• Journal of Radiation Protection and Research
• /
• v.39 no.1
• /
• pp.46-53
• /
• 2014

For the case of radiation emergency, it is required to assess internal contamination of the public, including children as well as adults. The objective of the present study was to assess counting efficiency of a whole body counter by human body size and standing position of the measurement person. In this study, the FASTSCAN whole body counter used at National Radiation Emergency Medical Center of Korean Institute of Radiological and Medical Science was simulated by a radiation transport computer code. The simulation results of the counting efficiencies agreed well with measurements within the 2% of discrepancy for 4-year child and 5% for adults. The standing positions of the people were adjusted by body size to find the consistent trend of the counting efficiencies by human body size. Body size scaling factors of the whole body counter were derived to consider human body size and improve the measurement accuracy. The counting efficiency assessment methodology in this study can be successively used to improve the measurement accuracy when using a whole body counter for the case of radiation emergency.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2016.11a
• /
• pp.140-141
• /
• 2016

기존의 사람 계수 측정 시스템은 적외선 빔이나 열 감지 영상 장치를 통해 측정하였다. 하지만 이와 같은 방법으로 측정하면 객체가 들어가거나 나가는 정보는 제공하지 않는다. 이에 본 논문은 고정된 카메라를 이용하여 각 사람의 피부색과 옷차림 등의 RGB 정보를 이용한 사람 계수 측정 기법을 제안한다. RGB카메라 영상을 통하여 객체의 RGB 히스토그램을 얻은 후 각 객체에 대해 Bhattacharyya metric을 통한 histogram similarity을 계산하여 객체 추적 및 분류를 통해 사람 계수 측정을 한다. 제안된 시스템은 C/C++을 기반으로 구현하여, 사람 계수 측정 성능을 평가하였다.

• Journal of Satellite, Information and Communications
• /
• v.8 no.4
• /
• pp.26-29
• /
• 2013

In order to estimate people crowdedness in public area, the texture based method or motion based method can be used. In this paper we have proposed a mixed method. By designating the region of interest, we made the degree of crowdedness more accurate. The feature normalization also reduced the image distortion which results from difference of camera angle. The proposed system was optimized to real time embedded hardware system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.6 no.1
• /
• pp.1-5
• /
• 2013

The number of pedestrians is considered essential information which can be used to control a person who makes a entrance or a exit into a building. The number of pedestrians, also, can be used to help to manage pedestrian traffic and the volume of pedestrian flow within the building. Due to the fact there is incorrect detection by occluded, shadows, and illumination, however, difficulty can arise in existing system which is for detection and counts of a person who makes a entrance or a exit into a building. In this paper, it is minimized that the change of illumination and the effect of shadow through the transmitted image from camera which is created and processed with great adaptability. The accuracy of the calculations can be increase as well by using Kalman Filter and Mean-Shift Algorithm in order to avoid overlapped counts. As a result of the test, it is proved that the count method that shows the accuracy of 95.4% should be effective for detection and counts.

• Smart Media Journal
• /
• v.9 no.3
• /
• pp.122-129
• /
• 2020

The possibility that an asymptotic coronavirus-19 infected person around the world is not aware of his infection and can spread it to people around him is still a very important issue in that the public is not free from anxiety and fear over the spread of the epidemic. In this paper, the K-means clustering algorithm and deep learning-based crowd aggregation were proposed to determine the possibility of contact with confirmed cases of infectious diseases. As a result of 300 iterations of all input learning images, the PSNR value was 21.51, and the final MAE value for the entire data set was 67.984. This means the average absolute error between observations and the average absolute error of fewer than 4,000 people in each CCTV scene, including the calculation of the distance and infection rate from the confirmed patient and the surrounding persons, the net group of potential patient movements, and the prediction of the infection rate.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.6
• /
• pp.1555-1560
• /
• 2012

Recently, it has been increasing attention on health care that can provide remote medical service to an aging population, people with disabilities and people having a medical checkup periodically due to increasing people's average life span. Home health care system should provide reasonable cost, on-line basic health status monitoring, embedded basic medical helper function and intuitive interface. In this paper, we developed a prototype of 3G networked pulse measurement system that can detect pulse signal information from subject's fingertip using the optical sensor. The prototype had been analyzed in terms of abnormalities, feeling, timing and pulse counting accuracy. Finally we evaluated its suitability.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.43 no.5
• /
• pp.667-674
• /
• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.

