• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.859-866
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• 2021

Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.25 no.1
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• pp.30-40
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• 2022

Purpose: Pediatric acute appendicitis has a stable incidence rate in Western countries with an annual change of -0.36%. However, a sharp increase was observed in the Asian region. The Indonesian Health Department reveals appendicitis as the fourth most infectious disease, with more than 64,000 patients annually. Hence, there is an urgent need to identify and evaluate the risk factors and diagnostic modalities for accurate diagnosis and early treatment. This study also clarifies the usage of pediatric appendicitis score (PAS) for children <5 years of age. Methods: The current study employed a cross-sectional design with purposive sampling through demographic and PAS questionnaires with ultrasound sonography (USG) results. The analysis was performed using the chi-square and Mann-Whitney tests and logistic regression. Results: This study included 21 qualified patients with an average age of 6.76±4.679 years, weighing 21.72±10.437 kg, and who had been hospitalized for 4.24±1.513 days in Siloam Teaching Hospital. Compared to the surgical gold standard, PAS and USG have moderate sensitivity and specificity. Bodyweight and stay duration were significant for appendicitis (p<0.05); however, all were confounders in the multivariate regression analysis. Incidentally, a risk prediction model was generated with an area under the curve of 72.73%, sensitivity of 100.0%, specificity of 54.5%, and a cut-off value of 151. Conclusion: PAS outperforms USG in the sensitivity of diagnosing appendicitis, whereas USG outperforms PAS in terms of specificity. This study demonstrates the use of PAS in children under 5 years old. Meanwhile, no risk factors were significant in multivariate pediatric acute appendicitis risk factors.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Journal of Veterinary Science
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• v.21 no.1
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• pp.10.1-10.12
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• 2020

Blood perfusion of skeletal muscle and callus was evaluated using contrast-enhanced ultrasonography (CEUS) in a canine osteotomy model to determine the applicability of CEUS in the assessment of neovascularization during fracture healing and to compare the vascular signals on CEUS between external skeletal fixation and cast-applied dogs. In 6 Beagle dogs, a simple transverse osteotomy was performed at the left tibial shaft and external skeletal fixation (n = 3) or a cast (n = 3) was applied. Radiography, power Doppler ultrasonography (power Doppler), and CEUS were performed until complete union was achieved. On CEUS, vascular changes were quantitatively evaluated by measuring peak intensity (PI) and time to PI in the soft tissue and callus and by counting the vascular signals. Vascular signals from the soft tissue were detected on power Doppler and CEUS on day 2. Significantly more vascular signals were detected by CEUS than by power Doppler. On CEUS, PI in the surrounding soft tissue was markedly increased after the fracture line appeared indistinctively changed on radiography in all dogs. In the cast-applied dogs, vascular signals from the periosteal and endosteal callus were detected on CEUS before mineralized callus was observed on radiography. CEUS was useful in assessing the vascularity of soft tissue and callus, particularly in indirect fracture healing, and provided indications of a normally healing fracture.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.163-170
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• 2023

This paper introduces the semantic modeling technology for autonomous control of manufacturing facilities and status definition algorithm. With the development of digital twin technology and various ICT technologies of the smart factory, a new production management model is being built in the manufacturing industry. Based on the advanced smart manufacturing technology, the status determination algorithm was presented as a methodology to quickly identify and respond to problems with autonomous control and facilities in the factory. But the existing status determination algorithm informs the user or administrator of error information through the grid map and is presented as a model for coping with it. However, the advancement and direction of smart manufacturing technology is diversifying into flexible production and production tailored to consumer needs. Accordingly, in this paper, a technology that can design and build a factory using a semantic-based Linked List data structure and provide only necessary information to users or managers through graph-based information is introduced to improve management efficiency. This methodology can be used as a structure suitable for flexible production and small-volume production of various types.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.845-850
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• 2023

In this paper, we propose a method for diagnosing ball bearing vibration using transfer learning. STFT, which can analyze vibration signals in time-frequency, was used as input to CNN to diagnose failures. In order to rapidly learn CNN-based deep artificial neural networks and improve diagnostic performance, we proposed a transfer learning-based deep learning learning technique. For transfer learning, the feature extractor and classifier were selectively learned using a VGG-based image classification model, the data set for learning was publicly available ball bearing vibration data provided by Case Western Reserve University, and performance was evaluated by comparing the proposed method with the existing CNN model. Experimental results not only prove that transfer learning is useful for condition diagnosis in ball bearing vibration data, but also allow other industries to use transfer learning to improve condition diagnosis.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.528-533
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• 2023

Airlines tried to introduce training programs in connection with practical work in order to provide more effective education and training. To this end, airlines have been conducting evidence-based training(EBT) to strengthen the practical capabilities of aviation personnel and enhance safety culture. Airlines can systematically evaluate the capabilities and practical capabilities of aviation personnel by analyzing operational data and case studies for effective EBT model development. In addition, EBT models can be constructed by applying technical methods such as crew resource management (CRM) and a holistic approach that includes human factors. Due to the introduction of EBT, airlines will establish diagnostic and feedback systems for pilots' practical work, provide personalized education, and establish an education and training system that verifies the effectiveness of education through educational outcomes.

• Journal of Engineering Education Research
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• v.27 no.2
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• pp.35-50
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• 2024

The purpose of this study is to identify the core competencies of graduate students at A research university in the context of graduate education in science and engineering, and to develop and validate a diagnostic tool to measure them. To achieve the research objectives, first, 6 factors and 18 sub-competencies of core competencies were derived based on a review of domestic and foreign studies, cases of excellent research-centered overseas universities, and interviews with members of A University. Second, a theoretical model was constructed by deriving behavioral indicators based on the core competencies and sub-competencies, and a preliminary survey was conducted on 188 graduate students of University A to verify the statistical validity of the theoretical model. Results of exploratory and confirmatory factor analysis, the core competencies of graduate students at A research university consisted of 6 factors, 16 sub-competencies, and 77 items. Specifically, it included "Independent research capability(13 items)", "Social Entrepreneurship(10 items)", "Academic agility(15 items)", "Ingenious Challenges(15 items)", "Collegial Collaboration(9 items)", and "Mueunjae leadership(15 items)". This study contributes to the development of theories related to core competencies of graduate students in science and engineering, and has practical significance as a basis for a data-driven competency-based graduate education system.

• Steel and Composite Structures
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• v.53 no.1
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• pp.103-114
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• 2024

This study proposes a new hybrid approach that utilizes post-earthquake survey data and numerical analysis results from an evolving finite element routing model to capture vulnerability processes. In order to achieve cost-effective evaluation and optimization, this study introduced an online data evolution data platform. The proposed method consists of four stages: 1) development of diagnostic sensitivity curve; 2) determination of probability distribution parameters of throughput threshold through optimization; 3) update of distribution parameters using smart evolution method; 4) derivation of updated diffusion parameters. Produce a blending curve. The analytical curves were initially obtained based on a finite element model used to represent a similar RC building with an estimated (previous) capacity height in the damaged area. The previous data are updated based on the estimated empirical failure probabilities from the post-earthquake survey data, and the mixed sensitivity curve is constructed using the update (subsequent) that best describes the empirical failure probabilities. The results show that the earthquake rupture estimate is close to the empirical rupture probability and corresponds very accurately to the real engineering online practical analysis. The objectives of this paper are to obtain adequate, safe and affordable housing and basic services, promote inclusive and sustainable urbanization and participation, implement sustainable and disaster-resilient buildings, sustainable human settlement planning and management. Therefore, with the continuous development of artificial intelligence and management strategy, this goal is expected to be achieved in the near future.