• Journal of Sensor Science and Technology
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• v.20 no.3
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• pp.151-155
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• 2011

A variety of pattern recognition algorithms including neural networks may be applicable to the identification of odors. In this paper, an identification technique for an electronic odor sensing system applicable to wound state monitoring is presented. The performance of the radial basis function(RBF) network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those parameters are initialized by an ill-conditioned genetic fuzzy c-means algorithm, and the distribution of input patterns in the very first stage, the stochastic gradient(SG), is adapted. The adaptive RBF network with singular value decomposition(SVD), which provides additional adaptation capabilities to the RBF network, is used to process data from array-based gas sensors for early detection of wound infection in burn patients. The primary results indicate that infected patients can be distinguished from uninfected patients.

• Journal of Trauma and Injury
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• v.34 no.1
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• pp.75-78
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• 2021

A Morel-Lavallée lesion is a post-traumatic closed degloving soft tissue injury after blunt trauma. Infection and skin necrosis frequently occur if it is not treated properly in the early stages. However, there is no clearly established treatment algorithm. In the acute stage, it is mainly treated with aspiration, simple compression, and incisional drainage. In the chronic stage, sclerotherapy is usually performed. If skin necrosis develops, the necrotic tissue is resected and a skin graft is needed. We describe a case of acute Morel-Lavallée lesion in the buttock region that was treated with limited incisional drainage and negative-pressure wound therapy, and also present a review of the literature.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.63-72
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• 2007

Recently, as recognizing the risk about the infection of an infectious wastes, the problems about the management and treatment of the infectious wastes stand out socially. In this paper, as being possible monitoring whole processing from the origin of the infectious waste to the processing plant, using the RFID which is the kernel technology of the next generation, we tried to solve the second infection problem by inefficient treatment of the infectious wastes. Through the research suggesting in this paper, as storing and monitoring the procedural business articles and the problem about miss-writing and input error being found in management system like documentary writing by the existing manager and computation input by the web application, we can understand the management state, immediately. And the Bio information for the personal authentication is carried out through storing the feature vector calculation by the PCA algorithm, into the tag. It suggested more systematic and safer management plan than previous thing, as giving attention about the wastes to manager.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.349-360
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• 2020

Fire-blight disease is a kind of contagious disease affecting apples, pears, and some other members of the family Rosaceae. Due to its extremely strong infectivity, once an orchard is confirmed to be infected, all of the orchards located within 100 m must be buried under the ground, and the sites are prohibited to cultivate any fruit trees for 5 years. In South Korea, fire-blight was confirmed for the first time in the Ansung area in 2015, and the infection is still being identified every year. Traditional approaches to detect fire-blight are expensive and require much time, additionally, also the inspectors have the potential to transmit the pathogen, Thus, it is necessary to develop a remote, unmanned monitoring system for fire-blight to prevent the spread of the disease. This study was conducted to detect fire-blight on pear trees using discriminant analysis with color information collected from a rotary-wing drone. The images of the infected trees were obtained at a pear orchard in Cheonan using an RGB camera attached to a rotary-wing drone at an altitude of 4 m, and also using a smart phone RGB camera on the ground. RGB and Lab color spaces and discriminant analysis were used to develop the image processing algorithm. As a result, the proposed method had an accuracy of approximately 75% although the system still requires many flaws to be improved.

• Tuberculosis and Respiratory Diseases
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• v.69 no.2
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• pp.75-80
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• 2010

The year of 2009~2010 brought a number of concepts and new ideas were evaluated with promising results. However, some studies that challenged many beliefs. In acute respiratory distress syndrome (ARDS), recent clinical studies took into consideration of pathophysiologic changes of respiratory system compliance. Meta-analysis of positive end-expiratory pressure trials showed survival benefit of high positive end-expiratory pressure in ARDS. Until now, prone positioning did not show survival benefit in patients with ARDS. Extracorporeal membrane oxygenation (ECMO) based management improved survival in patients with severe ARDS. ECMO can be a management option in severe ARDS. Sedation is a standard practice in critically ill patients needing mechanical ventilation. However, Danish group reported less sedation of critically ill patients receiving mechanical ventilation was associated with an increase in days without ventilation. Although this single center study has some limitations, the overall results are promising. Use of maximal sterile barrier precautions (mask, sterile gown, sterile gloves, and large sterile drapes) with chlorhexidine-impregnated dressing reduced central venous catheter related infection. Selective oropharyngeal decontamination (application of topical antibiotics in the oropharynx) reduced the mortality rate of an intensive care unit (ICU) population. Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation (NICE-SUGAR) trial reported intensive glucose control increased mortality among adults in the ICU. Some of the results of above papers are promising. However, some ideas may need for more frequent individual assessment and increase the workload of ICU staffs. Before implementation of new practice in ICU, we should take into consideration of individual hospital situation including human and material resources.

