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A LoRaWAN Enabled Sensor-Based System for Detecting Rodent Activity 21

A LoRaWAN Enabled Sensor-Based System for Detecting Rodent A...

引用

21st International System-on-Chip Design Conference, ISOCC 2024

作者： Wang, Szu-Ting Wu, Shang-Sian Tseng, Kuo-Cheng Hsu, Li-Chuan Ke, Song-Min Lai, Shin-Chi National Formosa University The Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering & Smart Machinery Intelligent Manufacturing Research Center Huwei Taiwan

ISBN: (纸本)9798350377088

This study presents the design and implementation of a sensor-based system for detecting rodent activity. The proposed system utilizes infrared (IR) receivers for detecting rodent presence and can incorporate IP cameras to capture intruder images. Data collected by sensor nodes are transmitted to a database via Wi-Fi for web access. The system employs a reflective-type IR sensor comprising an emitter and a receiver aligned in the same direction to discern approaching or receding objects. Power-efficient operation is ensured through the use of the Heltec Wireless Stick Lite MCU and LoRa transceivers for data transmission. Results demonstrate the system's effectiveness in monitoring rodent behavior, with power consumption averaging 67.4mA to 69.5mA, supported by two 18650 batteries for up to 4 days of continuous operation. During the 18-day experiment duration, a total of 1,453 instances were recorded. © 2024 IEEE.

关键词： Wi-Fi

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Design of a Multilingual Translation System with Personalized Voice Characteristics 21

Design of a Multilingual Translation System with Personalize...

引用

21st International System-on-Chip Design Conference, ISOCC 2024

作者： Chen, Chen-Hua Lai, Guan-Yu Lee, Chuan Wu, Chang-Yu Liu, Kuan-Lin Lai, Shin-Chi Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. of Automation Engineering Huwei Taiwan National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan

ISBN: (纸本)9798350377088

This study combines AI voice synthesis with IoT to create a real-time multilingual translation system. Front-end devices capture the speaker's voice, which is then played back in both original and translated forms, enabling consecutive interpreting. This system is ideal for multilingual scenarios. After digitization, audio signals undergo speech-to-text conversion, multilingual translation, and text-to-speech processing. Speech synthesis matches the speaker's voice characteristics, allowing listeners to hear the translation in the speaker's voice. Both voice-to-text conversion and translated audio playback use Wi-Fi for data transmission, facilitating future applications. In validation, the average translation and transmission time for each speech segment were 410 ms and 3.38 s, respectively. The synthesized voice similarity analysis achieved an accuracy of 88%, showcasing the method's potential for system integration and development. © 2024 IEEE.

关键词： Wi-Fi

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

引用

21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Insect Classification Using Spatial Pyramid Pooling and Convolution Neural Network

Insect Classification Using Spatial Pyramid Pooling and Conv...

引用

IEEE Asia-Pacific Conference on Circuits and Systems

作者： Chang-Yu Wu Song-Min Ke Hoh-Siang Liao Chen-Hua Chen Ying-Hsiu Hung Shin-Chi Lai Department of Automation Engineering Master’s Program National Formosa University Huwei Taiwan Department of Automation Engineering National Formosa University Huwei Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

This study aims to enhance the accuracy and efficiency of insect classification using spatial pyramid pooling convolutional neural networks (SPP-CNN). Improving traditional convolutional neural networks has resulted in poor performance when dealing with the diversity of insects and images of various sizes. The dataset, provided by a collaborating company, includes six different insect categories. Given the relatively limited number of samples in the existing dataset, we employed various data augmentation techniques to enrich our training samples, such as sharpening, adding noise, resizing, adjusting brightness, and randomly rotating images. Experiments demonstrate that SPP-CNN shows superior performance in insect classification tasks compared to efficientNetV2. The proposed architecture has achieved an accuracy rate of $83.8 \%$, which is $3.9 \%$ higher than the $79.9 \%$ accuracy of EfficientNetV2 architectures. This improvement indicates that incorporating the spatial pyramid pooling layer significantly enhances the accuracy of insect image classification. Moreover, the adaptability of SPP-CNN to various image resolutions without the need for retraining significantly boosts its practicality for real-world applications.

关键词： Training Accuracy Image resolution Insects Noise Neural networks Data models Convolutional neural networks Integrated circuit modeling Image classification

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Intelligent Status Diagnosis System for Water Circulation Device 10

Intelligent Status Diagnosis System for Water Circulation De...

