检索结果-内蒙古大学图书馆

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Hapnes Toba Elizabeth Wianto Maya Malinda Agung Wijaya Al Halim Chien-Hsu Chen Master Program in Computer Science Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Visual Communication Design Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Management Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Informatics Universitas Kristen Maranatha Bandung Indonesia Industrial Design Department National Cheng Kung University Tainan Taiwan

This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Speaker Voice Cloning in Indonesian Using Sequence to Sequence Speech Synthesis

SSRN

引用

SSRN 2022年

作者： Awangga, Rolly Maulana Majesty, Dinda Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung40151 Indonesia

Multi-Speaker Voice Cloning is a system that can produce various voices based on voice samples and text input. Voice cloning can be an alternative to get audio data quickly without recording, which requires a lot of time, cost, and effort. Sequence to Sequence Speech Synthesis produces sounds that are naturally similar to speech equivalent to the human voice. Sequence to Sequence Speech Synthesis is a technology for generating speech directly from the text, consisting of attention-based predictive network features. Multi-Speaker Voice Clone consists of speaker encoder, encoder-decoder, and vocoder models. Tacotron-2 as model encoder-decoder and WaveNet as a vocoder. The speaker encoder will produce a speaker embedding to recognize the sound from the speaker. Tacotron-2 will embed text into the mel scale spectrogram. WaveNet will synthesize the time domain waveform from the mel scale spectrogram and generate sound predictions based on the samples. This model uses a multi-speaker voice dataset in Indonesian. Combining the three models produces a value of 3.28 for speech naturalness and 1.94 for speech similarity. © 2022, The Authors. All rights reserved.

关键词： Cloning

来源：评论

学校读者我要写书评

暂无评论

An Analysis of Fluid Intake Assessment Approaches for Fluid Intake Monitoring System

Biosensors

引用

Biosensors 2024年第1期14卷 14页

作者： Hsieh, Chia-Yeh Huang, Hsiang-Yun Chan, Chia-Tai Chiu, Li-Tzu Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City24205 Taiwan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City11221 Taiwan

Monitoring fluid intake is essential to help people manage their individual fluid intake behaviors and achieve adequate hydration. Previous studies of fluid intake assessment approaches based on inertial sensors can be categorized into wrist-worn-based and smart-container-based approaches. This study aims to analyze wrist-worn-based and smart-container-based fluid intake assessment approaches using inertial sensors. The comparison of these two approaches should be analyzed according to gesture recognition and volume estimation. In addition, the influence of the fill level and sip size information on the performance is explored in this study. The accuracy of gesture recognition with postprocessing is 92.89% and 91.8% for the wrist-worn-based approach and smart-container-based approach, respectively. For volume estimation, sip-size-dependent models can achieve better performance than general SVR models for both wrist-worn-based and smart-container-based approaches. The improvement of MAPE, MAD, and RMSE can reach over 50% except MAPE for small sip sizes. The results demonstrate that the sip size information and recognition performance are important for fluid intake assessment approaches. © 2023 by the authors.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

Wearable-based Frozen Shoulder Rehabilitation Exercise Recognition using Machine Learning Approaches

Wearable-based Frozen Shoulder Rehabilitation Exercise Recog...

引用

2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Liu, Chien-Pin Lai, Chih-Chun Liu, Kai-Chun Hsieh, Chia-Yeh Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Enginnering Taipei City112 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City115 Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications New Taipei City242062 Taiwan

ISBN: (纸本)9781665493840

Frozen shoulder is a disease that causes shoulder pain and stiffness. It limits the range of movement of the shoulder and has a great impact on the quality of daily life. One of the common treatment methods is to do frozen shoulder rehabilitation exercises. However, patients often fail to follow the instructions of the physical therapists during the program of home-based rehabilitation. Furthermore, clinical professionals are unavailable to track and monitor the home-based rehabilitation exercise performance of patients. To support clinical monitoring, we develop a wearable-based frozen shoulder rehabilitation exercise recognizer using different machine learning models and deep learning models. The proposed methods can automatically identify movement/silence segments from continuous signals and classify types of frozen shoulder rehabilitation exercises. Besides, we propose a finite state machine and fragmentation revision mechanism for error correction. Twenty subjects are invited to perform six types of rehabilitation exercises. The proposed methods achieve the best result of 95.6% accuracy, 95.83% F-score for the identification of movement/silence and 95.58% accuracy, 95.49% F-score for classification of exercise type, respectively. The results demonstrate the feasibility of the proposed method to automatically monitor the frozen shoulder rehabilitation exercise, which has the potential to provide objective, continuous and quantitative information for telerehabilitation. © 2023 IEEE.

关键词： Patient rehabilitation

来源：评论

学校读者我要写书评

暂无评论

Automated Diagnostic Radiographs of COVID-19 Based on Deep Learning Method

Automated Diagnostic Radiographs of COVID-19 Based on Deep L...

引用

2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022

作者： Ting, Jyun-Siou Shongwe, Njabulo Lin, Ruei-Chi Huang, Cheng-Ta Yuan Ze University Department of Information Management Taoyuan City Taiwan Yuan Ze University International Bachelor Program in Informatics Taoyuan City Taiwan Far Eastern Memorial Hospital Department of Medical Imaging New Taipei City Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

The COVID-19 outbreak has had a serious impact on Taiwan's health care system. Deep Learning is an effective technology to help doctors make the most appropriate medical decisions for every patient in this crisis. In this study, we select four state-of-the-art Deep Learning-Xception, MobileNetv2, DenseNet169, and DenseNet201. Additionally, Transfer Learning is used for pre-training them before four models individually classify normal and positive COVID-19 chest X-ray images. Lastly, the best results reached 98.58% accuracy, 98.58% precision, and 98.42% recall. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Liu, Chien-Pin Li, Ju-Hsuan Chu, En-Ping Hsieh, Chia-Yeh Liu, Kai-Chun Chan, Chia-Tai Tsao, Yu National Yang Ming Chiao Tung University Department of Biomedical Engineering Taipei City112 Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications New Taipei City242062 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City115 Taiwan

ISBN: (纸本)9781665493840

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. © 2023 IEEE.

