• 한국건축시공학회지
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• 제21권3호
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• pp.189-194
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• 2021

본 연구에서는 Lab. 스케일 LIBS 장치를 제작하여 모르타르 내 염화물 분석에서의 LIBS 적용성과 재현성 검토를 수행하였다. 염화물 함량을 조절한 모르타르를 대상으로 기존의 분석방법(XRF, 전위차 적정법)과 LIBS 분석을 동시에 진행하였다. LIBS 분석 결과, 염소이온은 837.59nm 파장에서 검출되었고, 다양한 농도 구간에서의 정밀도를 향상시키기 위하여 전기장 강화를 통한 약 50배의 LIBS 신호증폭을 구현하였다. 수용액 기반의 재현성을 검증을 통하여 LIBS 신호 강도와 Cl농도 사이의 높은 상관관계를 확인할수 있었으며, 콘크리트 염해 내구성 진단에 LIBS적용 가능성을 확인하였다.

• Imaging Science in Dentistry
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• 제52권4호
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.179-186
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• 2023

For the future prosperity of any society, the sound growth of children is essential. Autism Spectrum Disorder (ASD) is a neurobehavioral disorder which has an impact on social interaction of autistic child and has an undesirable effect on his learning, speaking, and responding skills. These children have over or under sensitivity issues of touching, smelling, and hearing. Its symptoms usually appear in the child of 4- to 11-year-old but parents did not pay attention to it and could not detect it at early stages. The process to diagnose in recent time is clinical sessions that are very time consuming and expensive. To complement the conventional method, machine learning techniques are being used. In this way, it improves the required time and precision for diagnosis. We have applied TFLite model on image based dataset to predict the autism based on facial features of child. Afterwards, various machine learning techniques were trained that includes Logistic Regression, KNN, Gaussian Naïve Bayes, Random Forest and Multi-Layer Perceptron using Autism Spectrum Quotient (AQ) dataset to improve the accuracy of the ASD detection. On image based dataset, TFLite model shows 80% accuracy and based on AQ dataset, we have achieved 100% accuracy from Logistic Regression and MLP models.

• 한국방사선학회논문지
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• 제17권6호
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• pp.873-879
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• 2023

본 연구는 인공지능(AI)을 사용하여 흉부 엑스레이 이미지에서 이물질을 탐지하는 방법을 탐구하였다. 의료영상학, 특히 흉부 엑스레이는 폐렴이나 폐암과 같은 질병을 진단하는 데 매우 중요한 역할을 한다. 영상의학 검사가 증가함에 따라 AI는 효율적이고 빠른 진단을 위한 중요한 도구가 되었다. 하지만 이미지에는 단추나 브래지어 와이어와 같은 일상적인 장신구를 포함한 이물질이 포함될 수 있어 정확한 판독을 방해할 수 있다. 본 연구에서는 이러한 이물질을 정확하게 식별하는 AI 알고리즘을 개발하였고, 미국 국립보건원 흉부 엑스레이 데이터셋을 가공하여 YOLOv8 모델을 기반으로 처리하였다. 그 결과 정확도, 정밀도, 리콜, F1-score가 모두 0.91에 가까울 정도로 높은 탐지 성능을 보였다. 이번 연구는 AI의 뛰어난 성능에도 불구하고 이미지 내 이물질로 인해 판독 결과가 왜곡될 수 있는 문제점을 해결함으로써 영상의학 분야에서 AI의 혁신적인 역할과 함께, 임상 구현에 필수적인 정확성에 기반하여 신뢰성을 강조하였다.

• Journal of Korean Neurosurgical Society
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• 제66권6호
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2945-2965
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• 2023

The laser inertial measurement unit is a precision device widely used in rocket navigation system and other equipment, and its quality is directly related to navigation accuracy. In the quality evaluation of laser inertial measurement unit, there is inevitably uncertainty in the index input information. First, the input numerical information is in interval form. Second, the index input grade and the quality evaluation result grade are given according to different national standards. So, it is a key step to transform the interval information input by the index into the data form consistent with the evaluation result grade. In the case of uncertain input, this paper puts forward a method based on probability distribution to solve the problem of asymmetry between the reference grade given by the index and the evaluation result grade when evaluating the quality of laser inertial measurement unit. By mapping the numerical relationship between the designated reference level and the evaluation reference level of the index information under different distributions, the index evidence symmetrical with the evaluation reference level is given. After the uncertain input information is transformed into evidence of interval degree distribution by this method, the information fusion of interval degree distribution evidence is carried out by interval evidential reasoning algorithm, and the evaluation result is obtained by projection covariance matrix adaptive evolution strategy optimization. Taking a five-meter redundant laser inertial measurement unit as an example, the applicability and effectiveness of this method are verified.

• International Journal of Computer Science & Network Security
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• 제24권6호
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• pp.8-16
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• 2024

Communication with the deaf has always been crucial. Deaf and hard-of-hearing persons can now express their thoughts and opinions to teachers through sign language, which has become a universal language and a very effective tool. This helps to improve their education. This facilitates and simplifies the referral procedure between them and the teachers. There are various bodily movements used in sign language, including those of arms, legs, and face. Pure expressiveness, proximity, and shared interests are examples of nonverbal physical communication that is distinct from gestures that convey a particular message. The meanings of gestures vary depending on your social or cultural background and are quite unique. Sign language prediction recognition is a highly popular and Research is ongoing in this area, and the SVM has shown value. Research in a number of fields where SVMs struggle has encouraged the development of numerous applications, such as SVM for enormous data sets, SVM for multi-classification, and SVM for unbalanced data sets.Without a precise diagnosis of the signs, right control measures cannot be applied when they are needed. One of the methods that is frequently utilized for the identification and categorization of sign languages is image processing. African Buffalo Optimization using Support Vector Machine (ABO+SVM) classification technology is used in this work to help identify and categorize peoples' sign languages. Segmentation by K-means clustering is used to first identify the sign region, after which color and texture features are extracted. The accuracy, sensitivity, Precision, specificity, and F1-score of the proposed system African Buffalo Optimization using Support Vector Machine (ABOSVM) are validated against the existing classifiers SVM, CNN, and PSO+ANN.

