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4th International Conference on High Voltage Engineering and Power Systems, ICHVEPS 2023

作者： Maeda, Haruka Sitompul, Sandro Fujita, Goro Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

The use of high-penetration solar photovoltaics (PV) to transmission networks is complicated by voltage rise. Infractions of voltage occur when voltage levels surpass the permitted limits. Several solutions have been suggested to avoid voltage breaches, including volt-VAR control on inverters. In this research, the impact of utilizing dynamic volt-VAR control as opposed to static volt-VAR control in a high-penetration photovoltaic (PV) system is compared to the impact of applying static VAR compensator (SVC). With dynamic volt-VAR control, which takes into account the inverter's available reactive power capacity, it is possible to adjust the reactive power contribution of the PV inverter. The quantity of available capacity will affect the force of the control, as expressed by the droop gain. The transmission system's controls are evaluated for one year to determine the number of uncompensated voltage occurrences. The number of violations that the control cannot compensate for affects how much reactive power the SVC must provide. © 2023 IEEE.

关键词： Reactive power

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Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models 2

Multi-weather Cross-view Geo-localization Using Denoising Di...

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2nd Workshop on UAVs in Multimedia

作者： Feng, Tongtong Li, Qing Wang, Xin Wang, Mingzi Li, Guangyao Zhu, Wenwu Department of Computer Science and Technology Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China Department of Computer Science and Technology BNRist Tsinghua University Beijing China Tbsi Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798400712067

Cross-view geo-localization in GNSS-denied environments aims to determine an unknown location by matching drone-view images with the correct geo-tagged satellite-view images from a large gallery. Recent research shows that learning discriminative image representations under specific weather conditions can significantly enhance performance. However, the frequent occurrence of unseen extreme weather conditions hinders progress. This paper introduces MCGF, a Multi-weather Cross-view Geo-localization Framework designed to dynamically adapt to unseen weather conditions. MCGF establishes a joint optimization between image restoration and geo-localization using denoising diffusion models. For image restoration, MCGF incorporates a shared encoder and a lightweight restoration module to help the backbone eliminate weather-specific information. For geo-localization, MCGF uses EVA-02 as a backbone for feature extraction, with cross-entropy loss for training and cosine distance for testing. Extensive experiments on University160k-WX demonstrate that MCGF achieves competitive results for geo-localization in varying weather conditions. © 2024 Owner/Author.

关键词： Image reconstruction

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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2022 IEEE Creative Communication and Innovative technology, ICCIT 2022

作者： Yanfi, Yanfi Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University Binus Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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Towards Robust Temporal Activity Localization Learning with Noisy Labels 30

Towards Robust Temporal Activity Localization Learning with ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Daizong Qu, Xiaoye Fang, Xiang Dong, Jianfeng Zhou, Pan Nan, Guoshun Tang, Keke Fang, Wanlong Cheng, Yu Wangxuan Institute of Computer Technology Peking University China School of Cyber Science and Engineering Huazhong University of Science of Technology China Interdisciplinary Graduate Programme Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang Gongshang University China School of Cyberspace Security Beijing University of Posts and Telecommunications China Cyberspace Institute of Advanced Technology Guangzhou University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9782493814104

This paper addresses the task of temporal activity localization (TAL). Although recent works have made significant progress in TAL research, almost all of them implicitly assume that the dense frame-level correspondences in each video-query pair are correctly annotated. However, in reality, such an assumption is extremely expensive and even impossible to satisfy due to subjective labeling. To alleviate this issue, in this paper, we explore a new TAL setting termed Noisy Temporal activity localization (NTAL), where a TAL model should be robust to the mixed training data with noisy moment boundaries. Inspired by the memorization effect of neural networks, we propose a novel method called Co-Teaching Regularizer (CTR) for NTAL. Specifically, we first learn a Gaussian Mixture Model to divide the mixed training data into preliminary clean and noisy subsets. Subsequently, we refine the labels of the two subsets by an adaptive prediction function so that their true positive and false positive samples could be identified. To avoid single model being prone to its mistakes learned by the mixed data, we adopt a co-teaching paradigm, which utilizes two models sharing the same framework to teach each other for robust learning. A curriculum strategy is further introduced to gradually learn the moment confidence from easy to hard. Experiments on three datasets demonstrate that our CTR is significantly more robust to the noisy training data compared to the existing methods. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Gaussian distribution

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Automatic Highlight Generation of Soccer Videos 14

Automatic Highlight Generation of Soccer Videos

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14th International Conference on Information and Communication technology Convergence, ICTC 2023

作者： Park, Jiwoo Jwa, Younkyung Kwak, Jiin Lim, Jihun Kim, Sehee Pohang University of Science and Technology Ai Graduate School Pohang Korea Republic of Gwangju Institute of Science and Technology Ai Graduate School Gwangju Korea Republic of Ulsan National Institute of Science and Technology Ai Graduate School Ulsan Korea Republic of Korea University Department of Mathematics Seoul Korea Republic of Seoul National University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

