• Journal of the Korean Society of Safety
• /
• v.36 no.1
• /
• pp.26-35
• /
• 2021

Governmental bodies generally conduct investigations for product safety management once new products are released to the market, in order to prevent distribution of illegal and defective items. Further, market surveillance activities are regularly conducted by the government to ensure distribution of safe products and recall any hazardous products. The safety investigations often involve a consumer monitoring system wherein consumer organizations participate in market monitoring to conduct surveys on illegal and defective products. As a result, the monitoring results of individual consumer organizations are available separately, but an integrated analysis of the data from all consumer organizations cannot be performed, thereby deterring comprehensive evaluation of the consumer monitoring system. In this study, we analyze the individual monitoring results of consumer organizations to understand the overall status and performance of comprehensive market monitoring and present the directions for desirable market surveillance policies. To this end, the effectiveness of market surveillance related to the distribution of products is verified through analysis of the interrelationships between the monitoring processes and performances of consumer organizations as well as their impact on the performances of the monitoring implementation processes; moreover, several improvement points and direction points are presented for more desirable monitoring of the consumer markets.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2005.11a
• /
• pp.123-126
• /
• 2005

Wireless Sensor Networks will revolutionize applications such as environmental monitoring, home automation, and logistics. We developed forest fire surveillance system. In this paper, Considering the fact that in Korea, during November to May, forest fires occur very frequently causing catastrophic damages on the valuable environment, Although exists other forest fire surveillance system such as surveillance camera tower, infrared ray sensor system and satellite system. Preexistence surveillance system can't real-time surveillance, monitoring, database and automatic alarm. But, forest fire surveillance system(FFSS) support above. In this paper, we describes a system development approach for a wireless sensor network based FFSS that is to be used to measure temperature and humidity as well as being fitted with a smoke detector. Such a device can be used as an early warning fire detection system and real-time surveillance in the area of a bush fire or endangered public infrastructure. Once the system has being development, a mesh network topology will be implemented with the chosen sensor node with the aim of developing a sophisticated mesh network.

• Journal of Advanced Navigation Technology
• /
• v.15 no.5
• /
• pp.906-912
• /
• 2011

In this paper, we structured the intelligent cluster surveillance network which can be managed scattered-the emergency light without communication line. Will not be interfered in location of emergency light and not to be will be able to establish the system. And the surveillance network there is by a destroyer and the communication relay system is born breakdown but the dead zone without condition of emergency light implemented the emergency light management system where the monitoring and management are possible. And we implemented the emergency light surveillance equipment and the emergency light monitoring system. Some experiments were conducted to verify so as to proposed system, and the system processes such as the surveillance network, the status information measurement and initialization were well performed.

• Journal of the Semiconductor & Display Technology
• /
• v.14 no.3
• /
• pp.37-43
• /
• 2015

In this paper, a new background subtraction algorithm for video based fence monitoring surveillance systems is proposed. We adopt the sampling based background subtraction technique and focus on the two main issues: handling highly dynamic environment and handling the flickering nature of pulse based IR (infrared) lamp. Natural scenes from fence monitoring system are usually composed of several dynamic entities such as swaying trees, moving water, waves and rain. To deal with such dynamic backgrounds, we utilize the confidence factor for each background value of the input image. For the flickering IR lamp, the original sampling based technique is extended to handle double background models. Experimental results revealed that our method works well in real fence monitoring surveillance systems.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2021.05a
• /
• pp.40-41
• /
• 2021

The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1027-1028
• /
• 2008

Most of the urban transit operation company in Korea have a passive surveillance system to monitor the status of the passengers and facilities in the urban transit service area. The surveillance system is based on CCTV, closed circuit television, and several sensors, such as a fire sensor. However, this system has some limitations to prevent and cope with the emergency quickly. So the urban transit operation companies have plans to be change their surveillance system to be active. The active surveillance system has an intelligent function to detect the event predefined by managers automatically. To construct the active surveillance system, there are a standard concept design and a function analysis. In this paper, we propose the classification of the functions of the active surveillance system for urban transit. We divide the functions into five parts, ordinary monitoring, safety monitoring, environment monitoring, administration support, and record management. And we describe the systems related to the every functions to clarify the classified functions.

• Journal of the Korea Institute of Building Construction
• /
• v.21 no.5
• /
• pp.397-408
• /
• 2021

The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Fashion & Textile Research Journal
• /
• v.24 no.2
• /
• pp.195-205
• /
• 2022

This study examines the image management behavior according to self-monitoring, self-objectification of profile-based SNS users. Questionnaires were administered to 313 SNS users including both men and women in their 20s to 30s. The SPSS 25.0 package was utilized for data analysis, which included frequency analysis, factor analysis, Cronbach's ?, t-test, and regression analysis. The study analyzed self-monitoring in 2 groups (high, low), self-objectification for 2 factors (body surveillance, body shame), and image management behavior for 5 factors (fashion oriented, instrumentality, conformity, ostentation, interpersonal disposition). The results revealed: first, self-monitoring groups exhibited significant differences in self-objectification. The higher self-monitoring group was more influenced by body surveillance and body shame compared to the low self-monitoring group. Second, self-objectification had a positive influence on all the factors of image management behavior. Especially, body surveillance demonstrated a high influence on instrumentality and body shame showed a high influence on ostentation. Third, the self-monitoring groups showed significant differences in all the factors of image management behavior. The higher self-monitoring group demonstrated more influence of image management behavior compared to the low self-monitoring group. These results provide useful information in understanding the influence of social media on users' psychological attitude and consciousness toward their body and image management behavior.

• Korean Journal of Veterinary Research
• /
• v.60 no.1
• /
• pp.1-7
• /
• 2020

We investigated the genotypes of Leptospira spp. detected in symptomatic dogs in Thailand. During April to December 2012, 6 out of 41 client-owned dogs were diagnosed with leptospirosis based on polymerase chain reaction tests. All of the infected dogs showed clinical symptoms related to leptospirosis. Direct genotyping of the causative agent of the canine leptospirosis was conducted from the archival DNA samples extracted from urine or blood of those 6 infected dogs. Sequencing of the partial 16S rRNA and lipL32 genes from all samples identified Leptospira (L.) interrogans as the infecting species. Multilocus sequence typing tests were successful for 2 out of 6 samples. The sequence type (ST) was identified as ST50 for both samples where the profile corresponded to L. interrogans species and Bataviae serogroup. The presence of this genotype of Leptospira has never been reported in Thailand. Thus, our findings showed the existence of ST50 L. interrogans serogroup Bataviae and the ability to cause leptospirosis in dogs in Thailand.

• Journal of the Korea Convergence Society
• /
• v.11 no.3
• /
• pp.77-83
• /
• 2020

Recently, intelligent predictive surveillance system has emerged. It is a system that can probabilistically predict the future situation and event based on the existing data beyond the scope of the current object or object motion and situation recognition. Since such intelligent predictive monitoring system has a high possibility of handling personal information, security consideration is essential for protecting personal information. The existing video surveillance framework has limitations in terms of privacy. In this paper, we proposed a security framework for intelligent predictive surveillance system. In the proposed method, detailed components for each unit are specified by dividing them into terminals, transmission, monitoring, and monitoring layers. In particular, it supports active personal information protection in the video surveillance process by supporting detailed access control and de-identification.