• Journal of Microbiology and Biotechnology
• /
• v.32 no.10
• /
• pp.1344-1354
• /
• 2022

Laryngeal cancer is one of the highest incidence, most prevalently diagnosed head and neck cancers, making it critically necessary to probe effective targets for laryngeal cancer treatment. Here, real-time quantitative reverse transcription PCR (qRT-PCR) and western blot analysis were used to detect gene expression levels in laryngeal cancer cell lines. Fluorescence in situ hybridization (FISH) and subcellular fractionation assays were used to detect the subcellular location. Functional assays encompassing Cell Counting Kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), transwell and wound healing assays were performed to examine the effects of target genes on cell proliferation and migration in laryngeal cancer. The in vivo effects were proved by animal experiments. RNA-binding protein immunoprecipitation (RIP), RNA pulldown and luciferase reporter assays were used to investigate the underlying regulatory mechanisms. The results showed that KIF26B antisense RNA 1 (KIF26B-AS1) propels cell proliferation and migration in laryngeal cancer and regulates the toll-like receptor 4 (TLR4) signaling pathway. KIF26B-AS1 also recruits FUS to stabilize TLR4 mRNA, consequently activating the TLR4 signaling pathway. Furthermore, KIF26B-AS1 plays an oncogenic role in laryngeal cancer via upregulating TLR4 expression as well as the FUS/TLR4 pathway axis, findings which offer novel insight for targeted therapies in the treatment of laryngeal cancer patients.

• Korean Security Journal
• /
• no.58
• /
• pp.9-34
• /
• 2019

The Korean Police Force is equipped with the dual responsibility to not only protect the constitutional right to protest, but also prevent potential disorder and misconduct might be caused by the abuse of such a right. To this end, the Korean national police employ the crowd counting methodology, termed 'Maximum Figure at Any One Time' with a view to dispatching the proportionate number of police officers to protest scenes for safety management. However, protest organizers rather take advantage of 'Cumulative Figure' methodology, the purpose of which being to publicize the wide recognition of success, noticeably by demonstrating that as many people as possible support for their cause or voice. Hence, different estimates generated by different methods have raised serious political issues in Korean society. Nevertheless, it is found out that there are only three existing academic studies in Korea regarding crowd counting methods, and they are mainly geared towards comparing the two methods, unfortunately without any attempt to analyze the foreign literature in details. Keeping the research gap in mind, the research conducts a comprehensive review of the foreign literature with relation to protest crowd counting methods. Derived from the review and analysis, the counting methods can be broadly categorized into the three models such as: 1) Grid/Density Model, 2) Moving Crowds Model, and 3) Electronic & Non-Image Model. In the end, the research provides brief explanations regarding specific research findings per each model, and further, suggests some policy implications for the development of more accurate crowd counting methodology at protests in Korea.

• Journal of the Society of Disaster Information
• /
• v.20 no.1
• /
• pp.199-205
• /
• 2024

Purpose: This study aims to improve the recognition rate of Auto People Counting (APC) in accurately identifying and providing information on remaining evacuees in disaster-vulnerable facilities such as nursing homes to firefighting and other response agencies in the event of a disaster. Methods: In this study, a baseline model was established using CNN (Convolutional Neural Network) models to improve the algorithm for recognizing images of incoming and outgoing individuals through cameras installed in actual disaster-vulnerable facilities operating APC systems. Various algorithms were analyzed, and the top seven candidates were selected. The research was conducted by utilizing transfer learning models to select the optimal algorithm with the best performance. Results: Experiment results confirmed the precision and recall of Densenet201 and Resnet152v2 models, which exhibited the best performance in terms of time and accuracy. It was observed that both models demonstrated 100% accuracy for all labels, with Densenet201 model showing superior performance. Conclusion: The optimal algorithm applicable to APC among various artificial intelligence algorithms was selected. Further research on algorithm analysis and learning is required to accurately identify the incoming and outgoing individuals in disaster-vulnerable facilities in various disaster situations such as emergencies in the future.