• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.22-27
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• 2020

Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.126-132
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• 2022

At present, due to the rapid spread of smartphones and activation of IoT, malicious codes are disseminated using SNS, or intelligent intrusions such as intelligent APT and ransomware are in progress. The damage caused by the intelligent intrusion is also becoming more consequential, threatening, and emergent than the previous intrusion. Therefore, in this paper, we propose an intelligent intrusion situation-aware reasoning system to detect transgression behavior made by such intelligent malicious code. The proposed system was used to detect and respond to various intelligent intrusions at an early stage. The anticipated system is composed of an event monitor, event manager, situation manager, response manager, and database, and through close interaction between each component, it identifies the previously recognized intrusive behavior and learns about the new invasive activities. It was detected through the function to improve the performance of the inference device. In addition, it was found that the proposed system detects and responds to intelligent intrusions through the state of detecting ransomware, which is an intelligent intrusion type.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1105-1114
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• 2021

Due to the recent surge in popularity of cryptocurrency, the threat of cryptojacking, a malicious code for mining cryptocurrencies, is increasing. In particular, web-based cryptojacking is easy to attack because the victim can mine cryptocurrencies using the victim's PC resources just by accessing the website and simply adding mining scripts. The cryptojacking attack causes poor performance and malfunction. It can also cause hardware failure due to overheating and aging caused by mining. Cryptojacking is difficult for victims to recognize the damage, so research is needed to efficiently detect and block cryptojacking. In this work, we take representative distinct symptoms of cryptojacking as an indicator and propose a new architecture. We utilized the K-Nearst Neighbors(KNN) model, which trained computer performance indicators as behavior-based dynamic analysis techniques. In addition, a K-means model, which trained the frequency of malicious script words for script similarity-based static analysis techniques, was utilized. The KNN model had 99.6% accuracy, and the K-means model had a silhouette coefficient of 0.61 for normal clusters.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.328-335
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• 2022

With the advance of robot capabilities and functionalities, construction robots assisting construction workers have been increasingly deployed on construction sites to improve safety, efficiency and productivity. For close-proximity human-robot collaboration in construction sites, robots need to be aware of the context, especially construction worker's behavior, in real-time to avoid collision with workers. To recognize human behavior, most previous studies obtained 3D human poses using a single camera or an RGB-depth (RGB-D) camera. However, single-camera detection has limitations such as occlusions, detection failure, and sensor malfunction, and an RGB-D camera may suffer from interference from lighting conditions and surface material. To address these issues, this study proposes a novel method of 3D human pose estimation by extracting 2D location of each joint from multiple images captured at the same time from different viewpoints, fusing each joint's 2D locations, and estimating the 3D joint location. For higher accuracy, the probabilistic representation is used to extract the 2D location of the joints, considering each joint location extracted from images as a noisy partial observation. Then, this study estimates the 3D human pose by fusing the probabilistic 2D joint locations to maximize the likelihood. The proposed method was evaluated in both simulation and laboratory settings, and the results demonstrated the accuracy of estimation and the feasibility in practice. This study contributes to ensuring human safety in close-proximity human-robot collaboration by providing a novel method of 3D human pose estimation.

• The Journal of Korean Association of Computer Education
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• v.13 no.6
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• pp.91-98
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• 2010

NAC(Network Access Control) has been developed as a solution for the security of end-point user, to be a target computer of worm attack which does not use security patch of OS and install Anti-Virus, which spreads the viruses in the Intra-net. Currently the NAC products in market have a sufficient technology of pre-connect, but insufficient one of post-connect which detects the threats after the connect through regular authentication. Therefore NAC users have been suffered from Zero-day attacks and malware infection. In this paper, to solve the problems in the post-connect step we generate the normal behavior profiles using the traffic information of each host, host information through agent, information of open port and network configuration modification through network scanner addition to authentication of host and inspection of policy violation used before. Based on these we propose the system to detect the hosts infected malware.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.5995-5998
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• 2012

