• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.760-761
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• 2019

Smart manufacturing is a process of integrating computer-related technologies in production and by doing so, achieving more efficient production management. The recent development of supercomputers has led to the broad utilization of artificial intelligence (AI) and machine learning techniques useful in predicting specific patterns. Despite the usefulness of AI and machine learning techniques in smart manufacturing processes, there are many fundamental issues with the direct deployment of these technologies related to data management. In this paper, we focus on solving the outlier detection issue in smart manufacturing applications. More specifically, we apply a state-of-the-art outlier detection technique, called Elliptic Envelope, to detect anomalies in simulation-based collected data.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.199-203
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• 2019

In this paper, we propose the efficient impulsive noise mitigation scheme for power line communication (PLC) systems in smart grid applications. The proposed scheme estimates the channel impulsive noise information of receiver by applying machine learning. Then, the estimated impulsive noise is updated in data base. In the modulator, the impulsive noise which reduces the PLC performance is effectively mitigated through proposed technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the conventional model. As a result, the proposed noise mitigation improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC systems for smart grid.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 추계학술대회
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• pp.15-15
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• 2017

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.142-142
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• 2022

• 한국융합학회논문지
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• 제1권1호
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• pp.49-55
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• 2010

정보통신기술 발전에 따라 많은 장치들간의 통신과 네트워킹의 수용이 이뤄지고 있다. 장치간의 통신을 위한 융합 사업이 빠르게 발전되어지고 있다. IT 융합 통신은 무선통신에서 차후 개척분야의 하나로 여겨지고 있다. 본 논문에서는 IT 융합 구조에서 M2M, 지능형 자동차, 스마트그리드, U-헬스케어에 대한 보안 위협요소를 분석하였다. 임베디드 시스템 보안, 포렌식 보안, 사용자 인증과 키관리 기법에 대한 IT 융합 보안의 방향을 제안하였다.

• 스마트미디어저널
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• 제5권3호
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• pp.30-34
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• 2016

Nowadays, there are a lot of Korean documents, which often need to be identified in one of printed or handwritten text. Early methods for the identification use structural features, which can be simple and easy to apply to text of a specific font, but its performance depends on the font type and characteristics of the text. Recently, the bag-of-words model has been used for the identification, which can be invariant to changes in font size, distortions or modifications to the text. The method based on bag-of-words model includes three steps: word segmentation using connected component grouping, feature extraction, and finally classification using SVM(Support Vector Machine). In this paper, bag-of-words model based method is proposed using SURF(Speeded Up Robust Feature) for the identification of machine printed and handwritten text in Korean documents. The experiment shows that the proposed method outperforms methods based on structural features.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.16-30
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• 2023

The elderly population is increasing owing to a low fertility rate and an aging population. In addition, life expectancy is increasing, and the advancement of medicine has increased the importance of health to most people. Therefore, government and companies are developing and supporting smart healthcare, which is a health-related product or industry, and providing related services. Moreover, with the development of the Internet, many people are managing their health through online searches. The most convenient way to achieve such management is by consuming nutritional supplements or seasonal foods to prevent a nutrient deficiency. However, before implementing such methods, knowing the nutrient status of the individual is difficult, and even if a test method is developed, the cost of the test will be a burden. To solve this problem, we developed a questionnaire related to nutrient classification twice, based upon which an adaptive algorithm was designed. This algorithm was designed as a machine learning based algorithm for nutrient classification and its accuracy was much better than the other machine learning algorithm.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• IEIE Transactions on Smart Processing and Computing
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• 제5권1호
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• pp.17-21
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• 2016

This paper investigates the importance of the computational overhead when machine learning methods, such as SVM, LASSO, AdaBoosting and AdaBagging, are used for automatic security classification.