• Journal of Veterinary Clinics
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• v.23 no.2
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• pp.105-110
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• 2006

A three-layer, feed-forward artificial neural network (ANN) with sixteen input neurons, three hidden neurons, and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as possible in laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 and November 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 cases for training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristics of the layer house, management practice, flock size, and the output was either presence or absence of IB. ANN was trained using training set with a back-propagation algorithm and test set was used to determine the network's capability to predict outcomes that it has never seen. Diagnostic performance of the trained network was evaluated by constructing receiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determine the best positivity criterion for the model. Several different ANNs with different structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training set. Sensitivity and specificity of the trained neural network was 95.5% (42/44, 95% CI, 84.5-99.4) and 93.9% (31/33, 95% CI, 79.8-99.3), respectively. For testing set, AVC of the ROC curve for the IBV_D1 network was 0.948 (SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnostic accuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificity together with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Based on these findings, the authors conclude that neural network can be successfully applied to the development of a screening model for identifying IB infection in laying hen flocks.

• Journal of The Korea Institute of Healthcare Architecture
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• v.28 no.4
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• pp.41-50
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• 2022

Purpose: This study aims to suggest an analysis model finding the relationship between building scale characteristics of Public-use facilities and infectious disease outbreaks around senior leisure welfare facilities and the features and their scopes where quarantine resources are to be concentrated. Methods: Reviewing previous studies found the user characteristics of senior leisure welfare facilities and scale characteristics of urban architectures. The data preprocessing was performed after collecting building data and infectious disease outbreak data in the analysis area. This study derived data for attributes of building size and frequency of infectious disease outbreaks in Public-use facilities around senior leisure welfare facilities. A computing algorithm was implemented to analyze the correlation between the building size characteristics and the infectious disease outbreak frequency as per the change of the spatial scope. Results: The results of this study are as follows: First, the suggested model was to analyze the correlation between the infection frequency and the number of senior leisure welfare facilities, the number of Public-use facilities, building area, total floor area, site area, height, building-to-land ratio, and floor area ratio varied as per the change of spatial scope. Second, correlation results varied between the infection frequency and the number of senior leisure welfare facilities, the number of Public-use facilities, building area, total floor area, site area, height, building-to-land ratio, and floor area ratio. Third, a negative correlation appeared in the analysis between the number of senior leisure welfare facilities and infection frequency. And positive correlations appeared noticeably in the study between the number of Public-use facilities, building area, total floor area, height, building-to-land ratio, and floor area ratio. Implications: This study can be used as primary data on the utilization of limited quarantine resources by analyzing the relationship between the Public-use facilities around the senior leisure welfare facilities and the spread of infectious diseases. In addition, it suggests that infectious disease prevention measures are necessary considering the spatial scope of the analysis area and the size of buildings.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.1-7
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• 2015

As recent smartphone is used around the world, all of the subscribers of the mobile communication is up to 47.7% about 24 million people. Smartphone has a vulnerability to security, and security-related incidents are increased in damage with the smartphone. However, precautions have been made, rather than analysis of the infection of most of the damage occurs after the damaged except for the case of the expert by way of conventional post-countermeasure. In this paper, we implement a mobile-based malware analysis systems apply a virtualization technology. It is designed to analyze the behavior through it. Virtualization is a technique that provides a logical resources to the guest by abstracting the physical characteristics of computing resources. The virtualization technology can improve the efficiency of resources by integrating with cloud computing services to servers, networks, storage, and computing resources to provide a flexible. In addition, we propose a system that can be prepared in advance to buy a security from a user perspective.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.744-748
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• 2012

Botnet is a network that consists of bot hosts infected by malware. The C&C server of centralized botnet, which is being used widely, is relatively easy to detect, while detecting P2P botnet is not a trivial problem because of the existence of many avoiding techniques. In this paper, we separate the network into inner and outer sub-network at the location of the router, and analyze the method of detecting botnet using path of packet and infection probability. We have extended Dye-Pumping algorithm in order to detect P2P botnet members more accurately, and we expect that the analysis of the results can be used as a basis of techniques that detect and block P2P botnet in the networks.

• The Journal of the Korean Society for Microbiology
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• v.34 no.6
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• pp.561-571
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• 1999

Diagnosis of mycobacterial infection is dependent upon the isolation and identification of causative agents. The procedures involved are time consuming and technically demanding. To improve the laborious identification process mycobacterial systematics supported by gene analysis is feasible, being particularly useful for slowly growing or uncultivable mycobacteria. To complement genetic analysis for the differentiation and identification of mycobacterial species, an alternative marker gene, rpoB encoding the ${\beta}$ subunit of RNA polymerase, was investigated. rpoB DNAs (342 bp) were amplified from 52 reference strains of mycobacteria including Mycobacterium tuberculosis H37Rv (ATCC 27294) and clinical isolates by the PCR. The nucleotide sequences were directly determined (306 bp) and aligned using the multiple alignment algorithm in the MegAlign package (DNASTAR) and MEGA program. A phylogenetic tree was constructed with a neighborhood joining method. Comparative sequence analysis of rpoB DNA provided the basis for species differentiation. By being grouped into species-specific clusters with low sequence divergence among strains belonging to same species, all the clinical isolates could be easily identified. Furthermore RFLP analysis enabled rapid identification of clinical isolates.