• Journal of the Ergonomics Society of Korea
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• v.5 no.2
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• pp.11-15
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• 1986

In this paper, we consider the validity of a human probabilistic learning model applied to the perdiction of errors associated with the absolute identification of tones. It is shown that the probabilistic learning model describes the human error process adequately. The model parameters are estimated by two methods which are the method of maximum likelihood, and the method of mement. The MLE version of the model has the better predictive power but the ME version is more readily obtainable and may be more practical.

• Analytical Science and Technology
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• v.17 no.3
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• pp.211-216
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• 2004

Commercial one-step rapid fecal occult blood (FOB) kit which was used as a screening test to detect traces of blood in stool samples was evaluated for the feasibility of the forensic identification of human blood. The sensitivity was determined and compared with the conventional Leucomalichite green (LMG) method. In addition, the specificity of the kit and the effects of various chemicals and environmental factors were examined. FOB kit was specific for human hemoglobin and more sensitive than LMG test (approximately 100 times). FOB kit showed positive band using at least 1,000,000-fold diluted human blood. The antigen was very stable regardless of storage temperature and boiling. The positive reaction was not affected by LMG and Luminol, the traditional tests for identification of bloodstain. As a results, FOB test kit could be effectively applied to identification of human blood at crime scene and crime laboratories.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1046-1048
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• 2003

Recently, various researches in respect of the positioning technologies using satellites and the other sensors have made location-based services (LBS) more common and accurate. Consequently, concern about position information has been increasing. However, since these positioning systems only focus on user's position, it is difficult to know the user's attitude or detailed behaviors at the specific position. It is worthy to study on how to acquire such human attitude or behavior, because those information is useful to know the context of the user. In this paper, the sensor unit consisting of three dimensional accelerometer was attached to human body, and autonomously measured the perpendicular acceleration of ordinary human behaviors including activity modes such as walking, running, and transportation mode using transportation such as a train, a bus, and an elevator. Subsequently, using the classified measurement results, the method to identify the human activity modes was proposed.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.16-22
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• 2018

This paper presents a solution for personal profiling system based on user-oriented tracking. Here, we introduce a new way to identify and track humans by using two types of cameras: dome and face camera. Dome camera has a wide view angle so that it is suitable for tracking human movement in large area. However, it is difficult to identify a person only by using dome camera because it only sees the target from above. Thus, face camera is employed to obtain facial information for identifying a person. In addition, we also propose a new mechanism to locate human on targeted location by using grid-cell system. These result in a system which has the capability of maintaining human identity and tracking human activity (movement) effectively.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.117-124
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• 2008

This paper introduces a novel approach for automated mapping of a map feature that is vinyl green house in high spatial resolution imagery Some map features have their unique spatial patterns. These patterns are normally detected in high spatial resolution remotely sensed data by human recognition system. When spatial patterns can be applied to map feature identification, it will improve image classification accuracy and will be contributed a lot to feature identification. In this study, an automated feature identification approach using spatial aucorrelation is developed, specifically for the vinyl green house that has distinctive spatial pattern in its array. The algorithm aimed to develop the method without any human intervention such as digitizing. The method can investigate the characteristics of repeated spatial pattern of vinyl green house. The repeated spatial pattern comes from the orderly array of vinyl green house. For this, object-based approaches are essential because the pattern is recognized when the shapes that are consists of the groups of pixels are involved. The experimental result shows very effective vinyl house extraction. The targeted three vinyl green houses were exactly identified in the IKONOS image for a part of Jeju area.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.499-509
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• 2013

Seafarers are one of the main engines driving economic growth in the maritime sector. The International Maritime (IMO) Organization estimated that there were approximately 1.5 million seafarers around the world engaged in international trade in 2012. Data have shown that human casualties in maritime accidents around Japan have shown an increasing trend over the last ten years. One cause is human error, which is inseparable from the human element that influences mariner's decisions and actions. The Personal Identification (PIN) Safe method is one way to systematically identify substandard and unsafe actions by considering the error taxonomies associated with various scenarios for a maritime system. The results are based on analysis of the role of the human element in commonly reported unsafe actions when interacting with equipment and other systems. Furthermore, patterns of influencing shaping factors were observed on the basis of data processing; the aim of this study was to promote safety culture and provide an opportunity to improve safety at sea.

• Journal of Microbiology and Biotechnology
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• v.20 no.3
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• pp.558-562
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• 2010

SDS-PAGE of extracted surface-associated proteins of Lactobacillus rhamnosus strains E/N, Oxy, and Pen, was performed. The obtained protein patterns allowed differentiation of the examined strains, which was not accomplished by the commonly used RAPD genotypic method. The differentiation by the SDS-PAGE method proved to be a useful tool for strain-specific identification, which was further confirmed by 2DE analysis. Therefore, it can be used as an alternative or complementary method for both conventional and genotypic identification procedures, especially when closely related lactobacilli isolates are identified.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• Journal of Microbiology and Biotechnology
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• v.21 no.4
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• pp.421-429
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• 2011

For precise identification of a Lactobacillus K1 isolate, LC-MS/MS analysis of the putative surface layer protein was performed. The results obtained from LTQ-FT-ICR mass spectrometry confirmed that the analyzed protein spot is the surface layer protein originating from Lb. helveticus species. Moreover, the identified protein has the highest similarity with the surface layer protein from Lb. helveticus R0052. To evaluate the proteomic study, multilocus sequence analysis of selected housekeeping gene sequences was performed. Combination of 16S rRNA sequencing with partial sequences for the genes encoding the RNA polymerase alpha subunit (rpoA), phenylalanyl-tRNA synthase alpha subunit (pheS), translational elongation factor Tu (tuf), and Hsp60 chaperonins (groEL) also allowed to classify the analyzed isolate as Lb. helveticus. Further classification at the strain level was achieved by sequencing of the slp gene. This gene showed 99.8% identity with the corresponding slp gene of Lb. helveticus R0052, which is in good agreement with data obtained by nano-HPLC coupled to an LTQ-FT-ICR mass spectrometer. Finally, LC-MS/MS analysis of surface layer proteins extracted from three other Lactobacillus strains proved that the proposed method is the appropriate molecular tool for the identification of S-layer-possessing lactobacilli at the species and even strain levels.

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