• 한국멀티미디어학회논문지
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• 제18권3호
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• pp.359-367
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• 2015

In this paper, we propose intelligent fire learning and detection system using hybrid visual attention mechanism of human. Proposed fire learning system generates leaned data by learning process of fire and smoke images. The features used as learning feature are selected among many features which are extracted based on bottom-up visual attention mechanism of human, and these features are modified as learned data by calculating average and standard variation of them. Proposed fire detection system uses learned data which is generated in fire learning system and features of input image to detect fire.

• 한국정밀공학회지
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• 제24권6호
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• pp.66-71
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• 2007

Development of a locomotive mechanism for the capsule type endoscopes will largely enhance their ability to diagnose disease of digestive organs. As a part of it, there should be provided a detection device of their position in human organs for the purpose of observation and motion control. In this paper, a permanent magnet outside human body was employed to project magnetic field on a capsule type endoscope, while its position dependent flux density was measured by three hall-effect sensors which were orthogonally installed inside the capsule. In order to detect the 2-D position data of the capsule with three hall-effect sensors including the roll, pitch and yaw angle, the permanent magnet was extra translated during the measurement. In this way, the 2-D coordinates and three rotation angles of a capsule endoscope on the same motion plane with the permanent magnet could be detected. The working principle and performance test results of the capsule position detection device were introduced in this paper showing that they could be also applied to 6-DOF position detection.

• 한국컴퓨터정보학회논문지
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• 제24권3호
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• pp.87-93
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• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• Journal of Microbiology
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• 제39권3호
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• pp.226-228
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• 2001

Human lymphotropic herpesvirus is known to be a major pathogen associated with various diseases in bone marrow transplantation (BMT) recipients. A multiplex nested-polymerase chain reaction (PCR) method was developed for the simultaneous detection of human lymphotropic herpesviruses, including Ebstein-Barr virus (EBV), cytomegalovirus (CMV), and human herpesvirus 6 variants A and B (HHV6-A, HHV6-B). To demonstrate the usefulness of multiplex PCR for the analysis of clinical samples, peripheral blood mononuclear cells and serum from BMT recipients were analysed. The results skewed that a clear detection could be made between EBV, HCMV and HHV-6. This multiplex PCR assay is an efficient and cost-effective approach to the analysis of large numbers of samples to determine the epidemiological importance of EBV HCMV and HHV-6.

• Journal of Information Processing Systems
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• 제12권3호
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• pp.489-501
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• 2016

As interest in the Internet increases, related technologies are also quickly progressing. As smart devices become more widely used, interest is growing in words are missing here like "improving the" or "figuring out how to use the" future Internet to resolve the fundamental issues of transmission quality and security. The future Internet is being studied to improve the limits of existing Internet structures and to reflect new requirements. In particular, research on words are missing here like "finding new forms of" or "applying new forms of" or "studying various types of" or "finding ways to provide more" reliable communication to connect the Internet to various services is in demand. In this paper, we analyze the security threats caused by malicious activities in the future Internet and propose a human behavior analysis-based security service model for malware detection and intrusion prevention to provide more reliable communication. Our proposed service model provides high reliability services by responding to security threats by detecting various malware intrusions and protocol authentications based on human behavior.

• Nuclear Engineering and Technology
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• 제33권2호
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• pp.225-230
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• 2001

The aim of this study is to describe a usefulness of nuclear analytical technique in assessing and comparing the concentration levels through the analysis of uranium using human hair sample in the field of environment. A fission track detection technique was applied to determine the uranium concentration in human hair. Hair samples were collected from two groups of people - a) workers not dealing with uranium directly, and b) workers possibly contaminated with uranium. The concentration of $^{235}$ U for the first group varied from <1 to 39 ng/g and the second group can be estimated up to the level of $\mu$g/g. Radiographs of heavy-duty work samples contained high dense “hot spots” along a single hair. After washing in acetone and distilled water, external contamination was not totally removed. Insoluble uranium compounds were not completely washed out. The (n, f)- radiography technique, having high sensitivity, and capable of getting information on uranium content at each point of a single hair, is an excellent tool for environmental monitoring.

• 전기학회논문지
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• 제62권6호
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• pp.811-817
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• 2013

In this study we present a system for pulse-rate period detection and human sensibility evaluation based on the wireless medical telemetry service (WMTS) used for transmission of data from medical telemetry devices to various medical facilities and services. We develop a medical-purpose specific WMTS communication module to transmit biometric signals. From the pulse rate variability(PRV) signal, we attempt to classify positive and negative emotional states based on analysis of the ratio of LF/HF in the frequency domain. We measure the data reception rate according to distance in order to test the performance of the WMTS module and analyze the effects on human sensibility evaluation.

• IEIE Transactions on Smart Processing and Computing
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• 제2권3호
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• pp.117-129
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• 2013

The increasing demand for monitoring wildfires and their impact on the land surface have prompted studies of burned area extraction and analysis. To differentiate burned and unburned area, the earlier method of the Moderate Resolution Imaging Spectro-radiometer (MODIS) Burned Area Detection Algorithm was proposed to estimate the change in land surface based on the reflectance energy. The energy, whose wavelengths are sensitive to burning, was selected to calculate the change parameter $Z_{score}$. This method was applied using the MODIS images to produce a MODIS Burned Area product. The approach was to simplify this algorithm to make it compatible with the Landsat 7 ETM+ SLC-off images. To extract the refined version of burned regions, post-processing was carried out by applying a median filter, dilation morphology algorithm, and finally a gap filling method. The experimental results showed that the detailed burned areas extracted from the proposed method exhibited more spatial details than those of the MODIS Burned products in the large U.S areas. The results also revealed the discontinuous distribution of burned regions in Vietnam forests.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.173-176
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• 2002

This paper describes an implementation of fast face detection algorithm. This algorithm can robustly detect human faces with unknown sizes and positions in complex backgrounds. This paper provides a powerful face detection algorithm using skin color segmenting. Skin Color is modeled by a Gaussian distribution in the HSI color space among different persons within the same race, Oriental. The main feature of the Algorithm is achieved face detection robust to illumination changes and a simple adaptive thresholding technique for skin color segmentation is employed to achieve robust face detection.