or opportunities that can support human activities in the economic, political, socio-cultural, health, and other fields. And alsothe author tries to analyze about industry 4.0 era for Indonesia’s health sector with SWOT Analysis to know about Strenght, Weaknesses, Opportunities, and Threats. However, the challenges of technological advances can pose a threat to countries that are not ready to facethem, one of which is cyber attacks that can threaten data security. Therefore, it is necessary to be prepared in terms of data security by cooperating with various parties involved in it.

关键词： Law Data security Weapons Government Data protection Security National security

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Terahertz Time-Domain Imaging for Radiotherapy Treatment Monitoring in Pancreatic Ductal Adenocarcinoma

Terahertz Time-Domain Imaging for Radiotherapy Treatment Mon...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Ivan Komissarov Scott Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York Laboratory for Laser Energetics University of Rochester Rochester New York Department of Surgery University of Rochester Medical Center Rochester New York Department of Electrical and Computer Engineering University of Rochester Rochester New York

ISBN: (纸本)9798350369311

Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refractive index and absorption coefficient were used as imaging markers extracted with the maximum a-posteriori probability (MAP) estimation algorithm. The results were then compared with the terahertz maps of untreated murine PDAC tissue samples and demonstrated that terahertz imaging can probe physical changes with high sensitivity in the tissue post treatment.

关键词： Maximum a posteriori estimation Terahertz wave imaging Laser theory Sensitivity Refractive index Radiation therapy Time-domain analysis Spatial resolution Probes Monitoring

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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Autonomous car using CNN deep learning algorithm 2

Autonomous car using CNN deep learning algorithm

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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Sonata, I. Heryadi, Y. Lukas, L. Wibowo, A. Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Jl. Kyai H. Syahdan No.9 Jakarta11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jl Jend. Sudirman No.51 Jakarta12930 Indonesia

Autonomous cars have become an interesting discussion in recent years. Several major automotive industries such as Tesla, GM and Nissan are trying to become pioneers in autonomous car technology. Giant technology companies such as Google Waymo, Baidu and Aptiv also developing autonomous car technology. Several technological approaches are carried out in implementing autonomous cars for recognizing surrounding situations, such as radar, lidar, sonar, GPS, and odometry. An automatic control system is used to control navigation based on the data obtained from these sensors. This paper will discuss the use of CNN deep learning algorithm for recognizing the surrounding environment in creating the automatic navigation required by autonomous cars. The system designed will create and learn the data set that will be taken in advance and the learning outcomes will be implemented in an open simulation system. This simulation shows high accuracy in learning to navigate the autonomous car by observing the surrounding environment. © Published under licence by IOP Publishing Ltd.

关键词： Autonomous vehicles

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

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SN computer science 2021年第1期2卷 1-11页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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Construction Process Quality Control Assisted Mobile Application 8

Construction Process Quality Control Assisted Mobile Applica...

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8th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2021

作者： Kruachottikul, Pravee Cooharojananone, Nagul Phanomchoeng, Gridsada Wongsanao, Keetapat Thabsuwan, Sirada Suwannatrai, Kuptipong Pongsuttisatta, Watcharapong Ganyaporngul, Sirapavee Kovitanggoon, Kittikul Vajarodaya, Napatsapa Graduate School Chulalongkorn University Technopreneurship and Innovation Management Program Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Mechanical Engineering Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Solitech Innovations and Technologies Co. Ltd. Bangkok Thailand

ISBN: (纸本)9781665428958

Digital transformation plays a big role in many industries, including construction. One of the obstacles to manage large-scale and complex construction projects is inefficient communication between involving personnel. Most problems are such as misunderstanding contexts, incomplete information, ignorance, missing multimedia files, etc. In Thailand, organizations adopt digital transformation by utilizing a variety of applications and software to complete their projects, such as LINE, G-Suite, Asana, etc. Users have to spend a lot of time to learn each application and software to be able to achieve maximum performance. Construction process quality control assisted application is the new tool developed for uniting all needs and solving communication problems. Requirements are gathered by five interviews with three major organizations, the Thailand Department of Highway, Team Built Co., Ltd., and one major construction company. Requirements were analyzed to extract the most useful features that are beneficial to all users. React Native and the Expo framework were used to develop the frontend part of the application. For the backend part, the application used microservice architecture with various cloud services, like Amazon AWS, Expo Server, and Firebase Cloud Messaging. In the end, the application was put through hand-on tests by 13 on-site personnel. The final results showed the average scores of the user interface, hands-on experience, and benefits for the users were 4, 4.16, and 3.92 (out of 5), respectively, in which the total satisfaction score is 4.03(out of 5). © 2021 IEEE.

关键词： Construction

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RHC: A Dataset for In-Room and Out-Room Human Counting

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Procedia computer science 2021年 179卷 33-39页

作者： Bens Pardamean Hery Harjono Muljo Faizal Abid Herman Albert Susanto Tjeng Wawan Cenggoro Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In recent years, research in human counting from CCTV (Closed Circuit Television) images have found an increasing demand to be deployed in real-world applications. The applications have been implemented in various settings, both indoor and outdoor. In the case of indoor setting, we found a type of room setting that conveys a problem to human counting model if we need to count only humans inside a room. With this respect, we present RHC (Room Human Counting) dataset, which images are captured in the aforementioned setting. The dataset can be used to develop a robust model that can differentiate between humans inside and outside a room. The dataset is publicly available at https://***/datasets/vt5c8h6kmh/1.

关键词： dataset human counting crowd counting in-room out-room

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