• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2009년도 추계학술대회
• /
• pp.1046-1048
• /
• 2009

협업통신은 다중경로페이딩의 문제를 해결하고 전송전력소모를 감소시키기위한 효과적인 기술이다. 릴레이선택과 전력할당은 협업통신의 성능을 결정하는 중요한 요소이다. 본 논문에서는 센서네트워크에서 네트워크수명 극대화를 위해 새로운 형태의 다중 릴레이선택 방법과 전력할당 알고리즘을 제안한다. 제안하는 릴레이 선택 알고리즘은 채널상태 뿐아니라 각 노드의 잔여전력을 함께 고려함으로써 전송전력을 극소화하고 네트워크의 수명을 증가시킨다. 시뮬레이션결과는 제안된 알고리즘이 기존의 방식에비해 더 긴 네트워크 수명을 갖을 수 있음을 보여준다.

• Communications for Statistical Applications and Methods
• /
• 제31권3호
• /
• pp.337-347
• /
• 2024

Kernel density estimation is a prevalent technique employed for nonparametric density estimation, enabling direct estimation from the data itself. This estimation involves two crucial elements: selection of the kernel function and the determination of the appropriate bandwidth. The selection of the bandwidth plays an important role in kernel density estimation, which has been developed over the past decade. A range of methods is available for selecting the bandwidth, including the plug-in bandwidth. In this article, the proposed plug-in bandwidth is introduced, which leverages shifted Chebyshev series-based approximation to determine the optimal bandwidth. Through a simulation study, the performance of the suggested bandwidth is analyzed to reveal its favorable performance across a wide range of distributions and sample sizes compared to alternative bandwidths. The proposed bandwidth is also applied for kernel density estimation on real dataset. The outcomes obtained from the proposed bandwidth indicate a favorable selection. Hence, this article serves as motivation to explore additional plug-in bandwidths that rely on function approximations utilizing alternative series expansions.

• 정보처리학회논문지B
• /
• 제10B권6호
• /
• pp.695-704
• /
• 2003

본 논문은 멀티미디어 데이터 스트림들을 이동단말기에서 연출(play)하기 위해 스트림의 특성을 파악한 후 변환 시킬 스트림을 선정하여 트랜스코딩하는 스케줄링 기법을 제시하였다. 이것은 연출될 모든 스트림들을 트랜스코딩하는 것보다 선정정책에 의해 특정 스트림을 선택하여 트랜스코딩하는 것이 최초 재생 지연 시간을 줄일 수 있는 장점이 있다. 따라서 본 논문은 멀티미디어 데이터 스트림들의 요구 대역폭을 네트워크 대역폭 보다 낮추면서, 이동 단말기에서 멀티미디어 데이터 스트림들이 재생되기까지의 최초 재생 지연시간을 최소화시킬 수 있는 방법으로 EPOB(End Point of Over Bandwidth) 기반의 트랜스코딩 스트림 선정 정책을 제안하였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제2권4호
• /
• pp.219-226
• /
• 2013

특징 선택은 기계 학습 및 패턴 인식 분야에서 중요한 이슈 중 하나로, 분류 정확도를 향상시키기 위해 원본 데이터가 주어졌을 때 가장 좋은 성능을 보여줄 수 있는 데이터의 부분집합을 찾아내는 방법이다. 즉, 분류기의 분류 목적에 가장 밀접하게 연관되어 있는 특징들만을 추출하여 새로운 데이터를 생성하는 것이다. 본 논문에서는 소프트웨어 재사용의 성공 요인과 실패 요인에 대한 분류 정확도를 향상시키기 위해 특징 부분 집합을 찾는 실험을 하였다. 그리고 기존 연구들과 비교 분석한 결과 본 논문에서 찾은 특징 부분 집합으로 분류했을 때 가장 좋은 분류 정확도를 보임을 확인하였다.

