• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.411-412
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• 2023

Speech emotion recognition(SER) is one of the interesting topics in the machine learning field. By developing SER, we can get numerous benefits. By using a convolutional neural network and Long Short Term Memory (LSTM ) method as a part of Artificial intelligence, the SER system can be built.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.526-527
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• 2023

Speech emotion recognition(SER) is one of the interesting topics in the machine learning field. By developing multi-modal speech emotion recognition system, we can get numerous benefits. This paper explain about fusing BERT as the text recognizer and CNN as the speech recognizer to built a multi-modal SER system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.5
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• pp.275-282
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• 2018

Using isogeometric analysis(IGA) gives more accurate results for higher order mode in eigenvalue problem than using the finite element method(FEM). This is because the FEM has $C^0$ continuity between elements, whereas IGA guarantee $C^{P-1}$ between elements for p-th order basis functions. In this paper, a mode based reduced model is constructed by using IGA and dynamic behavior analysis is performed using this advantage. Craig-Bampton(CB) method is applied to construct the reduced model. Several numerical examples were performed to compare the eigenvalue analysis results for various order of element basis function by applying the IGA and FEM to simple rod analysis. We have confirmed that numerical error increases in the higher order mode as the continuity between elements decreases in the IGA by allowing internal knots multiplicity. The accuracy of the solution can be improved by using the IGA with high inter-element continuity when high-frequency external force acts on the reduced model for dynamic behavior analysis.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.391-398
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• 2023

In this paper, a penalized maximum likelihood estimation (PMLE) method that applies a penalty to increase the accuracy of a basis-screening-based Kriging model (BSKM) is introduced. The maximum order and set of basis functions used in the BSKM are determined according to their importance. In this regard, the cross-validation error (CVE) for the basis functions is employed as an indicator of importance. When constructing the Kriging model (KM), the maximum order of basis functions is determined, the importance of each basis function is evaluated according to the corresponding maximum order, and finally the optimal set of basis functions is determined. This optimal set is created by adding basis functions one by one in order of importance until the CVE of the KM is minimized. In this process, the KM must be generated repeatedly. Simultaneously, hyper-parameters representing correlations between datasets must be calculated through the maximum likelihood evaluation method. Given that the optimal set of basis functions depends on such hyper-parameters, it has a significant impact on the accuracy of the KM. The PMLE method is applied to accurately calculate hyper-parameters. It was confirmed that the accuracy of a BSKM can be improved by applying it to Branin-Hoo problem.

• Electronics and Telecommunications Trends
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• v.27 no.2
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• pp.31-40
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• 2012

광대역 통신 기술은 일반 사용자를 대상으로 고속의 데이터 전송 및 고품질의 음성통화와 영상통화 등을 포함하는 멀티미디어 서비스 제공을 위해 발전 중이다. 나아가 국민의 안전과 생명보호를 위한 공공안전 및 재난구조(PPDR, Public Protection and Disaster Relief)를 위한 범국가적 통신망과 원격 감시, 측정, 보고 및 제어 등을 위한 스마트그리드(smart grid) 및 M2M(Machine-to-Machine) 통신을 위한 광대역 통신망 구축이 기대된다. 본고에서는 공공안전 및 재난구조 통신과 M2M 통신 응용을 지원하는 광대역 무선접속 표준화 동향과 기술 개념을 살펴보고자 한다.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.366-371
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• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

• Journal of the Korean Society of Safety
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• v.37 no.4
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• pp.1-10
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• 2022

The purposes of this study were to suggest 1) a future direction for Korea's machine tool industry and 2) how to secure the safety and reliability of emerging intelligent or automated machine tooling. The study concludes that, overseas, the machine tool industry is growing again while promoting innovation by converging with ICT. Accordingly, Korea also promotes ICT innovation to advance the machine tool industry, which is at the core of the national economy. As a result, unlike in the past, the frequency of serious injuries like entrapment accidents has recently decreased, while the proportion of collision accidents has increased. In addition, a new type of accident has become possible. Since ICT is network-based, the distinction between work and rest can become ambiguous; there is a risk of hacking, working hours and places are flexible and there are risk factors for diseases like chronic fatigue due to overload of specific personnel. As robots and automation are introduced, there is also a high probability of problems caused by physical and psychological burdens on system operators and resulting fatigue.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• IE interfaces
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• v.25 no.3
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• pp.338-346
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• 2012

Regarding the green environment, it is important to boost the spread of environmentally friendly vending machines and to operate the vehicles for their inventory replenishments while minimizing emissions of greenhouse gases. In general, the vending machine management company lacks capability to operate the supply chain effectively in an integrated way under the dynamic, complex, and stochastic environment. This paper presents a decision support system, termed DSSVM, for the operations of the general and smart vending machine supply chains with stock-out-based, one stage item substitution in a green logistics environment. The DSSVM supports the estimation of item demand and substitution probabilities, determination of operation parameters, supply chain analysis, what-if analysis, and $CO_2$ analysis for which various analytical models are employed.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.17-21
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• 2017

In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.