• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.1
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• pp.129-135
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• 1995

This study aims to compare the heat release calculated using the ensemble average of pressure data with the heat release calculated using the least squares method for pressure data. This paper propose a heat release computation method that can analyze the most correct, straight and simple method to analyse combustion phenomenon. In conclusion, we found that the least squares method of third-order was the best computational method for heat release calculation.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.524-527
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• 2022

본 연구에서는 머신러닝(Machine Learning, ML)과 딥러닝(Deep Learning, DL) 모델을 앙상블(Ensemble)하여 어떠한 주가 예측 방법이 우수한지에 대한 연구를 하고자 한다. 연구에 사용된 모델은 하이퍼파라미터(Hyperparameter) 조정을 통하여 최적의 결과를 출력한다. 앙상블 방법은 머신러닝과 딥러닝 모델의 앙상블, 머신러닝 모델의 앙상블, 딥러닝 모델의 앙상블이다. 세 가지 방법으로 얻은 결과를 평균 제곱근 오차(Root Mean Squared Error, RMSE)로 비교 분석하여 최적의 방법을 찾고자 한다. 제안한 방법은 주가 예측 연구의 시간과 비용을 절약하고, 최적 성능 모델 판별에 도움이 될 수 있다고 사료된다.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.305-312
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• 2023

Physics-Informed Neural Network (PINN) is used to invert bubble size distributions from attenuation losses. By considering a linear system for the bubble population inversion, Adaptive Learned Iterative Shrinkage Thresholding Algorithm (Ada-LISTA), which has been solved linear systems in image processing, is used as a neural network architecture in PINN. Furthermore, a regularization based on the linear system is added to a loss function of PINN and it makes a PINN have better generalization by a solution satisfying the bubble physics. To evaluate an uncertainty of bubble estimation, deep ensemble is adopted. 20 Ada-LISTAs with different initial values are trained using the same training dataset. During test with attenuation losses different from those in the training dataset, the bubble size distribution and corresponding uncertainty are indicated by average and variance of 20 estimations, respectively. Deep ensemble Ada-LISTA demonstrate superior performance in inverting bubble size distributions than the conventional convex optimization solver of CVX.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.35-38
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• 2023

포트홀은 주행하는 자동차와 접촉이 이뤄지면 차체나 운전자에게 충격을 주고 제어를 잃게 하여 도로 위 안전을 위협할 수 있다. 포트홀의 검출을 위한 국내 동향으로는 진동을 이용한 방식과 신고시스템 이용한 방식과 영상 인식을 기반한 방식이 있다. 이 중 영상 인식 기반 방식은 보급이 쉽고 비용이 저렴하나, 컴퓨터 비전 알고리즘은 영상의 품질에 따라 정확도가 달라지는 문제가 있었다. 이를 보완하기 위해 영상 인식 기반의 딥러닝 모델을 사용한다. 따라서, 본 논문에서는 사전 학습된 딥러닝 모델의 정확도 향상을 위한 Feature Level Ensemble 기법을 제안한다. 제안된 기법은 사전 학습된 CNN 모델 중 Test 데이터의 정확도 기준 Top-3 모델을 선정하여 각 딥러닝 모델의 Feature Map을 Concatenate하고 이를 Fully-Connected(FC) Layer로 입력하여 구현한다. Feature Level Ensemble 기법이 적용된 딥러닝 모델은 평균 대비 3.76%의 정확도 향상을 보였으며, Top-1 모델인 ShuffleNet보다 0.94%의 정확도 향상을 보였다. 결론적으로 본 논문에서 제안된 기법은 사전 학습된 모델들을 이용하여 각 모델의 다양한 특징을 통해 기존 모델 대비 정확도의 향상을 이룰 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.2
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• pp.31-43
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• 2000

