• Journal of Information Processing Systems
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• v.2 no.1
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• pp.23-27
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• 2006

This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many features and a target value. We classified them into positive and negative data based on the target value. We then divided the range of the feature values into 10 intervals and calculated the distribution of the intervals in each positive and negative data. Then, we selected the features and the intervals of the features for which the distributional differences are over a certain threshold. Using the selected intervals and features, we could obtain the reduced training data. In the experiments, we will show that the reduced training data can reduce the training time of the neural network by about 40%, and we can obtain more profit on simulated stock trading using the trained functions as well.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.185-194
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• 2007

This paper presents a motor selection technique for a manipulator design that is used in a multipurpose dog-horse robot. Since the dynamics of a manipulator and its servo drives are closely related to each other, it requires a repetitive analysis to determine a suitable motor. In order to simplify this procedure, Straete et al. proposed a simple normalization method to separate the load dependent terms and the motor dependent terms. This technique is adopted in this paper for selecting a motor in designing a manipulator.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.1-6
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• 2024

Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1149-1157
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• 2003

As an Embedded system combining hardware and software gets more complicated, the importance of the embedded software test increases. Especially, it is mandatory to test the embedded software in the system which has high safety level. In embedded system, it is necessary to develop a test technique to detect faults in interaction between hardware and software. In this paper, we propose a test data selection technique using a fault injection technique for the faults in interaction between hardware and software in embedded system and we apply our technique to the Digital Plant Protection System and analyze effectiveness of the proposed technique through experiments.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.896-908
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• 2003

Interaction technique in .3D virtual environment is a decisive factor that affects the immersion and presence felt by users in virtual space. Especially, in fields that require exquisite manipulation of objects such as electronic manuals in desktop environment, interaction technique that supports effective and natural manipulation of object is in demand. In this paper, 3D scene graph can be internally divided and reconstructed to a list defending on the users selection and through moving focus among the selection candidate objects list, the user can select 3D object more accurately Also, by providing various feedbacks for each manipulation stage, more effective manipulation is possible. The proposed technique can be used as 3D user interface in areas that require exquisite object manipulation.

• Journal of the Korean Society of Safety
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• v.25 no.6
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• pp.180-185
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• 2010

We have to develop more correct and systematic way to choose Hazardous Highway Segments. In this research, we applied CRP(Continuous Risk Profile) technique which developed by UC Berkeley Traffic Safety Center in year of 2007, and can analyze yearly dangerous level tendency of continuity in the route of main road that is under California Department of Transportation(Caltrans). We changed standard of CRP to suit in Korean circumstance with consideration in radius of curve and traffic volume. For the verification by actual accident data, we embodiment the CRP by using the data from total of 587 case of accident in latest 10 years in Gyeong-Bu Highways, the amount of 56km. Finally, the effectiveness of technique in this research has been verified by obtained same result with current method for Hazardous Highway Segments. In addition, when calculating the Hazardous Highway Segments with technique that presented in this research we obtained following statements. First, identified dangerous level of continuity in the route by using CRP. Second, Accurate of Actual Hazardous Highway Segments selection has been developed by using last 10 year's data and profile making which provide simplicity analyze of Tendency. Third, after reforming the way of selection, effective range has been wider than former selection and it gives advantage for the policy side.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.1-10
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• 2022

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.11
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• pp.24-29
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• 2010

Selection-type relaying protocol, which is one of cooperative relaying protocols, provides low decoding complexity and improved system performance due to selection diversity. In this paper, we deal with linear precoding technique that minimize the error probability of cooperative MIMO system. Under the assumption that full channel state information is available at whole nodes, linear source and relay precoders, which minimize mean squared error of the estimated symbol vector, are proposed. Moreover, unlikely to the conventional selection-type relaying protocol using a fixed threshold signal-to-noise-ratio, new transmission link selection algorithm selects direct link or relay link as a transmission link, is introduced. Simulation results show that the proposed linear precoder with the transmission link selection algorithm outperforms the conventional precoders for two-hop relaying protocols or selection-type relaying protocols.

• Journal of Animal Science and Technology
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• v.55 no.6
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• pp.501-508
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• 2013

This study was conducted to determine genetic correlations among carcass traits measured by ultrasound and real carcass measurements and to estimate indirect selection responses for real carcass traits based on ultrasound measurements in Hanwoo cows. To accomplish this, 22,080 ultrasound measurement records from 17,926 cows collected from 2001 to 2012 and 11,907 carcass records obtained from fattened cattle from 2008 to 2012 were used. Genetic parameters were estimated based on eye muscle area (EMA), backfat thickness (BF) and marbling score (MS) measured by ultrasound-scanning of live cows and using the official technique on chilled bovine half-carcasses after slaughtering. Heritability and genetic correlation for carcass traits were estimated using a mixed model equation that consisted of environmental effects as fixed parameters and additive genetic effects and residual effects as random parameters, assuming that traits were different between ultrasound and carcass measurements. This statistical method was applied to the average information restricted maximum likelihood method. The heritability of EMA, BF and MS measured by ultrasound were 0.33, 0.61 and 0.46, respectively, while the heritability estimates of the corresponding traits based on carcass measurements were 0.29, 0.40 and 0.38, respectively and the genetic correlation between ultrasound and carcass traits for EMA, BF and MS were 0.41, 0.78 and 0.67, respectively. The genetic correlation between ultrasound and carcass traits was highly positive. Additionally, the selection response for marbling score was estimated to be 0.42 per generation if the cows were selected based on the ultrasound scan marbling score with an assumed selection intensity of 0.8. Overall, these results indicate that the ultrasound scan technique would be applicable to judging cow selection for genetically improved meat quality.

• Journal of Information Technology Applications and Management
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• v.19 no.3
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• pp.19-30
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• 2012

Material selection is a complex problem in the design and development of products for diverse engineering applications. This paper is aimed to present a fuzzy decision making approach to deal with the material selection in engineering design problems. A fuzzy multi criteria decision-making model is proposed for solving the material selection problem. The proposed model makes use of fuzzy TOPSIS (Technique for Order reference by Similarity to Ideal Solution) with trapezoidal numbers for evaluating the criteria and ranking the alternatives. And result is compared with fuzzy VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, means Multi criteria Optimisation and Compromise Solution) which is proposed by Jeya Girubha and Vinodh [2012]. The present paper is aimed to also improve literature of fuzzy decision making for material selection problem.