• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.459-463
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• 2009

Background modeling and motion detection is the one of the most significant real time video processing technique. Until now, many researches are conducted into the topic but it still needs much time for robustness. It is more important when other algorithms are used together such as object tracking, classification or behavior understanding. In this paper, we propose efficient multi-modal background modeling methods which can be understood as simplified learning method of Gaussian mixture model. We present its validity using numerical methods and experimentally show detecting performance.

• Reproductive and Developmental Biology
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• v.36 no.2
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• pp.103-108
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• 2012

Anestrus is one of the most important production limiting disorders in dairy buffaloes and its underlying causes have been a current topic of studies. The objectives of this study were to explore the causes of anestrus in buffaloes with the application of ultrasonography. Two examinations were performed by transrectal ultrasonography at 12 days apart in buffalo cows that were not seen in oestrus at 60 or more days postpartum. As high as 54.5% buffaloes had silent ovulation and 45.5% suffered from the true anestrus with ovarian dysfunction. The duration of anestrus after calving was 60~90, 91~120, 121~180 and 181~365 days in 27%, 32%, 18% and 23% buffalo cows, respectively. Treatment with prostaglandin of cyclic buffalo cows with a corpus luteum (72.7%) resulted in higher estrous rate as compared with close observation of estrus (23.1%) by the farmer (p=0.021). Acyclic buffalo cows without any corpus luteum on ovaries were successfully treated with gonadotropin releasing hormone (70%), resulting in higher estrous detection rate than those treated with a vitamin-mineral mixture (20%) (p=0.035). In conclusion, poor heat detection due to silent ovulation is the most important cause of apparent anoestrus in buffaloes; however the percentage of the true anestrous is also quite high in postpartum buffaloes.

• International Journal of High-Rise Buildings
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• v.2 no.4
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• pp.331-340
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• 2013

The main topic of this paper is to show a possibility of indoor air quality enhancement and the fan energy savings in underground parking facilities by applying the demand-controlled ventilation (DCV) strategy based on the real-time variation of the traffic load. The established ventilation rate is estimated by considering the passing distance, CO emission rate, idling time of a vehicle, and the floor area of the parking facility. However, they are hard to be integrated into the real-time DCV control. As a solution to this problem, the minimum ventilation rate per a single vehicle is derived in this research based on the actual ventilation data acquired from several existing underground parking facilities. And then its applicability to the DCV based on the real-time variation of the traffic load is verified by simulating the real-time carbon monoxide concentration variation. The energy saving potentials of the proposed DCV strategy is also checked by comparing it with those for the current underground parking facility ventilation systems found in the open literature.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.18-26
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• 1999

Recently, multiuser detection has been investigated as an interesting topic because of its capability of eliminating multiuser interference and resistance to the near-far problem. In this paper, we consider a quasi maximum likelihood detector in the reverse link system which used antenna arrays in quasi-synchronous channels. It is also shown that the proposed system can suppress the interuser interference without using the side information of the other users. It is also shown that the performance of the proposed system is superior to that of the conventional matched filter system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1066-1079
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• 2023

Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.55-66
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• 2009

In this paper, I will discuss how the Internet has spread rapidly in our lives. Large portals and social networks experience service attacks that access personal customers' databases. This interferes with normal service through DDoS (Distribute Denial of Service Attack), which is the topic I want to discuss. Among the types of DDoS, TCP SYN Flooding attacks are rarely found because they use few traffics and its attacking type is regular transaction. The purpose of this study is to find and suggest the method for accurate detection of the attacks. Through the analysis of TCP SYN Flooding attacks, we find that these attacks cause Backscatter effect. This study is about the algorithm which detects the attacks of TCP SYN Flooding by the study of Backscatter effect.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.200-214
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• 2024

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.

• Convergence Security Journal
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• v.24 no.1
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• pp.77-82
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• 2024

Recent research has seen significant improvements in various object detection and classification models overall. However, the study of object detection and classification in situations where objects are partially obscured remains an intriguing research topic. Particularly in the military domain, unmanned combat systems are often used to detect and classify objects, which are typically partially concealed or camouflaged in military scenarios. In this study, a method is proposed to enhance the classification performance of partially obscured objects. This method involves adding occlusions to specific parts of object images, considering the surrounding environment, and has been shown to improve the classification performance for concealed and obscured objects. Experimental results demonstrate that the proposed method leads to enhanced object classification compared to conventional methods for concealed and obscured objects.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.430-433
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• 1996

The shape of an object plays a very important role in pattern analysis and classification. Roughly, the researches on this topic can be classified into three fields, i.e. (i) edge detection, (ii) dominant points extraction, and (iii) shape recognition and classification. Many works have been done in these three fields. However, it is very seldom to see the research that discusses the connection relationship of objects. This problem is very important in robot assembly systems. Therefore, here we focus on this problem and discuss how to recover the connection relationship of planar objects. Our method is based on the partial curve identification algorithm. The experiment results show the efficiency and validity of this method.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.883-890
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• 2020

In this paper, we propose a variational expectation-maximization algorithm that computes posterior probabilities from Latent Dirichlet Allocation (LDA) model. The algorithm approximates the intractable posterior distribution of a document term matrix generated from a corpus made up by 50 papers. It approximates the posterior by searching the local optima using lower bound of the true posterior distribution. Moreover, it maximizes the lower bound of the log-likelihood of the true posterior by minimizing the relative entropy of the prior and the posterior distribution known as KL-Divergence. The experimental results indicate that documents clustered to image classification and segmentation are correlated at 0.79 while those clustered to object detection and image segmentation are highly correlated at 0.96. The proposed variational inference algorithm performs efficiently and faster than Gibbs sampling at a computational time of 0.029s.