• Title, Summary, Keyword: C4.5 Algorithm

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Selecting variables for evidence-diagnosis of paralysis disease using CHAID algorithm

  • Shin, Yan-Kyu
    • 한국데이터정보과학회:학술대회논문집
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    • pp.76-78
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    • 2001
  • Variable selection in oriental medical research is considered. Decision tree analysis algorithms such as CHAID, CART, C4.5 and QUEST have been successfully applied to a medical research. Paralysis disease is a highly dangerous and murderous disease which accompanied with a great deal of severe physical handicap. In this paper, we explore the use of CHAID algorithm for selecting variables for evidence-diagnosis of paralysis, disease. Empirical results comparing our proposed method to the method using Wilks $\lambda$ given.

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A Basic Study on Control Algorithm for Car HVAC (승용차 공기조화 제어 알고리즘 기초연구)

  • Shin, Young-Gy
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.22 no.5
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    • pp.275-281
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    • 2010
  • Car HVAC is one of main factors influencing a potential customer's first impression. It should be fault-free, which requires the most stable control performance. So, the control algorithm consists of a proportional feedback only, not with an integral action needed for elimination of steady-state errors. To reduce the errors and make the response faster, feedforward algorithm based on predicted thermal load is added. To evaluate the performance, car HVAC is dynamically modelled and its control logic is simulated. The results shows that the proportional feedback leads to about $4^{\circ}C$ of steady-state error. When the feedback is combined with the feedforward algorithm and with a set value update based on disturbances, it predicts less than $1^{\circ}C$ of control error and improved thermal comfort.

Data Mining for Knowledge Management in a Health Insurance Domain

  • Chae, Young-Moon;Ho, Seung-Hee;Cho, Kyoung-Won;Lee, Dong-Ha;Ji, Sun-Ha
    • Journal of Intelligence and Information Systems
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    • v.6 no.1
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    • pp.73-82
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    • 2000
  • This study examined the characteristicso f the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms CHAID (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) since logistic regression has assumed a major position in the healthcare field as a method for predicting or classifying health outcomes based on the specific characteristics of each individual case. This comparison was performed using the test set of 4,588 beneficiaries and the training set of 13,689 beneficiaries that were used to develop the models. On the contrary to the previous study CHAID algorithm performed better than logistic regression in predicting hypertension but C5.0 had the lowest predictive power. In addition CHAID algorithm and association rule also provided the segment characteristics for the risk factors that may be used in developing hypertension management programs. This showed that data mining approach can be a useful analytic tool for predicting and classifying health outcomes data.

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Implementation of Parallel Volume Rendering Using the Sequential Shear-Warp Algorithm (순차 Shear-Warp 알고리즘을 이용한 병렬볼륨렌더링의 구현)

  • Kim, Eung-Kon
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.6
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    • pp.1620-1632
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    • 1998
  • This paper presents a fast parallel algorithm for volume rendering and its implementation using C language and MPI MasPar Programming Language) on the 4,096 processor MasPar MP-2 machine. This parallel algorithm is a parallelization hased on the Lacroute' s sequential shear - warp algorithm currently acknowledged to be the fastest sequential volume rendering algorithm. This algorithm reduces communication overheads by using the sheared space partition scheme and the load balancing technique using load estimates from the previous iteration, and the number of voxels to be processed by using the run-length encoded volume data structure.Actual performance is 3 to 4 frames/second on the human hrain scan dataset of $128\times128\times128$ voxels. Because of the scalability of this algorithm, performance of ]2-16 frames/sc.'cond is expected on the 16,384 processor MasPar MP-2 machine. It is expected that implementation on more current SIMD or MIMD architectures would provide 3O~60 frames/second on large volumes.

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A Study on the Combined Decision Tree(C4.5) and Neural Network Algorithm for Classification of Mobile Telecommunication Customer (이동통신고객 분류를 위한 의사결정나무(C4.5)와 신경망 결합 알고리즘에 관한 연구)

  • 이극노;이홍철
    • Journal of Intelligence and Information Systems
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    • v.9 no.1
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    • pp.139-155
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    • 2003
  • This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

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Charge-coupled analog-to-Digital Converter (전하결합소자를 이용한 Analog-to-Digital 변화기)

  • 경종민;김충기
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.18 no.5
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    • pp.1-9
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    • 1981
  • Experimental results on a 4-bit charge-coupled A/D converter are described. Major operations in the successive approximation algorithm are implemented in a monolithic chip, CCADC, which was fabricated usir p-channel CCD technology, with its die size of 4,200 mil2 Typical operating frequency range has been found out to be from 500Hz to 200kHz. In that frequency range, no missing code has been found in the whole signal range of 2.4 volts for ramp signal slewing at 1 LSB/(sampling time). A discussion is made on several layout techniques to conserve the nominal binary ratio of (8:4:2:1) among the areas of four adjacent potential wells (M wells), whose charge storing capacities correspond to each bit magnitude - 3.6 pC, 1.8 pC, 0.9 pC, and 0.45 pC nominal in the order of MSB to the LSB. The effect of 'dump slot', which is responsible for the excessive nonlinearity (2$\frac{1}{2}$LSB) in the A/D converter, is explained. A novel input scheme called 'slot zero insertion' to circumvent the deleterious effects of the dump slot is described with the experimental results.

