• Title/Summary/Keyword: Neural network prediction

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An Automatic Pattern Recognition Algorithm for Identifying the Spatio-temporal Congestion Evolution Patterns in Freeway Historic Data (고속도로 이력데이터에 포함된 정체 시공간 전개 패턴 자동인식 알고리즘 개발)

  • Park, Eun Mi;Oh, Hyun Sun
    • Journal of Korean Society of Transportation
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    • v.32 no.5
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    • pp.522-530
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    • 2014
  • Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

Image Quality Assessment by Combining Masking Texture and Perceptual Color Difference Model

  • Tang, Zhisen;Zheng, Yuanlin;Wang, Wei;Liao, Kaiyang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.7
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    • pp.2938-2956
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    • 2020
  • Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

Search Trend's Effects On Forecasting the Number of Outbound Passengers of the Incheon Airport (포탈의 검색 트렌드를 활용한 인천공항 출국자 수 예측 연구)

  • Shin, Euiseob;Yang, Dong-Heon;Sohn, Sei Chang;Huh, Moonhaeng;Baek, Seokchul
    • Journal of IKEEE
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    • v.21 no.1
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    • pp.13-23
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    • 2017
  • Short-term prediction of the number of passengers at the airport is very essential for the efficient and stable operation of the airport. Here, to forecast the immigration of Incheon International Airport, we perform the predictive modeling of Korean and Chinese outbound travelers comprising most of immigration. We conduct the Granger Causality test between the number of outbound travelers and related search trend data to confirm the correlation. It is found that the forecasting with both "outbound travelers" and "search term trends" data outperforms the one only with "outbound travelers" data. This is because search activities are done before doing something and this study confirms that search trend data inherently possess the potential for prediction.

Landslide susceptibility assessment using feature selection-based machine learning models

  • Liu, Lei-Lei;Yang, Can;Wang, Xiao-Mi
    • Geomechanics and Engineering
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    • v.25 no.1
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    • pp.1-16
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    • 2021
  • Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

Efficient Osteoporosis Prediction Using A Pair of Ensemble Models

  • Choi, Se-Heon;Hwang, Dong-Hwan;Kim, Do-Hyeon;Bak, So-Hyeon;Kim, Yoon
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.12
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    • pp.45-52
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    • 2021
  • In this paper, we propose a prediction model for osteopenia and osteoporosis based on a convolutional neural network(CNN) using computed tomography(CT) images. In a single CT image, CNN had a limitation in utilizing important local features for diagnosis. So we propose a compound model which has two identical structures. As an input, two different texture images are used, which are converted from a single normalized CT image. The two networks train different information by using dissimilarity loss function. As a result, our model trains various features in a single CT image which includes important local features, then we ensemble them to improve the accuracy of predicting osteopenia and osteoporosis. In experiment results, our method shows an accuracy of 77.11% and the feature visualize of this model is confirmed by using Grad-CAM.

Sea Ice Type Classification with Optical Remote Sensing Data (광학영상에서의 해빙종류 분류 연구)

  • Chi, Junhwa;Kim, Hyun-cheol
    • Korean Journal of Remote Sensing
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    • v.34 no.6_2
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    • pp.1239-1249
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    • 2018
  • Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

Mid-Term Energy Demand Forecasting Using Conditional Restricted Boltzmann Machine (조건적 제한된 볼츠만머신을 이용한 중기 전력 수요 예측)

  • Kim, Soo-Hyun;Sun, Young-Ghyu;Lee, Dong-gu;Sim, Is-sac;Hwang, Yu-Min;Kim, Hyun-Soo;Kim, Hyung-suk;Kim, Jin-Young
    • Journal of IKEEE
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    • v.23 no.1
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    • pp.127-133
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    • 2019
  • Electric power demand forecasting is one of the important research areas for future smart grid introduction. However, It is difficult to predict because it is affected by many external factors. Traditional methods of forecasting power demand have been limited in making accurate prediction because they use raw power data. In this paper, a probability-based CRBM is proposed to solve the problem of electric power demand prediction using raw power data. The stochastic model is suitable to capture the probabilistic characteristics of electric power data. In order to compare the mid-term power demand forecasting performance of the proposed model, we compared the performance with Recurrent Neural Network(RNN). Performance comparison using electric power data provided by the University of Massachusetts showed that the proposed algorithm results in better performance in mid-term energy demand forecasting.

