• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.10-20
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• 2012

This study shows how to estimate AADT(Annual Average Daily Traffic) on temporary count data using new grouping method. This study deals with clustering permanent traffic counts using monthly adjustment factor, daily adjustment factor and a percentage of hourly volume. This study uses a percentage of hourly volume comparing with other studies. Cluster analysis is used and 5 groups is suitable. First, make average of monthly adjustment factor, average of daily adjustment factor, a percentage of hourly volume for each group. Next estimate AADT using 24 hour volume(not holiday) and two adjustment factors. Goodness of fit test is used to find what groups are applicable. MAPE(Mean Absolute Percentage Error) is 8.7% in this method. It is under 1.5% comparing with other method(using adjustment factors in same section). This method is better than other studies because it can apply all temporary counts data.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.306-311
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• 2005

To analyze the consumer's purchase pattern, we must consider a factor which is a cultural, social, individual, psychological and so on. If we consider the internal state by the consumer's purchase, Both the consumer's purchase action and the purchase factor can be predicted, so the corporation can use effectively in suitable goods development in a consumer's preference. These factors need a technology that treat uncertain information, because it is difficult to analyze by directly information processing. Therefore, bayesian network manages elements those the observation of inner state such as consumer's purchase is difficult. In addition, it is interpretable about data that the observation is impossible. In this paper, we examine the seller's know-how and the way of consumer's purchase to analyze consumer's purchase action pattern through goods purchase. Also, we compose the bayesian network based on the examined data, and propose the method that predicts purchase patterns. Finally, we remove the data including unnecessary attribute using the bayesian network, and analyze the consumer's Purchase pattern using Kohonen's SOM method.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.425-430
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• 2015

This paper is a study to improve the classification efficiency of rotating objects by using deep neural networks to which a deep learning algorithm was applied. For the classification experiment of rotating objects, COIL-20 is used as data and total 3 types of classifiers are compared and analyzed. 3 types of classifiers used in the study include PCA classifier to derive a feature value while reducing the dimension of data by using Principal Component Analysis and classify by using euclidean distance, MLP classifier of the way of reducing the error energy by using error back-propagation algorithm and finally, deep learning applied DBN classifier of the way of increasing the probability of observing learning data through pre-training and reducing the error energy through fine-tuning. In order to identify the structure-specific error rate of the deep neural networks, the experiment is carried out while changing the number of hidden layers and number of hidden neurons. The classifier using DBN showed the lowest error rate. Its structure of deep neural networks with 2 hidden layers showed a high recognition rate by moving parameters to a location helpful for recognition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.42-48
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• 2010

In this paper, we proposed analysis performance improvement technique for T-DMB watermarking based on MBOK spread spectrum. By applying the proposed scheme to T-DMB system, it allows additional data transmission and improves spectral efficiency of data transmission through watermarking spreading code. Using MBOK spread spectrum technique, data rate improved 2m-times existing direct sequence spread spectrum. However, since hardware complexity increase as the value of m becomes large, the method should be used with optimal situation. We analyze T-DMB watermarking techniques performance with MBOK spread spectrum and averaging. As a result, we confirm DER performance of MBOK spread spectrum scheme and it is shown system performance is improved as the number of averaging increases. The results of the paper can be applied to wireless multimedia digital broadcasting system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.139-144
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• 2008

Generally, real system that is the object of decision-making is very variable and sometimes it lies situations with uncertainty. To solve these problem, it has used statistical methods as significance level, certainty factor, sensitivity analysis and so on. In this paper, we propose a method for fuzzy decision-making based on MFAC(Multiple Fuzzy Alpha Cut) to improve the definiteness of classification results with similarity evaluation. In the proposed method, MFAC is used for extracting multiple a ${\alpha}$-level with proximity degree at proximity relation between relative Hamming distance and max-min method and for minimizing the number of data which are associated with the partition intervals extracted by MFAC. To determine final alternative of decision-making, we compute the weighted value between extracted data by MFAC From the experimental results, we can see the fact that the proposed method is simpler and more definite than classification performance of the conventional methods and determines an alternative efficiently for decision-maker by testing significance of sample data through statistical method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.183-193
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• 2019

This study developed a shockwave detection and prediction of their extinction point method based on continuous wavelet transform using trajectory data from probe vehicles equipped with automotive sensors.. To analyze the effectiveness of the proposed method, this paper proposed two measures which are a distance error between the extinction points of the predictor and an time-location error of the extinction points. The proposed concept was proved using the micro simulation based experiment with three exogenous variables of traffic volume, lane-close duration, market penetration of probe vehicles. The analysis results show that the proposed method is capable of detecting the traffic shockwaves as well as predicting their extinction point, and also that the accuracy of the proposed method is highly dependent on the rate of the probe vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.103-114
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• 2023

This study collected various data of urban roadways to analyze the effect of travel speed change, and a GRU-based short-term travel speed prediction model was developed using such big data. The baseline model and the double exponential smoothing model were selected as comparison models, and prediction errors were evaluated using the RMSE index. The model evaluation results revealed that the average RMSE of the baseline model and the double exponential smoothing model were 7.46 and 5.94, respectively. The average RMSE predicted by the GRU model was 5.08. Although there are deviations for each of the 15 links, most cases showed minimal errors in the GRU model, and the additional scatter plot analysis presented the same result. These results indicate that the prediction error can be reduced, and the model application speed can be improved when applying the GRU-based model in the process of generating travel speed information on urban roadways.

• Smart Structures and Systems
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• v.33 no.5
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• pp.365-374
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• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.70-75
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• 2018

Smart Factory is an intelligent factory that can enhance productivity, quality, customer satisfaction, etc. by applying information and communications technology to the entire production process including design & development, manufacture, and distribution & logistics. The precise amount of data generated in a smart factory varies depending on the factory's size and state of facilities. Regardless, it would be difficult to apply traditional production management systems to a smart factory environment, as it generates vast amounts of data. For this reason, the need for a distributed big-data processing system has risen, which can process a large amount of data. Therefore, this article has designed a Gluster File System (GlusterFS)-based distributed big-data processing system that can be used in a smart factory environment. Compared to existing distributed processing systems, the proposed distributed big-data processing system reduces the system load and the risk of data loss through the distribution and management of network traffic.