• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.455-464
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• 2022

There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.449-451
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• 2021

Cyber-attacks targeting endpoints have developed sophisticatedly into targeted and intelligent attacks, Advanced Persistent Threat (APT) targeting the Industrial Internet of Things (IIoT) has increased accordingly. Machine learning-based Endpoint Detection and Response (EDR) solutions combine and complement rule-based conventional security tools to effectively defend against APT attacks are gaining attention. However, universal EDR solutions have a high false positive rate, and needs high-level analysts to monitor and analyze a tremendous amount of alerts. Therefore, the process of optimizing machine learning-based EDR solutions that consider the characteristics and vulnerabilities of IIoT environment is essential. In this study, we analyze the flow and impact of IIoT targeted APT cases and compare the method of machine learning-based APT detection EDR solutions.

• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.17-25
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• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.823-826
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• 2021

The recent COVID-19 pandemic has accentuated the need for faster and more accurate ways of diagnosing certain diseases for there to be safer and more effective early responses that help to prevent a total outbreak. In this work, we would like to approach this issue through machine learning algorithms to investigate whether or not they could serve as a viable replacement for conventional diagnosis. Through a process of training and testing various algorithms, we analyzed how successfully they can predict a patient's COVID-19 diagnosis based on a list of symptoms and also identified which algorithm is the most effective at doing so. If the necessary data, containing the symptoms and diagnoses of different cases, is provided, this method can be utilized to make a probable diagnosis of any disease besides COVID-19. This method can be used in conjunction with or in lieu of conventional diagnosis depending on the situation: if there is a lack of testing facilities or test kits, this method can be employed as it is inexhaustible and it could also be used in situations where a conventional diagnosis is proven to be inaccurate.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1413-1415
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• 2004

This paper proposes a SVM(Support Vector Machine) training algorithm to control a service robot with voice command. The service robot with a stereo vision system and dual manipulators of four degrees of freedom implements a User-Dependent Voice Control System. The training of SVM algorithm that is one of the statistical learning theories leads to a QP(quadratic programming) problem. In this paper, we present an efficient SVM speech recognition scheme especially based on less learning data comparing with conventional approaches. SVM discriminator decides rejection or acceptance of user's extracted voice features by the MFCC(Mel Frequency Cepstrum Coefficient). Among several SVM kernels, the exponential RBF function gives the best classification and the accurate user recognition. The numerical simulation and the experiment verified the usefulness of the proposed algorithm.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• Journal of information and communication convergence engineering
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• v.22 no.3
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• pp.199-206
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• 2024

The network security of Plug-and-Charge (PnC) technology in electric vehicle charging systems is typically achieved through the well-known Transport Layer Security (TLS) protocol, which causes high communication overhead. To reduce this overhead, a differential authentication method employing different schemes for individual users has been proposed. However, decisions use a simple threshold approach and no quantitative performance evaluation should be made. In this study, we determined each user's trust using several machine learning algorithms with their charging patterns and compared them. The experimental results reveal that the proposed approach outperforms the conventional approach by 41.36% in terms of round-trip time efficiency, demonstrating its effectiveness in reducing the TLS overhead. In addition, we show the simulation results for three user authentication methods and capture the performance variations under CPU busy waiting scenarios.