• Animal Bioscience
• /
• 제36권1호
• /
• pp.10-18
• /
• 2023

Objective: In this study, we aimed to position the Hungarian Merino among other Merinoderived sheep breeds, explore the characteristics of our sampled animals' genetic similarity network within the breed, and highlight single nucleotide polymorphisms (SNPs) associated with daily weight-gain. Methods: Hungarian Merino (n = 138) was genotyped on Ovine SNP50 Bead Chip (Illumina, San Diego, CA, USA) and positioned among 30 Merino and Merino-derived breeds (n = 555). Population characteristics were obtained via PLINK, SVS, Admixture, and Treemix software, within-breed network was analysed with python networkx 2.3 library. Daily weight gain of Hungarian Merino was standardised to 60 days and was collected from the database of the Association of Hungarian Sheep and Goat Breeders. For the identification of loci associated with daily weight gain, a multi-locus mixed-model was used. Results: Supporting the breed's written history, the closest breeds to Hungarian Merino were Estremadura and Rambouillet (pairwise F_ST values are 0.035 and 0.036, respectively). Among Hungarian Merino, a highly centralised connectedness has been revealed by network analysis of pairwise values of identity-by-state, where the animal in the central node had a betweenness centrality value equal to 0.936. Probing of daily weight gain against the SNP data of Hungarian Merinos revealed five associated loci. Two of them, OAR8_17854216.1 and s42441.1 on chromosome 8 and 9 (-log₁₀P>22, false discovery rate<5.5e-20) and one locus on chromosome 20, s28948.1 (-log₁₀P = 13.46, false discovery rate = 4.1e-11), were close to the markers reported in other breeds concerning daily weight gain, six-month weight, and post-weaning gain. Conclusion: The position of Hungarian Merino among other Merino breeds has been determined. We have described the similarity network of the individuals to be applied in breeding practices and highlighted several markers useful for elevating the daily weight gain of Hungarian Merino.

• 국제학술발표논문집
• /
• The 3th International Conference on Construction Engineering and Project Management
• /
• pp.700-706
• /
• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권4호
• /
• pp.1572-1593
• /
• 2018

The diversity and fast growth of Internet traffic volume are highly influenced by mobile and computer applications being developed. Moreover, the developed applications are too dynamic to be identified and monitored by network administrators. Several approaches have been proposed to identify network applications, however, are still not robust enough to identify modern applications. This paper proposes both, TSA (Traffic collection, Signature generation and Applications identification) system and a derived algorithm so called CSP (Contiguous Sequential Patterns) to identify applications for management and security in IP networks. The major focus of this paper is the CSP algorithm which is automated in two modules (Signature generation and Applications identification) of the proposed system. The proposed CSP algorithm generates DNA-like unique signatures capable of identifying applications and their individual services. In this paper, we show that the algorithm is suitable for generating efficient signatures to identify applications and application services in high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권11호
• /
• pp.5631-5652
• /
• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• 교육시설 논문지
• /
• 제21권2호
• /
• pp.25-32
• /
• 2014

Many Korean university campuses are located on hilly terrain where the hierarchy of the path system is unclear. Therefore, it is difficult to analyze the pedestrian network through space syntax, in which only horizontal direction changes are considered as depths of space. The purpose of this study is to compare pedestrian movement patterns and space syntax analysis in order to find their relevance to each other and the relationship between them. We conducted a survey regarding the most-visited buildings and pathways at S-University, which is located on a hilly area in Seoul. The survey results were compared with the Space Syntax integration map by regression analysis. For the segments where the relationship between pedestrian volume and integration was weak, field observations were conducted. As a result, topographical aspects, functional aspects, and location aspects were observed as the main influential factors. In addition, the research proposes that adding an extra axial line per vertical directional change can potentially compensate for the low relevance of stairs. This study suggests the possibility and the necessity of three-dimensional space syntax programs and emphasizes the importance of campus planning for the pedestrian environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제8권3호
• /
• pp.838-856
• /
• 2014