This paper presents a novel algorithm lot extraction of the eye and mouth fields (facial features) from 2D gray level face images. First of all, it has been found that Eigenfeatures, derived from the eigenvalues and the eigenvectors of the binary edge data set constructed from the eye and mouth fields are very good features to locate these fields. The Eigenfeatures, extracted from the positive and negative training samples for the facial features, ate used to train a MultiLayer Perceptron(MLP) whose output indicates the degree to which a particular image window contains the eye or the mouth within itself. Second, to ensure robustness, the ensemble network consisting of multiple MLPs is used instead of a single MLP. The output of the ensemble network becomes the average of the multiple locations of the field each found by the constituent MLPs. Finally, in order to reduce the computation time, we extracted the coarse search region lot eyes and mouth by using prior information on face images. The advantages of the proposed approach includes that only a small number of frontal faces are sufficient to train the nets and furthermore, lends themselves to good generalization to non-frontal poses and even to other people's faces. It was also experimentally verified that the proposed algorithm is robust against slight variations of facial size and pose due to the generalization characteristics of neural networks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.362-374
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• 2018

The purpose of this study is to identify and improve the satisfaction of people attending the Jeju International Wind Festival, the representative festival in Jeju. The Jeju International Wind Ensemble Festival is a festival in which people communicate with each other through music. The objective of this study is to investigate user satisfaction of the International Wind Ensemble Festival and to make improvement plans. The average satisfaction rate for the event was 75.8% (Satisfaction + Great satisfaction). In addition, the average program level of the Jeju International Wind Ensemble Festival and average appropriateness of the event space were 86.9% (Satisfaction + Great satisfaction) and 85.2% (Satisfaction + Great satisfaction), respectively. The disadvantage is that public transportation that should be improved. According to results of the survey through the interview, the town wind ensemble festival and outside concert enabled performers to communicate with the audience through melody. In order for the Jeju International Orchestra Festival to take root as a festival for citizens, it is necessary to increase the convenience of transportation, secure personnel in charge of the secretariat, revitalize the Jeju International Orchestra Festival website, and promote volunteer activities. We present future research direction.

• Journal of Navigation and Port Research
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• v.27 no.4
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• pp.455-463
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• 2003

Time series of suspended sediment concentration, surface elevation and velocity were measured and analysed to investigate the role of waves and the predominance of infra-gravity wave component for sediment suspension phenomena in the surf zone. For the investigation in detail, we adopted the cross spectral analysis method between suspended sediment concentration and the characteristic values of wave, and ensemble average analysis method about long-period wave component, which is dominant to sediment suspension in the measurement point. The obtained results are summarized as follows: 1)The relationship between suspended sediment concentration and the characteristic values of wave is stronger for the long-period standing wave components(about 60s and 30s where the nodal point of the first mode and the anti-nodal point of the second mode are located at the measurement point, respectively) than the long wave components(about 100s), which have the most energetic power, 2) and also, it is cleared that suspended sediment concentration is increased in the case of the phase, the velocity components of the first mode long-period standing wave(60sec) were accelerated toward on-shore direction, that is, the water surface in offshore side is higher than on-shore side.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.328-337
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• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.

• Transactions of the Korean Society of Mechanical Engineers
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• v.8 no.6
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• pp.599-603
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• 1984

Field equations describing the effective behavior of general heterogeneous media are derived from the integral balance relations through the methods of distribution and convolution.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.675-689
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• 2014

This study applied Ensemble Empirical Mode Decomposition(EEMD), a new methodology to define the timing of spring onset over the Republic of Korea and to examine its spatio-temporal change. Also this study identified the relationship between spring onet timing and some atmospheric variations, and figured out synoptic factors which affect the timing of spring onset. The averaged spring onset timing for the period of 1974-2011 was 11th, March in Republic of Korea. In general, the spring onset timing was later with higher latitude and altitude regions, and it was later in inland regions than in costal ones. The correlation analysis has been carried out to find out the factors which affect spring onset timing, and global annual mean temperature, Arctic Oscillation(AO), Siberian High had a significant correlation with spring onset timing. The multiple regression analysis was conducted with three indices which were related to spring onset timing, and the model explained 64.7%. As a result of multiple regression analysis, the effect of annual mean temperature was the greatest and that of AO was the second. To find out synoptic factors affecting spring onset timing, the synoptic analysis has been carried out. As a result the intensity of meridional circulation represented as the major factor affect spring onset timing.