• Convergence Security Journal
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• v.24 no.1
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• pp.77-82
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• 2024

Recent research has seen significant improvements in various object detection and classification models overall. However, the study of object detection and classification in situations where objects are partially obscured remains an intriguing research topic. Particularly in the military domain, unmanned combat systems are often used to detect and classify objects, which are typically partially concealed or camouflaged in military scenarios. In this study, a method is proposed to enhance the classification performance of partially obscured objects. This method involves adding occlusions to specific parts of object images, considering the surrounding environment, and has been shown to improve the classification performance for concealed and obscured objects. Experimental results demonstrate that the proposed method leads to enhanced object classification compared to conventional methods for concealed and obscured objects.

• Journal of Communications and Networks
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• v.11 no.6
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.1048-1056
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• 1998

In this paper, a fractal image coding based on discrete wavelet transform is proposed to improve PSNR at low bit rates and reduce computational complexity of encoding process. The proposed method takes the absolute value of discrete wavelet transform coefficients, and then constructs significant coefficients trees, which indicate the positions and signs of the significant coefficients. This method improves PSNR and reduces computational complexity of mapping contracted domain pool onto range block, by matching only the significant coefficients of range block to coefficients of contracted domain block. Also, this paper proposes a classification scheme which minimizes the number of contracted domain blocks compared with range block. This scheme significantly reduces the number of range and contracted domain block comparison.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1457-1465
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• 2019

3D convolution is to stack multiple consecutive frames to form a cube, and then apply the 3D convolution kernel in the cube. In this structure, each feature map of the convolutional layer is connected to multiple adjacent sequential frames in the previous layer, thus capturing the motion information. However, due to the changes of pedestrian posture, motion and position, the convolution at the same place is inappropriate, and when the 3D convolution kernel is convoluted in the time domain, only time domain features of three consecutive frames can be extracted, which is not a good enough to get action information. This paper proposes an action recognition method based on feature fusion of 3D convolutional neural network. Based on the VGG16 network model, sending a pre-acquired optical flow image for learning, then get the time domain features, and then the feature of the time domain is extracted from the features extracted by the 3D convolutional neural network. Finally, the behavior classification is done by the SVM classifier.

• Child Health Nursing Research
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• v.25 no.2
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• pp.143-153
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• 2019

Purpose: The purpose of this study was to examine the frequency, perceived importance and competence of pediatric emergency nursing practice (PENP) in nurses who cared for pediatric patients in the emergency department. Methods: This cross-sectional descriptive study analyzed 175 emergency department nurses caring for pediatric patients at 7 university hospitals with more than 500 beds, located in Seoul and Gyeonggi Province. The measurement tool was modified from the Classification of Standard Nursing Activities to measure the frequency, importance of PENP, and nursing competence. It comprised 143 items in 16 domains of PENP. Results: The most frequent nursing practice was the domain of 'nursing records and environmental management' and the least frequent practice was the 'research and consulting' domain. The nursing care domain perceived to be most important by nurses was 'specialized intensive nursing care'. The nursing care domain with the highest level of nursing competence was 'hygiene care', and the domain with the least level of nursing competence was the 'research and consulting'. Conclusion: These results will be utilized as basic data for future pediatric emergency nursing education initiatives and for establishing priorities of nursing policy to improve health care for children admitted to the emergency department.

• Journal of Microbiology and Biotechnology
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• v.34 no.7
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• pp.1385-1394
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• 2024