• The Korean Journal of Medicine
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• 제99권2호
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• pp.96-103
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• 2024

Lung cancer is the leading cause of cancer death in Republic of Korea. After their initial diagnosis, only 10-20% of patients with advanced non-small cell lung cancer (NSCLC) survive for 5 years of longer. Given enormous advances in therapeutics such as novel targeted therapies and immunotherapies, survival rates are improving for advanced patients with NSCLC; 5-year survival rates range from 15% to 50%, contingent upon the biomarker. Detection of the specific molecular alteration as biomarker is thus crucial for identifying subgroups of NSCLC that contain therpapeutically targetable oncogenic drivers. This review examines the process of diagnosing lung adenocarcinoma with dominant biomarkers in order to customize treatment with appropriate targeted therapy.

• 핵의학기술
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• 제14권1호
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• pp.138-142
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• 2010

PTH 측정은 체내 칼슘 및 이온화 칼슘의 수치와 함께 갑상선 절제술 후 저칼슘혈증을 예측하는 유용한 지표로서 임상에서는 신속한 결과 보고를 요구하고 있으나 검사반응시간이 길어 이에 상응하지 못하고 있다. 따라서 본 연구는 PTH 검사반응시간에 변화를 주어 신속한 결과 보고를 함으로서, 저칼슘혈증 예측 지표의 유용성을 평가하고자 한다. 2009년 7~8월까지 PTH를 검사한 환자들(n=131)을 대상으로 하였으며, 검사방법은 면역방사계수법으로 반응시간은 18${\pm}$2시간(Overnight), 0.5, 3, 6시간으로 구분하여 상관관계, 정밀도(10회 반복), 회수율을 측정하였다. 또한 조기 저칼슘 혈증 예측지표의 유용성을 평가하기 위해 민감도, 특이도, 양성예측도, 정확도를 비교 분석 하였다. 상관관계에서는 overnight을 기준으로 0.5시간은 $R^2$=0.987, 3시간은 $R^2$=0.993, 6시간은 $R^2$=0.996로 나타났다. 정밀도(%CV${\pm}$SD)에서 0.5시간은 $15.92{\pm}15.54$, 3시간은 $6.91{\pm}7.38$, 6시간은 $4.30{\pm}4.69$, Overnight은 $4.59{\pm}2.59$로 측정되었다. 회수율(%Mean${\pm}$SD)에서 0.5시간은 $96.8{\pm}5.44$, 3시간은 $102.6{\pm}4.35$, 6시간은 $100.7{\pm}2.56$, Overnight은 $102.2{\pm}5.98$로 측정되었다. 양성 예측치(cut-off point=15 pg/mL)를 기준으로 저칼슘혈증을 예측할 때, 0.5시간일 때 민감도 97.5% 특이도 96.0% 양성예측도 86.6% 정확도 84.7%였고, 3시간일 때 민감도 97.5% 특이도 100% 양성예측도 100% 정확도 97.5%였고, 6시간일때 민감도 97.5% 특이도 92.3% 양성예측도 92.8% 정확도 90.6%였다. Overnight법과 비교할 때 검사반응시간 3시간에서 98.3%로 가장 높은 일치율을 보였고, Kappa와 상관관계 또한 우수하였다. 이에 검사반응시간을 3시간으로 단축시켜 신속한 결과를 보고함으로써 환자의 저칼슘혈증을 조기에 예측하여 칼슘제 투여 등 적절한 조치를 취하여 환자의 증상발현을 막을 수 있는 유용한 지표로 활용될 것이다.

• 한국터널지하공간학회 논문집
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• 제21권4호
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• pp.479-499
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• 2019

공용중인 터널의 조사 및 평가는 시설물안전법의 안전점검 및 정밀안전진단 세부지침(터널)편에 따라 외관조사와 내구성조사를 근거하여 상태평가를 수행하고 있다. 본 연구에서는 종방향균열의 발생요인 파악을 위해 준공 후 10년이 경과되어 최초 정밀안전진단이 실시된 NATM터널 12개에 대해 균열과 라이닝의 두께를 검토하여, 이를 반영한 상태평가 수정사항을 모색하였다. 종방향균열에 대한 굴착지반의 영향을 검토하기 위해 지보패턴, 이를 구성하는 지보재 시공 조건, 라이닝의 재료적 특성, GPR탐사를 통한 라이닝 두께 등을 터널 형상 및 용도에 따라 4개의 그룹으로 나누어 자료를 분석하였다. 균열발생밀도는 숏크리트, 록볼트, 강지보재의 지보재 지지능력에 따라 변화되나 굴착지보패턴에 따른 균열발생과의 연관성은 낮은 것으로 나타났다. 라이닝 재료적인 특징인 물-시멘트비, 시멘트함유량, 강도 등이 균열발생에 영향을 주는 것으로 나타났다. 또한, GPR탐사의 라이닝 두께를 반영한 상태평가에서는 평균 0.03 정도(분포, 0.001~0.071)의 점수증가를 야기하는 것으로 확인되어 향후 라이닝 두께 부족을 고려하는 현실적인 상태평가 방안에 대한 검토가 필요하다.