Due to soccer's immense popularity, numerous soccer matches are played each year and people watch or record these matches. However, considering that the duration of a game of soccer is at least 90 minutes and that important scenes such as goals and passes take up a small portion of the total time, we inevitably look for highlights rather than the entire game. Therefore, we propose a model that automatically generates a highlight video of a full soccer match in this paper. For this purpose, we searched diverse action-spotting models to choose a backbone model for our highlight classifier. Through various experiments, NetVLAD++ is selected as a backbone model and we created a fairly natural highlight generator by combining NetVLAD++ and our highlight classifier. Our results are meaningful because they can be applied in various fields and have huge room for improvement. © 2023 IEEE.

关键词： Automatic Video Summarization Highlight Generation Soccer Video Highlight

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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes 8

Systematic Literature Review on Statistics and Machine Learn...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning rice phenotypes prediction statistics

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Real-time Implementation of Human Detection Based on Informed-Filters Using C++ AMP for Aerial Images

Journal of the Institute of Image Electronics Engineers of J...

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Journal of the Institute of Image Electronics Engineers of Japan 2023年第2期52卷 329-338页

作者： Tsuyama, Masahiko Xue, Junfeng Morioka, Junya Oki, Takuro Miyamoto, Ryusuke Department of Computer Science School of Science and Technology Meiji University Japan Department of Computer Science Graduate School of Science and Technology Meiji University Japan

To adopt Image-Assisted Routing to construct sensor network dynamically for real-time vital sensing, a real-time system for human detection using aerial images is indispensable. When DJI Phantom 4 Pro V2.0 is used to obtain visual information, the system must be implemented using DJI Windows SDK that is designed to develop a software based on Universal Windows Platform (UWP). The most significant problem of UWP-based development is limitation about usage of CUDA that is popular for parallel programming using NVIDIA GPUs. To solve this problem, we have implemented Informed-Filters with C++ AMP that is available for parallel implementation using GPUs on UWP. Experimental results using 1920 × 1080 pixels images showed that the detection accuracy by the proposed implementation was better than detectors based on deep neural networks and the detection speed reached to 41.8 fps. © 2023 Institute of Image Electronics Engineers of Japan. All rights reserved.

关键词： Deep neural networks

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Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing 4

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Hidayaturrahman Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo School of Computer Science Bina Nusatara University Mathematics Department Jakarta11480 Indonesia Bina Nusatara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation. © 2023 IEEE.

关键词： Color

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Development of “Peter's First-Aid Adventure” Virtual Reality-Based Serious Game in First-Aid Education: Usability Analysis of Virtual Reality Interaction Tools

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Procedia computer science 2024年 245卷 309-319页

作者： Alexander Rainer Angeline Setiono Kevin Leonardrich Dimas Ramdhan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Incidents that threaten lives can occur unexpectedly, highlighting the importance of immediate first aid. Studies show that Virtual Reality (VR)-based Serious Games (SG) are excellent alternatives for learning, especially for physical training, as they provide immersive and realistic scenarios where users can learn and practice their skills in a safe environment. Therefore, this research proposes the design of a VR-based SG for learning first aid procedures. A total of 31 participants tested the game's usability using both VR controllers and hand tracking, playing the game with both tools in random order, and then completing the XR-GUQ and Interaction Categories questionnaire. The results indicate that controllers offer higher usability than hand tracking. However, the game can still effectively utilize both interaction tools, given their satisfactory usability ratings.

关键词： Serious Games Virtual Reality First Aid Hand Tracking Usability Health Interaction Tools Education

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Player evaluation and learning outcomes of “NusaVenturas”: A serious game for enhancing player knowledge and interest in Indonesian traditional foods

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Procedia computer science 2024年 245卷 320-329页

作者： Vito Sebastian Sanjaya Nicholas Valenthinus Tanoto Nicholas Tandinata Dimas Ramdhan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The popularity of Indonesian traditional food has been declining due to the increasing influence of foreign cuisine. This study presents the development method that combining Game Development Life Cycle (GDLC) with Experiential Game Learning Design Framework and evaluation of a serious game "NusaVenturas" using usability, learning outcome, and Game Experience Questionnaire (GEQ) that designed to introduce, promote interest, educate, and preserve knowledge of Indonesian traditional foods. "NusaVenturas" is a unique 2D serious game that integrates puzzle-platformer elements with time-management cooking mechanics. The study involved 37 participants aged 18 to 23. Evaluation results indicate that "NusaVenturas" significantly enhances players' knowledge of Indonesian traditional foods, with statistical analysis confirming the significance of the findings.

关键词： serious games culture indonesian foods usability learning outcomes game experience questionnaire

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