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A Study on Labeling Algorithm of ECG Signal using Fuzzy Clustering (퍼지 클러스터링을 이용한 심전도 신호의 구분 알고리즘에 관한 연구)

  • Kong, In-Wook;Kweon, Hyuk-Je;Lee, Jeong-Whan;Lee, Myoung-Ho
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.4
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    • pp.427-436
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    • 1999
  • This paper describes an ECG signal labeling algorithm based on fuzzy clustering, which is very useful to the automated ECG diagnosis. The existing labeling methods compares the crosscorrelations of each wave form using IF-THEN binary logic, which tends to recognize the same wave forms such as different things when the wave forms have a little morphological variation. To prevent this error, we have proposed as ECG signal labeling algorithm using fuzzy clustering. The center and the membership function of a cluster is calculated by a cluster validity function. The dominant cluster type is determined by RR interval, and the representative beat of each cluster is determined by MF (Membership Function). The problem of IF-THEN binary logic is solved by FCM (Fuzzy C-Means). The MF and the result of FCM can be effectively used in the automated fuzzy inference -ECG diagnosis.

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Temperature Control Algorithm for Reefer Container (냉동컨테이너 온도 제어 알고리즘에 관한 연구)

  • Moon, Young-Sik;Park, Shin-Jun;Jung, Jun-Woo;Choi, Hyung-Rim;Kim, Jae-Joong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.12
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    • pp.2380-2386
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    • 2017
  • Fresh agricultural product in Korea are currently being exported overseas through expensive air transportation, supported by the governments's farm export subsidies. However all members of the World Trade Organization(WTO) must halt government subsidies by 2023. Accordingly, it is necessary to use marine transport capable of carrying freight at low cost. Reefer containers are used for marine transportation of fresh produce but it have a problem due to the temperature difference inside the reefer container which causes of fresh cargo and drop in freshness during sea transportation. In order to solve the problem, we developed a temperature control algorithm for reefer container that maintain a constant temperature and minimizes the deviation inside reefer container. The result showed that the maintained a constant temperature within a maximum of $0.5^{\circ}C$ based on the set-point of $4.0^{\circ}C$ inside reefer container.

A SOC Coefficient Factor Calibration Method to improve accuracy Of The Lithium Battery Equivalence Model (리튬 배터리 등가모델의 정확도 개선을 위한 SOC 계수 보정법)

  • Lee, Dae-Gun;Jung, Won-Jae;Jang, Jong-Eun;Park, Jun-Seok
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.4
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    • pp.99-107
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    • 2017
  • This paper proposes a battery model coefficient correction method for improving the accuracy of existing lithium battery equivalent models. BMS(battery management system) has been researched and developed to minimize shortening of battery life by keeping SOC(state of charge) and state of charge of lithium battery used in various industrial fields such as EV. However, the cell balancing operation based on the battery cell voltage can not follow the SOC change due to the internal resistance and the capacitor. Various battery equivalent models have been studied for estimation of battery SOC according to the internal resistance of the battery and capacitors. However, it is difficult to apply the same to all the batteries, and it tis difficult to estimate the battery state in the transient state. The existing battery electrical equivalent model study simulates charging and discharging dynamic characteristics of one kind of battery with error rate of 5~10% and it is not suitable to apply to actual battery having different electric characteristics. Therefore, this paper proposes a battery model coefficient correction algorithm that is suitable for real battery operating environments with different models and capacities, and can simulate dynamic characteristics with an error rate of less than 5%. To verify proposed battery model coefficient calibration method, a lithium battery of 3.7V rated voltage, 280 mAh, 1600 mAh capacity used, and a two stage RC tank model was used as an electrical equivalent model of a lithium battery. The battery charge/discharge test and model verification were performed using four C-rate of 0.25C, 0.5C, 0.75C, and 1C. The proposed battery model coefficient correction algorithm was applied to two battery models, The error rate of the discharge characteristics and the transient state characteristics is 2.13% at the maximum.

Fuzzy Inference Systems Based on FCM Clustering Algorithm for Nonlinear Process (비선형 공정을 위한 FCM 클러스터링 알고리즘 기반 퍼지 추론 시스템)

  • Park, Keon-Jun;Kang, Hyung-Kil;Kim, Yong-Kab
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.5 no.4
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    • pp.224-231
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    • 2012
  • In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.