• 종양간호연구
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• 제10권1호
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• pp.59-67
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• 2010

Purpose: The purpose of this study was the development and validation of knowledge scale for patients with breast cancer (KS-Br) in Korea. Methods: The process included the construction of a conceptual framework, generation of preliminary items, and the test of validity and reliability of the scale. Thirty-seven items were developed through an evaluation process by 10 experts and 24 items were finally confirmed through item analysis. Psychometric testing was performed with a convenient sample of 303 women with breast cancer. The data was analyzed using independent t-test, Pearson's correlation, and calculation of KR-20. Results: Participants averaged 70.8% correct on the test. The KS-Br has 24 items consisting of 5 categories: incidence of breast cancer, diagnosis and treatment, symptom management, sexuality, and maintenance of daily life. Validity was supported by the use of content validity, known-group technique, and criterion-related validity. Women who had undergone education intervention scored significantly higher than women who had not (p<.001). KS-Br scores were significantly correlated with those of Mishel's Illness Uncertainty Scale (r=-.214, p<.001). Internal consistency of the KS-Br was appropriate (KR20=.805). Conclusion: This study reveals that the KS-Br is reliable and valid scale to measure the knowledge of breast cancer. Therefore, this scale can be effectively utilized to assess the knowledge of patients with breast cancer regarding their disease.

• Asian Pacific Journal of Cancer Prevention
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• 제16권8호
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• pp.3527-3531
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• 2015

Background: Serbia is one of the countries with highest incidence and mortality rates for cervical cancer in Central and South Eastern Europe. Introducing a risk index could provide a powerful means for targeting groups at high likelihood of having an abnormal cervical smear and increase efficiency of screening. The aim of the present study was to create and assess validity ofa index for prediction of an abnormal Pap test result. Materials and Methods: The study population was drawn from patients attending Departments for Women's Health in two primary health care centers in Serbia. Out of 525 respondents 350 were randomly selected and data obtained from them were used as the index creation dataset. Data obtained from the remaining 175 were used as an index validation data set. Results: Age at first intercourse under 18, more than 4 sexual partners, history of STD and multiparity were attributed statistical weights 16, 15, 14 and 13, respectively. The distribution of index scores in index-creation data set showed that most respondents had a score 0 (54.9%). In the index-creation dataset mean index score was 10.3 (SD-13.8), and in the validation dataset the mean was 9.1 (SD=13.2). Conclusions: The advantage of such scoring system is that it is simple, consisting of only four elements, so it could be applied to identify women with high risk for cervical cancer that would be referred for further examination.

• The Journal of Korean Physical Therapy
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• 제27권4호
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• pp.214-220
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• 2015

Purpose: The purpose of this study was to investigate the availability of software for rehabilitation with the Kinect sensor by presenting an efficient algorithm based on machine learning when classifying the motion data of the PNF pattern if the subjects were wearing a patient gown. Methods: The motion data of the PNF pattern for upper extremities were collected by Kinect sensor. The data were obtained from 8 normal university students without the limitation of upper extremities. The subjects, wearing a T-shirt, performed the PNF patterns, D1 and D2 flexion, extensions, 30 times; the same protocol was repeated while wearing a patient gown to compare the classification performance of algorithms. For comparison of performance, we chose four algorithms, Naive Bayes Classifier, C4.5, Multilayer Perceptron, and Hidden Markov Model. The motion data for wearing a T-shirt were used for the training set, and 10 fold cross-validation test was performed. The motion data for wearing a gown were used for the test set. Results: The results showed that all of the algorithms performed well with 10 fold cross-validation test. However, when classifying the data with a hospital gown, Hidden Markov model (HMM) was the best algorithm for classifying the motion of PNF. Conclusion: We showed that HMM is the most efficient algorithm that could handle the sequence data related to time. Thus, we suggested that the algorithm which considered the sequence of motion, such as HMM, would be selected when developing software for rehabilitation which required determining the correctness of the motion.

• 한국위성정보통신학회논문지
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• 제6권2호
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• pp.1-9
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• 2011

국내 최초의 정지궤도 관측 위성인 천리안(통신해양기상위성)이 2010년 6월 27일에 성공적으로 발사되었다. 천리안에의 기상탑재체와 해양탑재체의 원시 영상은 지상에서 처리 과정을 거쳐 사용자에게 전달되다. 한국항공우주연구원의 주도로 국내 개발된 송수신자료전처리시스템은 원시 영상에 복사 및 기하 보정을 수행하고, 전처리된 영상과 부가 자료들을 위성을 통해 사용자들에게 분배하는 기능을 수행한다. 궤도상 시험을 성공적으로 완료한 송수신자료전처리시스템은 기상위성센터, 해양위성센터, 그리고 위성운영센터에 설치되어 현재 정상 운영 중에 있다. 궤도상 시험 기간 동안 송수신자료전처리시스템의 기능과 성능에 대한 검증은 1) 영상 송수신, 2) 기상 및 해양 영상의 전처리, 그리고 3) 사용자 분배 기능으로 나뉘어 수행되었다. 이 논문은 천리안 위성 발사 후 수행된 송수신자료전처리시스템의 궤도상 시험 운영 검증 결과를 기술한다.

