• The KIPS Transactions:PartA
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• v.13A no.3 s.100
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• pp.261-266
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• 2006

Priority queues can be used in applications such as scheduling, sorting, and shortest path network problem. Fibonacci heap, pairing heap, and M-heap are priority queues based on pointers. This paper proposes a modified M-heap with an way representation, called MA-heap, that resolves the problem mentioned in [1]. The MA-heap takes O(1) amortized time and O(logn) time to insert an element and delete the max/min element, respectively. These time complexities are the same as those of the M-heap. In addition, it is much easier to implement an MA-heap than a heap proposed in [5] since it is based on the simple traditional heap.

• Journal of IKEEE
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• v.17 no.1
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• pp.29-35
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• 2013

In this paper, we suggest an improved Convex Hull algorithm considering sort in plane point set. This algorithm has low computational complexity since processing data are reduced by characteristic of extreme points. Also it obtains a complete convex set with just one processing using an convex vertex discrimination criterion. Initially it requires sorting of point set. However we can't quickly sort because of its heavy operations. This problem was solved by replacing value and index. We measure the execution time of algorithms by generating a random set of points. The results of the experiment show that it is about 2 times faster than the existing algorithm.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.23-48
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• 2013

This study compared and analyzed 15 CBMR (Content-based Music Retrieval) systems accessible on the web in terms of DB size and type, query type, access point, input and output type, and search functions, with reviewing features of music information and techniques used for transforming or transcribing of music sources, extracting and segmenting melodies, extracting and indexing features of music, and matching algorithms for CBMR systems. Application of text information retrieval techniques such as inverted indexing, N-gram indexing, Boolean search, truncation, keyword and phrase search, normalization, filtering, browsing, exact matching, similarity measure using edit distance, sorting, etc. to enhancing the CBMR; effort for increasing DB size and usability; and problems in extracting melodies, deleting stop notes in queries, and using solfege as pitch information were found as the results of analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.483-503
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• 2015

This paper analyzes the optimal user density and power allocation for Device-to-Device (D2D) communication underlaying cellular networks on multiple bands with the target of maximizing the D2D transmission capacity. The entire network is modeled by Poisson point process (PPP) which based on stochastic geometry. Then in order to ensure the outage probabilities of both cellular and D2D communication, a sum capacity optimization problem for D2D system on multiple bands is proposed. Using convex optimization, the optimal D2D density is obtained in closed-form when the D2D transmission power is determined. Next the optimal D2D transmission power is obtained in closed-form when the D2D density is fixed. Based on the former two conclusions, an iterative algorithm for the optimal D2D density and power allocation on multiple bands is proposed. Finally, the simulation results not only demonstrate the D2D performance, density and power on each band are constrained by cellular communication as well as the interference of the entire system, but also verifies the superiority of the proposed algorithm over sorting-based and removal algorithms.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.117-124
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• 2009

In the environment where various and complicated threat signals exist, RWR (Radar Warning Receiver), which can warn pilot of the existence of threats, has long been a necessary electronic warfare (EW) system to improve survivability of aircraft. The angle of arrival (AOA) information, the most reliable sorting parameter in the RWR, is measured by means of four-quadrant amplitude comparison direction finding (DF) technique. Each of four antennas (usually spiral antenna) of DF unit covers one of four quadrant zones, with 90 degrees apart with nearby antenna. According to the location of antenna installed in helicopter, RWR is subject to signal loss and interference by helicopter body and structures including tail bumper, rotor blade, and so on, causing a difficulty of detecting hostile emitters. In this paper, the performance degradation caused by signal interference by tail rotor blades has been estimated by measuring amplitude video signals into which RWR converts RF signals in case a part of antenna is screened by real tail rotor blade in anechoic chamber. The results show that corruption of pulse amplitude (PA) is main cause of DF error. We have proposed two algorithms for resolving the interference by tail rotor blades as below: First, expand the AOA group range for pulse grouping at the first signal analysis phase. Second, merge each of pulse trains with the other, that signal parameter except PRI and AOA is similar, after the first signal analysis phase. The presented method makes it possible to use RWR by reducing interference caused by blade screening in case antenna is screened by tail rotor blades.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.2
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• pp.27-38
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• 2018

