• Steel and Composite Structures
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• v.37 no.2
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• pp.193-209
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• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.1
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• pp.1-11
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• 2016

We investigate compressive sensing techniques to estimate sparse channel in Orthogonal Frequency Division Multiplexing(OFDM) systems. In the case of large channel delay spread, compressive sensing may not be applicable because it is affected by length of measurement vectors. In this paper, we increase length of measurement vector adding pilot information to OFDM data block. The increased measurement vector improves probability of finding path delay set and Mean Squared Error(MSE) performance. Simulation results show that signal recovery performance of a proposed scheme is better than conventional schemes.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1103-1110
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• 2020

In this study, a segmented model with Upside-Down bathtub shaped failure intensity for a repairable system are proposed under the assumption that the occurrences of the failures of a repairable system follow the Non-Homogeneous Poisson Process. The proposed segmented model is the compound model of S-PLP and LIP (Segmented Power Law Process and Logistic Intensity Process), that fits the separate failure intensity functions on each segment of time interval. The maximum likelihood estimation is used for estimating the parameters of the S-PLP and LIP model. The case study of system A shows that the S-PLP and LIP model fits better than the other models when compared by AICc (Akaike Information Criterion corrected) and MSE (Mean Squared Error). And it also implies that the S-PLP and LIP model can be useful for explaining the failure intensities of similar systems.

• Journal of IKEEE
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• v.24 no.4
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.321-322
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• 2023

본 논문에서는 Scikit-learn 패키지의 LinearRegression 모델과 Keras 딥러닝 모델을 활용하여 수도권 도시 환경 요인에 따른 범죄 발생 건수를 예측 모델을 제안한다. 연구 방법으로 범죄 발생과 유의미한 관계가 있다고 파악되는 수도권의 각 자치구 별 데이터셋을 분석하여, CCTV, 파출소, 가로등의 수가 범죄 발생에 유의미한 영향을 끼치는 것을 확인하였다. 독립 변수들 간에 Scale을 줄이고자 정규화를 진행했고, 종속변수의 정규성 확보를 위해 로그변환을 취했다. 손실 함수는 회귀문제에서 사용되는 'relu'함수를 사용했고 모델의 성능을 확인할 수 있는 지표로 MSE(Mean Squared Error)를 사용해 모델을 구성하였다. 본 논문에서 설계한 이 프로그램은 범죄 발생율이 높은 지역구에 경찰 인력의 추가적 배치, 안전 시설 확충 등 실무적 조치를 취함에 있어 근거를 제공할 수 있을 것으로 사료된다.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.203-212
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• 2024

Simultaneous motion is essential in the activities of daily living (ADL). For motion intention recognition, surface electromyogram (sEMG) and corresponding motion label is necessary. However, this process is time-consuming and it may increase the burden of the user. Therefore, we propose a simultaneous motion recognition framework using data augmentation based on muscle activation model. The model consists of multiple point sources to be optimized while the number of point sources and their initial parameters are automatically determined. From the experimental results, it is shown that the framework has generated the data which are similar to the real one. This aspect is quantified with the following two metrics: structural similarity index measure (SSIM) and mean squared error (MSE). Furthermore, with k-nearest neighbor (k-NN) or support vector machine (SVM), the classification accuracy is also enhanced with the proposed framework. From these results, it can be concluded that the generalization property of the training data is enhanced and the classification accuracy is increased accordingly. We expect that this framework reduces the burden of the user from the excessive and time-consuming data acquisition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.

• Journal of Biomedical Engineering Research
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• v.45 no.3
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• pp.118-127
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• 2024

Commonly deep learning methods for enhancing the quality of medical images use unpaired dataset due to the impracticality of acquiring paired dataset through commercial imaging system. In this paper, we propose a supervised learning method to enhance the quality of ultrasound images. The U-net model is designed by incorporating a divide-and-conquer approach that divides and processes an image into four parts to overcome data shortage and shorten the learning time. The proposed model is trained using paired dataset consisting of 828 pairs of low-quality and high-quality images with a resolution of 512x512 pixels obtained by varying the number of channels for the same subject. Out of a total of 828 pairs of images, 684 pairs are used as the training dataset, while the remaining 144 pairs served as the test dataset. In the test results, the average Mean Squared Error (MSE) was reduced from 87.6884 in the low-quality images to 45.5108 in the restored images. Additionally, the average Peak Signal-to-Noise Ratio (PSNR) was improved from 28.7550 to 31.8063, and the average Structural Similarity Index (SSIM) was increased from 0.4755 to 0.8511, demonstrating significant enhancements in image quality.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.89-94
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• 1999

In vector quantization (VQ), mean squared error (MSE) is widely used as a distance measure between vectors. But the distance between averages appears as a dominant quantity in MSE. In the case of image vectors, the coincidence of edge pattern is also important considering human visual system (HVS). Therefore, this paper presents a new distance measure using the variance of difference (VD) as a criterion for the coincidence of edge pattern. By using this in the VQ encoding, we can reduce the degradation of edge region in the reconstructed image. And applying this to the codebook design, we can obtain the final codebook that has a lot of various edge codevectors instead of redundant shade ones.