• 컴퓨터교육학회논문지
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• 제21권6호
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• pp.27-37
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• 2018

본 논문에서는 임베디드 시스템의 어플리케이션 계층 소프트웨어 학습 도구로 사용할 수 있는 엘리베이터 시뮬레이터의 설계 및 개발 방법과 이를 이용한 교수학습방법을 제안한다. 본 시뮬레이터는 학생들에게 하드웨어와 임베디드 OS 계층의 이슈를 배제한 어플리케이션 계층에서 엘리베이터 시스템의 동작 원리와 제어 방법을 소프트웨어로 구현할 수 있는 환경을 제공하여, 반응(reactive)적이며 실시간(real-time)적인 특징을 갖는 임베디드 어플리케이션 개발 경험을 가질 수 있도록 한다. 아울러 본 논문에서는 시뮬레이터를 이용하여 단계별로 난이도가 높아지는 실습이 포함된 4주간의 임베디드 어플리케이션 소프트웨어 교육 과정을 제시하고, 실제 학생들을 대상으로 교육을 진행한 결과 학습 성취도 점수 83.3점을 얻어 본 교육 과정이 임베디드 어플리케이션 학습에 유의미한 효과가 있음을 입증하였다.

• 디지털산업정보학회논문지
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• 제17권3호
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• 대한임베디드공학회논문지
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• 제15권6호
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• pp.307-316
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• 2020

Machine learning model data is highly affected by performance. preprocessing is needed to enable analysis of various types of data, such as letters, numbers, and special characters. This paper proposes a development environment that aims to process categorical and continuous data according to the type of missing values in stage 1, implementing the function of selecting the best performing algorithm in stage 2 and automating the process of checking model performance in stage 3. Using this model, machine learning models can be created without prior knowledge of data preprocessing.

• 대한임베디드공학회논문지
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• 제14권4호
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• pp.207-218
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• 2019

The purpose of this study is to develop a model that can systematically study the whole learning process of machine learning. Since the existing model describes the learning process with minimum coding, it can learn the progress of machine learning sequentially through the new model, and can visualize each process using the tensor flow. The new model used all of the existing model algorithms and confirmed the importance of the variables that affect the target variable, survival. The used to classification training data into training and verification, and to evaluate the performance of the model with test data. As a result of the final analysis, the ensemble techniques is the all tutorial model showed high performance, and the maximum performance of the model was improved by maximum 5.2% when compared with the existing model using. In future research, it is necessary to construct an environment in which machine learning can be learned regardless of the data preprocessing method and OS that can learn a model that is better than the existing performance.

• 대한임베디드공학회논문지
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• 제15권1호
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• pp.1-8
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• 2020

A derivation method for a quantized gradient for machine learning on an embedded system is proposed, in this paper. The proposed differentiation method induces the quantized gradient vector to an objective function and provides that the validation of the directional derivation. Moreover, mathematical analysis shows that the sequence yielded by the learning equation based on the proposed quantization converges to the optimal point of the quantized objective function when the quantized parameter is sufficiently large. The simulation result shows that the optimization solver based on the proposed quantized method represents sufficient performance in comparison to the conventional method based on the floating-point system.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권7호
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• pp.417-423
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• 2017

최근, 딥러닝을 사용 가능한 임베디드 디바이스가 상용화됨에 따라 임베디드 시스템 영역에서도 딥러닝 활용에 대한 다양한 연구가 진행되고 있다. 그러나 임베디드 시스템을 고성능 PC 환경과 비교하면 상대적으로 저사양의 CPU/GPU 프로세서와 메모리를 탑재하고 있으므로 딥러닝 기술의 적용에 있어서 많은 제약이 있다. 본 논문에서는 다양한 최신 딥러닝 네트워크들을 임베디드 디바이스에 적용했을때의 성능을 시간과 전력이라는 관점에서 실험적으로 평가한다. 또한, 호스트 CPU와 GPU 디바이스간의 메모리를 공유하는 임베디드 시스템들의 아키텍처적인 특성을 이용하여 메모리 복사를 줄임으로써 실시간 성능과 저전력성을 높이는 방법을 제시한다. 제안된 방법은 대표적인 공개 딥러닝 프레임워크인 Caffe를 수정하여 구현되었으며, 임베디드 GPU를 탑재한 NVIDIA Jetson TK1에서 성능평가 되었다. 실험결과, 대부분의 딥러닝 네트워크에서 뚜렷한 성능향상을 관찰할 수 있었다. 특히, 메모리 사용량이 높은 AlexNet에서 약 33%의 이미지 인식 속도 단축과 50%의 소비 전력량 감소를 관찰할 수 있었다.

• 한국산학기술학회논문지
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• 제11권9호
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• pp.3417-3421
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• 2010

USN은 인간의 생활공간, 생활기기, 기계 등 모든 사물에 컴퓨팅 및 네트워킹 기능을 부여하여 환경과 상황의 자동인지를 통해 사용자에게 최적의 서비스를 가능하게 함으로써 인간생활의 편리성과 안정성을 고도화 하는 기술이라 할 수 있다. 본 논문에서는 이와 같은 USN기술을 이용하여 학습자의 학습환경을 실시간으로 파악하여 이를 기초로 최적의 학습환경으로 만들어주기 위한 학습지원시스템을 임베디드 시스템 기반으로 개발한다.

• 한국기계가공학회지
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• 제7권4호
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• pp.82-95
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• 2008

As society has changed to being more knowledge-based, it is necessary that change of paradigm is incorporated into engineering education and the education goals and the assessment method of educational outcomes is developed to promptly meet the needs of the times. A purpose of this study is to measure learning outcomes in coursework of engineering college every semester, which ultimately provides to validate program outcomes. We looked into teaching-learning style of course in the engineering college and analyzed its grade method and tool. By use of a survey, we derived a reasonable method to measure for the learning outcomes in course and presented tools for course-embedded assessment to measure that learning outcomes had been tied to their objectives. These tools are effective to determine that program outcomes and education goals have been achieved, ultimately. In addition, it will help that instruction builds a loop system for better.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.100-104
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• 2020

Over the past several years, deep learning has been widely used for feature extraction in image and video for various applications such as object classification and facial recognition. This paper introduces an implantation of embedded Linux system for embossed digits recognition using CNN based deep learning methods. For this purpose, we implemented a coin recognition system based on deep learning with the Keras open source library on Raspberry PI. The performance evaluation has been made with the success rate of coin classification using the images captured with ultra-wide angle camera on Raspberry PI. The simulation result shows 98% of the success rate on average.