• Title/Summary/Keyword: Intelligent Data Analysis

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Analysis of Taxi Combined Surcharge System Using DTG Data (DTG 데이터를 활용한 택시 복합할증제 분석)

  • Kim, Seoung bum;Kim, Ho seon;Jung, Jong heon
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.19 no.6
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    • pp.152-162
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    • 2020
  • In the urban and rural complex, taxis move from downtown to rural areas for business purposes, and operate a combined surcharge system that preserves losses when they back to downtown. However, complaints related to the abolition of the compound surcharge system are increasing due to deformed operation that does not fit the purpose of the system. When the combinedsurcharge system is abolished, the taxi industry can be hit hard by the decrease in profits, and local governments are inevitable to support it. However, it is difficult to set the size of the subsidy considering the decrease of actual income. This study is to estimate the income reduction in the abolition of the combined surcharge system by scientific and objective method by analyzing the DTG data and the sales data collected from the digital driving recorder installed in the corporate taxi of the urban and rural complex area (e.g., Tongyeong city). This study is meaningful in that it used DTG data to solve the current issues in the real region and suggested the use of new DTG data.

Classification and Prediction of Highway Accident Characteristics Using Vehicle Black Box Data (블랙박스 영상 기반 고속도로 사고유형 분류 및 사고 심각도 예측 평가)

  • Junhan Cho;Sungjun Lee;Seongmin Park;Juneyoung Park
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.6
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    • pp.132-145
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    • 2022
  • This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

Emerging Data Management Tools and Their Implications for Decision Support

  • Eorm, Sean B.;Novikova, Elena;Yoo, Sangjin
    • Journal of Korea Society of Industrial Information Systems
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    • v.2 no.2
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    • pp.189-207
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    • 1997
  • Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

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DSS Architectures to Support Data Mining Activities for Supply Chain Management (데이터 마이닝을 활용한 공급사슬관리 의사결정지원시스템의 구조에 관한 연구)

  • Jhee, Won-Chul;Suh, Min-Soo
    • Asia pacific journal of information systems
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    • v.8 no.3
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    • pp.51-73
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    • 1998
  • This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

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Study on Imputation Methods of Missing Real-Time Traffic Data (실시간 누락 교통자료의 대체기법에 관한 연구)

  • Jang Jin-hwan;Ryu Seung-ki;Moon Hak-yong;Byun Sang-cheal
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.3 no.1 s.4
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    • pp.45-52
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    • 2004
  • There are many cities installing ITS(Intelligent Transportation Systems) and running TMC(Trafnc Management Center) to improve mobility and safety of roadway transportation by providing roadway information to drivers. There are many devices in ITS which collect real-time traffic data. We can obtain many valuable traffic data from the devices. But it's impossible to avoid missing traffic data for many reasons such as roadway condition, adversary weather, communication shutdown and problems of the devices itself. We couldn't do any secondary process such as travel time forecasting and other transportation related research due to the missing data. If we use the traffic data to produce AADT and DHV, essential data in roadway planning and design, We might get skewed data that could make big loss. Therefore, He study have explored some imputation techniques such as heuristic methods, regression model, EM algorithm and time-series analysis for the missing traffic volume data using some evaluating indices such as MAPE, RMSE, and Inequality coefficient. We could get the best result from time-series model generating 5.0$\%$, 0.03 and 110 as MAPE, Inequality coefficient and RMSE, respectively. Other techniques produce a little different results, but the results were very encouraging.

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A Study of Classification Analysis about Traffic Conditions Using Factor Analysis and Cluster Analysis (요인분석 및 군집분석을 활용한 교통상황 유형 분류분석)

  • Su-hwan Jeong;Kyeung-hee Han;Jaehyun (Jason) So;Choul-ki Lee
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.1
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    • pp.65-80
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    • 2023
  • In this study, a classification analysis was performed based on the type of traffic situation. The purpose was to derive the major variable factors that could represent the traffic situation. The TTI(Travel Time Index) was used as a criterion for determining traffic conditions, and analysis was performed using data generally detected by the Vehicle Detecting System(VDS). First, the major factors influencing the traffic situation were selected through factor analysis, and traffic conditions were clustered through a cluster analysis of the major factors. After that, variance analysis for each cluster was performed based on the TTI, and similar clusters were merged to categorize the type of traffic situation. The analysis derived, the maximum queue length and occupancy as major factors that could represent the traffic situation. Through this study, it is expected that efficient management of traffic congestion would be possible by just concentrating on the main variable factors that affect the traffic situation.

