• Journal of agricultural medicine and community health
• /
• v.34 no.2
• /
• pp.214-222
• /
• 2009

Objectives: This study was performed to evaluate the effect of navigator education program for cancer screening, which is designed for improvement in knowledge of cancer, perceived self efficacy and communication skill of the breast and cervical cancer screening for middle-aged and aged women in urban areas. Cancer screening navigator is lay health advisor who are educated for providing information, emotional support about cancer screening at the community. Methods: The subjects were 33 women at the age of 40-69 and educated for 12 hours through the education program. The control group subjects were 30 women. For statistical analysis, descriptive statistics and paired t-test were used with SPSS WIN 14.0. Results: Contents of education program were case of cancer early detection, benefit of breast cancer screening, benefit of cervical cancer screening, health care system for cancer screening, role of cancer screening navigator, communication skill, transtheoretical model and role play. Knowledge of cancer(t=4.267, p=0.000) and communication skill(t=4.947, p=0.000) of the women increased significantly after implementing the 12 hours education program. Conclusion: The results suggest that navigator education for cancer screening has an effect in increasing knowledge of cancer, and communication skill scores.

• Biomedical Science Letters
• /
• v.22 no.1
• /
• pp.24-28
• /
• 2016

Tuberculosis (TB) remains an unsolved community health problem since identification of its causing microorganism called Mycobacterium tuberculosis (MTB) by Robert Koch in 1882. Annually, eight million TB cases are newly reported and 2~3 million patients die from TB. Pulmonary TB is highly infectious and untreated pulmonary TB patients are believed to infect >10 people in a year. The conventional methods for diagnosis of TB are chest X-ray and isolation of the causing microorganisms from patient specimens. Screening of TB is conducted with smeared sputum in slides, and TB is confirmed by identification of MTB in cultured specimens. One of the fatal pitfalls of screening detection for smeared sputum is that it is impossible to distinguish MTB and other acid-fast bacilli (AFB) because they are stained equally with Ziehl-Neelsen (ZN) stain. Culture of MTB is the most reliable method for diagnosis of TB but it takes 4~8 weeks. In this report, we suggest a fast and highly-reliable MTB detection method that distinguishes AFB in sputum samples. Purified DNA from the AFB stained slide samples offered by The Korean Institute of Tuberculosis were used to detect infected MTB in patients. PCR, real-time PCR and reverse blot hybridization assay (REBA) methods were applied to purified DNA. Conclusively, the real-time PCR method was confirmed to produce high sensitivity and we were able to further detect drug-resistant MTB with REBA.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.25 no.5
• /
• pp.1115-1122
• /
• 2015

In recent years, the use of game bots by illegal programs has been expanded from individual to group scale; this brings about serious problems in online game industry. The gold farmers group creates an in-game social community so-called "guild" to obtain a large amount of game money and manage game bots efficiently. Although game developers detect game bots by detection algorithms, the algorithms can detect only part of the gold farmers group. In this paper, we propose a detection method for the gold farmers group on a basis of normal and bot guilds characteristic analysis. In order to differentiate normal and bots guild, we analyze transaction patterns for individuals, auction house and chatting. With the analyzed results, we can detect game bot guilds. We demonstrate the feasibility of the proposed methods with real datasets from one of the popular online games named AION in Korea.

• Journal of Korea Multimedia Society
• /
• v.13 no.7
• /
• pp.950-959
• /
• 2010

The Election paradigm can be used as a building block in many practical problems such as group communication, atomic commit and replicated data management where a protocol coordinator might be useful. The problem has been widely studied in the research community since one reason for this wide interest is that many distributed protocols need an election protocol. However, mobile ad hoc systems are more prone to failures than conventional distributed systems. Solving election in such an environment requires from a set of mobile nodes to choose a unique node as a leader based on its priority despite failures or disconnections of mobile nodes. In this paper, we describe a solution to the election problem from mobile ad hoc computing systems and it was proved by temporal logic. This solution is based on the Group Membership Detection algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.169-189
• /
• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.1
• /
• pp.227-232
• /
• 2015

Background: Breast cancer is the commonest cancer among Sri Lankan females, accounting for 26% of the cancer incidence in women. Early detection of breast cancer is conducted by public health midwives (PHMs) in the Well Woman Clinics. The aim of the present study was to determine the effectiveness of an educational intervention on improving knowledge, attitudes and practices (KAP) on breast cancer screening among PHMs in the district of Gampaha. Materials and Methods: Two Medical Officer of Health (MOH) areas in Gampaha district were selected using random sampling as intervention (IG) and control (CG) groups. All the PHMs in the two MOH areas participated in the study, with totals of 38 in IG and 47 in CG. They were exposed to an educational intervention with the objective of using them to subsequently conduct the same among 35-59 year women in the community. Following the intervention, post-intervention assessments were conducted at one month and six months to assess the effectiveness of the intervention. Results: The overall median scores for KAP among PHMs respectively were as follows. Pre-intervention: IG:58%(IQR: 53-69%), 90%(IQR: 70-100%) and 62%(IQR: 57-70%). CG: 64%(IQR: 56-69%), 90%(IQR: 70-90%) and 62%( IQR: 50-77%). Post-intervention: one month, IG:96%(IQR: 93-96%), 100%(IQR: 100-100%), and 85%(IQR: 81-89%). CG:67%(IQR: 60- 73%), 90%(IQR: 80-100%) and 65%(IQR: 50-73%). Post-intervention: six months, IG: 93% (IQR: 91-93%), 100%(IQR: 90-100%), and 81%(IQR: 77-89%). CG: 67%(IQR: 58- 71%), 90%(IQR: 90-100%), and 62%( IQR: 58-73%). All the above post-intervention scores of PHMs in the IG were significantly higher in comparison to CG (p<0.001). Conclusions: This planned educational intervention had a significant impact on improving KAP of PHMs for early detection of breast cancer in the Gampaha district.

• Smart Structures and Systems
• /
• v.24 no.5
• /
• pp.567-585
• /
• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.6
• /
• pp.7-14
• /
• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.93-103
• /
• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Journal of Radiation Protection and Research
• /
• v.34 no.3
• /
• pp.115-120
• /
• 2009

Since 1990s, some events - detection of a dirty bomb in a Russian nation park in 1995, 9/11 terrorist attack to WTC in 2001, discovery of Al-Qaeda's experimentation to build a dirty bomb in 2003 etc - have showed that nuclear or radiological terrorism relating to radioactive materials (hereinafter "radioactive materials" is referred to as "nuclear material, nuclear spent fuel and radioactive source") is not incredible but serious and credible threat. Thus, to respond to the new threat, the international community has not only strengthened security and physical protection of radioactive materials but also established prevention of and response to illicit trafficking of radioactive materials. In this regard, our government has enacted or revised the national regulatory framework with a view to improving security of radioactive materials and joined the international convention or agreement to meet this international trend. For the purpose of prevention of nuclear/radiological terrorism, this paper reviews physical characteristics of nuclear material and existing detection instruments used for prevention of illicit trafficking. Finally, national detection regime against nuclear/radiological terrorism based on paths of the smuggled radioactive materials to terrorist's target building/area, national topography and road networks, and defence-in-depth concept is suggested in this paper. This study should contribute to protect people's health, safety and environment from nuclear/radiological terrorism.