• Structural Engineering and Mechanics
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• 제87권3호
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• Advances in nano research
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• 제15권5호
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• pp.451-466
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• 2023

Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture.

• 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.

• 한국전자통신학회논문지
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• 제18권5호
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• pp.927-934
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• 2023

최근 반려동물 시장 규모가 커짐에 따라 반려동물 관련 사회적 문제도 대두되고 있다. 대표적으로 반려견 물림 사고, 유기견 문제, 안락사, 동물 학대 등이 있다. 대안으로 반려동물 관련 방송, 교육 앱 등 다양한 방식의 훈련 프로그램이 제공되고 있지만, 무엇을 먼저 가르쳐야 할지 모르는 초보 보호자들에게는 그리 효율적이지 못하다. 비교적 접근성이 용이한 훈련 앱이 다수 배포됐지만, 아직 사용자가 직접 훈련을 체험하며 익히는 방식의 앱은 부족한 실정이다. 이에 본 논문에서는 유니티 엔진을 활용해 더욱 효율적인 AR 기반의 반려견 훈련 모바일 앱을 제안한다. 사용성 평가 결과, 기존에 부재했던 요소의 추가로 사용자들 흥미도는 증대했고, 훈련 몰입감까지 제고되어 학습 효과가 향상됐다. 향후 개발 및 양산 검증까지 거쳐 배포된다면 반려동물 입양 계획을 세운 초보 보호자나 기존 보호자들에게 효과적인 훈련 앱이 될 것으로 기대된다.

• Korean Journal of Radiology
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• 제25권4호
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• pp.343-350
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• 2024

Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. Materials and Methods: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

• 한국컴퓨터정보학회논문지
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• 제29권2호
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• pp.67-78
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• 2024

4차 산업혁명과 인공지능이 대두되면서 사회구조가 변화하고 있으며, 미래 인재 양성을 위한 인공지능 교육에 대한 세계적인 관심이 높아지고 있다. 이에 본 연구는 고등학교 1학년 학습자를 위한 인공지능 융합 국어 교과 교육 프로그램을 개발하는 것을 목적으로 하여 ADDIE 모형에 근거하여 교수·학습 프로그램을 개발하였다. 교육 프로그램의 효과를 평가하기 위해 미래 핵심역량 4C(Collaboration-협업, Communication-의사소통, Critical Thinking-비판적 사고, Creativity-창의력)과 지식정보처리 역량에 대한 사전-사후 검사를 수행하였고 총 9차시 동안 4개의 작은 프로젝트들로 수업을 구성하여 학생들에게 인공지능을 융합한 국어 교과 교육의 새로운 경험을 제공하고자 하였다. 그 결과, 프로그램 적용 학생들은 모든 영역에서 미래 핵심역량의 향상을 나타냈으며, 만족도 및 질적 분석에서도 긍정적인 결과를 도출했다. 이를 통해 본 프로그램이 고등학교 국어 교육에 인공지능을 성공적으로 융합하여 학생들의 미래 인재 양성에 기여 할 수 있는 가능성을 제시하고자 한다.

• 공학교육연구
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• 제27권1호
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• pp.3-12
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• 2024

The degree-linked programs for employees, operated by joint training centers in specialized universities that have implemented work-study integrated programs, are educational programs that require an annual government budget of around 80 billion KRW. However, the 70+ universities running these programs face issues such as a decline in academic achievement and an increase in dropout rates among students. In this paper, I conducted multiple regression analysis based on observed and measured information to examine whether the participating students in these programs are achieving an appropriate level of academic performance and to identify the factors that universities need to invest in to achieve that level. To do this, I hypothesized a causal relationship between the university's input factors and students' academic achievement, and used the SPSS program to analyze the statistical data, confirming the validity of the hypothesis. The collected data for the study were obtained through a survey developed using a Likert 4-point scale, which quantified the distribution of grades among students enrolled in IT-related departments offering the degree-linked programs for employees and the emotional contact efforts made by the universities to motivate them for academic success. Particularly, through the results of multiple regression analysis, it was confirmed that these input factors, unlike those for students in general education programs, require more personalized and frequent interactions.

