• Title/Summary/Keyword: 예측성능 개선

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Prediction of Multi-Physical Analysis Using Machine Learning (기계학습을 이용한 다중물리해석 결과 예측)

  • Lee, Keun-Myoung;Kim, Kee-Young;Oh, Ung;Yoo, Sung-kyu;Song, Byeong-Suk
    • Journal of IKEEE
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    • v.20 no.1
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    • pp.94-102
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    • 2016
  • This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.

A Side Information Generation Using Adaptive Estimation and Its Performance Comparison in PDWZ CODEC (화소 영역 Wyner-Ziv코덱에서 적응적 예측을 통한 보조정보 생성 방식과 성능 비교)

  • Kim, Jin-Soo;Kim, Jae-Gon;Seo, Kwang-Deok
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.2
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    • pp.383-393
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    • 2010
  • DVC (Distributed Video Coding) allows us to explore the video statistics at the decoder side, resulting in a less complex encoder and more complex decoder. In this approach, it is important to generate a good prediction to the current Wyner-Ziv frame, called side information, which plays a crucial role in the overall performance of a DVC system. Conventional MCFI (motion compensated frame interpolation) techniques, which explore temporal correlations between neighbor frames of the current frame, preform the block-based or object-based motion estimation, but, they do not include the basis frame for the Wyner-Ziv frame. This paper proposes an efficient way to get better side information, by finding the average frame between neighbor frames and by comparing adaptively the candidate blocks. Through computer simulations, it is shown that the proposed method can improve the performance up to 0.4dB and provide better subjective and objective visual qualities in Wyner-Ziv CODEC.

A Study on the Prediction Model for Analysis of Water Quality in Gwangju Stream using Machine Learning Algorithm (머신러닝 학습 알고리즘을 이용한 광주천 수질 분석에 대한 예측 모델 연구)

  • Yu-Jeong Jeong;Jung-Jae Lee
    • The Journal of the Korea institute of electronic communication sciences
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    • v.19 no.3
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    • pp.531-538
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    • 2024
  • While the importance of the water quality environment is being emphasized, the water quality index for improving the water quality of urban rivers in Gwangju Metropolitan City is an important factor affecting the aquatic ecosystem and requires accurate prediction. In this paper, the XGBoost and LightGBM machine learning algorithms were used to compare the performance of the water quality inspection items of the downstream Pyeongchon Bridge and upstream BanghakBr_Gwangjucheon1 water systems, which are important points of Gwangju Stream, as a result of statistical verification, three water quality indicators, Nitrogen(TN), Nitrate(NO3), and Ammonia amount(NH3) were predicted, and the performance of the predictive model was evaluated by using RMSE, a regression model evaluation index. As a result of comparing the performance after cross-validation by implementing individual models for each water system, the XGBoost model showed excellent predictive ability.

Exploring Predictive Models for Student Success in National Physical Therapy Examination: Machine Learning Approach

  • Bokyung Kim;Yeonseop Lee;Jang-hoon Shin;Yusung Jang;Wansuk Choi
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.10
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    • pp.113-120
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    • 2024
  • This study aims to assess the effectiveness of machine learning models in predicting the pass rates of physical therapy students in national exams. Traditional grade prediction methods primarily rely on past academic performance or demographic data. However, this study employed machine learning and deep learning techniques to analyze mock test scores with the goal of improving prediction accuracy. Data from 1,242 students across five Korean universities were collected and preprocessed, followed by analysis using various models. Models, including those generated and fine-tuned with the assistance of ChatGPT-4, were applied to the dataset. The results showed that H2OAutoML (GBM2) performed the best with an accuracy of 98.4%, while TabNet, LightGBM, and RandomForest also demonstrated high performance. This study demonstrates the exceptional effectiveness of H2OAutoML (GBM2) in predicting national exam pass rates and suggests that these AI-assisted models can significantly contribute to medical education and policy.

