• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.331-337
• /
• 1997

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general descriptions of concepts from specific instances of these concepts. In many real life situations however new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overall set of instances. The method of learning presented here is based on a rough set concept proposed by Pawlak[2]. It is shown an algorithm to fund minimal set of rules using reduct change theorems giving criteria for minimum recalculation and an illustrative example.

• Journal of Electrical Engineering and information Science
• /
• v.3 no.1
• /
• pp.126-130
• /
• 1998

In this paper, a new interference rejection filter using the data-recycling technique is presented. The reference signal of the interference rejection filter is formed by using chip decisions, which is correlated with the narrowband interference components of the received signal. The decision feedback techniques reduce the distortion of the desired signal, which is introduced by the interference rejection filter through he use of feedback chip decisions. In order to update the filter coefficients a simple and efficient data-recycling technique that has improved performance over the conventional LMS algorithm is used. The performance of the proposed interference rejection filter is compared to filter with conventional LMS linear filter. The results show that the convergence speed of the proposed interference rejection filter using decision feedback and data-recycling technique is significantly faster than that of the conventional filters. Also, BER performance of the interference rejection filter demonstrates the superiority of the proposed filter algorithm.

• Journal of Korean Institute of Industrial Engineers
• /
• v.34 no.3
• /
• pp.289-295
• /
• 2008

The shared-route decision algorithms in multicasting communications networks to provide the internet-based services such as IPTV, remote education/health, and internet broadcasting are presented. The three main measures of maximum delay, average delay and estimated delay between each node and member are adopted. Under the Mesh network with the uniform random cost between each node, the algorithm's performance is compared to the optimal solution with the minimum cost by all enumeration. The simulation results show that the algorithm using the estimated delay outperforms the other two methods.

• The Journal of the Acoustical Society of Korea
• /
• v.17 no.4E
• /
• pp.11-16
• /
• 1998

For large vocabulary speech recognition using HMMs, context-dependent subword units have been often employed. However, when context-dependent phone models are used, they result in a system which has too may parameters to train. The problem of too many parameters and too little training data is absolutely crucial in the design of a statistical speech recognizer. Furthermore, when building large vocabulary speech recognition systems, unseen triphone problem is unavoidable. In this paper, we propose the modified phonetic decision tree algorithm for the automatic prediction of unseen triphones which has advantages solving these problems through following two experiments in Japanese contexts. The baseline experimental results show that the modified tree based clustering algorithm is effective for clustering and reducing the number of states without any degradation in performance. The task experimental results show that our proposed algorithm also has the advantage of providing a automatic prediction of unseen triphones.

• Journal of Korean Neurosurgical Society
• /
• v.59 no.4
• /
• pp.327-333
• /
• 2016

Adult spinal deformity (ASD) is one of the most challenging spinal disorders associated with broad range of clinical and radiological presentation. Correct selection of fusion levels in surgical planning for the management of adult spinal deformity is a complex task. Several classification systems and algorithms exist to assist surgeons in determining the appropriate levels to be instrumented. In this study, we describe our new simple decision making algorithm and selection of fusion level for ASD surgery in terms of adult idiopathic idiopathic scoliosis vs. degenerative scoliosis.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1768-1771
• /
• 2002

Automatic Transmission System(ATS) was designed to replace the human's manual operation of the gear box. So far, this system operates with the fixed shift pattern information. In this paper, new algorithm considering driver's operation tendency is proposed. Also, to get rid of the uselessly frequent shift of the ATS, the conditions and the status of the vehicle would be included for the evaluation in making a decision of shifting. A field test is done in a car equipped with the computer set connected to Transmission Control Units(TCU) to check the status of the test car, and it shows the excellency of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.10
• /
• pp.2679-2686
• /
• 1996

In this paper, we propsoed the two methods to improve the conventional decision feedback interference canceller in DS/SS communication systems. The data bit is obtained by correlating the PN sequence with the received signals to the present time k, and thus the errors in the reference signal can be reduced by newly deciding all the reference signals with the resultant data bit. Additionally the cancelled signals are computed with less weight for initial reference signals of low processing gain, and highly weighted as the processing gain goes up. the resulting interference canceller outperforms the existing ones. By simulation, we found the proposed algorithm has "2-3 dB" performance gain at BER 10$^{-3}$ compared to the conventional descision feedback algorithm.algorithm.

• Journal of Communications and Networks
• /
• v.9 no.3
• /
• pp.312-318
• /
• 2007

In this paper, we propose the optimal offset-time decision (OOD) algorithm which can effectively reduce the pre-transmission delay compared to the perfect isolation model, and can also be extended to general n priority classes while the target loss probability of each class is guaranteed for the variable offered load. In order to drive the OOD algorithm, we first analyze the loss probability of each priority class through class aggregation and iteration method; the analytic results obtained through the proposed algorithm are then validated with results garnered from extensive simulation tests.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.747-749
• /
• 2003

For forest fire monitoring in relatively cool area like Siberia, design of Decision Support System (DSS) is proposed. The DSS is consisted of three different algorithms to detect potential fires from NOAA AVHRR image. The algorithm developed by CCRS (Canada Center for Remote Sensing) uses fixed thresholds for multi-channel information like one by ESA (European Space Agency). The algorithm of IGBP (International Geosphere Biosphere Program) involves contextual information in deriving fire pixels. CCRS and IGBP algorithms are rather liberal compared to more conservative ESA algorithm. Fire pixel information from the three algorithms is presented to the user. The user considers all these information in making decision about the location fire takes place.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.4
• /
• pp.555-566
• /
• 2019

In order to prepare for the future warfare environment, which requires a faster operational tempo, it is necessary to utilize the fourth industrial revolution technology in the field of military operations. This study propose a methodology, 'machine learning based dynamic targeting', which can contribute to reduce required man-hour for dynamic targeting. Specifically, a decision tree algorithm is considered to apply to dynamic targeting process. The algorithm learns target prioritization patterns from JIPTL(Joint Integrated Prioritized Target List) which is the result of the deliberate targeting, and then learned algorithm rapidly(almost real-time) determines priorities for new targets that occur during ATO(Air Tasking Order) execution. An experiment is performed with artificially generated data to demonstrate the applicability of the methodology.