Background: Colorectal cancer is one of most common cancers in women and men and one of the major causes of death due to neoplasia. Colonoscopy is considered as the most accurate diagnostic procedure to detect colorectal cancer at the earlier stages. Objectives: The current study aimed to evaluate the effects of an education program using the Theory of Planned Behavior on promoting behavioral intention among first degree relatives of colorectal cancer patients. Materials and Methods: A quasi-experimental study conducted to evaluate the effectiveness of an educational program to promote attitudinal factors associated with early detection of colorectal cancer in 99 first degree relatives of colorectal cancer patients aged more than 20 years in Yazd city, Iran. A researcher made questionnaire forwhich validity and reliability were confirmed by expert point of view and pilot testing was employed for data collection. Questionnaires were filled in before and after educational intervention. The registered data were transferred to SPSS 19 and analyzed by paired T-test, Man-Whitney and Wilcaxon. Results: Mean scores of knowledge, attitude, perceived behavioral control and intention regarding colorectal cancer increased after education significantly (P<0.05). Conclusions: Application of the Theory of Planned Behavior has positive influence on promoting intention behavior. It is therefore recommended to apply educational programs to promote behavioral intention.

• Journal of KIBIM
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• v.6 no.3
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• pp.34-41
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• 2016

The previous studies argue that using the semantic properties of BIM objects is efficient for simulating the behaviors of autonomous, computer agents, called virtual-users, but such assumption is not proven via evidence-based research approaches. Hence, this present study aims to investigate the empirical effects of a human behavior simulation model equipped the semantics of spaces on the architecture-major students' operation and understanding of the simulation system, compared to a typical path-finding model. To achieve the aim, this study analyzed the survey and interview data, collected in the authentic design projects. The analysis indicates that (1) using a simulation model equipped the semantics of spaces helps the students' operation of the simulation, and (2) it also aids understanding the relationship between the variables of spaces and virtual-users (${\alpha}=0.74$). In addition, the qualitative data inform that the advantages of the simulation model that computes the semantics of spaces stem in the automatic behavioral changes of massive numbers of virtual-users, and efficient detection and activation on the what-if situations. The analysis also reveals that the simulation model has shortcomings in orchestrating the complex data structure between the semantics properties of spaces and virtual-users under multi-sequential scenarios. The results of this study contribute to develop a future design system combining BIM with human behavior simulation.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.179-186
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• 2019

This study was to investigate the factors influencing colorectal cancer(CRC) screening behavior using the health belief model(HBM). It was a descriptive cross-sectional survey. A total of 148 adults aged 50 or older participants were surveyed using structured questionnaires including general characteristics,, health beliefs, and behavioral variables. The data were analyzed by descriptive statistics, t-test, chi-square test and multiple logistic regression using SPSS/WIN 25.0 program. The significant factors influecing CRC screening behavior were perceived sensitivity, spousal experience of CRC screening and family history. Therefore, in order to improve the CRC screening rate, it is necessary to increase the perceived sensitivity through systematic education about the importance of early CRC screening. In addition, it is necessary to assess the spousal screening experience and the family history of subjects and to develop the education program using the partnership of the couple.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• Journal of Embryo Transfer
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• v.28 no.3
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• pp.177-184
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• 2013

The objective of this study was to analyze the accuracy of estrus detection of heat detector and analysis of estrus behavior (mounting and mounted), and the evaluation of conditions required for improving reproductive efficiency in Holstein dairy cows fitted with a estrous detector. The heat detection system consists of estrous detector based on wireless sensor and an electric bulletin board displayed estrus behavior data. When cow mounting other cows, the accuracy of estrus behavior displayed an electric bulletin board were 87.5% (mounting other cows only), 100% (mounting other cows but not standing), 80.0% (mounting other cows with standing for 1~4 seconds), 90.0% (mounting other cows but not standing for 1~4 seconds), 80% (mounting other cows with standing for more than 5 seconds) and 90.0% (mounting other cows but not standing for more than 5 seconds). When cow mounted other cows, the accuracy of estrus behavior displayed an electric bulletin board were 100% (mounted other cows but not standing), 100% (mounted other cows with standing for 1~4 seconds), 100% (mounted other cows but not standing for 1~4 seconds) and 100% (mounted other cows with standing for more than 5 seconds). Circadian distribution of first observed in estrus were 59.1% (am 8~pm 6) and 40.9% (pm 6~am 8). Distribution for the number of estrus behavior were 40.9% (less than 3 times), 36.4% (4~6 times) and 22.7% (more than 4 times). The conception rates relative to interval from first estrus behavior to insemination for estrus periods were 23.1% (less than 11 hours) and 55.6% (12~20 hours).