• Journal of the Korean Institute of Educational Facilities
• /
• v.26 no.4
• /
• pp.11-18
• /
• 2019

School Crime Prevention Throgh Environmental Design (CPTED) is in the process of building a design guide through a lot of research. However, it is common practice to conduct consulting through existing cases and utilize internal self-evaluation through similar projects, as the self-evaluation criteria are not open to public or unlike other certifications. Therefore, the purpose of this study is to contribute to the development of a more quantitative and objective practical manual by analyzing the evaluation elements of school crime prevention environment design provided by guidelines and guidelines of various organizations and local autonomous bodies involved in school design. As a result of quantifying the school facilities CPTED indicators so far and incorporating the current guidelines, 'Surveillance and access control' of 'Entrance space and parking space' is found to be main. If the various indicators presented above are applied in consideration of the specificity of the school facilities in the application of the school CPTED technique, in addition to the CPTED for physically preventing offenses committed by outsiders, it can be expected to prevent violence among students or social crimes in school education.

• Journal of Information Processing Systems
• /
• v.14 no.6
• /
• pp.1494-1507
• /
• 2018

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

• BLOOD RESEARCH
• /
• v.53 no.4
• /
• pp.294-298
• /
• 2018

Background Production of immunosuppressive enzymes such as indoleamine 2,3-dioxygenase (IDO) is one of the strategies employed by hematologic malignancies, including acute myeloid leukemia (AML), to circumvent immune surveillance. Moreover, IDO has the ability to convert $CD4^+CD25^-$ conventional T cells into regulatory T cells (Tregs). In this study, we evaluated the expression of IDO in cytogenetically normal acute myeloid leukemia (CN-AML) patients and its correlation with the Treg marker, FOXP3, as well as clinical and laboratory parameters. Methods Thirty-seven newly diagnosed CN-AML patients were enrolled in our study along with 22 healthy individuals. The expression of the IDO and FOXP3 genes was analyzed by SYBR Green real-time PCR. Results Both IDO and FOXP3 were highly upregulated in CN-AML patients compared to control groups (P=0.004 and P=0.031, respectively). A positive correlation was observed between IDO and FOXP3 expression among AML patients (r=0.512, P=0.001). Expression of IDO and FOXP3 showed no significant correlation with laboratory parameters such as white blood cell and platelet counts, hemoglobin levels, bone marrow blast percentage, gender, and FLT3 mutation status (P>0.05). Conclusion Higher IDO expression in CN-AML patients may be associated with an increased Treg phenotype which may promote disease progression and lead to poor prognosis of CN-AML patients.

• Journal of Climate Change Research
• /
• v.7 no.2
• /
• pp.205-215
• /
• 2016

The relationship between the climate and the number of heat-related patients in South Korea was analysed in this study. The number of the patients was 1,612 during the summer 2011 to 2015 according to the Heat-related Illness (HRI) surveillance system. The coefficient of determination between the number of the patients and the daily maximum temperature was higher than that between the number of them and the other elements: the daily mean/minimum temperature and relative humidity. The thresholds of daily maximum and minimum temperature in metropolitan cities (MC) were higher than those in regions except for MC (RMC). The higher the maximum and minimum temperature became, the more frequently the heat-related illness rate was observed. The regional difference of this rate was that the rate in RMC was higher than that in MC. Prolonged heat wave and tropical night tended to cause more patients, which continued for 20 days and 31 days of maximum values, respectively. On the other hand, the relative humidity was not proportional to the number of the patients which was rather decreasing at over 70% of relative humidity.