• 해양환경안전학회지
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• 제30권1호
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• pp.82-88
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• 2024

현대 해양 산업은 기술적 발전을 통해 신속한 발전을 이루고 있다. 이러한 발전을 주도하는 주요 기술 중 하나는 데이터 처리 기술이며, 이 중 자연어 처리 기법은 사람의 언어를 기계가 이해하고 처리할 수 있도록 하는 기술이다. 본 연구는 자연어 처리 기법을 통해 해양안전심판원의 재결서를 분석하여 이미 재결이 이루어진 선박 충돌사고의 원인 제공 비율을 학습한 후, 새로운 재결서를 입력하면 원인 제공 비율을 예측하는 모델을 개발하고자 하였다. 이 모델은 사고 당시 적용되는 항법과 원인 제공 비율에 영향을 주는 핵심 키워드의 가중치를 이용하여 사고의 원인 제공 비율을 계산하는 방식으로 구성하였다. 이 연구는 이러한 방식을 통해 제작한 모델의 정확도를 분석하고, 모델의 실무 적용 가능성을 검토함과 동시에 충돌사고 재발 방지 및 해양사고 당사자들의 분쟁 해결에 기여할 것으로 기대한다.

• Geomechanics and Engineering
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• 제36권5호
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• pp.465-474
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• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• 대한공업교육학회지
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• 제41권1호
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• pp.68-87
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• 2016

이 연구에서는 중소기업 특성화고 인력양성사업과 취업맞춤반의 성과 목표에 대한 타당도와 만족도를 분석함으로써 중소기업 특성화고 인력양성사업과 취업맞춤반의 효과적인 운영을 위한 시사점을 제시하기 위하여 107개 중소기업 특성화고 인력양성사업 참여 학교(공업 농업 계열)의 담당 교원 총 166명을 대상으로 설문조사를 시행하였다. 연구 결과는 다음과 같다. 첫째, 중소기업 특성화고 인력양성사업의 담당 교원은 본 사업의 목적을 특성화고의 취업 확대와 중소기업의 인력 제공으로 인식하고 있다. 그리고 본 사업이 학교 성과 향상에 중요하며 실제로도 성과 향상에 긍정적으로 영향을 미친다고 인식하고 있다. 둘째, 중소기업 특성화고 인력양성사업의 담당 교원은 중소기업에 대한 학생의 이해 증진, 중소기업에 대한 교원의 이해 증진, 특성화고에 대한 중소기업의 이해 증진, 학생의 직업관 함양, 취업 과정을 기반으로 한 진로지도 프로그램의 체계화, 산학협력 교육 강화, 학생의 기술수준 향상, 현장견학보다 현장체험 및 실습 위주의 현장학습 실시, 협약기업에 대한 맞춤형 기능인력 양성, 학생의 현장적응력 제고, 중소기업 취업률 제고, 중소기업 취업 기회 확대, 학교와 중소기업 간 취업 연계 기반 마련, 자기 조직(학교)의 외부에 대한 홍보, 산학협력 기회 확대, 산업 업종별 협회 및 단체와 학교 간 협력 체계 구축, 공동 교육 채용을 위한 취업 연계 모델 도입 및 운영, 교원의 현장 전문성 강화가 본 사업의 성과 목표로 타당하다고 인식하고 있다. 그러나 산학협력 교육 강화, 중소기업 취업률 제고를 제외한 나머지 성과 목표의 달성 정도에 대한 만족도는 타당도에 비해 상대적으로 낮다. 셋째, 중소기업 특성화고 인력양성사업의 담당 교원은 학생의 취업 기회 확대, 양질의 취업처 발굴, 산학협력 기회 확대, 현장 중심 교육 실시, 학생의 전공 기초 및 심화 기술 향상, 학생의 직무 수행에 필요한 문해 수리 팀워크 커뮤니케이션 능력 향상, 학생의 업무 태도 향상, 학생의 바람직한 진로 탐색 결정이 취업맞춤반의 성과 목표로 타당하다고 인식하고 있다. 그러나 성과 목표의 달성 정도에 대한 만족도는 타당도에 비해 상대적으로 낮다.