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IEEE symposium on computers and Communications (IsCC)

作者： Taiki Kato shinichi Yamagiwa Koichi Marumo Master's Program in Computer Science Degree Program in Systems and Information Engineering University of Tsukuba Tsukuba Ibalaki Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Ibalaki Japan

This paper focuses on a performance enhancement of communication performance by compressing data stream. AsE coding is an effective lossless data compression method for data stream. The software implementation of the coding/decoding method inevitably meets a performance mismatch in memory and storage devices. In the compressor side, it is predictable to decide the size of an original data block and is available to process a flexible buffer memory. However, the decompressor is not able to predict the buffer size because the original data size is not obvious before the decompression. This causes a performance mismatch in the filesystem level. This paper proposes a novel method to address the performance mismatch by applying a notification mechanism of compression size from the compressor. This paper describes the mechanism focusing on the system call usage. Through experimental evaluations, we show the performance improvement of the decompression performance for handling data stream.

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Toward Parallelization Technique for stream-based Lossless Data Compression

Toward Parallelization Technique for Stream-based Lossless D...

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IEEE International Conference on Big Data

作者： Taiki Kato shinichi Yamagiwa Koichi Wada Master’s Program in Computer Science Degree Program in Systems and Information Engineering University of Tsukuba Tsukuba Ibalaki Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Ibalaki Japan

IoT edge platform has become popular in various distributed environments. The edge devices need to communicate BigData among them or with the cloud servers by collaborating with AI technologies for finding events from the applications. Those devices exchange data streams from such as distributed sensors and remote image/video devices. We focus on an acceleration technique for the communication performance using a stream-based lossless data compression technology. This paper proposes a parallelization technique for the compression process in a software environment running on a multicore processor. The technique invokes concurrent compression processes assigned to multiple threads with splitting a data stream to chunks. The paper exposes three scheduling methods for assigning the chunks to the threads: in-order, hybrid and out-of-order. As an original data order of chunks must be obtained in decompression side, the proposed technique introduces packeting mechanisms in each chunk by adding headers to support the scheduling methods. Through experimental performance evaluations, we discuss the packeting overhead focusing on compression ratio and speedup by the parallelization with three scheduling methods.

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MINING FREQUENT sUBsTRUCTUREs FROM DEFOREsTATION OBJECTs

MINING FREQUENT SUBSTRUCTURES FROM DEFORESTATION OBJECTS

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IEEE International Geoscience and Remote sensing symposium

作者： Adeline M. Maciel Marcelino P. s. silva Maria Isabel s. Escada Master's Program in Computer Science Rio Grande do Norte State University Federal University of Semi-Arid Region National Institute for Space Research

ISBN: (纸本)9781467311601

The fight against deforestation is a priority for the environmental organizations, society and government. It demands the creation of methodologies and techniques that allow monitoring and intervening, efficiently and at reasonable costs, in areas susceptible to deforestation. Thus, the computational modeling of remote sensing data involves many challenges, including a large set of algorithms and techniques to extract strategic information contained in these data. This paper aims to employ graphs to represent relationships among deforestation objects captured from remote sensing data, and then extract patterns from them applying graph mining, that performs the search for frequent substructures using the FsG graph-based knowledge discovery algorithm, in order to identify frequent substructures among deforestation objects.

关键词： Remote sensing Deforestation Patterns Data Mining Graph Mining Frequent substructures

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Real-time recognition of traffic signs using opponent color filters

Real-time recognition of traffic signs using opponent color ...

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14th World Congress on Intelligent Transport systems, ITs 2007

作者： Baba, Kyoko Hirai, Yuzo Master's Program in Graduate School of Systems and Information Engineering University of Tsukuba 1-1-1 Ten-nodai Tsukuba Ibaraki 305-8573 Japan Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba 1-1-1 Ten-nodai Tsukuba Ibaraki 305-8573 Japan

ISBN: (纸本)9781617387777

In order to assist driver's vision, a real-time recognition system for traffic signs is proposed. After detecting sign candidates, biologically inspired opponent-color filters are used to extract symbol parts of signs. After normalizing the size of symbol, structural features are calculated to identify the sign. 5572 segmented images are used to design the algorithm. In a real-time system, the same sign in a sequence of frames is tracked, and a majority vote is used to integrate the recognition results. For test data, 93.8% recall rate and 99.3% precision rate could be attained. In-vehicle experiment also showed high recall and precision rates.

关键词： Traffic signs

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The matter of deep reinforcement learning towards practical ai applications 12

The matter of deep reinforcement learning towards practical ...

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12th International Conference on Ubi-Media Computing, Ubi-Media 2019

作者： Thaipisutikul, Tipajin Chen, Yi-Cheng Hui, Lin Chen, sheng-Chih Mongkolwat, Pattanasak shih, Timothy K. Department of Computer Science and Information Engineering National Central University Taiwan Department of Information Management National Central University Taiwan Department of Innovative Information and Technology Tamkang University Taiwan Master's Program of Digital Content and Technologies College of Communication National ChengChi University Taiwan Faculty of Information and Communication Technology Mahidol University Thailand

ISBN: (纸本)9781728128207

Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised and unsupervised real-world problems. However, RL is one of state-of-the-art topic due to the opaque aspects in design and implementation. Also, in which situation we will get performance gain from RL is still unclear. Therefore, This study firstly examines the impact of Experience Replay in Deep Q-Learning agent with self-Driving Car application. secondly, The impact of Eligibility Trace in RNN A3C agents with Breakout AI game application is studied. Our results indicated that these two techniques enhance RL performance by more than 20 percent as compared with traditional RL methods. © 2019 IEEE.

