• 인터넷정보학회논문지
• /
• 제22권5호
• /
• pp.1-8
• /
• 2021

Small-cells in heterogeneous networks are one of the important technologies to increase the coverage and capacity in 5G cellular networks. However, due to the randomly arranged small-cells, co-tier and cross-tier interference increase, deteriorating the system performance of the network. In order to manage the interference, some channel management methods use fractional frequency reuse(FFR) that divides the cell coverage into the inner region(IR) and outer region(OR) based on the distance from the macro base station(MBS). However, since it is impossible to properly measure the distance in the method with FFR, we propose a new interference aware FFR(IA-FFR) method to enhance the system performance. That is, the proposed IA-FFR method divides the MUEs and SBSs into the IR and OR groups based on the signal to interference plus noise ratio(SINR) of macro user equipments(MUEs) and received signals strength of small-cell base stations(SBSs) from the MBS, respectively, and then dynamically assigns subchannels to MUEs and small-cell user equipments. As a result, the proposed IA-FFR method outperforms other methods in terms of the system capacity and outage probability.

• Genomics & Informatics
• /
• 제16권4호
• /
• pp.23.1-23.5
• /
• 2018

Virus taxonomy was initially determined by clinical experiments based on phenotype. However, with the development of sequence analysis methods, genotype-based classification was also applied. With the development of genome sequence analysis technology, there is an increasing demand for virus taxonomy to be extended from in vivo and in vitro to in silico. In this study, we verified the consistency of the current International Committee on Taxonomy of Viruses taxonomy using an in silico approach, aiming to identify the specific sequence for each virus. We applied this approach to norovirus in Caliciviridae, which causes 90% of gastroenteritis cases worldwide. First, based on the dogma "protein structure determines its function," we hypothesized that the specific sequence can be identified by the specific structure. Firstly, we extracted the coding region (CDS). Secondly, the CDS protein sequences of each genus were annotated by the conserved domain database (CDD) search. Finally, the conserved domains of each genus in Caliciviridae are classified by RPS-BLAST with CDD. The analysis result is that Caliciviridae has sequences including RNA helicase in common. In case of Norovirus, Calicivirus coat protein C terminal and viral polyprotein N-terminal appears as a specific domain in Caliciviridae. It does not include in the other genera in Caliciviridae. If this method is utilized to detect specific conserved domains, it can be used as classification keywords based on protein functional structure. After determining the specific protein domains, the specific protein domain sequences would be converted to gene sequences. This sequences would be re-used one of viral bio-marks.

• 한국컴퓨터정보학회논문지
• /
• 제24권1호
• /
• pp.93-100
• /
• 2019

One of critical issues in dealing with 360-degree realistic contents is the performance degradation in searching and recognition process since they support up to 4K UHD quality and have all image angles including the front, back, left, right, top, and bottom parts of a screen. To solve this problem, in this paper, we propose an efficient search and comparison method for 360-degree realistic contents. The proposed method first corrects the distortion at the less distorted regions such as front, left and right parts of the image excluding severely distorted regions such as upper and lower parts, and then it extracts feature points at the corrected region and selects the representative images through sequence classification. When the query image is inputted, the search results are provided through feature points comparison. The experimental results of the proposed method shows that it can solve the problem of performance deterioration when 360-degree realistic contents are recognized comparing with traditional 2D contents.

• 농업과학연구
• /
• 제50권3호
• /
• pp.357-364
• /
• 2023

In this study, we propose a ripe tomato bunch localization method based on convolutional neural networks, to be applied in robotic harvesting systems. Tomato images were obtained from a smart greenhouse at the Rural Development Administration (RDA). The sample images for training were extracted based on tomato maturity and resized to 128 × 128 pixels for use in the classification model. The model was constructed based on four-layer convolutional neural networks, and the classes were determined based on stage of maturity, using a Softmax classifier. The localization of the ripe tomato bunch region was indicated on a class activation map. The class activation map could show the approximate location of the tomato bunch but tends to present a local part or a large part of the ripe tomato bunch region, which could lead to poor performance. Therefore, we suggest a recursive method to improve the performance of the model. The classification results indicated that the accuracy, precision, recall, and F1-score were 0.98, 0.87, 0.98, and 0.92, respectively. The localization performance was 0.52, estimated by the Intersection over Union (IoU), and through input recursion, the IoU was improved by 13%. Based on the results, the proposed localization of the ripe tomato bunch area can be incorporated in robotic harvesting systems to establish the optimal harvesting paths.

