• 한국수산과학회지
• /
• 제44권4호
• /
• pp.390-396
• /
• 2011

Southern rough shrimp Trachysalambria curvirostris is exploited mainly by small shrimp beam trawl in coastal regions of Korea. To determine the size selectivity of a shrimp beam trawl for this species, a series of comparative fishing experiments was conducted in the sea adjacent to Geoje Island off the southern cost of Korea in June and November, 2010, using codends with four different mesh sizes(14.2, 17.8, 25.5, and 35.3 mm). The extended Share Each Length's Catch Total(SELECT) analysis method, based on a multinomial distribution, was applied to the fishing data to obtain a master selection curve. The model with the estimated split parameters fit the catch data best. The master selection curve was estimated to be: s(R)=exp(15.183R-7.872)/[1+exp(15.183R-7.872)], where the relative carapace length, R, is the ratio of carapace length to mesh size. The relative carapace length for 50% retention was 0.518, and the selection range was 0.145. The results suggest that codends with a larger mesh size allow more small-sized shrimps to escape.

• 천문학회보
• /
• 제38권1호
• /
• pp.66.2-66.2
• /
• 2013

In this study, we present a pore-selection technique to estimate the size of pore. The estimation of the size of pore is important to examine the temporal evolution of size itself and corresponding intensity. The size of pore is typically estimated by applying the intensity threshold technique to the fixed box which contains the entire pore. The typical method has disadvantages in the following circumstances; there are small features near the pore or the image has low spatial resolution. In the former, it is difficult to define a box containing the pore only, excluding the small features near the pore. In the latter, the background and threshold intensity are insignificant due to the insufficient number of pixel in the box. To avoid these difficulties, we use a pore-selection technique which is simply based on the measurement of distances from the pore center. In addition, we will discuss the advantage of the technique for the imaging spectrograph data like the NST FISS.

• Communications for Statistical Applications and Methods
• /
• 제27권3호
• /
• pp.301-311
• /
• 2020

Schwarz's Bayesian information criterion (BIC) is one of the most popular criteria for model selection, that was derived under the assumption of independent and identical distribution. For correlated data in longitudinal studies, Jones (Statistics in Medicine, 30, 3050-3056, 2011) modified the BIC to select the best linear mixed effects model based on the effective sample size where the number of parameters in covariance structure was not considered. In this paper, we propose an extended Jones' modified BIC by considering covariance parameters. We conducted simulation studies under a variety of parameter configurations for linear mixed effects models. Our simulation study indicates that our proposed BIC performs better in model selection than Schwarz's BIC and Jones' modified BIC do in most scenarios. We also illustrate an example of smoking data using a longitudinal cohort of cancer patients.

• Journal of Animal Science and Technology
• /
• 제62권6호
• /
• pp.777-789
• /
• 2020

Intense artificial selection has been imposed to Luzhong mutton sheep population in the past years. Improvements on growth and reproductive performance are two breeding goals in the present herd. Although some progresses were phenotypically observed possibly due to inbreeding induced by strong selection in terms of these traits, the genomic evaluation was poorly understood. Therefore, a high-density SNP array was used to characterize the pattern of runs of homozygosity (ROH), estimate inbreeding and inbreeding depressions on early growth performance and litter size based upon ROH, and scan positive selection signatures of recent population. Consequently, a low inbreeding level was observed which had negative effects on litter size, but not on early growth performance. And 160 genes were under selection, of which some were reported to be linked to several traits of sheep including body weight, litter size, carcass and meat quality, milk yield and composition, fiber quality and health, and the top genes were associated with growth (growth hormone [GH]- growth hormone receptor [GHR]- Insulin-like growth factor 1 [IGF1] axis) and litter size (bone morphogenic proteins [BMPs]-associated). The effectiveness of previous breeding measures was highlighted, but purging selection was proposed to alleviate the inbreeding depression on litter size, providing some genomic insights to breeding management of Luzhong mutton sheep.

• 응용통계연구
• /
• 제26권5호
• /
• pp.821-834
• /
• 2013

Law 등 (2004)은 군집분석에서 변수선택을 위해 정규분포기반 "두각 혼합모형(salient mixture model)"의 사용을 제안하였다. 본 논문에서는 이 모형의 적합 상의 문제점과 변수선택의 결함을 지적하고 그 대안을 제시한다. 모의자료와 실자료를 바탕으로 제안된 방법이 기존의 방법보다 유용함을 보였다.

• Journal of Information Processing Systems
• /
• 제14권2호
• /
• pp.418-434
• /
• 2018

With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.

• Journal of Communications and Networks
• /
• 제17권3호
• /
• pp.306-320
• /
• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.

• 수산해양기술연구
• /
• 제30권2호
• /
• pp.86-96
• /
• 1994

In order to estimate the optimum harvesting mesh size of multispecies, the 24 species of catching data which were taken by fishing trial of trawl gear in Korean Southern Coast and East China Sea during 1991-1993 year were grouped and divided by the Cluster analysis method, considering first maturity length and body width, body height, body girth based on the first maturity length. With the same method, the above groups were subdivided by the potential escape such as possible escape index, range factor and selection factor. In case of the species devoid of selection parameters, these species were first subdivided by the use of possible escape index and length range factor. Next, the optimum harvesting mesh size of multispecies was properly classified according to the optimal mesh size of a fish estimated by first maturity length against selection factor. The results obtained are summarized as follows: 1. Each optimum harvesting mesh size of Psenopsis anomala, Priacanthus macra-canthus, Trachurus japonicus, Argyrosomus argentatus was 71.1-79.5mm, and Saurida undosquamis was 65.5mm. 2. Each optimum harvesting mesh size of Scomber japonicus, Pseudosciaena crosea, Pseudosciaena Polyactis, Sebastes thompsoni, Doderleinia berycoides was 78.5-85.6mm, and Bembras japonicus, Sphyraena pinguis was 48.4-51.3mm. 3. Each optimum harvesting mesh size of Zeus faber, Pampus argenteus, Zenopsis nebulosan was 118.4-124.1mm, and Caranx equula was 91.4mm, and Thamnaconus modestus was 131.2mm, and Pagrus major was 149.4mm. 4. Each optimum harvesting mesh size of Upeneus bensasi, Callanthias japonicus, Sardinops melanosticata, Konosirus punctatus was 36.8-42.8mm, and Acropoma japonicum was 21.2mm, and Apogon lineatus was 26.3mm.

• Geomechanics and Engineering
• /
• 제25권1호
• /
• pp.1-16
• /
• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
• /
• pp.396-398
• /
• 2005

IEC 60287-3-2 standards suggests the selection method of economic conductor size for power cables. This method deals solely with the economic choice of conductor size based on joule losses. This paper introduces the method and example study which is proposed by IEC standards.