• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• 통합자연과학논문집
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• 제14권2호
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• pp.50-56
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• 2021

Due to the recent increase in the mobile streaming market, mobile traffic is increasing exponentially. IMT-2020, named as the next generation mobile communication standard by ITU, is called the 5th generation mobile communication (5G), and is a technology that satisfies the data traffic capacity, low latency, high energy efficiency, and economic efficiency compared to the existing LTE (Long Term Evolution) system. 5G implements this technology by utilizing a high frequency band, but there is a problem of path loss due to the use of a high frequency band, which is greatly affected by system performance. In this paper, small cell technology was presented as a solution to the high frequency utilization of 5G mobile communication system, and furthermore, the system performance was improved by applying machine learning technology to macro communication and small cell communication method decision. It was found that the system performance was improved due to the technical application and the application of machine learning techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.702-723
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• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.

• 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.

• 한국정보통신학회논문지
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• 제24권2호
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• pp.225-230
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• 2020

기계학습은 데이터를 기반으로 한 컴퓨터를 학습시켜 컴퓨터 스스로 데이터의 경향성을 파악하게 하여 새로운 입력 데이터의 출력을 예측하도록 하는 알고리즘이다. 기계학습은 크게 지도학습, 비지도학습, 강화학습으로 나눌 수 있다. 지도학습은 데이터에 대한 레이블이 주어진 상태로 기계를 학습시키는 방법이다. 즉, 데이터 및 레이블의 쌍을 통해 해당 시스템의 함수를 추론하는 방법으로 새로운 입력 데이터에 대해서 추론한 함수를 이용하여 결과를 예측한다. 그리고 예측하는 결과 값이 연속 값이면 회귀분석, 예측하는 결과 값이 이산 값이면 분류로 사용된다. 새로운 붓꽃 데이터 Sepal length(5.01)과 Sepal width(3.43)을 이용하여 기초 데이터와 유클리드 거리를 분석하였다. 분석결과, 테이블 3의 8번(5, 3.4, setosa), 27번(5, 3.4, setosa), 41번(5, 3.5, setosa), 44번(5, 3.5, setosa) 그리고 40번(5.1, 3.4, setosa)의 데이터 순으로 유사도가 높은 붓꽃으로 분류되었다. 따라서 이론적 실무적 시사점을 제시하였다.

• 한국안전학회지
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• 제35권2호
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• pp.94-99
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• 2020

Bikers can be subjected to injuries from unexpected accidents even if they wear basic helmets. A properly designed airbag can efficiently protect the critical areas of the human body. This study introduces a wearable smart airbag system using machine learning techniques to protect human neck and shoulders. When a bicycle accident happens, a microprocessor analyzes the biker's motion data to recognize if it is a critical accident by comparing with accident classification models. These models are trained by a variety of possible accidents through machine learning techniques, like k-means and SVM methods. When the microprocessor decides it is a critical accident, it issues an actuation signal for the gas inflater to inflate the airbag. A protype of the wearable smart airbag with the machine learning techniques is developed and its performance is tested using a human dummy mounted on a moving cart.

• 한국멀티미디어학회논문지
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• 제20권6호
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• pp.927-934
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• 2017

As the technology of drone develops, the use of drone is increasing, In addition, the types of sensors that are inside of smart phones are becoming various and the accuracy is enhancing day by day. Various of researches are being progressed. Therefore, we need to control drone by using smart phone's sensors. In this paper, we propose the most suitable machine learning model that matches the gyro sensor data with drone's moving. First, we classified drone by it's moving of the gyro sensor value of 4 and 8 degree of freedom. After that, we made it to study machine learning. For the method of machine learning, we applied the One-Rule, Neural Network, Decision Tree, and Navie Bayesian. According to the result of experiment that we designated the value from gyro sensor as the attribute, we had the 97.3 percent of highest accuracy that came out from Naive Bayesian method using 2 attributes in 4 degree of freedom. On and the same, in 8 degree of freedom, Naive Bayesian method using 2 attributes showed the highest accuracy of 93.1 percent.

• 방송공학회논문지
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• 제22권2호
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• pp.253-256
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• 2017

본 논문에서는 기계학습 기반의 분류 방법을 이용하여 장애 음성을 검출하고자 한다. 음성 장애 중 마비말 장애는 뇌성마비, 파킨슨 질환, 뇌졸중 등 주로 뇌질환에 의해 발생하는 것으로 알려져 있다. 이러한 장애 음성을 검출함으로써 뇌졸중 등의 급성 뇌질환 발생에 대한 조기 처치가 가능하다. 장애 음성 검출은 입력 음성에 대한 특징벡터 추출과 기계학습을 이용한 분류과정을 통해 이루어질 수 있다. 실험을 위해서 장애 음성 DB인 TORGO 데이터를 사용하였으며, 10가지 기계학습 알고리즘과 다양한 특징벡터 스케일링 방법에 대해 장애 음성 검출 성능을 평가하였다.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• 한국산업융합학회 논문집
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• 제27권2_1호
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• pp.257-268
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• 2024

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. This paper utilizes various supervised classification machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes. Moreover, based on our finding the KNN and Decision models achieved the highest accuracy rates at 70%, surpassing other models. The Random Forest model followed closely with an accuracy score of 62%. Among the evaluated models, the Naive Bayes model demonstrated the lowest accuracy at 56%. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.