• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.112-116
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• 2024

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.4
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• pp.242-246
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• 2000

The Near Infrared Reflectance Spectroscopy(NIR) was used to evaluate nutritional diagnosis for rapid leaf analysis method, 177 'Fuji' apple and 130 'Campbell Early' grape leaves were measured by Near Infrared reflectance spectra in the NIR region(1,100~2.500nm). Total nitrogen content was measured by kjelldhal distillation, after salycilic acid-sulfuric acid digestion. An empirical equation to predict total nitrogen content from its spectral signature was developed by adapting the Near Infrared Reflectance Spectroscopy analysis(NIRa) technique and the results were apple-0.965(R). 0.086(SEC), grape-0.926(R), 0.152(SEC). Standard Error of Prediction(SEP) of NIRa for predicting the total nitrogen of apple and grape leaves was 0.360 and 0.210, respectively. It was concluded that Near infrared reflectance spectroscopy analysis is promising method for rapid analysis of apple and grape leaves.

• Fire Science and Engineering
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• v.18 no.3
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• pp.51-56
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• 2004

A fire outbreak in a reinforcement concrete structure looses the organism by the different contraction and expansion of hardened cement pastes and aggregate, and causes cracks by thermal stress, leading to the deterioration of the durability. So concrete reinforcement structure is damaged partial or whole structure system. Therefore diagnosis of deterioration is needed based on mechanism of fire deterioration in general concrete structures. Fundamental information and data on the properties of concrete exposed to high temperature are necessary for accurate diagnosis of deterioration. In this study, it was presented data for the accurate diagnosis and selection of repair and reinforcement system for the deteriorated concrete heated highly, various concrete such as standard design compressive strength, fine aggregate and admixture were exposed to a high temperature environment. And fundamental data were measured engineering properties such as explosive spatting, ultrasonic pulse velocity and compressive strength.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.105-108
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• 2010

The vehicle navigation system is a representative driver supporting system that available to present searching and guiding path functions, have been increased for usability. Under competition situation because of following the spreaded navigation market, to meet customer's needs about new given services, there are need differentiated services increasing dramatically. Also now, dash board indicates various vehicle's status and driver can aware of that. However it is not easy to know where is abnormal essentially and there are no devices to give warning to driver. Therefore, It is difficult to preserve accidents because it can't deal with various abnormal functions immediately on driving. In this paper, we proposed vehicle diagnosis program within navigation that is available to manage and to make a diagnosis of vehicle. And this program conform OBD-II standard, so it can transmit diagnosis data from ECU to navigation system using Bluetooth wireless communication protocol. Thus this program give enhanced services to customer as well as multimedia and geometry information services.

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.59-64
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• 2007

In these days, malicious codes have become reality and evolved significantly to become one of the greatest threats to the modern society where important information is stored, processed, and accessed through the internet and the computers. Computer virus is a common type of malicious codes. The standard techniques in anti-virus industry is still based on signatures matching. The detection mechanism searches for a signature pattern that identifies a particular virus or stain of viruses. Though more accurate in detecting known viruses, the technique falls short for detecting new or unknown viruses for which no identifying patterns present. To cope with this problem, anti-virus software has to incorporate the learning mechanism and heuristic. In this paper, we propose a fuzzy diagnosis system(FDS) using fuzzy c-means algorithm(FCM) for the cluster analysis and a decision status measure for giving a diagnosis. We compare proposed system FDS to three well known classifiers-KNN, RF, SVM. Experimental results show that the proposed approach can detect unknown viruses effectively.

• The korean journal of orthodontics
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• v.52 no.1
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• pp.3-19
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• 2022

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradient-weighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.1861-1864
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• 2016

Although early diagnosis of thyroid nodule type is very important, the diagnostic accuracy of standard tests is a challenging issue. We here aimed to find an optimal combination of factors to improve diagnostic accuracy for distinguishing malignant from benign thyroid nodules before surgery. In a prospective study from 2008 to 2012, 345 patients referred for thyroidectomy were enrolled. The sample size was split into a training set and testing set as a ratio of 7:3. The former was used for estimation and variable selection and obtaining a linear combination of factors. We utilized smoothly clipped absolute deviation (SCAD) logistic regression to achieve the sparse optimal combination of factors. To evaluate the performance of the estimated model in the testing set, a receiver operating characteristic (ROC) curve was utilized. The mean age of the examined patients (66 male and 279 female) was $40.9{\pm}13.4years$ (range 15- 90 years). Some 54.8% of the patients (24.3% male and 75.7% female) had benign and 45.2% (14% male and 86% female) malignant thyroid nodules. In addition to maximum diameters of nodules and lobes, their volumes were considered as related factors for malignancy prediction (a total of 16 factors). However, the SCAD method estimated the coefficients of 8 factors to be zero and eliminated them from the model. Hence a sparse model which combined the effects of 8 factors to distinguish malignant from benign thyroid nodules was generated. An optimal cut off point of the ROC curve for our estimated model was obtained (p=0.44) and the area under the curve (AUC) was equal to 77% (95% CI: 68%-85%). Sensitivity, specificity, positive predictive value and negative predictive values for this model were 70%, 72%, 71% and 76%, respectively. An increase of 10 percent and a greater accuracy rate in early diagnosis of thyroid nodule type by statistical methods (SCAD and ANN methods) compared with the results of FNA testing revealed that the statistical modeling methods are helpful in disease diagnosis. In addition, the factor ranking offered by these methods is valuable in the clinical context.

