• Journal of Biomedical Engineering Research
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• v.17 no.1
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• pp.19-24
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• 1996

The purpose of this paper is to design high-order (or radial) surface gradient coil (SGC), which can provide multi-dimensional spatial selection. Although the spatial Selection with High-Order gradienT (SHOT) can provide a 2-D selection with only one selective RF pulse, the high-order gradient pro- duced by conventional cylindrical-shape coils has not been clinically useful due to the large selection size caused by the limited radial gradient intensity. However, by using the proposed high-order SGCs located near the imaging region, the size of volume selection can be reduced to a clinically useflll size of 1-2 cm in diameter by applying stronger radial gradient field with much less gradient driving power. So far radial SGCs have been designed by using the field component method and may cause distortion in the selection shapes. In this paper, by using the target field approach for the coil design, selected volumes became almost circular. A 40 cm-by-40 cm $z^2$_surface gradient coil has been designed and implemented by using the target field approach. Phantom and volunteer studies have been performed Experimental results using spatially localized MRI show good agreement to the theoretically predicted behavior.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3A
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• pp.217-224
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• 2010

In this paper, we propose a hard handover algorithm for a base station's self-optimization, one of the automatic operational technologies for the 3GPP LTE systems. The proposed algorithm simultaneously considers a mixed target sell selection method for optimal selection and a multiple parameter based active hysteresis method with the received signal strength from adjacent cells and the cell load information of the candidate target cells from information exchanges between eNBs through X2 interface. The active hysteresis method chooses optimal handover hysteresis value considering the costs of the various environmental parameters effect to handover performance. The algorithm works on the optimal target cell and the hysteresis value selections for a base station's automatic operational optimization of the LTE system with the gathered informaton effects to the handover performance. The simulation results show distinguished handover performances in terms of the most important performance indexes of handover, handover failure rate and load balancing.

• Journal of Computing Science and Engineering
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• v.11 no.4
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• pp.121-129
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• 2017

Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and $l_{2,1}-norm$ sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The $l_{2,1}-norm$ sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.3
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• pp.366-377
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• 2016

This paper presents a vision-based guidance method that allows a fixed-wing aircraft to orbit around a target at a given radius. The guidance method uses a simple formula that regulates a relative side-bearing angle estimated by a vision system. The global asymptotic stability of the associated guidance law is demonstrated, and a linear analysis is performed to facilitate the proper selection of the relevant control parameters. A flight experiment is presented to demonstrate the feasibility and performance of the proposed guidance method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1166-1191
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• 2011

This paper presents an adaptive active tracking system with sector based scanning for a single PTZ camera. Dividing sectors on an image reduces the search space to shorten selection time so that the system can cover many targets. Upon the selection of a target, the system estimates the target trajectory to predict the zooming location with a finite amount of time for camera movement. Advanced estimation techniques using probabilistic reason suffer from the unknown object dynamics and the inaccurate estimation compromises the zooming level to prevent tracking failure. The proposed system uses the simple piecewise estimation with a few frames to cope with fast moving objects and/or slow camera movements. The target is tracked in multiple steps and the zooming time for each step is determined by maximizing the zooming level within the expected variation of object velocity and detection. The number of zooming steps is adaptively determined according to target speed. In addition, the iterative estimation of a zooming location with camera movement time compensates for the target prediction error due to the difference between speeds of a target and a camera. The effectiveness of the proposed method is validated by simulations and real time experiments.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.887-896
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• 2002

Based on SPICE (Software Process Improvement and Capability dEtermination) evaluation model, SPIRE (Software Process Improvement in Regions of Europe) is developed and published as a process improvement model for small and medium size organizations. However, practical selection guidelines or mapping rules between business goals and software processes do not exist within SPIRE. This research aims to construct an objective reference mapping table between business goals and software processes, and to propose a process selection method using the mapping table. The mapping table is constructed by the convergence of domestic software process experts' opinions using Delphi techniques. In the suggested process selection method, target processes are selected using the intuition of project participants or project managers as well as the reference mapping table. The feasibility of the proposed selection method has been reviewed by applying to two small software companies. Using the reference mapping table, we could select key processes which were passed over by project managers.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.124-129
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• 2006

This parer presents a method of ranking translation word selection for Korean verbs based on lexical knowledge contained in a bilingual Korean-English dictionary and WordNet that are easily obtainable knowledge resources. We focus on deciding which translation of the target word is the most appropriate using the measure of semantic relatedness through the 45 extended relations between possible translations of target word and some indicative clue words that play a role of predicate-arguments in source language text. In order to reduce the weight of application of possibly unwanted senses, we rank the possible word senses for each translation word by measuring semantic similarity between the translation word and its near synonyms. We report an average accuracy of $51\%$ with ten Korean ambiguous verbs. The evaluation suggests that our approach outperforms the default baseline performance and previous works.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.455-467
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• 2017

When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.

• Journal of Distribution Science
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• v.18 no.7
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• pp.63-72
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• 2020

Purpose: This study examines the market segmentation and strategy of PT INKA, a rolling stock manufacturer in Indonesia. Research design, data and methodology: The study used the MCDM (Multiple Criteria Decision Making) method specifically the AHP (Analytical Hierarchy Process). The AHP method was applied to identify the target market. This method or approach considers the market attractiveness and competitive strength criteria with quantified parameters. Results: a) Australia, Kenya, Tanzania, New Zealand, and India emerge as the top five target markets; b) There is justification for rolling stock manufacturers to allocate their resources in winning the market share. Conclusion: The main challenge confronting the rolling stock manufacturer is limited resources to acquire a particular market share despite abundant opportunities in this sector. Despite the mastery of technology and long experience in the industry, selecting a target market with multiple criteria could be difficult for an emerging rolling stock manufacturer in South East Asia.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.749-758
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• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.