• 대한임베디드공학회논문지
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• 제18권6호
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• pp.303-310
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• 2023

A single vision system limits the ability to accurately understand the spatial constraints and interactions between robots and dynamic workers caused by gantry robots and collaborative robots during production manufacturing. In this paper, we propose a 3D pose registration method for dynamic workers based on a multi-domain vision system for safety monitoring in manufacturing environments. This method uses OpenPose, a deep learning-based posture estimation model, to estimate the worker's dynamic two-dimensional posture in real-time and reconstruct it into three-dimensional coordinates. The 3D coordinates of the reconstructed multi-domain vision system were aligned using the ICP algorithm and then registered to a single 3D coordinate system. The proposed method showed effective performance in a manufacturing process environment with an average registration error of 0.0664 m and an average frame rate of 14.597 per second.

• 한국해양공학회지
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• 제38권2호
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• pp.53-63
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• 2024

Submerged breakwaters can be installed underneath floating structures to reduce the external wave loads acting on the structure. The objective of this study was to establish an optimization analysis framework to determine the corresponding shape and position of the submerged breakwater that can minimize or maximize the external forces acting on the floating structure. A two-dimensional frequency-domain boundary element method (FD-BEM) based on the linear potential theory was developed to perform the hydrodynamic analysis. A metaheuristic algorithm, the advanced particle swarm optimization, was newly coupled to the FD-BEM to perform the optimization analysis. The optimization analysis process was performed by calling FD-BEM for each generation, performing a numerical analysis of the design variables of each particle, and updating the design variables using the collected results. The results of the optimization analysis showed that the height of the submerged breakwater has a significant effect on the surface piercing body and that there is a specific area and position with an optimal value. In this study, the optimal values of the shape and position of a single submerged breakwater were determined and analyzed so that the external force acting on a surface piercing body was minimum or maximum.

• Asia pacific journal of information systems
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• 제32권2호
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.633-652
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• 2024

Software testing is an important phase as it ensures the software quality. The software testing process comprises of three steps: generation, execution, and evaluation of test cases. Literature claims the usage of single and multiple 'Unified Modeling Language' (UML) diagrams to generate test cases. Using multiple UML diagrams increases test case coverage. However, the existing approaches show limitations in test case generation from UML diagrams. Therefore, in this research study, we propose an approach to generate the test cases using UML State Chart Diagram (SCD), Activity Diagram (AD), and Sequence Diagram (SD). The proposed approach transforms UML diagrams into intermediate forms: SCD Graph, AD Graph, and SD Graph respectively. Furthermore, by integrating these three graphs, a System Testing Graph (STG) is formed. Finally, test cases are identified from STG by using a traversal algorithm such as Depth First Search (DFS) that is an optimization method. The results show that the proposed approach is better compared to existing approaches in terms of coverage and performance. Moreover, the generated test cases have the ability to detect faults at the unit level, integration, and system level testing.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 추계학술발표대회
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• pp.564-567
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• 2007

애드혹 네트워크는 무선 모바일 기기들만으로 구성 가능한 네트워크로서 무선 모바일기기의 특성상 제한된 에너지를 사용하게 된다. On-Demand 방식의 DSR(Dynamic Source Routing), PSR(Power-aware Source Routing)은 애드 혹 네트워크의 특성에 적합한 라우팅 프로토콜로 경로 탐색 시 브로드 캐스팅을 통해 단말기의 이동성과 전력 보유량을 고려하여 경로를 설정한다. 이러한 경로 탐색 기법은 네트워크를 구성하는 노드 수에 비례하여 경로 탐색시 소모되는 에너지 오버헤드가 증가하므로 대량의 노드들로 이루어진 애드혹 네트워크에서는 상당한 에너지 낭비를 초래한다. HPSR(Hierarchical Power-aware Source Routing)은 이러한 경로 탐색 오버헤드 문제를 보조경로를 이용해 감소 시켰지만, 초기 주 경로가 결정되는 시간에 보조경로 역시 동시에 결정 되는 특성을 가지고 있어, 경로를 이루는 노드의 에너지 정보가 시간적으로 낙후되고 실제 사용 시에는 에너지 효율이 낮은 경로가 되는 문제점이 발생한다. 본 논문에서는 노드들의 최신 에너지 정보로 부분 경로 설정을 함으로써 경로 탐색 오버헤드를 감소시킴과 동시에 변경된 경로의 에너지 효율도 최적화 시킬 수 있는 기법을 제안 한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 추계학술발표대회
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• pp.34-37
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• 2007