Collagenolytic proteases are widely used in the food, medical, pharmaceutical, cosmetic, and textile industries. Mesophilic collagenases exhibit collagenolytic activity under physiological conditions, but have limitations in efficiently degrading collagen-rich wastes, such as collagen from fish scales, at high temperatures due to their poor thermostability. Bacterial collagenolytic proteases are members of various proteinase families, including the bacterial collagenolytic metalloproteinase M9 and the bacterial collagenolytic serine proteinase families S1, S8, and S53. Notably, the C-terminal domains of collagenolytic proteases, such as the pre-peptidase C-terminal domain, the polycystic kidney disease-like domain, the collagen-binding domain, the proprotein convertase domain, and the β-jelly roll domain, exhibit collagen-binding or -swelling activity. These activities can induce conformational changes in collagen or the enzyme active sites, thereby enhancing the collagen-degrading efficiency. In addition, thermostable bacterial collagenolytic proteases can function at high temperatures, which increases their degradation efficiency since heat-denatured collagen is more susceptible to proteolysis and minimizes the risk of microbial contamination. To date, only a few thermophile-derived collagenolytic proteases have been characterized. TSS, a thermostable and halotolerant subtilisin-like serine collagenolytic protease, exhibits high collagenolytic activity at 60℃. In this review, we present and summarize the current research on A) the classification and nomenclature of thermostable and mesophilic collagenolytic proteases derived from diverse microorganisms, and B) the functional roles of their C-terminal domains. Furthermore, we analyze the cleavage specificity of the thermostable collagenolytic proteases within each family and comprehensively discuss the thermostable collagenolytic protease TSS.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.31-34
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• 2000

In this paper, we proposed multispectral image compression method using CIP (classified inter-channel prediction) and SVQ (selective vector quantization) in wavelet domain. First, multispectral image is wavelet transformed and classified into one of three classes considering reflection characteristics of the subband with the lowest resolution. Then, for a reference channel which has the highest correlation with other channels, the variable VQ is performed in the classified intra-channel to remove spatial redundancy. For other channels, the CIP is performed to remove spectral redundancy. Finally, the prediction error is reduced by performing SVQ. Experiments are carried out on a multispectral image. The results show that the proposed method reduce the bit rate at higher reconstructed image quality and improve the compression efficiency compared to conventional method.

• Journal of Biomedical Engineering Research
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• v.39 no.2
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• pp.69-79
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• 2018

The present study proposes an algorithm that can discriminate between normal subjects and paroxysmal atrial fibrillation (PAF) patients, which is conducted using electrocardiogram (ECG) without PAF events. For this, time-domain features and random forest classifier are used. Time-domain features are obtained from Poincare plot, Lorenz plot of ${\delta}RR$ interval, and morphology analysis. Afterward, three features are selected in total through feature selection. PAF patients and normal subjects are classified using random forest. The classification result showed that sensitivity and specificity were 81.82% and 95.24% respectively, the positive predictive value and negative predictive value were 96.43% and 76.92% respectively, and accuracy was 87.04%. The proposed algorithm had an advantage in terms of the computation requirement compared to existing algorithm, so it has suggested applicability in the more efficient prediction of PAF.

• Korean Journal of Biological Psychiatry
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• v.20 no.3
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• pp.63-65
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• 2013

The new edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) is published by the American Psychiatric Association. The diagnostic systems for mental disorders have come under criticism for relying on presenting signs and symptoms with the result that they do not adequately reflect relevant neurobiological and behavioral systems. Finally, the National Institute of Mental Health (NIMH) in the United States has suggested the Research Domain Criteria (RDoC) to develop a research classification system based upon dimensions of neurobiology and behavioral aspect. The present review introduces the RDoC as a new reaseach framework.

• Journal of Korean Academy of Nursing Administration
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• v.20 no.5
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• pp.545-557
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• 2014

Purpose: The purpose of this study was to develop a patient classification system based on nursing care intensity for patients with acute stroke-related symptoms and verify its validity and reliability. Methods: Data were collected between November, 2013 and February, 2014. The verification for content validity of the patient classification system was conducted by a group of seven professionals. Both interrater reliability and concurrent validity were verified at stroke units in tertiary hospitals. Results: The intensive nursing care for acute stroke patients consisted of 14 classified domains and 56 classified contents by adding 'neurological assessment and observation' and 'respiratory care': 'hygiene', 'nutrition', 'elimination', 'mobility and exercise', 'education or counselling', 'emotional support', 'communication', 'treatment and examination', 'medication', 'assessment and observation', 'neurological assessment and observation', 'respiratory care', 'coordination between departments', and 'discharge or transfer care'. Each domain was classified into four levels such as Class I, Class II, Class III, and Class IV. Conclusion: The results show that this patient classification system has satisfactory validity for content and concurrent and verified reliability and can be used to accurately estimate the demand for nursing care for patients in stroke units.