Ying-Chia Huang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ying-Chia Huang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Bo-Siang Chen
Department of Vehicle Engineering, Nan Kai University of Technology, No. 568, Zhongzheng Rd., Caotun Township, Nantou City 542020, Taiwan
1 shared publication - Hsin-Jung Tsai
Department of Electrical and Mechanical Technology, National Changhua University of Education Bao-Shan Campus, No. 2, Shi-Da Road, Changhua City 500208, Taiwan
1 shared publication - Hui-Ting Liang
Department of Finance, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd., Changhua 500208, Taiwan
1 shared publication - Tzu-Hsin Chu
Department of Electrical and Mechanical Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Road, Changhua City 500208, Taiwan
1 shared publication - Wei-Lun Huang
Medical Affairs Office, National Taiwan University Hospital, Taipei City, Taiwan
1 shared publication - Wei-Sho Ho
Department of Electrical and Mechanical Technology, National Changhua University of Education, Changhua City, Taiwan
1 shared publication
Latest Publications
- Open Access
Development of an Automotive Electronics Internship Assistance System Using a Fine-Tuned Llama 3 Large Language Model
This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10−5 across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ2 = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills.
Open AccessDevelopment of an Automotive Electronics Internship Assistance System Using a Fine-Tuned Llama 3 Large Language Model
This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10−5 across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ2 = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills.