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Development of a Work Management System Based on Speech and Speaker Recognition
Gaybulayev, Abdulaziz,Yunusov, Jahongir,Kim, Tae-Hyong Institute of Embedded Engineering of Korea 2021 대한임베디드공학회논문지 Vol.16 No.3
Voice interface can not only make daily life more convenient through artificial intelligence speakers but also improve the working environment of the factory. This paper presents a voice-assisted work management system that supports both speech and speaker recognition. This system is able to provide machine control and authorized worker authentication by voice at the same time. We applied two speech recognition methods, Google's Speech application programming interface (API) service, and DeepSpeech speech-to-text engine. For worker identification, the SincNet architecture for speaker recognition was adopted. We implemented a prototype of the work management system that provides voice control with 26 commands and identifies 100 workers by voice. Worker identification using our model was almost perfect, and the command recognition accuracy was 97.0% in Google API after post- processing and 92.0% in our DeepSpeech model.
Development of a Low-cost Industrial OCR System with an End-to-end Deep Learning Technology
Subedi, Bharat,Yunusov, Jahongir,Gaybulayev, Abdulaziz,Kim, Tae-Hyong Institute of Embedded Engineering of Korea 2020 대한임베디드공학회논문지 Vol.15 No.2
Optical character recognition (OCR) has been studied for decades because it is very useful in a variety of places. Nowadays, OCR's performance has improved significantly due to outstanding deep learning technology. Thus, there is an increasing demand for commercial-grade but affordable OCR systems. We have developed a low-cost, high-performance OCR system for the industry with the cheapest embedded developer kit that supports GPU acceleration. To achieve high accuracy for industrial use on limited computing resources, we chose a state-of-the-art text recognition algorithm that uses an end-to-end deep learning network as a baseline model. The model was then improved by replacing the feature extraction network with the best one suited to our conditions. Among the various candidate networks, EfficientNet-B3 has shown the best performance: excellent recognition accuracy with relatively low memory consumption. Besides, we have optimized the model written in TensorFlow's Python API using TensorFlow-TensorRT integration and TensorFlow's C++ API, respectively.
소량 데이터 딥러닝 기반 강판 표면 결함 검출 시스템 개발
게이뷸라예프 압둘라지즈,이나현,이기환,김태형,Gaybulayev, Abdulaziz,Lee, Na-Hyeon,Lee, Ki-Hwan,Kim, Tae-Hyong 대한임베디드공학회 2022 대한임베디드공학회논문지 Vol.17 No.3
Collecting and labeling sufficient training data, which is essential to deep learning-based visual inspection, is difficult for manufacturers to perform because it is very expensive. This paper presents a steel plate surface defect detection system with industrial-grade detection performance by training a small amount of steel plate surface images consisting of labeled and non-labeled data. To overcome the problem of lack of training data, we propose two data augmentation techniques: program-based augmentation, which generates defect images in a geometric way, and generative model-based augmentation, which learns the distribution of labeled data. We also propose a 4-step semi-supervised learning using pseudo labels and consistency training with fixed-size augmentation in order to utilize unlabeled data for training. The proposed technique obtained about 99% defect detection performance for four defect types by using 100 real images including labeled and unlabeled data.