Enhanced network speed and the appearance of various applications have recently resulted in the rapid increase of Internet users and the explosive growth of network traffic. Under this circumstance, Internet users are eager to receive reliable and Quality of Service (QoS)-guaranteed services. To provide reliable network services, network managers need to perform control measures involving dropping or blocking each traffic type. To manage a traffic type, it is necessary to rapidly measure and correctly analyze Internet traffic as well as classify network traffic according to applications. Such traffic classification result provides basic information for ensuring service-specific QoS. Several traffic classification methodologies have been introduced; however, there has been no favorable method in achieving optimal performance in terms of accuracy, completeness, and applicability in a real network environment. In this paper, we propose a method to classify Internet traffic as the first step to provide stable network services. We integrate the existing methodologies to compensate their weaknesses and to improve the overall accuracy and completeness of the classification. We prioritize the existing methodologies, which complement each other, in our integrated classification system.

• 한국항행학회논문지
• /
• 제19권1호
• /
• pp.53-59
• /
• 2015

노드들 간의 설치비용과 트래픽 요구량이 주어졌을 때, 이 조건을 만족하는 메쉬망을 설계하는 주요 고려사항으로는 설계시간, 비용, 지연, 신뢰성등 여러 가지가 있으며, 일반적으로 설계시간을 줄이고, 비용은 작게, 지연은 적게, 신뢰성이 높은 메쉬망을 설계하여야 한다. 설계시간에 대한 문제는 Aaron Kershenbaum이 제안한 MENTOR (mesh network topology optimization and routing) 알고리즘에 의해 최소화를 이루는데 성공하였지만 비용, 지연, 신뢰성에는 여전히 문제가 남아있다. 본 논문에서는 MENTOR의 설계시간의 장점을 유지하면서 다른 성능인자들을 만족시킬 수 있는 새로운 망설계 알고리즘을 제안하고자 한다. 제안된 알고리즘의 설계결과는 MENTOR 알고리즘의 성능인자들보다 개선되었음을 보여주었다.

• 한국지반신소재학회논문집
• /
• 제17권4호
• /
• pp.199-212
• /
• 2018

본 연구에서는 깊은 굴착에 따른 인접구조물의 손상 평가 및 벽체 구조물의 안정성 평가를 하기 위한 지표의 거동 및 벽체 부재력의 효율적인 예측기법에 대한 내용을 다루었다. 우선적으로 지표의 거동 및 벽체 부재력에 영향을 미치는 매개 변수에 대한 연구를 수행하였고, 이를 토대로 다양한 굴착 조건에 대해 수치해석을 실시한 결과를 통해 데이터베이스를 구축하였다. 구축된 데이터베이스를 토대로 벽체의 부재력과 지표의 거동 각각의 해석 결과에 대한 인공신경망 엔진 학습을 수행하였으며 학습된 인공신경망을 이용하여 예측된 결과와 사용된 데이터베이스의 결과를 비교하여 인공신경망 엔진이 벽체의 부재력 및 지표의 거동예측에 효율적임을 검증하였다.

• International Journal of Computer Science & Network Security
• /
• 제22권11호
• /
• pp.265-271
• /
• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• International Journal of Computer Science & Network Security
• /
• 제23권9호
• /
• pp.100-110
• /
• 2023

The Internet of Things (IoT) is the most creative and focused technology to be employed today. It increases the living conditions of both individuals and society. IoT offers the ability to recognize and incorporate physical devices across the globe through a single network by connecting different devices by using various technologies. As part of IoTs, significant questions are posed about access to computer and user privacy-related personal details. This article demonstrates the three-layer architecture composed of the sensor, routing, and implementation layer, respectively, by highlighting the security risks that can occur in various layers of an IoT architecture. The article also involves countermeasures and a convenient comparative analysis by discussing major attacks spanning from detectors to application. Furthermore, it deals with the basic protocols needed for IoT to establish a reliable connection between objects and items.