关键词： Reinforcement learning

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Comparison of disturbance rejection performance between sliding-mode control and equivalent-input-disturbance approach

Comparison of disturbance rejection performance between slid...

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IEEE International Conference on Mechatronics

作者： she, Jinhua Pan, Yaodong Hashimoto, Hiroshi Wu, Min School of Computer Science Tokyo Univ. of Tech. Hachioji 192-0982 Japan Control Systems Eng Honeywell Canada Mississauga ON L5L3S6 Canada Master Program of Innovation for Design and Eng Advanced Institute of Industrial Tech. Tokyo 140-0011 Japan School of Information Science and Engineering Central South Univ. Changsha 410083 China

ISBN: (纸本)9781612849836

sliding mode control (sMC) has been considered as a powerful method for disturbances rejection. On the other hand, we have presented an equivalent-input disturbance (EID) approach to reject disturbances. This paper takes a dual-stage feed drive as an example to compare these two methods. The example shows that, while the EID approach obtains almost the same disturbance rejection performance for matched disturbances as that of the sMC does, it can reject unmatched disturbances. This shows the superiority of the EID approach over sMC method. © 2011 IEEE.

关键词： sliding mode control

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3D Content Generation to Moodle Platform to support Anatomy Teaching and Learning

3D Content Generation to Moodle Platform to Support Anatomy ...

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IEEE/ACs International Conference of computer systems and Applications

作者： Fabricio Quintanilha Baptista Mario Popolin Neto Diego Roberto Colombo Dias Marcelo de Paiva Guimaraes Jose Remo Ferreira Brega Computer Science Department Sao Paulo State University - UNESP Federal Institute of Sao Paulo - IFSP Computer Science Department Federal University of Sao Joao Del Rei - UFSJ Open University of Brazil - UNIFESP Faccamp's Master Program

ISBN: (纸本)9781509043217

Anatomy learning is very important for many courses, and it requires understanding three-dimensional (3D) shapes, most forms of anatomy teaching are offered to students through two-dimensional (2D) resources, such as images and videos. Many software solutions were developed with the purpose of assisting anatomy teaching and learning, arranging varied techniques and assets, as interaction and visualization of 3D virtual models. Despite great features, these solutions lack standards utilization and pedagogical methodology. This paper aims to present a 3D content generation technique to Moodle, one of the most popular platforms for teaching and learning management, built upon a specific pedagogical methodology, and validated by educational experts. The standard used in this technique allow sharing and reuse of content not only among Moodle systems, which are used by a large number of educational institutions and professors but also in other Virtual Learning Environments.

关键词： Three-dimensional displays solid modeling Education standards Browsers Libraries software

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Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

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International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MsE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (Vss) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MsE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

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An Evaluation Of Hospital Accreditation From The survey With Text Vector Analysis Techniques

An Evaluation Of Hospital Accreditation From The Survey With...

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International Joint Conference on computer science and software Engineering (JCssE)

作者： Dittaphong Prapunwattana Aurawan Imsombut Picha suwannahitatorn Thammakorn saethang Biomedical Data Science Master's Program Faculty of Science Kasetsart University Bangkok Thailand Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand Thai Board of Prevention Medicine (Epidemiology) Phramongkutklao College of Medicine Bangkok Thailand

The Healthcare Accreditation Institute has an assessment and certification process for hospitals applying for Healthcare Accreditation. The assessment process requires a large number of text-based reports. The purpose of this research was to study the text analysis of the self-assessment reports of healthcare facilities and surveyor reports on issues related to the pharmaceutical system to evaluate and rate the accreditation of medical facilities. The natural language text vector analysis technique, together with the Universal sentence Encoder (UsE) was compared to Learning Lightweight Language-agnostic sentence Embeddings (LEALLA) for encoding data into a high-dimensional format. Next the sentence encoding feature was fed through a machine learning procedure, including artificial neural networks, logistic regression, and support vector machines to classify nursing facility accreditation ratings. The experimental results showed that the UsE embedding yielded better performance than the LEALLA embedding across all models with a precision of 0.70 but took slightly longer to encode feature sentences. This research could improve the performance of the analysis and scoring.

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Analysis of the 5Rs in Thailand Medication Error Classification through Natural Language Processing

Analysis of the 5Rs in Thailand Medication Error Classificat...

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International Joint Conference on computer science and software Engineering (JCssE)

作者： Peachyasitt Udomnuchaisup Aurawan Imsombut Picha suwannahitatorn Thammakorn saethang Biomedical Data Science Master's Program Faculty of Science Kasetsart University Bangkok Thailand Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand Thai Board of Prevention Medicine (Epidemiology) Phramongkutklao College of Medicine Bangkok Thailand

Medication errors threaten patient safety considerably, underscoring the necessity for enhanced detection and prevention techniques. A prevalent classification system in hospitals relies on the standard practice of medication administration known as the Five Rights (5R). This study seeks to develop an NLP-based tool designed to expand 5R error categorization coverage and alleviate the workload of medical professionals. The proposed method focuses on Thai medical text, incorporating Thai and English vocabulary. In this investigation, we developed a supervised learning classification framework using the Universal sentence Encoder (UsE) for sentence embedding, followed by an Artificial Neural Network (ANN) for model training. Additionally, we explored a zero-shot classification model employing pre-trained Large Language Models (PLMs). Our findings reveal that the supervised learning classification model provides the most favorable performance, albeit with the limitation of reliance on labeled datasets, which can be resource intensive. Conversely, the zero-shot classification framework's performance is less optimal. However, future advancements in Thai medical PLMs may improve efficacy and present a viable alternative for medical data analysis without dependence on labeled datasets. This initiative lays the groundwork for potential future applications and advantages within Thailand's medical domain.

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