• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.239-245
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• 2016

In the midst of continuing international recession, the rate of maritime traffic and marine leisure markets are consistently growing. The Republic of Korea controls the marine traffic volume through vessel traffic centers and various other management facilities. Nevertheless, the continuous growth and complexity of marine traffic is resulting in repeated occurrences of marine accidents. Recovery is very difficult in cases of human injuries or deaths caused by marine accidents due to its nature, and the scale of marine accidents is also becoming greater with advanced ship building technologies. Passenger ships, oil tankers, and other such vessels used for specific purposes requires a more detailed navigational status surveillance and analysis, and numerous research has been conducted with an objective for monitoring such special purpose vessels. However, the data elements transmitted from the ocean to the shore station are limited to AIS and ARPA. We are implementing IoT ship sensor collection and a syncing system capable of transmitting various ship sensing data to the shore station, and also proposing a Safe Navigation Status Analysis System utilizing the collected data.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.41-58
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• 1998

본 연구는 여러 가지 Data Mining 기법들로부터 도출된 지식과 AHP를 이용하여 도출된 전문가의 지식을 사용된 정보의 특성에 따라 조사하고, 이러한 각각의 지식들을 중심으로 부도예측 모형을 설계한 후, 각 모형의 특성 및 부도예측력에 대한 실증적 비교연구에 그 목적을 두고 있다. 사용된 Data Mining 기법들은 통계적 다중판별분석 모형, ID3 모형, 인공신경망 모형이며, 전문가 지식의 추출은 AHP를 사용하여 45명의 전문가로부터 부도와 관련하여 인터뷰 및 설문조사를 실시하였다. 특히 부도예측에 사용된 변수의 특성을 정량적 재무정보와 정성적 비재무정보로 나누어서 각 모형의 특성을 비교연구하였다. 연구결과 부도예측시 정성적정보의 중요성을 확인하였으며, 전문가의 지식을 기반으로한 AHP 모형이 위험예측모형으로 사용될 수 있음을 실증적으로 보여주었다.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.159-165
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• 2024

This study investigates the comprehensive system for collecting intelligent learning activity data tailored to learner-centered personalized education. We compared and analyzed the characteristics of xAPI, Caliper analytics, and cmi5, which are learning activity data collection standards, and established a system that allows not only standardized data but also non-standardized learning activity data to be stored as big data for artificial intelligence learning analysis. As a result, the system was structured into five stages: defining data types, standardizing learning data using xAPI, storing big data, conducting learning analysis (statistical and AI-based), and providing learner-tailored services. The aim was to establish a foundation for analyzing learning data using artificial intelligence technology. In future research, we will divide the entire system into three stages, implement and execute it, and correct and supplement any shortcomings in the design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.392-399
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• 2013

Due to the development of IT technology and the information age, a dependency of the network over the most of our lives have grown to a greater extent. Although it provides us to get various useful information and service, it also has negative effectiveness that can provide network intruder with vulnerable roots. In other words, we need to urgently cope with theses serious security problem causing service disableness or system connected to network obstacle with exploiting various packet information. Many experts in a field of security are making an effort to develop the various security solutions to respond against these threats, but existing solutions have a lot of problems such as lack of storage capacity and performance degradation along with the massive increase of packet data volume. Therefore we propose the packet analysis model to apply issuing Big Data technology in the field of security. That is, we used NoSQL which is technology of massive data storage to collect the packet data growing massive and implemented the packet analysis model based on K-means clustering using MapReudce which is distributed programming framework, and then we have shown its high performance by experimenting.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.2 s.10
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• pp.29-43
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• 2006

Highway ARS service made several times handling of cellular phone for accept the one information. But, use the cellular phone while driving is against the law that 'Road Traffic Act' and wield influence on safety by degrade driver's attention that causes reduced section of concentration. On this study, propose a new type service that more useful and safer witch improved of existing ARS service to it served for cellular phone. For the analyze problem in existing ARS service, collect and analysis that ARS using status data and highway overall speed data, and then offer a better service type which based on improvements to that. Also, make a comparative analysis including measure of degree about easy to use and safety between two services by using the Stated Preference method, as a result of verifies the effect of new type service Finally, for measure of the effect the value of improved ARS service type that used willingness to pay in CVM method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.57-69
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• 2007

The most important problem in logistic activities may be to decrease the transportation efficiency due to the traffic congestion in urban areas. The traffic congestion reduces the average travel speed of freight vehicles, and then increases the travel time. These problems can lead the logistic system to be inefficient. As a result, it causes an increase of transportation costs. In addition, the increased cost is a main barrier for the transition to an advanced logistic system. This study focuses on the analysis of key factors choosing commercial freight vehicles using Logistic regression-analysis with RP (Revealed Preference) data to solve the increase of private freight cars and transportation costs. Additionally, this paper presents policies to promote good use of commercial freight vehicles based on the results of this study.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.29-35
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• 2005

The task for chromosome analysis and diagnosis by experienced cytogenetists are being concerned as repetitive, time consuming job and expensive. For that reason, intelligent agent based on chromosome knowledge base using web has been developed to be able to analyze chromosomes and obtain necessary advises from the knowledge base instead of human experts. That is to say, the knowledge base of IF THEN production rule was implemented to a knowledge domain with normal and abnormal chromosomes, and then the inference results by the knowledge base could enter the inference data into the database. Experimental data were composed of normal chromosomes of 2,736 cases and abnormal chromosomes of 259 cases that have been obtained from GTG-banding metaphase peripheral blood and amniotic fluid samples. The completed intelligent agent for the chromosome knowledge base provides variously morphological information by analysis of normal or abnormal chromosomes also has the advantage of being able to consult with the user on the chromosome analysis and diagnosis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.149-161
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• 2009

Electronic control systems of vehicle are rapidly developed to keep balance of a driver`s safety and the legal, social needs. The driver assistance systems are putted into practical use according to the cost drop in hardware and highly efficient sensor, etc. This study has developed a lane and vehicle detection program using CCD camera. The Risky Driving Analysis Program based on vision systems is developed by combining a risky driving detection algorithm formed in previous study with lane and vehicle detection program suggested in this study. Risky driving detection programs developed in this study with information coming from the vehicle moving data and lane data are useful in efficiently analyzing the cause and effect of risky driving behavior.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.571-576
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• 2013

Despite the development of the Internet, both in terms of technology and coverage, there are still remote areas and scenarios where connectivity is very difficult to achieve. Pocket Switched Network is a network paradigm that takes the advantage of human mobility to disseminate data. Factors such as mobility of nodes, link failures, discharged batteries, are among the challenges that may compromise connectivity in these networks. This paper presents a performance analysis of existing routing schemes for PSN in terms of delivery probability, overhead ratio, average latency and average residual energy when the number of nodes is increased. We seek to identify a scheme that maximizes data delivery while minimizing communication overhead and thus extending the network lifetime.