• Toxicological Research
• /
• 제33권4호
• /
• pp.315-323
• /
• 2017

Preconceptual sex selection is still a highly debatable process whereby X- and Y-chromosome-bearing spermatozoa are isolated prior to fertilization of the oocyte. Although various separation techniques are available, none can guarantee 100% accuracy. The aim of this study was to separate X- and Y-chromosome-bearing spermatozoa using methods based on the viability difference between the X- and Y-chromosome-bearing spermatozoa. A total of 18 experimental semen samples were used, written consent was obtained from all donors and results were analysed in a blinded fashion. Spermatozoa were exposed to different pH values (5.5, 6.5, 7.5, 8.5, and 9.5), increased temperatures ($37^{\circ}C$, $41^{\circ}C$, and $45^{\circ}C$) and ROS level ($50{\mu}M$, $750{\mu}M$, and $1,000{\mu}M$). The live and dead cell separation was done through a modified swim-up technique. Changes in the sex-chromosome ratio of samples were established by double-label fluorescent in situ hybridization (FISH) before and after processing. The results indicated successful enrichment of X-chromosome-bearing spermatozoa upon incubation in acidic media, increased temperatures, and elevated $H_2O_2$. This study demonstrated the potential role for exploring the physiological differences between X-and Y-chromosome-bearing spermatozoa in the development of preconceptual gender selection.

• International Journal of Computer Science & Network Security
• /
• 제21권1호
• /
• pp.40-48
• /
• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• The Journal of Asian Finance, Economics and Business
• /
• 제9권2호
• /
• pp.347-353
• /
• 2022

Using the Heckman two-step procedure, this study investigates the relationship between management accounting implementation and firm effectiveness. The research data for this study was acquired from 450 publicly traded companies in Vietnam; however, the final sample only includes 304 responses containing useful information. The reliability analysis was used to evaluate the acquired data to examine the qualities of constructs and the dimensions that make them up. Then, the Heckman two-step technique was performed to analyze the causal connection from the acceptance of management accounting to firm effectiveness allowing for the effect of environmental uncertainty and organizational characteristics on the likelihood of adopting management accounting. The empirical findings show that management accounting acceptance determines firm effectiveness; however, the research model on the relationship between management accounting adoption and firm effectiveness has a sample selection bias. The main conclusions of this study are that there is a difference in the effects of management accounting adoption on business effectiveness when sample selection bias is not taken into consideration. When potential sample selection bias is taken into account by integrating environmental uncertainty and organizational characteristics in the research model, the effect of adopting management accounting on company effectiveness becomes minor.

• 한국기계가공학회지
• /
• 제7권3호
• /
• pp.87-95
• /
• 2008

The purpose of this study is to provide a decision support to select an appropriate rapid prototyping(RP) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model for molding, material property, build time and part cost that greatly affect the performance of RP machines. However, the selection of a RP is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate RP machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify RP machines that the users consider. After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of RP machines.

• Journal of Computing Science and Engineering
• /
• 제11권4호
• /
• pp.121-129
• /
• 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.

• 한국컴퓨터정보학회논문지
• /
• 제21권4호
• /
• pp.63-71
• /
• 2016

This paper is about applying efficient data mining method which improves the score calculation and proper building performance of credit ranking score system. The main idea of this data mining technique is accomplishing such objectives by applying Correlation based Feature Selection which could also be used to verify the properness of existing rank scores quickly. This study selected 2047 manufacturing companies on KOSPI market during the period of 2009 to 2013, which have their own credit rank scores given by NICE information service agency. Regarding the relevant financial variables, total 80 variables were collected from KIS-Value and DART (Data Analysis, Retrieval and Transfer System). If correlation based feature selection could select more important variables, then required information and cost would be reduced significantly. Through analysis, this study show that the proposed correlation based feature selection method improves selection and classification process of credit rank system so that the accuracy and credibility would be increased while the cost for building system would be decreased.