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arXiv 2023年

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems" using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). Copyright © 2023, The Authors. All rights reserved.

关键词： Fall detection

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tin-Han Chi Kai-Chun Liu Chia-Yeh Hsieh Yu Tsao Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

关键词： Computational modeling System performance Aging Transformers Reliability Fall detection Usability

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Ontology Design of Influential People Identification Using Centrality

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： Rolly Maulana Awangga Muhammad Yusril Helmi Setyawan Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Indonesia

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

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SQL Collaborative Learning Framework Based on SOA

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： S Armiati RM Awangga Applied Program of Informatics Management Politeknik Pos Indonesia Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia

The research is focused on designing collaborative learning-oriented framework fulfilment service in teaching SQL Oracle 10g. Framework built a foundation of academic fulfilment service performed by a layer of the working unit in collaboration with program Studi Manajemen Informatika. In the design phase defined what form of collaboration models and information technology proposed for program Studi Manajemen Informatika by using a framework of collaboration inspired by the stages of modelling a Service Oriented Architecture (SOA). Stages begin with analyzing subsystems, this activity is used to determine subsystem involved and reliance as well as workflow between the subsystems. After the service can be identified, the second phase is designing the component specifications, which details the components that are implemented in the service to include the data, rules, services, profiles can be configured, and variations. The third stage is to allocate service, set the service to the subsystems that have been identified, and its components. Implementation framework contributes to the teaching guides and application architecture that can be used as a landing realize an increase in service by applying information technology.

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Improving TOGAF ADM 9.1 Migration Planning Phase by ITIL V3 Service Transition

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： Nisa Hanum Harani Arry Akhmad Arman Rolly Maulana Awangga Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology

Modification planning of business transformation involving technological utilization required a system of transition and migration planning process. Planning of system migration activity is the most important. The migration process is including complex elements such as business re-engineering, transition scheme mapping, data transformation, application development, individual involvement by computer and trial interaction. TOGAF ADM is the framework and method of enterprise architecture implementation. TOGAF ADM provides a manual refer to the architecture and migration planning. The planning includes an implementation solution, in this case, IT solution, but when the solution becomes an IT operational planning, TOGAF could not handle it. This paper presents a new model framework detail transitions process of integration between TOGAF and ITIL. We evaluated our models in field study inside a private university.

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Sampeu: Servicing Web Map Tile Service over Web Map Service to Increase Computation Performance

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IOP Conference Series: Earth and Environmental Science 2018年第1期145卷

作者： R M Awangga Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung 40151 Indonesia

Open Geospatial Consortium (OGC) has a standard for cartographic over web service; there are Web Map Service (WMS) used by MapServer, Web Feature Service (WFS), and Web Map Tile Service. There is research on comparing two cartography information between WMS and WFS, so this research extends to proposed solution by comparing WMS and WMTS computation cost and created a product called Sampeu. Sampeu has proposed a solution by creating WMTS protocol over WMS. WMTS work on the tile-ing system where the data transform from big picture to part of the Tile is. A lot of Tile making a pyramid of cartography and serving to client one by one, so the computation cost can be decreasing.

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Peuyeum: A Geospatial URL Encrypted Web Framework Using Advance Encryption Standard-Cipher Block Chaining Mode

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IOP Conference Series: Earth and Environmental Science 2018年第1期145卷

作者： R M Awangga Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung 40151 Indonesia

Many researches proposes geospatial web framework over the popularity of the Internet. Based on that, research on securing geospatial web framework is necessary. In this research aimed Peuyeum. Peuyeum is geospatial web framework with Encrypted Universal Resource Locator (URL). Advance Encryption Standard (AES)-Cipher Block Chaining (CBC) chosen as the method in this research. By calculating attack time, the brute force attack will reduce by this approach and resistance time will improve.

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AI in Industry: Real-World Applications and Case Studies

TechRxiv

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TechRxiv 2023年

作者： Rahmaniar, Wahyu Maarif, Alfian ul Haq, Qazi Mazhar Iskandar, Muchammad Edo Institute of Innovative Research Tokyo Institute of Technology Kanagawa226-8503 Japan Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55166 Indonesia International Bachelor Program in Informatics and Computer Science Yuan Ze University Taoyuan320315 Taiwan Ezy Industries Jakarta Indonesia

Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation and efficiency in various industries. This paper examines the real-world application of AI in multiple sectors, including healthcare, finance, agriculture, retail, energy, and automotive. Several case studies are described to understand better the practical applications, results, and challenges of implementing AI. While many industries have reaped enormous benefits from AI, inherent challenges include data privacy, the potential for bias, and the continuing demand for skilled labor. This comprehensive review aims to provide AI application insights to professionals and researchers. Thus, as AI grows, there may be challenges and avenues for future research. © 2023, CC BY.

关键词： Agriculture

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Deep Learning-based Fall Detection Algorithm Using Ensemble ...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Chien-Pin Liu Ju-Hsuan Li En-Ping Chu Chia-Yeh Hsieh Kai-Chun Liu Chia-Tai Chan Yu Tsao Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a “long-lie.” Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD.

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

arXiv

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arXiv 2023年

作者： Liu, Chien-Pin Li, Ju-Hsuan Chu, En-Ping Hsieh, Chia-Yeh Liu, Kai-Chun Chan, Chia-Tai Tsao, Yu Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei112 Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei242062 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115 Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie." Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD. Copyright © 2023, The Authors. All rights reserved.

关键词： Fall detection

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