Face recognition is still a challenging problem in pattern recognition field which is affected by different factors such as facial expression, illumination, pose etc. The facial feature such as eyes, nose, and mouth constitute a complete face. Mouth feature of face is under the undesirable effect of facial expression as many factors contribute the low performance. We proposed a new approach for face recognition under facial expression applying two cascaded classifiers to improve recognition rate. All facial expression images are treated by general purpose classifier at first stage. All rejected images (applying threshold) are used for adaptation using GA for improvement in recognition rate. We apply Gabor Wavelet as a general classifier and Gabor wavelet with Genetic Algorithm for adaptation under expression variance to solve this issue. We have designed, implemented and demonstrated our proposed approach addressing this issue. FERET face image dataset have been chosen for training and testing and we have achieved a very good success.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.119-126
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• 2024

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pretrain Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score.

• 소성∙가공
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• 제33권2호
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• pp.96-102
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• 2024

This study addresses evaluation of performance of hardening model for a titanium alloy (Ti6Al4V) based on the artificial neural network (ANN) regarding the strain rate and the temperature. Uniaxial compression tests were carried out at different strain rates from 0.001 /s to 10 /s and temperatures from 575 ℃ To 975 ℃. Using the experimental data, ANN models were trained and tested with different hyperparameters, such as size of hidden layer and optimizer. The input features were determined with the equivalent plastic strain, strain rate, and temperature while the output value was set to the equivalent stress. When the number of data is sufficient with a smooth tendency, both the Bayesian regulation (BR) and the Levenberg-Marquardt (LM) show good performance to predict the flow behavior. However, only BR algorithm shows a predictability when the number of data is insufficient. Furthermore, a proper size of the hidden layer must be confirmed to describe the behavior with the limited number of the data.

• 센서학회지
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• 제33권3호
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• pp.139-146
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• 2024

Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical molecules owing to its exceptional sensitivity in obtaining unique spectra, offering nondestructive classification capabilities for target analytes. Herein, we demonstrate an innovative strategy that provides significant machine learning (ML)-enabled predictive SERS platforms through surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/graphene SERS platforms showed exceptional sensitivity (10^-7 M) due to the collaborative strong correlation between the localized electromagnetic effect and the enhanced chemical bonding reactivity. The chemical and physical properties of the demonstrated SERS platform were systematically investigated using microscopy and spectroscopic analysis. Furthermore, an innovative strategy employing ML is proposed to predict various analytes based on a featured Raman spectral database. Using a customized data-preprocessing algorithm, the feature data for ML were extracted from the Raman peak characteristic information, such as intensity, position, and width, from the SERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-fold cross-validation (k = 5), showing 99% prediction accuracy.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2024년도 춘계학술발표대회
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• pp.367-370
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• 2024

양자컴퓨터 시대가 눈앞에 도래한 지금 차세대 암호로 주목받고 있는 양자 내성 암호는 다양한 수학적 알고리즘에 안전성을 기반하고 있으나 이 안전성을 위협하는 대표적인 공격 기법 중 하나인 부채널 분석 공격에 대응하기 위한 노력들이 계속되어 왔다. 이 논문에서는 코드 기반 양자 내성 암호를 중심으로 알고리즘에 위협적인 부채널 분석 공격에 대한 연구 동향을 분석하였다. 그리고 NIST 에서 PQC 표준화를 위해 Round 를 진행 중인 후보 중 하나인 코드 기반 알고리즘 MEDS 에 대해 소개하고, MEDS 알고리즘의 최적화를 위해 기존에 연구되었던 코드 기반 암호에 대한 부채널 분석 공격 대응 측면에서의 알고리즘의 안전성 확보라는 보안 비용과 하드웨어 가속 등을 통한 성능 향상이 적절한 조화를 이룰 수 있도록 설계하기 위한 방안에 대해 알아보았다.