• Korean Journal of Radiology
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• v.24 no.5
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• pp.454-464
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• 2023

Objective: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. Materials and Methods: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. Results: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. Conclusion: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.72-74
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• 2007

This paper presents a Windows graphic developed by the authors for the education and training of power system. Object-oriented programming is a major trend in computer software because it increases flexibility of large-scale software systems. An efficient platform for power system simulation applications has been proposed. This paper presents an intuitive Windows-based program for the power system analysis. The advantages of the object-oriented approach are demonstrated with an implementation of the graphical program. It provides a graphical interface for designing the one-line diagram of the bus and analyzing the output of the simulations. A graphical editor to visually edit the power system, diagram, results processing and exporting and graphic presentations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1146-1151
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• 2018

Recently, researches on the intelligence of electric power facilities have been trying to apply artificial intelligence techniques as computer platforms have improved. In particular, faults occurring in substation should be able to quickly identify possible faults and minimize power fault recovery time. This paper presents fault location technique for 154kV substation using neural network. We constructed a training matrix based on the operating conditions of the circuit breaker and IED to identify the fault location of each component of the target 154kV substation, such as line, bus, and transformer. After performing the training to identify the fault location by the neural network using Weka software, the performance of fault location discrimination of the designed neural network was confirmed.

• International Journal of Knowledge Content Development & Technology
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• v.9 no.1
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• pp.43-56
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• 2019

The intricacies of web development have orchestrated a paradigm shift in academic libraries. The study explored literature on the status of automation of academic libraries and web development in Nigeria. It was established that the functions of library management software are abysmally under-utilized in academic libraries in Nigeria, thus, progression of automation projects remain a swinging pendulum. The paper, therefore, recommends that librarians should acquire the relevant training and plan strategically for all automation projects aimed at incorporating the intricacies of the web and ICTs into library services to fully utilize the functions of the library management systems in line with web developmental stages, thus, be able to compete globally.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.29-35
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• 2019

In this paper, the effects of educational contents on job behavior, the effects of job behavior on job effectiveness and the effects of educational contents on job effectiveness were studied when working in the Software field. For this purpose, a questionnaire survey was conducted on the learning workers who conducted the training in the IT field, and 302 valid questionnaires were used for the analysis. The research model was set up to test exploratory factor and confirmatory factor analysis and hypothesis, and the research hypothesis was tested by applying structural equation. The effects of job behavior on job effectiveness were positively related to job satisfaction, customer orientation, and organizational commitment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.697-698
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• 2023

최근 딥 러닝 분야의 기술이 발달하면서 Chat GPT, Google Bard와 같은 자연어 처리 기술이 확대되고 있고 이미지 객체를 분석하는 CLIP, BLIP와 같은 기술도 발전되고 있다. 그러나 전시회와 같은 예술 분야는 딥 러닝 기술 기반의 이미지 데이터 활용이 제한적이다. 본 논문은 전시회장에서의 그림 내부의 객체 데이터를 분석하기 위해 이미지 객체 분석 기술을 사용하고 자연어 처리 기반으로 관람객이 특정 그림에 대한 질문을 입력하면 해당 그림을 식별하는 방법을 제시한다. 이를 통해 관람객이 원하는 그림을 선별하여 관람할 수 있도록 한다.

• The Journal of Korean Association of Computer Education
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• v.21 no.6
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• pp.27-37
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• 2018

In this paper, we propose a design and development method of an elevator simulator that can be used as an embedded application layer software learning tool and a teaching and learning method using it. The simulator provides students with an environment to implement the operating principle and control method of the elevator system in the application layer excluding the issues of hardware and embedded OS layer. This allows students to have a reactive and real-time embedded application development experience. In addition, we present a four-week embedded application software training course with hands-on exercises that add step-by-step functionality using a simulator. As a result of training for actual students, we obtained 83.3 points of learning achievement score and proved that the curriculum has a significant effect on embedded application learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.789-791
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• 2014

The software education has many interest in the world because a lot of help to improve logical thinking and problem solving training. However, to implement such a program using languages such as C or Java, you should be aware of the syntax, understanding of computer architecture, and many library. So, it is difficult for many ordinary student to implement program. Therefore, we need easy access to the programming to address the life program by coding for students. In this paper, we introduce such useful software training program or Internet Web site for students.

• Journal of Astronomy and Space Sciences
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• v.41 no.2
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• pp.61-78
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• 2024

This study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the least-squares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the difficulty in training deep learning models in space, we showed that actual experimental results could be predicted through software simulations on the ground. We integrated deep learning with nonlinear least squares (NLS) to predict the pose from a single spacecraft image in real time. We constructed a virtual environment capable of mass-producing synthetic images to train a deep learning model. This study proposed a method for training a deep learning model using pure synthetic images. Further, a visual-based real-time estimation system suitable for use in a flight testbed was constructed. Consequently, it was verified that the hardware experimental results could be predicted from software simulations with the same environment and relative distance. This study showed that a deep learning model trained using only synthetic images can be sufficiently applied to real images. Thus, this study proposed a real-time pose estimation software for automatic docking and demonstrated that the method constructed with only synthetic data was applicable in space.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.25-32
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• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.