引用

10th International Conference on Applied System Innovation, ICASI 2024

作者： Lee, Jeng-Dao Ye, Yu-Cheng Ho, Chi-Ting Liu, Guan-Zhi Chen, Lin-Yin Hung, Ying-Hsiu Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Department of Automation Engineering Yunlin Hu-Wei Taiwan National Formosa University Department of Mechanical Design Engineering Yunlin Hu-Wei Taiwan Doctor's Program of Smart Industry Technology Research and Design National Formosa University No. 64 Wenhua Road Hu-Wei Town. Yunlin Hu-Wei Taiwan

ISBN: (纸本)9798350394924

This study employs AI to classify the vibration states of equipment during operation to ensure the health of the equipment. Initially, a vibration simulation system was established in the laboratory using a servo motor. The digitized data measured by a vibration sensor was transmitted to a PC and stored in SQLite. The vibration data was then applied to a Temporal Convolutional Network (TCN) model for training. After completing the capability of status identification in the vibration simulation system, the model was applied to the water-pumping motor of the water circulation system in the Wire Electrical Discharge Machine (WEDM). The intelligent vibration status diagnosis system identified whether the operation of the WEDM water circulation system was normal at different water-pumping motor speeds. This reduces the need for manual equipment fault detection and allows for immediate detection of motor status, minimizing accidents and product losses. © 2024 IEEE.

关键词： Fault detection

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A LoRaWAN Enabled Sensor-Based System for Detecting Rodent Activity

A LoRaWAN Enabled Sensor-Based System for Detecting Rodent A...

引用

International SoC Design Conference, ISOCC

作者： Szu-Ting Wang Shang-Sian Wu Kuo-Cheng Tseng Li-Chuan Hsu Song-Min Ke Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

关键词： Wireless communication Wireless sensor networks Rodents Receivers Cameras Transceivers IP networks Reliability Monitoring Wireless fidelity

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Developing a Floating Robot for Mechanical Control of Water Hyacinth 3

Developing a Floating Robot for Mechanical Control of Water ...

引用

3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Elsayed, Mostafa E.A. Mohamed, Ahmed K.A. Shaaban, Mahmoud Abdelrahman, Mohamed A. Ahmed, Mohamed E. A. E. Rashed, Mostafa R. Benha University Automation and Energy Technology Lab. Shoubra Faculty of Engineering Mechanical Engineering Department Cairo Egypt Benha University Agricultural Power and Machinery Faculty of Agriculture Department of Agricultural Engineering Kalubia Egypt School of Engineering and Applied Sciences Nile University Smart Engineering Systems Center Mechanical Engineering Program Giza Egypt Benha University Combustion and Energy Technology Lab. Shoubra Faculty of Engineering Mechanical Engineering Department Cairo Egypt Benha University Shoubra Faculty of Engineering Mechanical Engineering Department Cairo Egypt University for Technology and Information Faculty of Engineering Modern Mechanical Engineering Department Cairo Egypt

ISBN: (纸本)9798350306231

Water hyacinth has become widespread in freshwater bodies in several countries, leading to various issues, particularly its substantial water consumption and the resulting need to control its growth. Among the different methods of controlling water hyacinth, mechanical control stands out as the most effective and environmentally friendly approach. A proposed harvester robot has been developed, capable of efficiently cutting and collecting water hyacinth from the water's surface. The robot boasts several advantages, including its ability to operate in narrow water paths and lakes, use clean energy sources to preserve the water ecology, employ a GPS module for safety and error detection, and generate maps for evaluating work and predicting annual growth. When compared to other technologies dealing with water hyacinth, this innovative water hyacinth harvesting robot presents a significantly lower cost of 0.01 /kg, making it an optimal solution for addressing water hyacinth problems. © 2023 IEEE.

关键词： Weed control

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Design of a Multilingual Translation System with Personalized Voice Characteristics

Design of a Multilingual Translation System with Personalize...

引用

International SoC Design Conference, ISOCC

作者： Chen-Hua Chen Guan-Yu Lai Chuan Lee Chang-Yu Wu Kuan-Lin Liu Shin-Chi Lai Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan Dept. of Automation Engineering National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

关键词： Accuracy System integration Real-time systems Text to speech Data communication Artificial intelligence Wireless fidelity Speech to text

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

引用

International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Speech Abusive Language Detection System Using MFCC Speech Feature Extraction and Convolutional Neural Network

Speech Abusive Language Detection System Using MFCC Speech F...

引用

Consumer Technology (ICCT-Pacific), International Conference on

作者： Yi-Chang Zhu Ying-Hsiu Hung Yen-Ching Chang Jhen-Kai Tang Wen-Kai Tsai Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Department of Electrical Engineering Master's Program National Formosa University Huwei Taiwan Dept. Electrical Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

As bullying rapidly spreads in schools, many parents are becoming increasingly concerned about their children being subjected to bullying by peers, which could negatively impact their physical and mental health. This paper proposes an abusive language detection system that combines Mel-frequency Cepstrum Coefficients (MFCC) speech features with a Convolutional Neural Network (CNN). Audio files which contain daily abusive phrases in the Minnan dialect are initially preprocesses to eliminate non-speech interference. Subsequently, the MFCC algorithm is applied to extract speech features that closely capture auditory characteristics. The CNN classification model is trained to learn the MFCC features and classify the phrases into their respective categories. Experimental results indicate that the CNN model achieves an average recognition accuracy of 91.9%. Finally, the CNN model was deployed on an embedded platform, where it achieves an average recognition time of 0.54 seconds per phrase and an average recognition accuracy of 86.25%.

关键词： Pediatrics Accuracy Semantics Speech recognition Mental health Speech enhancement Feature extraction Convolutional neural networks Mel frequency cepstral coefficient Optimization

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