Environmental planning includes resource allocation and spatial planning process for the conservation and management of environment. Because the spatialization of the environmental planning is not specifically addressed in the relevant statutes, it actually depends on the qualitative methodology such as expert judgement. The results of the qualitative methodology have the advantage that the accumulated knowledge and intuition of the experts can be utilized. However, it is difficult to objectively judge whether it is enough to solve the original problem or whether it is the best of the possible scenarios. Therefore, this study proposed a methodology to quantitatively and objectively spatialize various environmental planning. At first, we suggested a quantitative spatial planning model based on an optimization algorithm. Secondly, we applied this model to two kinds of environmental planning and discussed about the model performance to present the applicability. Since the models were developed based on conceptual study site, there was a limitation in showing possibility of practical use. However, we expected that this study can contribute to the fields related to environmental planning by suggesting flexible and novel methodology.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.12
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• pp.1980-1990
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• 2018

Objective: The aim of this study was to discover the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) that were found in selective sweep regions of the Landrace genome Methods: Whole-genome re-sequencing data were obtained from 40 pigs, including 14 Landrace, 16 Yorkshire, and 10 wild boars, which were generated with the Illumina HiSeq 2000 platform. The nsSNPs in the selective sweep regions of the Landrace genome were identified, and the impacts of these variations on protein function were predicted to reveal their potential association with traits of the Landrace breed, such as reproductive capacity. Results: Total of 53,998 nsSNPs in the mapped regions of pigs were identified, and among them, 345 nsSNPs were found in the selective sweep regions of the Landrace genome which were reported previously. The genes featuring these nsSNPs fell into various functional categories, such as reproductive capacity or growth and development during the perinatal period. The impacts of amino acid sequence changes by nsSNPs on protein function were predicted using two in silico SNP prediction algorithms, i.e., sorting intolerant from tolerant and polymorphism phenotyping v2, to reveal their potential roles in biological processes that might be associated with the reproductive capacity of the Landrace breed. Conclusion: The findings elucidated the domestication history of the Landrace breed and illustrated how Landrace domestication led to patterns of genetic variation related to superior reproductive capacity. Our novel findings will help understand the process of Landrace domestication at the genome level and provide SNPs that are informative for breeding.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.849-858
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• 2004

Most of supervised teaming algorithms could be applied after that continuous variables are transformed to categorical ones at the preprocessing stage in order to avoid the difficulty of processing continuous variables. This preprocessing stage is called global discretization, uses the class distribution list called bins. But, when data are large and the range of the variable to be discretized is very large, many sorting and merging should be performed to produce a single bin because most of global discretization methods need a single bin. Also, if new data are added, they have to perform discretization from scratch to construct categories influenced by the data because the existing methods perform discretization in batch mode. This paper proposes a method that extracts sample points and performs discretization from these sample points in order to solve these problems. Because the approach in this paper does not require merging for producing a single bin, it is efficient when large data are needed to be discretized. In this study, an experiment using real and synthetic datasets was made to compare the proposed method with an existing one.

• Resources Recycling
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• v.15 no.6 s.74
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• pp.3-9
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• 2006

In this study, a machine vision system using a color recognition method has been designed and developed to automatically sort out specified materials from a mixture, especially Cu and other non-ferrous metal scraps from a mixture of iron scraps. The system consists of a CCD camera, light sources, a frame grabber, conveying devices and an air-nozzle ejector, and is program-controlled by a image processing algorithms. The ejectors designed to be operated by an I/O interface communication with a hardware controller. In the functional tests of the system, its efficiency in the separation of Cu scraps from its mixture with Fe ones reaches to 90% or more at a conveying speed of 15m/min, and thus the system is proven to be excellent in terms of the separating efficiency. Therefore, it is expected that the system can be commercialized in the industry of shredder makers if an automated sorting system of high speed is realized.