• 한국환경생태학회지
• /
• 제22권6호
• /
• pp.666-678
• /
• 2008

최근 한국에서는 각종 개발계획에서 자연환경과 생태계를 구체적으로 고려할 수 있는 실천방안으로서 비오톱에 대한 관심이 높아지면서 각 지자체마다 비오톱지도 작성에 대한 요구가 빠르게 확산되고 있다. 하지만, 아직 통일된 비오톱 유형 및 분류체계, 분류기준에 대한 표준안이 없으며, 지자체마다 서로 다른 방법론이 적용되고 있는 실정이다. 이러한 문제의식 속에서 우선 지금까지 선행된 국내 외사례의 비오톱 유형 및 분류체계, 분류기준 등을 비교 검토하여, 비오톱 유형분류 및 분류체계의 초안을 작성하였다. 또한, 선정된 대표지역의 현장조사와 자문회의 등의 계속적인 피드백 과정을 통하여 한국에 적합한 비오톱 유형 및 분류체계를 개발하고자 하였다. 조사결과 국내사례의 비오톱 유형분류체계는 2단계나 3단계 분류체계가 혼합되어 있으며, 주로 대분류, 소분류의 2단구조로 구성되어있다. 또한, 일반적으로 적용되고 있는 비오톱 유형분류기준으로는 토지이용, 토양피복율, 녹피율, 식생 등 이었다. 본 연구에서는 비오톱 유형분류를 위해 대분류(biotope class), 중분류(biotore group), 소분류(biotope type), 세분류(sub-biotope type)의 4단계 분류체계를 제시하였으며, 대분류 13개 유형, 중분류 45개 유형, 소분류 127개 유형으로 비오톱유형을 분류하였다. 하지만, 비오톱 유형분류는 지역의 특성이 고려되어야 하므로, 본 연구에서 제안한 분류체계를 기반으로 하여 소분류 및 세분류 단계에서 새로운 유형들이 계속적으로 추가 보완되어야 할 것이다.

• 한국결정성장학회지
• /
• 제11권4호
• /
• pp.160-165
• /
• 2001

The effects of various metals on Field Aided Lateral Crystallization (FALC) behaviors of amorphous silicon (a-Si) were investigated. Under an influence of electric field, metals such s Cu, Ni and Co were found to fasten the lateral crystallization toward a metal-free region, exhibiting a typical FALC behavior while the lateral crystallization of a-Si was not obvious for Pd. However, Au, Al and Cr did not induce the lateral crystallization of a-Si in metal-free region. Such phenomenological differences in various metals were studied in terms of dominant diffusing species (DDS) in the reaction between metal and Si. It was judged that the applied electric field enhanced the crystallization velocity by accelerating the diffusion of metal atoms since the occurrence of lateral crystallization would be strongly dependent on the diffusion of metal atoms than that of Si atoms. Therefore, it was concluded that he only metal-dominant diffusing species in the reaction between metal and Si results in the crystallization of a-Si in metal-free region.

• Journal of information and communication convergence engineering
• /
• 제6권1호
• /
• pp.94-99
• /
• 2008

Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy C-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
• /
• pp.186-188
• /
• 2005

Hand region detection in images is an important process in many computer vision applications. It is a process that usually starts at a pixel-level, and that involves a pre-process of color space transformation followed by a classification process. A color space transformation is assumed to increase separability between skin classes for hands and non-skin classes for other parts, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the color space transformation does bring those benefits to the problem of hand region detection on a dataset of images with different hand postures, backgrounds, people, and illuminations. Results indicate that best of the color space is the normalized RGB.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2012년도 하계학술대회
• /
• pp.262-265
• /
• 2012

In this paper we propose a new deinterlacing method with block classification and motion vector smoothing. The proposed method classifies a block, then depending on the region it belongs to, the motion estimation or line averaging is applied. To classify a block its variance is calculated. Then, for those blocks that belong to simple non-texture region the line averaging is done while motion estimation is applied to complex region. The motion vector field is smoothed using median filter what yields more accurate interpolation. In the experiments for the subjective evaluation, the proposed method bas shown satisfying results in terms of computation time consumption and peak signal-to-noise ratio. Due to the simplicity of the algorithm computation time was drastically decreased.

• 대한원격탐사학회지
• /
• 제20권5호
• /
• pp.353-359
• /
• 2004

This study proposes an approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. In the proposed method, the image is segmented using a spatial region growing based on hierarchical clustering, and fuzzy training is then employed to find the sample classes that well represent the ground truth. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes. The experimental results show that the new scheme proposed in this study could be used to select the regions with different characteristics existed on the scene of observed image as an alternative of field survey that is so expensive.