关键词： Fall detection

来源：评论

学校读者我要写书评

暂无评论

Data Mining Approach for River Flood Hazard Time-Series: Using a Combination of Triple Exponential Smoothing and Neural Networks, in Demak

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第19期49卷 183-196页

作者： Hakim, Dimara Kusuma Gernowo, Rahmat Nirwansyah, Anang Widhi Haryanto, Totok Department of Informatics Universitas Muhammadiyah Purwokerto 53182 Indonesia Department of Physic Faculty of Mathematics and Natural Sciences Diponegoro University Indonesia Department of Geography Education Universitas Muhammadiyah Purwokerto 53182 Indonesia Bachelor of Management Program Faculty of Economics and Business Universitas Muhammadiyah Purwokerto 53182 Indonesia

Conventional flood hazard maps depict a static perspective on flood risk and are helpful in flood forecasting. Then, time-series flood forecasting research was developed and widely conducted. Still, it has limitations because the univariate method used does not consider the influence of other variables, unlike regression. Also, the determination of flood hazard weights, which is usually conducted through empirical studies using AHP, has a good level of accuracy but has the drawback of expert subjectivity. A new approach is proposed for flood hazard forecasting, combining the Triple ES method with NN and determining weights in a data-driven manner using NN. A combination of time series and regression methods (significantly non-linear), namely Triple ES with NN, results in good accuracy with an MSE value of 3.03, MAE 1.20, RMSE 1.74, R2 0.54, and a MAPE of 32.93%. The evaluation results for flood hazard weight determination with an MSE of 0.0111 and a MAPE of 7.81% show promising results, and the weights can be used in Hazard Flood GIS. Visualization in the form of a GIS Hazard Map can be done after all related raster data have been combined. The computational outcomes, particularly the MSE and MAPE values, demonstrate the effectiveness of the proposed approach, providing a clear understanding of the model's performance. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Linear regression

来源：评论

学校读者我要写书评

暂无评论

YNote: A Novel Music Notation for Fine-Tuning LLMs in Music Generation

arXiv

引用

arXiv 2025年

作者： Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Hsu, Chun-Chieh Hsu, Tsai-Ling Wu, Cheng-Han Shih, Timothy K. Lin, Yu-Cheng Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs. These formats are difficult for both machines and humans to interpret due to their variability and intricate structure. To address these challenges, we introduce YNote, a simplified music notation system that uses only four characters to represent a note and its pitch. YNote’s fixed format ensures consistency, making it easy to read and more suitable for fine-tuning LLMs. In our experiments, we fine-tuned GPT-2 (124M) on a YNote-encoded dataset and achieved BLEU and ROUGE scores of 0.883 and 0.766, respectively. With just two notes as prompts, the model was able to generate coherent and stylistically relevant music. We believe YNote offers a practical alternative to existing music notations for machine learning applications and has the potential to significantly enhance the quality of music generation using LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

引用

International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Catur Supriyanto Abu Salam Junta Zeniarja Adi Wijaya Bachelor Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Department of Health Information Management Universitas Indonesia Maju Jakarta Indonesia

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset’s size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving the quality of communicating with dementia patients: A virtual reality-based simulated communication approach

引用

British Journal of Educational Technology 2025年第1期56卷 167-189页

作者： Lin, Hui-Chen Huang, Hsin Tsai, Chia-Kuang Chang, Shao-Chen School of Nursing College of Nursing Taipei Medical University Taipei Taiwan Research Center in Nursing Clinical Practice Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Nursing Wan Fang Hospital Taipei Medical University Taipei Taiwan School of Nursing National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Neurology Tri Service General Hospital National Defense Medical Center Taipei Taiwan International Bachelor Program in Informatics and the Department of Information Communication Yuan Ze University Taoyuan Taiwan

Dementia patients may have language barriers and decreased comprehension ability. Their family caregivers can feel frustrated when communicating with them. Poor communication hinders family caregivers from obtaining accurate health information about patients, and may also increase their emotional burden, affecting patient care quality. The present study developed a virtual reality-based simulated communication training (VRSCT) system and applied it to a training course for family caregivers of dementia patients. It allowed family caregivers to simulate real-world situations in a VR environment, experience the daily communication barriers and stress with dementia patients, and apply their acquired knowledge and skills to solve related problems. This study used a randomised control experimental design with mixed analysis methods. A total of 63 family caregivers were recruited and randomly divided into the experimental group (N = 32) learning with the VRSCT system to interact with virtual dementia patients and practice communication skills, and the control group (N = 31) using the traditional role-playing method for practice. Quantitative data were analysed to determine participants' knowledge of dementia care, attitudes, communication confidence and skills. In addition, the qualitative method was used to analyse the participants' discussion records. The results showed that by using the VRSCT approach, participants significantly improved their knowledge of dementia care, attitudes, communication confidence and communication skills compared to the control group. In addition, participants reported that through the real-time feedback of the VRSCT system, they could recognise their previous incorrect communication approach. As a result, they adjusted their communication strategies and increased their self-confidence. Practitioner notes What is already known about this topic Situational simulation helps learners improve their communication skills in a safe environment. Virt

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：