• Journal of Dairy Science and Biotechnology
• /
• v.38 no.4
• /
• pp.169-188
• /
• 2020

Salmonella spp. is the most common cause of gastrointestinal food poisoning worldwide, and human salmonellosis is mostly caused by the consumption of contaminated food. Therefore, the development of rapid detection methods for Salmoenlla spp. and rapid identification of the source of infection by subtyping are important for the surveillance and monitoring of food-borne salmonellosis. Therefore, this review introduces (1) History and nomenclature of Salmoenlla spp., (2) Epidemiology of Salmoenlla spp., (3) Detection methods for Salmoenlla spp. - conventional culture method, genetic detection method, molecular detection methods, and aptamer, and (4) Subtyping methods for Salmoenlla spp. - pulsed-field gel electrophoresis and repetitive sequence-based polymerase chain reaction (PCR).

• International Journal of Computer Science & Network Security
• /
• v.22 no.11
• /
• pp.77-86
• /
• 2022

The destructive impact of contagious diseases outbreaks on all life facets necessitates developing effective solutions to control these diseases outbreaks. This research proposes an end-to-end, data-driven platform which consists of multiple modules that are working in harmony to achieve a concrete goal: early detection of contagious diseases outbreaks (i.e., epidemic diseases detection). Achieving that goal enables decision makers and people in power to act promptly, resulting in robust prevention management of contagious diseases. It must be clear that the goal of this proposed platform is not to predict or forecast the spread of contagious diseases, rather, its goal is to promptly detect contagious diseases outbreaks as they happen. The front end of the proposed platform is a web-based dashboard that visualizes diseases outbreaks in real-time on a real map. These outbreaks are detected via another component of the platform which utilizes data mining techniques and algorithms on gathered datasets. Those gathered datasets are managed by yet another component. Specifically, a mobile application will be the main source of data to the platform. Being a vital component of the platform, the datasets are managed by a DBMS that is specifically tailored for this platform. Preliminary results are presented to showcase the performance of a prototype of the proposed platform.

• International Journal of Computer Science & Network Security
• /
• v.22 no.11
• /
• pp.121-126
• /
• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

• The Journal of Korea Robotics Society
• /
• v.18 no.3
• /
• pp.299-307
• /
• 2023

In the midst of accelerating wars around the world, unmanned robot technology that can guarantee the safety of human life is emerging. ERP-42 is a modular platform that can be used according to the application. In the field of defense, it can be used for transporting supplies, reconnaissance and surveillance, and medical evacuation in conflict areas. Due to the nature of the military environment, atypical environments are predominant, and in such environments, the platform's path followability is an important part of mission performance. This paper focuses on reducing the minimum turning radius in terms of improving path followability. The minimum turning radius of the existing 2WS/2WD in-wheel platform was reduced by increasing the torque of the independent driving in-wheel motor on the rear wheel to generate oversteer. To determine the degree of oversteer, two GPS were attached to the center of the front and rear wheelbases and measured. A closed-loop speed control method was used to maintain a constant rotational speed of each wheel despite changes in load or torque.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.182-189
• /
• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• Safety and Health at Work
• /
• v.15 no.2
• /
• pp.123-128
• /
• 2024

Background: Coal miners are highly prone to occupational health risks, such as black lung disease. This study aims to assess the prevalence of black lung disease and the factors associated with black lung disease among coal miners in Asia. Method: This systematic review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, searched through the scientific literature of the following databases: EBSCO, ScienceDirect, PubMed, and Scopus. We selected articles that studied black lung disease among coal miners from 48 countries in Asia and were published between 2014 and 2023. Article quality was evaluated using the Critical Appraisal Skills Program. Result: The seven articles that we review studied a total of 653,635 coal miners from various types of coal mines from three countries in Asia. Of these miners, 59,998 experienced black lung disease. Black lung disease is prevalent among 9.18% of coal miners in Asia, which is approximately four times higher than the worldwide prevalence. Common factors that influence black lung disease in Asia include age, years of dust exposure, smoking, drinking, working types, and sizes of mines, type of mines, respiratory functions, spirometry parameters, tenure, lack of attention to occupational health, inefficient surveillance, and weak occupational health service. Conclusion: Although the prevalence of black lung disease among coal miners in Asia is considerably high, it can be addressed through effective prevention measures, monitoring, control, and case reporting.