• The Journal of Pediatrics of Korean Medicine
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• v.31 no.1
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• pp.52-62
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• 2017

Objectives The purpose of this study is to review pre-existing clinical practice guidelines for autism spectrum disorders, and refer those in developing a new practice guideline. Methods A total of 9 existing clinical practice guidelines for autism spectrum disorder developed from 2010 to 2016 were searched by Google scholar and Pubmed, and were reviewed those literatures in three parts: general, diagnosis & evaluation, and intervention. Results There were no consistency in the recommendation methods of 9 clinical care guidelines (such as the method of rating and recommendation intensity for diagnosis, evaluation, and treatment). However, in the diagnosis and evaluation section, frequently used evaluation and diagnostic tools are mentioned in most clinical practice guidelines, and the types of pharmacologic and non-pharmacological treatments that are mainly recommended in treatment are equally mentioned in most clinical practice guidelines could confirm. Conclusions 1. Some guideline recommendations are graded according to each criterion. Recommendations presented in various databases were based on systematic reviews or other literatures. The most utilized database were PsycINFO, CINAHL, Cochrane. 2. DSM-5 and ICD-10 were the most common used diagnostic criteria, and DSM-IV was used as a diagnostic standard in the guideline published before 2013. The tools used for diagnosis and evaluation were also varied. However, most recommended ones were ADI-R, ADOS-G, and DISCO. 3. Treatment was largely divided into pharmacological intervention and non-pharmacological intervention. In some guideline, the interventions were divided into pediatric and adult. Most of the pharmacological interventions were not recommended due to lack of evidence, but in cases in which specific symptoms were aimed, they recommended to seek professional help. 4. In addition to interventions, each guideline referred to supportive interventions that may be helpful in the daily life of patients with ASD, which may need to be addressed in future clinical guidelines.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.117-122
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• 2008

Although the pulse diagnosis is the one of the most important diagnostic process to traditional medical doctors, there is no proper communication tool between experts and trainees. In this paper, we have developed a indentation training system which consists of a hardware measuring indent pressure on artificial arm quantitatively and a software providing a indentation training program. The hardware for measurement of indent pressure profile includes 3 load cells embedded in the artificial arm, signal amplification part and digitization part, NI-USB 6009 with 200Hz sampling rate. For setting up a relationship table between weights and output voltages, 8 standard weights were used. To evaluate this hardware, 3 oriental medical specialists were involved and their indent pressure profile were recorded three times respectively. From these, it was found that pulse diagnosis process could be divided into 3 periods and the maximum load were $500g{\cdot}f$ approximately while doctors perform a pulse diagnosis. The indentation training program was implemented with LabView and designed to monitor the differences between the pressure profile of a expert and that of a trainee so to offer some visual feedback to the trainee. Also, this program could provide the trends of training performances. With this developed system, the education of pulse diagnosis is expected to be more quantitative and effective.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.638-649
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• 2019

The purpose of this study was to investigate the usefulness of the cognitive function test tool in the clinical or multi-life environment for the elderly and high-risk demented subjects after the development of the non-clinical dementia early diagnosis test content(Co-Wis) based on the contents of the International Clinical Data Standard(CDISC TAUG-Alzheimer's v 2.0, SDTMIG v3.3) And to verify the validity and reliability of the data. To do this, after searching for dementia diagnosis process, we developed a non-clinical dementia diagnosis content(Co-Wis) that can supplement the shortcomings of the existing paper test. We selected 30 subjects from elders who were over 60 years old and verified the validity of test and the reliability of retest among cognitive domains of the Korean MMSE-K, Seoul Neuropsychological Test(SNSB-II) and non-medication dementia diagnosis content(Co-Wis). As a result, we showed high correlation and reliability in all cognitive domains. However, the limitations of insufficient subjects and regional distribution were identified. Based on the results of the study, we discussed the necessity of supplementing and expanding further studies such as various methods of verifying validity and reliability.