• Design & Manufacturing
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• 제17권1호
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• pp.27-32
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• 2023

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.369-374
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• 2004

Web-based analysis programs for reliability assessment of machine tools were developed in this study. First, the reliability data analysis program was developed to search for failure rate using failure data and reliability test data of mechanical part. Second, failure mode analysis was developed through performance tests like circular movement test vibration test for machine tools. This analysis program shows correlation between failure mode and performance test result. Third, tool life was predicted by correlation between flank wear and cutting time, using the extended Taylor tool life equation in turning data and the equivalently converted equation in order to apply ball endmill data to Taylor tool life equation in milling data. All the information related to input and result data can be stored in theses programs.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• 한국공작기계학회논문집
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• 제14권1호
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• pp.15-23
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• 2005

For reliability assessment for machine tools, failure mode analyses by two viewpoints were studied in this paper. First, this study developed the reliability data analysis program, which searches f3r optimal failure distribution like failure rate or MTBF(Mean Time Between Failure) using failure data and reliability test data of mechanical parts in the web. Moreover, this data analysis program saves both failure data or reliability data and their failure rate or MTBF for database establishment. Second, this paper conducted failure mode analysis through such performance tests as circular movement test and vibration testing for machine tools when reliability data is not available. A developed web-based analysis program shows correlations between failure mode and performance test result and also accumulates all the data. These kinds of data analysis programs and stored data furnish valuable information for improving the reliability of mechanical system.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.157-163
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• 2023

Recently, as businesses and data types become more complex and diverse, efficient data analysis using machine learning is required. However, since communication in the cloud environment is greatly affected by network latency, data analysis is not smooth if information delay occurs. In this paper, SPT (Safe Proper Time) was applied to the cluster-based machine learning data analysis agent proposed in previous studies to solve this delay problem. SPT is a method of remotely and directly accessing memory to a cluster that processes data between layers, effectively improving data transfer speed and ensuring timeliness and reliability of data transfer.

• Journal of the Korean Data and Information Science Society
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• 제12권2호
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• pp.83-93
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• 2001

We investigate a kernel approach to discriminant analysis for binary classification as a machine learning point of view. Our view of the kernel approach follows support vector method which is one of the most promising techniques in the area of machine learning. As usual discriminant analysis, the kernel method can discriminate an object most likely belongs to. Moreover, it has some advantage over discriminant analysis such as data compression and computing time.

• Journal of Multimedia Information System
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• 제5권3호
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

• 응용통계연구
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• 제29권6호
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• pp.1107-1116
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• 2016

최근의 탐사 천문학 관측으로부터 대용량 관측 자료가 획득되면서, 기존의 일상적인 자료 분석 방법에 큰 변화가 있었다. 고전적인 통계적인 추론과 더불어 기계학습 방법들이, 자료의 표준화로부터 물리적인 모델을 추론하는 단계까지 자료 분석의 전 과정에서 활용되어 왔다. 적은 비용으로 대형 검출 기기들을 이용할 수 있게 되고, 더불어서 고속의 컴퓨터 네트워크를 통해서 대용량의 자료들을 쉽게 공유할 수 있게 되면서, 기존의 다양한 천문학 자료 분석의 문제들에 대해서 기계학습을 활용하는 것이 보편화되고 있다. 일반적으로 대용량 천문학 자료의 분석은, 자료의 시간과 공간 분포가 가지는 비 균질성 때문에 야기되는 효과를 고려해야 하는 문제를 가진다. 오늘날 증가하는 자료의 규모는 자연스럽게 기계학습의 활용과 더불어 병렬 분산 컴퓨팅을 필요로 하고 있다. 그러나 이러한 병렬 분산 분석 환경의 일반적인 자료 분석에서의 활용은 아직 활발하지 않은 상황이다. 천문학에서 기계학습을 사용하는데 있어서, 충분한 학습 자료를 관측을 통해 획득하는 것이 어렵고, 그래서 다양한 출처의 자료를 모아서 학습 자료를 수집해야 는 것이 일반적이다. 따라서 앞으로 준 지도학습이나 앙상블 학습과 같은 방법의 역할이 중요해 질 것으로 예상된다.

• 유공압시스템학회논문집
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• 제8권4호
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• pp.24-31
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• 2011

Double-color Injection molding machine is the assembly of many kinds of mechanical, fluid power part and electric electronic control system. From in these, fluid power is a part where becomes the first core of this machine. Fluid power systems of double-color injection molding machine are modelled and analyzed using a commercial program AMESim. Partial system models which is divided according to functional operation are made and its analysis results shows how design parameters work on operational characteristics like pressure, flow rates, displacement at each node and so on. Analysis modeling and compared the data which gets from experiment and the analysis result which has a reliability got data. The results made by analysis will be used design of fluid power circuit for developing a double-color injection molding machine.