• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.117-122
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• 2021

Autonomous driving allows cars to drive without people and is being studied very actively thanks to the recent development of artificial intelligence technology. Among artificial intelligence technologies, deep reinforcement learning is used most effectively. Deep reinforcement learning requires us to build a neural network using an appropriate activation function. So far, many activation functions have been suggested, but different performances have been shown depending on the field of application. This paper compares and evaluates the performance of which activation function is effective when using deep reinforcement learning to learn autonomous driving on highways. To this end, the performance metrics to be used in the evaluation were defined and the values of the metrics according to each activation function were compared in graphs. As a result, when Mish was used, the reward was higher on average than other activation functions, and the difference from the activation function with the lowest reward was 9.8%.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.1-5
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• 2021

Although CT has an advantage in describing the three-dimensional anatomical structure of the human body, it also has a disadvantage in that high doses are exposed to the patient. Recently, a deep learning-based image reconstruction method has been used to reduce patient dose. The purpose of this study is to analyze the dose reduction and image quality improvement of deep learning-based reconstruction (DLR) on the adult's chest CT examination. Adult lung phantom was used for image acquisition and analysis. Lung phantom was scanned at ultra-low-dose (ULD), low-dose (LD), and standard dose (SD) modes, and images were reconstructed using FBP (Filtered back projection), IR (Iterative reconstruction), DLR (Deep learning reconstruction) algorithms. Image quality variations with respect to varying imaging doses were evaluated using noise and SNR. At ULD mode, the noise of the DLR image was reduced by 62.42% compared to the FBP image, and at SD mode, the SNR of the DLR image was increased by 159.60% compared to the SNR of the FBP image. Based on this study, it is anticipated that the DLR will not only substantially reduce the chest CT dose but also drastic improvement of the image quality.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.5
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• pp.1-9
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• 2021

Children's drawings are widely used in the diagnosis of children's psychology as a means of expressing inner feelings. This paper proposes a children's drawings-based object detection algorithm applicable to children's psychology analysis. First, the sketch area from the picture was extracted and the data labeling process was also performed. Then, we trained and evaluated a Faster R-CNN based object detection model using the labeled datasets. Based on the detection results, information about the drawing's area, position, or color histogram is calculated to analyze primitive information about the drawings quickly and easily. The results of this paper show that Artificial Intelligence-based object detection algorithms were helpful in terms of psychological analysis using children's drawings.

• Korean Medical Education Review
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• v.23 no.3
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• pp.160-167
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• 2021

Rapid developments in technology as part of the Fourth Industrial Revolution have created a demand for educational technology (EdTech) and a gradual transition from traditional teaching and learning to EdTech-assisted learning in medical education. EdTech is a portmanteau (blended word) combining the concepts of education and technology, and it refers to various attempts to solve education-related problems through information and communication technology. The aim of this study was to explore the use of key EdTech applications in medical education programs. A scoping review was conducted by searching three databases (PubMed, CINAHL, and Educational Sources) for articles published from 2000 to June 2021. Twenty-one studies were found that presented relevant descriptions of the effectiveness of EdTech in medical education programs. Studies on the application and effectiveness of EdTech were categorized as follows: (1) artificial intelligence with learner-adaptive evaluation and feedback, (2) augmented/virtual reality for improving learning participation and academic achievement through immersive learning, and (3) social media/social networking services with learner-directed knowledge generation, sharing, and dissemination in medical communities. Although this review reports the effectiveness of EdTech in various medical education programs, the number of studies and the validity of the identified research designs are insufficient to confirm the educational effects of EdTech. Future studies should utilize suitable research designs and examine the instructional objectives achievable by EdTech-based applications to strengthen the evidence base supporting the application of EdTech by medical educators and institutions.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.327-337
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• 2021