Statistical Method and Deep Learning Model for Sea Surface Temperature Prediction (수온 데이터 예측 연구를 위한 통계적 방법과 딥러닝 모델 적용 연구)

  • Moon-Won Cho;Heung-Bae Choi;Myeong-Soo Han;Eun-Song Jung;Tae-Soon Kang
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.29 no.6
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    • pp.543-551
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    • 2023
  • As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

Very short-term rainfall prediction based on radar image learning using deep neural network (심층신경망을 이용한 레이더 영상 학습 기반 초단시간 강우예측)

  • Yoon, Seongsim;Park, Heeseong;Shin, Hongjoon
    • Journal of Korea Water Resources Association
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    • v.53 no.12
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    • pp.1159-1172
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    • 2020
  • This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

A Study on the Performance Improvements of Congestion Control of Multiple Time Scale Under in TCP-MT network (TCP-MT네트워크에서 다중 시간 간격을 이용한 혼잡제어 성능 개선에 관한 연구)

  • Cho, Hyun-Seob;Oh, Myoung-Kwan
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.9 no.1
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    • pp.75-80
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    • 2008
  • Measurement of network traffic have shown that the self-similarity is a ubiquitous phenomenon spanning across diverse network environments. In previous work, we have explored the feasibility of exploiting the long-range correlation structure in a self-similar traffic for the congestion control. We have advanced the framework of the multiple time scale congestion control and showed its effectiveness at enhancing performance for the rate-based feedback control. Our contribution is threefold. First, we define a modular extension of the TCP-a function called with a simple interface-that applies to various flavours of the TCP-e.g., Tahoe, Reno, Vegas and show that it significantly improves performance. Second, we show that a multiple time scale TCP endows the underlying feedback control with proactivity by bridging the uncertainty gap associated with reactive controls which is exacerbated by the high delay-bandwidth product in broadband wide area networks. Third, we investigate the influence of the three traffic control dimensions-tracking ability, connection duration, and fairness-on performance.

An Iterative Side Information Refinement Based on Block-Adaptive Search in Distributed Video Coding (분산 비디오 부호화에서 블록별 적응적 탐색에 기초한 반복적인 보조정보 보정기법)

  • Kim, Jin-Soo;Yun, Mong-Han;Kim, Jae-Gon;Seo, Kwang-Deok
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.2
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    • pp.355-363
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    • 2011
  • Recently, as one of several methods to improve the performance of DVC(Distributed Video Coding) system, many research works are focusing on the iterative refinement of side information. Most of the conventional techniques are mainly based on the relationship between the reconstruction level and side information, or the vector median filtering of motion vectors, but, their performance improvements are restricted. In order to overcome the performance limit of the conventional schemes, in this paper, a side information generation scheme is designed by measuring the block-cost estimation. Then, by adaptively selecting the compensation mode using the received bit-plane information, we propose a block-adaptive iterative refinement which is efficient for non-symmetric moving objects. Computer simulations show that, by using the proposed refinement method, the performance can be improved up to 0.2 dB in rate-distortion.

Optimal Design of Rotor Profile of Internal Gear Pump for Noise Reduction (내접형 기어펌프용 로터의 소음저감을 위한 치형 최적설계)

  • Bae, Jun Ho;Bae, Won Byong;Joo, Ung Tak;Kim, Chul
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.38 no.3
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    • pp.295-302
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    • 2014
  • An internal gear pump is required to improve the flow rate and noise. Furthermore, such pumps are durable and small in size. In this study, equations of the rotor shape with multiple profiles (ellipse 1-involute-ellipse 2) were derived, and relevant performance parameters (pressure angle, irregularity, and specific sliding) of the internal gear pump to noise were predicted and compared. Rotor profiles and performance parameters were obtained by a prediction algorithm for theoretical analysis, and the effects between the design parameters and the performance parameters were analyzed. Based on the analysis results of the effects, an optimal design of the rotor profile was proposed, and a noise test of the prototype was performed to evaluate the reliability of the design.

Improving Clustering Performance Using Gene Ontology (유전자 온톨로지를 활용한 클러스터링 성능 향상 기법)

  • Ko, Song;Kang, Bo-Yeong;Kim, Dae-Won
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.6
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    • pp.802-808
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    • 2009
  • Recently many researches have been presented to improve the clustering performance of gene expression data by incorporating Gene Ontology into the process of clustering. In particular, Kustra et al. showed higher performance improvement by exploiting Biological Process Ontology compared to the typical expression-based clustering. This paper extends the work of Kustra et al. by performing extensive experiments on the way of incorporating GO structures. To this end, we used three ontological distance measures (Lin's, Resnik's, Jiang's) and three GO structures (BP, CC, MF) for the yeast expression data. From all test cases, We found that clustering performances were remarkably improved by incorporating GO; especially, Resnik's distance measure based on Biological Process Ontology was the best.