• The Korean Journal of Pain
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• 제34권4호
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• pp.501-508
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• 2021

Background: Yellow flags are psychosocial factors shown to be indicative of long-term chronicity and disability. The purpose of the study was to evaluate the psychometric properties of the Turkish Yellow Flag Questionnaire (YFQ) in patients with chronic musculoskeletal pain (CMP). Methods: The cross-cultural adaptation was conducted with translation and back-translation of the original version. Reliability (internal consistency and test-retest) was examined for 231 patients with CMP. Construct validity was assessed by correlating the YFQ with the Hospital Anxiety and Depression Scale (HADS), Orebro Musculoskeletal Pain Questionnaire (OMPQ), and Tampa Kinesiophobia Scale (TKS). Factorial validity was examined with both exploratory and confirmatory factorial analysis. Results: The YFQ showed excellent test/retest reliability with an Intraclass correlation coefficient of 0.82. The internal consistency was moderate (Cronbach's alpha of 0.797). As a result of the exploratory factor analysis, there were 7 domains compatible with the original version. As a result of confirmatory factor analysis, the seven-factor structure of YFQ was confirmed. There was a statistically significant correlation between YFQ-total score and OMPQ (r = 0.57, P < 0.001), HADS-anxiety (r = 0.32, P < 0.001), HADS-depression (r = 0.44, P < 0.001), and TKS (r = 0.37, P < 0.001). Conclusions: This study's results provide considerable evidence that the Turkish version of the YFQ has appropriate psychometric properties, including test-retest reliability, internal consistency, construct validity and factorial validity. It can be used for evaluating psychosocial impact in patients with CMP.

• 분석과학
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• 제34권2호
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• pp.68-77
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• 2021

Cannabis is one of the most abused drugs in Korea. The main psychoactive component in cannabis, Δ9-tetrahydrocannabinol, is metabolized to 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THCCOOH) and THCCOOH-glucuronide (THCCOOH-glu) in the human liver, whereby the amount of THCCOOH-glu found in urine is twice as high as that of THCCOOH. The analytical process adapted by the majority of urine drug-testing programs involves a two-step method consisting of an initial immunoassay-based screening test followed by a confirmatory test if the screening test result is positive. In this study, a qualitative gas chromatography-mass spectrometry (GC-MS) method was developed and validated for the detection of THCCOOH in human urine, where THCCOOH-glu was converted into THCCOOH by alkaline hydrolysis. For purification of the urine extract prior to instrumental analysis, high-speed centrifugation was used to minimize interference. In addition, an injection-port derivatization method using ethyl acetate and N,O-bis(trimethylsilyl)-trifluoroacetamide containing 1 % trimethylchlorosilane was employed to reduce the time required for derivatization, and an aliquot of the final solution was injected into the GC-MS. The method was validated by measuring the selectivity, limit of detection (LOD), and repeatability. The sensitivity, specificity, precision, accuracy, Kappa, F-measure, false positive, and false negative rate were determined by comparing the GC-MS results with those obtained using the immunoassay. The LOD was determined to be 0.32 ng/mL, while the repeatability was within 9.1 % for THCCOOH. Furthermore, a comparison study was carried out, whereby the screening immunoassay exhibited a sensitivity of 86.4 % and a specificity of 100 % compared to GC-MS. The applicability of the developed method was examined by analyzing spiked urine and forensic urine samples obtained from suspected cannabis abusers (n = 221).

• 대한건축학회논문집:구조계
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• 제36권3호
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• pp.113-120
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• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• 커뮤니케이션디자인학연구
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• 제57권
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• pp.256-271
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• 2016

사용자 중심 디자인을 실행하기 위하여 사용자의 니즈를 도출하고 도출된 니즈를 기반으로 아이데이션을 실시하고 아이디어 수렴을 통해 디자인 컨셉을 개발하고 있다. 이 중 사용자 및 전문가에 대한 리서치는 상당한 시간과 비용이 소요되는 과정으로 리서치 결과에 대한 타당성은 디자인컨셉을 도출하기 이전 단계에서 자체점검 할 수 있는 방법이 부재한 상태이다. 이 논문은 사용성 테스트 방식을 확장하여 맥락사용성 테스트 방법에 대한 과정을 설계하여 적용해 본 것에 의의가 있다. 이 논문에선 의료기기 중 웨어러블자동제세동기(WCD) 개발에 적용되어 테스트를 실시하였으며 개발기간이 비교적 오래 걸리는 의료기기 분야의 케이스가 다양하게 확장된다면 평가항목 및 평가결과에 대한 레퍼런스를 축적하여 체계화 할 수 있을 것이라 판단된다. 또한 이 경우 더욱 효과적인 방법으로 발전할 수 있을 것으로 기대되며 그에 따른 많은 후속연구가 필요한 것으로 판단된다.

• 대한치과교정학회지
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• 제52권2호
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• 한국체육학회지인문사회과학편
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• 제54권5호
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• pp.207-219
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• 2015

본 연구는 Martindale et al.(2010)이 개발한 스포츠영재육성환경 질문지(TDEQ)를 개념검토, 내적구조검토, 외적관계검토 등의 개념적, 통계적 검증단계를 거쳐 우리문화에 적합하게 활용될 수 있는지를 확인하였다. 번역본 작성 절차에 따라 문항에 대한 개념검토가 이루어졌고, 제작된 질문지는 244명(고등학생 : 117명, 대학생 : 127명)의 학생선수를 대상으로 자료가 수집되었다. 내적구조는 탐색적/확인적 요인분석, 신뢰도분석 등을 통해 검토되었고, 그 결과 5요인 32문항의 한국판 TDEQ가 최종 완성되었다. 또한 상관분석과 집단차이분석을 이용하여 외적관계에 대한 검토가 이루어졌으며, 타당도를 추가로 확보하였다. 본 연구를 통해 검증된 한국판 TDEQ는 스포츠영재에 대한 탐색, 발굴, 선택, 육성 등의 단계에서 진단 및 평가도구로 활용될 수 있을 것이다.