• Journal of Navigation and Port Research
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• v.26 no.3
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• pp.337-344
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• 2002

Although the number of site for ship sale and purchase are increasing year by year, we can not find the agent system for retrieving the necessary data automatically and efficiently. The object of this paper is to find the design structure of the intelligent agent systems by using wrapper technology. This paper is composed of two contents : design of retrieving system for agent and its application to ship sale and purchase. This paper will be evaluated in terms that its target domain is ship sale and purchase. In the result of the study, agent process is composed of reading URL, taking the source data, processing tag, pattern analysis, and storing the contents analysed.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.297-305
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• 2008

Recently, kernel methods have attracted great interests in the areas of pattern classification, function approximation, and anomaly detection. The role of the kernel is particularly important in the methods such as SVM(support vector machine) and KPCA(kernel principal component analysis), for it can generalize the conventional linear machines to be capable of efficiently handling nonlinearities. This paper considers the problem of combining radar and rain gauge observations utilizing the regression approach based on the kernel-based gaussian process and support vector learning. The data-assimilation results of the considered methods are reported for the radar and rain gauge observations collected over the region covering parts of Gangwon, Kyungbuk, and Chungbuk provinces of Korea, along with performance comparison.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.551-560
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• 2021

Due to the low strength and high compressibility characteristics, the loess deposits tunnels are prone to large deformations and collapse. An accurate stability evaluation for loess deposits is of considerable significance in deformation control and safety work during tunnel construction. 37 groups of representative data based on real loess deposits cases were adopted to establish the stability evaluation model for the tunnel project in Yan'an, China. Physical and mechanical indices, including water content, cohesion, internal friction angle, elastic modulus, and poisson ratio are selected as index system on the stability level of loess. The data set is randomly divided into 80% as the training set and 20% as the test set. Firstly, principal component analysis (PCA) is used to convert the five index system to three linearly independent principal components X1, X2 and X3. Then, the principal components were used as input vectors for probabilistic neural network (PNN) to map the nonlinear relationship between the index system and stability level of loess. Furthermore, Leave-One-Out cross validation was applied for the training set to find the suitable smoothing factor. At last, the established model with the target smoothing factor 0.04 was applied for the test set, and a 100% prediction accuracy rate was obtained. This intelligent classification method for loess deposits can be easily conducted, which has wide potential applications in evaluating loess deposits.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.54-63
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• 2023

Pedestrian traffic needs to be accurately quantified to predict effectively pedestrian traffic accidents, however, pedestrian traffic is more difficult to measure than vehicle traffic. In this study, we suggest the time-and cost-effective application of mobile closed-circuit television (CCTV) using a smartphone as an alternative that can collect and analyze real-time data with little. In the present investigation, the pedestrian-vehicle conflict that can develop into an accident was defined as the pedestrian accident exposure. After installing mobile CCTV in 40 sections of Dongseong-ro, Daegu, the pedestrian accident exposure was estimated through negative binomial regression analysis using the collected data. The results of the analysis showed statistically significant changes in the pedestrian accident exposure variables. Based on the present results, a pedestrian accident exposure estimation model was developed which can be used in sections where pedestrian accidents may occur.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.43-52
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• 2023

Traffic accidents among children have been decreasing after the installation of yellow carpets. However, the explanatory power of the causal relationship between yellow carpet installation and traffic accidents is still insufficient. The yellow carpet effect was analyzed in greater depth using virtual reality (VR) simulation experiments in various situation that could not be evaluated in existing actual vehicle research studies due to difficulties or risks in implementation. A target site where an actual yellow carpet was installed was selected and, implemented into a virtual environment. Subjects were made to, were gaze measurement equipment and ride the simulator. The visual/driving behavior before and after yellow carpet installation was compared, and a t-test analysis was performed for statistical verification. All the results were found to be statistically significant.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.29-47
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• 2024

Many domestic and foreign studies derive factors that significantly affect the use of shared E-scooters based on performance data, but few studies have been conducted with comparative analysis models using predictive power, applying them to other regions. Therefore, by clearly establishing detailed influencing factors and scope in Gwangjin-gu and Gangnam-gu by using domestic shared E-scooter performance data, this study enhances predictive power, and the Geographically Weighted Regression model is derived through spatial autocorrelation verification. Based on the results, the direction of a construction model created from regional differences was presented, and major implications from the user's perspective are derived based on the difference between actual use and the model's prediction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.31-42
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• 2019

Among the driving information recorded in the event data recorder (EDR), the speed information of the vehicle before the traffic accident is a very important factor that determines the punishment of the driver of the accident vehicle, the identification of the offender and the victim, and the possibility of avoiding the accident. Also, by analyzing the EDR data, the deceleration of the accident vehicle can be analyzed. In this study, the results of the braking test of the previous study and the analysis of the EDR data of the traffic accident vehicle were compared to suggest an appropriate deceleration value applicable to the calculation of the stopping distance. As a result of the braking test of the vehicle equipped with ABS of the previous study, the average deceleration of the vehicle was 0.79g ~ 0.94g. In addition, the deceleration value was calculated from 0.92g to 0.94g in the recent automobile safety evaluation braking test conducted by the Korea Automobile Testing & Research Institute. In addition, the deceleration value of 0.55g ~ 0.71g was calculated through the analysis of EDR data performed in this study, and the value was smaller than the deceleration value measured in the braking experiment of the previous study.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of the Korean Institute of Gas
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• v.22 no.5
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• pp.107-113
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• 2018

While accident data are used to show alertness to accidents or to review similar cases, the analysis of nature of accident data its association with surrounding environment is very insufficient. Therefore, it is very necessary to demonstrate the possibility of an accident for a particular region by developing analysis techniques with the related accident data. The purpose of this study is to develop an analysis model and implement a system that produces regional accident probability based on historical weather information data and accident and reporting data. In other words, the system is designed and developed to create models by k-NN and decision tree algorithms with optional user-environment variables based on the probability between weather and accidents about many particular region of Korea. In the future, the models developed in this study are intended to be used to analyze and calculate the risk of a more narrow area.