• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.3
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• pp.113-118
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• 2012

Collision detection plays a key role in collision mitigation system. The malfunction of the collision mitigation system can result in another dangerous situation or unexpected feeling to driver and passenger. To prevent this situation, the collision time, offset, and collision decision should be determined from the appropriate collision detection algorithm. This study focuses on a method to determine the time to collision (TTC) and frontal offset (FO) between the ego vehicle and the target object. The path prediction method using the ego vehicle information is proposed to improve the accuracy of TTC and FO. The path prediction method utilizes the ego vehicle motion data for better prediction performance. The proposed algorithm is developed based on laser scanner. The performance of the proposed detection algorithm is validated in simulations and experiments.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.21-30
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• 1997

In accelerated life tests, the failure time of an item is observed under a high stress level, and based on the time the performances of items are investigated at the normal stress level. In this paper, when the mean of the prior of a failure rate is known in the exponential lifetime distribution with censored accelerated failure time data, we utilize the empirical Bayesian method by using the moment estimators in order to estimate the parameters of the prior distribution and obtain the empirical Bayesian predictive density and predictive intervals for a future observation under the normal stress level.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.147-158
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• 2021

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.

• The Journal of Information Systems
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• v.27 no.4
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• pp.115-128
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• 2018

Purpose The purpose of this study is to predict future behaviors of business process. Specifically, this study tried to predict the last activities of process instances. It contributes to overcoming the limitations of existing approaches that they do not accurately reflect the actual behavior of business process and it requires a lot of effort and time every time they are applied to specific processes. Design/methodology/approach This study proposed a novel approach based using deep learning in the form of dynamic recurrent neural networks. To improve the accuracy of our prediction model based on the approach, we tried to adopt the latest techniques including new initialization functions(Xavier and He initializations). The proposed approach has been verified using real-life data of a domestic small and medium-sized business. Findings According to the experiment result, our approach achieves better prediction accuracy than the latest approach based on the static recurrent neural networks. It is also proved that much less effort and time are required to predict the behavior of business processes.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.401-413
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• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.497-514
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• 2023

Purpose: This study aims to estimate the demand for various public parking lots in Seoul by clustering similar demand types of parking lots and predicting the demand for new public parking lots. Methods: We examined real-time parking information data and used time series clustering analysis to cluster public parking lots with similar demand patterns. We also performed various regression analyses of parking demand based on diverse heterogeneous data that affect parking demand and proposed a parking demand prediction model. Results: As a result of cluster analysis, 68 public parking lots in Seoul were clustered into four types with similar demand patterns. We also identified key variables impacting parking demand and obtained a precise model for predicting parking demands. Conclusion: The proposed prediction model can be used to improve the efficiency and publicity of public parking lots in Seoul, and can be used as a basis for constructing new public parking lots that meet the actual demand. Future research could include studies on demand estimation models for each type of parking lot, and studies on the impact of parking lot usage patterns on demand.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1131-1141
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• 2021

In this study, the artificial neural network model is applied for real-time dam inflow prediction and then evaluated for the prediction lead times (1, 3, 6 hr) in dam basins in Korea. For the training and testing the model, hourly precipitation and inflow are used as input data according to average annual inflow. The results show that the model performance for up to 6 hour is acceptable because the NSE is 0.57 to 0.79 or higher. Totally, the predictive performance of the model in dry seasons is weaker than the performance in wet seasons, and this difference in performance increases in the larger basin. For the 6 hour prediction lead time, the model performance changes as the sequence length increases. These changes are significant for the dry season with increasing sequence length compared to the wet season. Also, with increasing the sequence length, the prediction performance of the model improved during the dry season. Comparison of observed and predicted hydrographs for flood events showed that although the shape of the prediction hydrograph is similar to the observed hydrograph, the peak flow tends to be underestimated and the peak time is delayed depending on the prediction lead time.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).