• 한국컴퓨터정보학회논문지
• /
• 제24권5호
• /
• pp.149-156
• /
• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

• 한국정밀공학회지
• /
• 제30권6호
• /
• pp.622-628
• /
• 2013

Due to the development of compounding technology, there is a considerable increase in the number of high performance rubbers in the world. Accordingly, there are rapid growing requests about high performance tires such as UHP tire and Run-flat tire. However, it is extremely difficult to investigate the friction coefficient of tire tread rubbers. An alternative solution must be developed with the reliability of high-speed linear friction test machines. The use of friction test machines can be expected to improve rubber friction researches. In this paper, we propose a new kind of high-speed linear friction test machine. We have designed and manufactured various mechanisms for friction tests. The final goals are to design and manufacture friction test machines that can investigate friction coefficients efficiently and rapidly. The performance of the proposed high-speed linear friction test machine is evaluated experimentally; however additional study should be necessary for safer and more reliable experimentation.

• Journal of Magnetics
• /
• 제20권3호
• /
• pp.265-272
• /
• 2015

In recent years, flux switching machines (FSMs) have been an attractive research topic owing to their tremendous advantages of robust rotor structure, high torque, and high power capability suitable for intensive applications. However, most of the investigations are focusing on the inner-rotor structure, which is incongruous for direct drive applications. In this study, high torque and power densities of a new 12S-14P outer-rotor permanent magnet (PM) FSM with a DC excitation coil was investigated based on two-dimensional finite element analysis for in-wheel direct drive electric vehicle (EV). Based on some design restrictions and specifications, design refinements were conducted on the original design machine by using the deterministic optimization approach. With only 1.0 kg PM, the final design machine achieved the maximum torque and power densities of 12.4 Nm/kg and 5.93 kW/kg, respectively, slightly better than the inner-rotor HEFSM and interior PM synchronous machine design for EV.

• Nuclear Engineering and Technology
• /
• 제47권5호
• /
• pp.624-632
• /
• 2015

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable.

• 한국전자통신학회논문지
• /
• 제14권5호
• /
• pp.943-950
• /
• 2019

머신 러닝은 인력을 대체함으로써 업무 효율성을 크게 높일 수 있다. 특히 4차 산업혁명 시대의 요청에 따라 인공지능을 포함한 머신 러닝의 중요성은 점점 커지고 있다. 본 논문은 MLP, RNN, LSTM, ANFIS 신경망 알고리즘 이용하여, 월별 전력 거래량을 예측한다. 본 논문에서는 통계청에서 제공하는 월별 전력 거래량과 월별 전력 거래금액, 최종에너지 소비량, 자동차용 경유 가격에 대한 2001~2017년까지의 공공 데이터를 사용하였다. 본 논문은 제시하는 각각의 알고리즘들을 학습시키고, 알고리즘이 예측하는 시계열 그래프를 이용하여 예측 결과를 보여주고 RMSE를 이용하여 이들 중에서 가장 우수한 알고리즘 제시한다.

• 한국기계가공학회지
• /
• 제18권5호
• /
• pp.37-47
• /
• 2019

Recently, as demand for various paper containers has rapidly grown, it is inevitable that paper cup-forming machines have increased their manufacturing speed. However, the faster manufacturing speed naturally brings more frequent manufacturing failures, which decreases manufacturing efficiency. As such, it is necessary to develop a system that monitors the failures in real time and diagnoses the failure progress in advance. In this research, a paper cup-forming machine diagnosis system was developed. Three major failure targets, paper deviation, temperature failure, and abnormal vibration, which dominantly affect the manufacturing process when they occur, were monitored and diagnosed. To evaluate the developed diagnosis system, extensive experiments were performed with the actual data gathered from the paper cup-forming machine. Furthermore, the desired system validation was obtained. The proposed system is expected to anticipate and prevent serious promising failures in advance and lower the final defect rate considerably.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권1호
• /
• pp.38-59
• /
• 2022

Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.

• Communications for Statistical Applications and Methods
• /
• 제29권2호
• /
• pp.177-191
• /
• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• 패션비즈니스
• /
• 제16권6호
• /
• pp.21-35
• /
• 2012

In consideration of the injuries and deaths occurring at manufacturing sites due to the use of inappropriate work clothes or safety devices, this study aims to categorize manufacturing work processes to develop functional work clothes for heavy industries including the automobile, machine and shipbuilding industries in South Korea. Defining the features of the work environments and work postures of these industries provided for a categorization of the work processes which would enable the development of suitable work clothes for each work process' category. The results of the study based on a questionnaire survey are as follows: Work process category 1, including steel panel pressing and auto body assembly, final inspection (in automobile) and inspection (in machine), requires work clothes with upper body and arm mobility and performance to protect from the toxic fume factor. Work process category 2, consisting of welding (in automobile), cutting-and-forming (in machine) and attachment-and-construction (in shipbuilding), requires clothing elasticity, durability and heat and fire resistance. Work process category 3 comprising welding and grinding in the machine and shipbuilding industries, requires work clothes' tear resistance and elasticity, particularly for lateral bending mobility, and work clothes' sleeves' and pants' hemlines with sealed designs to defend against iron filing penetration, as well as incombustible and heat-resistant material performance. Finally, work process category 4, including painting in machine and shipbuilding, requires work clothes with waterproofing, air permeability, thermal performance, elasticity, durability and abrasion resistance.

• 한국분무공학회지
• /
• 제27권2호
• /
• pp.57-65
• /
• 2022

The purpose of this study is to use machine learning to build a model capable of predicting the flash boiling spray characteristics. In this study, the flash boiling spray was visualized using Shadowgraph visualization technology, and then the spray image was processed with MATLAB to obtain quantitative data of spray characteristics. The experimental conditions were used as input, and the spray characteristics were used as output to train the machine learning model. For the machine learning model, the XGB (extreme gradient boosting) algorithm was used. Finally, the performance of machine learning model was evaluated using R² and RMSE (root mean square error). In order to have enough data to train the machine learning model, this study used 12 injectors with different design parameters, and set various fuel temperatures and ambient pressures, resulting in about 12,000 data. By comparing the performance of the model with different amounts of training data, it was found that the number of training data must reach at least 7,000 before the model can show optimal performance. The model showed different prediction performances for different spray characteristics. Compared with the upstream spray angle and the downstream spray angle, the model had the best prediction performance for the spray tip penetration. In addition, the prediction performance of the model showed a relatively poor trend in the initial stage of injection and the final stage of injection. The model performance is expired to be further enhanced by optimizing the hyper-parameters input into the model.