• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1356-1376
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• 2023

Software effort estimation is one of the most difficult tasks in software development whereas predictability is also of equal importance for strategic management. Accurate prediction of the actual cost that will be incurred in software development can be very beneficial for the strategic management. This study discusses the latest trends in software estimation focusing on analogy-based techniques to show how they have improved the accuracy for software effort estimation. It applies the standard deviation technique to the expected value of analogy-based estimates to improve accuracy. In more than 60 percent cases the applied technique of this study helped in improving the accuracy of software estimation by reducing the Magnitude of Relative Error (MRE). The technique is simple and it calculates the expected value of cost or time and then uses different confidence levels which help in making more accurate commitments to the customers.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 추계학술대회 논문집
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• pp.75-76
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• 2010

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5143-5158
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• 2018

Improving the intrusion detection system (IDS) is a pressing need for cyber security world. With the growth of computer networks, there are constantly daily new attacks. Machine Learning (ML) is one of the most important fields which have great contribution to address the intrusion detection issues. One of these issues relates to the imbalance of the diverse classes of network traffic. Accuracy paradox is a result of training ML algorithm with imbalanced classes. Most of the previous efforts concern improving the overall accuracy of these models which is truly important. However, even they improved the total accuracy of the system; it fell in the accuracy paradox. The seriousness of the threat caused by the minor classes and the pitfalls of the previous efforts to address this issue is the motive for this work. In this paper, we consolidated stratified sampling, cost function and weighted Support Vector Machine (WSVM) method to address the accuracy paradox of ID problem. This model achieved good results of total accuracy and superior results in the small classes like the User-To-Remote and Remote-To-Local attacks using the improved version of the benchmark dataset KDDCup99 which is called NSL-KDD.

• 소성∙가공
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• 제27권3호
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• pp.154-159
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• 2018

In the rectangular bar multi-stage drawing process, the cross-section dimensional accuracy of the rectangular bar varies depending on the aspect ratio and process conditions. It is very important to predict the dimensional error of the cross-section occurring in the multi-stage drawing process according to the aspect ratio of the rectangular bar and the half die angle of each pass. In this study, a process map for improving the dimensional accuracy according to the aspect ratio was derived in the drawing process of a rectangular bar. FE-simulation of the multi-stage shape drawing process was carried out with four types of rectangular bar. The results of the FE-simulation were trained to the nonlinear relationship between the shape parameters using an Artificial Neural Network (ANN), and the process maps were derived from them. The optimum half die angles were determined from the process maps on the dimensional accuracy. The validity of the suggested process map for aspect ratios 1.25~2:1 were verified through FE-simulation and experimentation.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.

• 로봇학회논문지
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• 제10권1호
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• pp.16-23
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• 2015

This paper presents a method of improving the pose recognition accuracy of objects by using Kinect sensor. First, by using the SURF algorithm, which is one of the most widely used local features point algorithms, we modify inner parameters of the algorithm for efficient object recognition. The proposed method is adjusting the distance between the box filter, modifying Hessian matrix, and eliminating improper key points. In the second, the object orientation is estimated based on the homography. Finally the novel approach of Auto-scaling method is proposed to improve accuracy of object pose estimation. The proposed algorithm is experimentally tested with objects in the plane and its effectiveness is validated.

• 대한음성학회지:말소리
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• 제63호
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• pp.113-124
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• 2007

In this paper, we propose a use of confidence vector as an intermediate input feature for multi-stage based speech recognition architecture to improve recognition accuracy. A multi-stage speech recognition structure is introduced as a method to reduce the computational complexity of the decoding procedure and then accomplish faster speech recognition. Conventional multi-stage speech recognition is usually composed of three stages, acoustic search, lexical search, and acoustic re-scoring. In this paper, we focus on improving the accuracy of the lexical decoding by introducing a confidence vector as an input feature instead of phoneme which was used typically. We take experimental results on 220K Korean Point-of-Interest (POI) domain and the experimental results show that the proposed method contributes on improving accuracy.

• 디지털산업정보학회논문지
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• 제7권3호
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• pp.107-113
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• 2011

An OFDMA(Orthogonal Frequency Division Multiple Access) includes a MIMO(Multi-Input Multi-Output) scheme for improving spectral efficiency and data throughput. Recognizing that the performance of MIMO system is heavily dependent upon the accuracy of channel estimation, we propose a novel channel estimation for the MIMO scheme based on OFDMA. Conventional interpolation-based channel estimation suffers from poor estimation error at specific subcarriers. Proposed scheme makes use of a planar interpolation instead of linear interpolation for those subcarriers of bad accuracy. Simulation results show that the proposed scheme improves the performance of MIMO system by improving the accuracy in channel estimation especially for the adverse subcarrier positions. It is observed that the proposed scheme outperforms the conventional method by about 2dB in terms of both mean squared error and overall bit error rate with a reasonable computational complexity.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.1071-1072
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• 2013

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.131-137
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• 1989

The effect of kinematic model choice on robot calibration is examined. This paper presents a complete formulation to identify the actual robot kinematic parameters directly from position data. The method presented in this paper applies to any serial link manipulator with arbitrary order and combination of revolute and prismatic joint.