How Query by humming, a Music Information Retrieval System, is Being Used in the Music Education Classroom

  • Bradshaw, Brian
    • Journal of Multimedia Information System
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    • v.4 no.3
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    • pp.99-106
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    • 2017
  • This study does a qualitative and quantitative analysis of how music by humming is being used by music educators in the classroom. Music by humming is part division of music information retrieval. In order to define what a music information retrieval system is first I need to define what it is. Berger and Lafferty (1999) define information retrieval as "someone doing a query to a retrieval system, a user begins with an information need. This need is an ideal document- perfect fit for the user, but almost certainly not present in the retrieval system's collection of documents. From this ideal document, the user selects a group of identifying terms. In the context of traditional IR, one could view this group of terms as akin to expanded query." Music Information Retrieval has its background in information systems, data mining, intelligent systems, library science, music history and music theory. Three rounds of surveys using question pro where completed. The study found that there were variances in knowledge, training and level of awareness of query by humming, music information retrieval systems. Those variance relationships where based on music specialty, level that they teach, and age of the respondents.

A Study on Intelligent Technique for Correlation Application of Overcurrent and Leakage Current Signals in the Indoor Wiring (옥내배선에서 과전류와 누전 신호의 상관관계 적용을 위한 지능형 기법 연구)

  • Kim, Doo-Hyun;Kim, Eun-Jin;Kim, Sung-Chul
    • Journal of the Korean Society of Safety
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    • v.30 no.4
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    • pp.14-19
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    • 2015
  • The purpose of this paper is to study the correlation application that electrical fire causes occurs for overcurrent and leakage current signals in the indoor wiring. In the order to purpose, the causes data of overcurrent, or leakage current of electrical fire are drawn out referring to past studies, consulting with experts and experimental data. The correlation application was then applied with fuzzy logic of intelligent technique. To check the reliability and performance of the correlation application, modified center of area(CoA) was adopted to calculate the possibility that electrical fire occurs, whose value was then compared to the results. The chance of electrical fire calculated is higher when two causes of fire are put into the CoA of the correlation application of this paper than that of when each cause is separately put into the CoA. The correlation application developed in this study enables better analysis on possible electrical fire due to overcurrent, or leakage current and provides managers with the possibility of electrical fire so that they can better manage at a time of overcurrent, or leakage current.

A Probe Prevention Model for Detection of Denial of Service Attack on TCP Protocol (TCP 프로토콜을 사용하는 서비스거부공격 탐지를 위한 침입시도 방지 모델)

  • Lee, Se-Yul;Kim, Yong-Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.4
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    • pp.491-498
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    • 2003
  • The advanced computer network technology enables connectivity of computers through an open network environment. There has been growing numbers of security threat to the networks. Therefore, it requires intrusion detection and prevention technologies. In this paper, we propose a network based intrusion detection model using FCM(Fuzzy Cognitive Maps) that can detect intrusion by the DoS attack detection method adopting the packet analyses. A DoS attack appears in the form of the Probe and Syn Flooding attack which is a typical example. The SPuF(Syn flooding Preventer using Fussy cognitive maps) model captures and analyzes the packet informations to detect Syn flooding attack. Using the result of analysis of decision module, which utilized FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance comparison, the "KDD′99 Competition Data Set" made by MIT Lincoln Labs was used. The result of simulating the "KDD′99 Competition Data Set" in the SPuF model shows that the probe detection rates were over 97 percentages.

A Construction Method of Expert Systems in an Integrated Environment

  • Chen, Hui
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2001.01a
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    • pp.211-218
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    • 2001
  • This paper introduces a method of constructing expert systems in an integrated environment for automatic software design. This integrated environment may be applicable from top-level system architecture design, data flow diagram design down to flow chart and coding. The system is integrated with three CASE tools, FSD (Functional Structure Diagram), DFD (Data Flow Diagram) and structured chart PAD (Problem Analysis Diagram), and respective expert systems with automatic design capability by reusing past design. The construction way of these expert systems is based on systematic acquisition of design knowledge stemmed from a systematic design work process of well-matured developers. The design knowledge is automatically acquired from respective documents and stored in the respective knowledge bases. By reusing it, a similar software system may be designed automatically. In order to develop these expert systems in a short period, these design knowledge is expressed by the unified frame structure, functions of th expert system units are partitioned mono-functions and then standardized components. As a result, the design cost of an expert system can be reduced to standard work procedures. Another feature of this paper is to introduce the integrated environment for automatic software design. This system features an essentially zero start-up cost for automatic design resulting in substantial saving of design man-hours in the resulting in substantial saving of design man-hours in the design life cycle, and the expected increase in software productivity after enough design experiences are accumulated.

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