Comparison of Machine Learning Classification Models for the Development of Simulators for General X-ray Examination Education (일반엑스선검사 교육용 시뮬레이터 개발을 위한 기계학습 분류모델 비교)

  • Lee, In-Ja;Park, Chae-Yeon;Lee, Jun-Ho
    • Journal of radiological science and technology
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    • v.45 no.2
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    • pp.111-116
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    • 2022
  • In this study, the applicability of machine learning for the development of a simulator for general X-ray examination education is evaluated. To this end, k-nearest neighbor(kNN), support vector machine(SVM) and neural network(NN) classification models are analyzed to present the most suitable model by analyzing the results. Image data was obtained by taking 100 photos each corresponding to Posterior anterior(PA), Posterior anterior oblique(Obl), Lateral(Lat), Fan lateral(Fan lat). 70% of the acquired 400 image data were used as training sets for learning machine learning models and 30% were used as test sets for evaluation. and prediction model was constructed for right-handed PA, Obl, Lat, Fan lat image classification. Based on the data set, after constructing the classification model using the kNN, SVM, and NN models, each model was compared through an error matrix. As a result of the evaluation, the accuracy of kNN was 0.967 area under curve(AUC) was 0.993, and the accuracy of SVM was 0.992 AUC was 1.000. The accuracy of NN was 0.992 and AUC was 0.999, which was slightly lower in kNN, but all three models recorded high accuracy and AUC. In this study, right-handed PA, Obl, Lat, Fan lat images were classified and predicted using the machine learning classification models, kNN, SVM, and NN models. The prediction showed that SVM and NN were the same at 0.992, and AUC was similar at 1.000 and 0.999, indicating that both models showed high predictive power and were applicable to educational simulators.

Deep Learning-based Pes Planus Classification Model Using Transfer Learning

  • Kim, Yeonho;Kim, Namgyu
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.4
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    • pp.21-28
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    • 2021
  • This study proposes a deep learning-based flat foot classification methodology using transfer learning. We used a transfer learning with VGG16 pre-trained model and a data augmentation technique to generate a model with high predictive accuracy from a total of 176 image data consisting of 88 flat feet and 88 normal feet. To evaluate the performance of the proposed model, we performed an experiment comparing the prediction accuracy of the basic CNN-based model and the prediction model derived through the proposed methodology. In the case of the basic CNN model, the training accuracy was 77.27%, the validation accuracy was 61.36%, and the test accuracy was 59.09%. Meanwhile, in the case of our proposed model, the training accuracy was 94.32%, the validation accuracy was 86.36%, and the test accuracy was 84.09%, indicating that the accuracy of our model was significantly higher than that of the basic CNN model.

Flight State Prediction Techniques Using a Hybrid CNN-LSTM Model (CNN-LSTM 혼합모델을 이용한 비행상태 예측 기법)

  • Park, Jinsang;Song, Min jae;Choi, Eun ju;Kim, Byoung soo;Moon, Young ho
    • Journal of Aerospace System Engineering
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    • v.16 no.4
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    • pp.45-52
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    • 2022
  • In the field of UAM, which is attracting attention as a next-generation transportation system, technology developments for using UAVs have been actively conducted in recent years. Since UAVs adopted with these technologies are mainly operated in urban areas, it is imperative that accidents are prevented. However, it is not easy to predict the abnormal flight state of an UAV causing a crash, because of its strong non-linearity. In this paper, we propose a method for predicting a flight state of an UAV, based on a CNN-LSTM hybrid model. To predict flight state variables at a specific point in the future, the proposed model combines the CNN model extracting temporal and spatial features between flight data, with the LSTM model extracting a short and long-term temporal dependence of the extracted features. Simulation results show that the proposed method has better performance than the prediction methods, which are based on the existing artificial neural network model.