The paper acknowledges that many human resources are needed on the research and development (R&D) process of artificial intelligence (AI), and discusses on factors to consider on the current method of development. Enfin, in order to enhance efficiency of AI development, it seems possible through labour division of a few managers and numerous ordinary workers as a type of light industry. Thus, the research team names the development process of AI, which maximizes production efficiency by handling digital resources named 'data' with mechanical equipment called 'computer', as digital light industry of fourth industrial era. As experienced during the previous Industrial Revolution, if human resources are efficiently distributed and utilized, digital light industry would be able to expect progress no less than the second Industrial Revolution, and human resources development for this is considered urgent.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.47-52
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• 2021

Recently, demand for artificial intelligence solutions for production process management has been increasing in the manufacturing industry. However, through the application of AI solutions in the manufacturing industry, there are limitations to legacy smart factory solutions such as POP and MES.Therefore, in order to overcome this, this paper aims to improve production management efficiency by applying guidance, an artificial intelligence concept, to image recognition systems. In the system flow, As_is To be separated and actual work flow was applied, and the process was improved for overall productivity efficiency. The pre-processing plan for AI guidance learning was established and the relevant AI model was designed, developed, and simulated, resulting in a 97% recognition rate.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• Journal of Korea Game Society
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• v.21 no.5
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• pp.17-28
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• 2021

Reinforcement learning, in which artificial intelligence develops itself to find the best solution to problems, is a technology that is highly valuable in many fields. In particular, the game field has the advantage of providing a virtual environment for problem-solving to reinforcement learning artificial intelligence, and reinforcement learning agents solve problems about their environment by identifying information about their situation and environment using observations. In this experiment, the instant dungeon environment of the RPG game was simplified and produced and various observation variables related to the field of view were set to the agent. As a result of the experiment, it was possible to figure out how much each set variable affects the learning speed, and these results can be referred to in the study of game RPG reinforcement learning.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1187-1198
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• 2021

Sea wind isrecently drawing attraction as one of the sources of renewable energy. Thisstudy describes a new method to produce a 10 m resolution sea wind field using Sentinel-1 images and low-resolution NWP (Numerical Weather Prediction) data with artificial intelligence technique. The experiment for the South East coast in Korea, 2015-2020,showed a 40% decreased MAE (Mean Absolute Error) than the generic CMOD (C-band Model) function, and the CC (correlation coefficient) of our method was 0.901 and 0.826, respectively, for the U and V wind components. We created 10m resolution sea wind maps for the study area, which showed a typical trend of wind distribution and a spatially detailed wind pattern as well. The proposed method can be applied to surveying for wind power and information service for coastal disaster prevention and leisure activities.

• Journal of Gastric Cancer
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• v.21 no.3
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• pp.279-297
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• 2021

Purpose: Various changes in nutrition, metabolism, immunity, and psychological status occur through multiple mechanisms after gastrectomy. The purpose of this study was to predict disease status after gastrectomy by analyzing diseases pattern that occur or change after gastrectomy. Materials and Methods: A retrospective cohort study was conducted using nationwide claims data. Patients with gastric cancer who underwent gastrectomy or endoscopic resection were included in the study. Eighteen target diseases were selected and categorized based on their underlying mechanism. The incidence of each target disease was compared by dividing the study sample into those who underwent gastrectomy (cases) and those who underwent endoscopic resection for early gastric cancer (controls). The cases were matched with controls using propensity score matching. Thereafter, Cox proportional hazard models were used to evaluate intergroup differences in disease incidence after gastrectomy. Results: A total of 97,634 patients who underwent gastrectomy (84,830) or endoscopic resection (12,804) were included. The incidence of cholecystitis (P<0.0001), pancreatitis (P=0.034), acute kidney injury (P=0.0083), anemia (P<0.0001), and inguinal hernia (P=0.0007) were higher after gastrectomy, while incidence of dyslipidemia (P<0.0001), vascular diseases (ischemic heart disease, stroke, and atherosclerosis; P<0.0001, P<0.0001, and P=0.0005), and Parkinson's disease (P=0.0093) were lower after gastrectomy. Conclusions: This study identifies diseases that may occur after gastrectomy in patients with gastric cancer.