• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.1-17
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• 2021

Self-potential (SP) surveys measure naturally occurring differences in electrical potential in the absence of artificial sources and have been applied to various fields since the first application in mineral explorations. Among various causes of SP occurrences, streaming potential is generated by the flow of groundwater, and makes SP surveys suitable for the exploration of groundwater table fluctuation, fractures, sinkholes and landslide occurrences. Recently, there has been many studies that applied SP surveys to monitor water leakage through dikes and embankments. In this review paper, we first review the characteristics and theoretical backgrounds of streaming potential in saturated or unsaturated porous media to introduce it in the embankment among various application field. After the review of the background theory, we review the past cases of field SP surveys on dikes and embankments and also the characteristics of field streaming potential data in the surveys. Further, by analyzing past studies of qualitative as well as quantitative interpretation of SP survey data, we show the possibility of quantitative interpretation of streaming potential data obtained on dikes and embankments. Consequently, it is hope that this review paper helps researches on SP surveys on dikes and embankments, and provides basis for interpretation methods of the SP data to identify leaked area and further leakage rate (or permeability).

• Journal of Environmental Science International
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• v.18 no.2
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• pp.129-139
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• 2009

Development of an artificial neural network model was presented to predict the daily maximum $SO_2$ concentration in the urban-industrial area of Ulsan. The network model was trained during April through September for 2000-2005 using $SO_2$ potential parameters estimated from meteorological and air quality data which are closely related to daily maximum $SO_2$ concentrations. Meteorological data were obtained from regional modeling results, upper air soundings and surface field measurements and were then used to create the $SO_2$ potential parameters such as synoptic conditions, mixing heights, atmospheric stabilities, and surface conditions. In particular, two-stage clustering techniques were used to identify potential index representing major synoptic conditions associated with high $SO_2$ concentration. Two neural network models were developed and tested in different conditions for prediction: the first model was set up to predict daily maximum $SO_2$ at 5 PM on the previous day, and the second was 10 AM for a given forecast day using an additional potential factors related with urban emissions in the early morning. The results showed that the developed models can predict the daily maximum $SO_2$ concentrations with good simulation accuracy of 87% and 96% for the first and second model. respectively, but the limitation of predictive capability was found at a higher or lower concentrations. The increased accuracy for the second model demonstrates that improvements can be made by utilizing more recent air quality data for initialization of the model.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.47-54
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• 2023

This article reviews recent advances and perspectives in neuroscience related to consciousness, cognition, and neural networks in the brain. The neural mechanisms underlying cognitive processes, such as perception, attention, memory, and decision-making, are explored. The article also examines how these processes give rise to our experience of consciousness. The implications of these findings for our understanding of the brain and its functions are presented, as well as potential applications of this knowledge in fields such as medicine, psychology, and artificial intelligence. Additionally, the article explores the concept of a quantum viewpoint concerning consciousness, cognition, and creativity and how incorporating DNA as a key element could reconcile classical and quantum perspectives on human behaviour, consciousness, and cognition, as explained by genomic psychological theory. Furthermore, the article explains how the human brain processes external stimuli through the sensory nervous system and how it can be simulated using an artificial neural network (ANN) consisting of one input layer, multiple hidden layers, and an output layer. The law of learning is also discussed, explaining how ANNs work and how the modification of weight values affects the output and input values. The article concludes with a discussion of future research directions in this field, highlighting the potential for further discoveries and advancements in our understanding of the brain and its functions.

• Journal of Medicine and Life Science
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• v.20 no.2
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• pp.53-59
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• 2023

This study explores the potential use of artificial intelligence (AI)-based services, specifically ChatGPT-3.5, in medical education. The application of this technology is acknowledged as a valuable tool for simulating authentic clinical scenarios and enhancing learners' diagnostic and communication skills. To construct a case, students received ChatGPT training using a clinical ethics casebook titled "Clinical Ethics Cases and Commentaries for Medical Students and Physicians." Subsequently, a role-play script was generated based on this training. The initial draft of the script was reviewed by two medical professors and was further optimized using ChatGPT-3.5. Consequently, a comprehensive role-play script, accurately reflecting real-world clinical situations, was successfully developed. This study demonstrates the potential for effectively integrating AI technology into medical education and provides a solution to overcome limitations in developing role-play scripts within conventional educational settings. However, the study acknowledges that AI cannot always generate flawless role-play scripts and recognizes the necessity of addressing these limitations and ethical concerns. The research explores both the potential and limitations of employing AI in the early stages of medical education, suggesting that future studies should focus on overcoming these limitations while further investigating the potential applications of AI in this field.

• International Journal of Oral Biology
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• v.48 no.4
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• pp.45-49
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• 2023

Digital dentistry has witnessed significant advancements in recent years, driven by extensive research following the introduction of cutting-edge technologies such as CAD/CAM and 3D oral scanners. Until now, 2D images obtained via x-ray or CT scans were critical to detect anomalies and for decision-making. This review describes the main principles and applications of supervised, unsupervised, and reinforcement learning in medical applications. In this context, we present a diverse range of artificial intelligence networks with potential applications in dentistry, accompanied by existing results in the field.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.13-23
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• 1996

When artificial potential field approach is used to avoid obstacle, the problem can be occurred in case that manipulator selects the path which across over an obstacle among paths. In thiscase manipulator can't reach the desired goal form obstacle. This problem is a case of structual local minimum. so this paper proposes the method to solve structual local minimum in this case. The method is that the manipulator goes via temporary goal. This paper proposes that visual region concept to select the temporary goal. The temporary goal is selected on the border of the visual region. To prove its effectiveness, two simulation examples are done by two link manipulator in two dimension and by three link manipulator in three dimension.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.03a
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• pp.431-438
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• 1999

An assessment of liquefaction potential is made in principle by comparing the shear stress induced by earthquake to the liquefaction strength of the soil. In this study, a modified method based on Seed and Idriss theory is developed for evaluating liquefaction potential. The shear stress in the ground can be evaluated with seismic response analysis and the liquefaction strength of the soil can be investigated by using cyclic triaxial tests. The cyclic triaxial tests are conducted in two different conditions in order to investigate the factors affecting liquefaction strength such as cyclic shear stress amplitude and relative density. And performance of the modified method in practical examples is demonstrated by applying it to liquefaction analysis of artificial zones with dimensions and material properties similar to those in a typical field. From the result, the modified method for assessing liquefaction potential can successfully evaluate the safety factor under moderate magnitude(M=6.5) of earthquake.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.741-753
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• 2018

Local drought in South Korea has recently increased interest in the efficient use of groundwater and then induces a growing need to introduce artificial recharge of groundwater that stores water in sedimentary layer. In order to evaluate the potential artificial recharge sites in the alluvial basins in Chungcheongnamdo province, an AHP (Analytical hierarchy process) model consisting of three primary and seven secondary factors was developed in this study. In the AHP model, adding candidate sites changes final evaluation score through a mathematical calculation process. By contrast ANN (Artificial neural network) model always provides an unchanged score for each candidate area. Therefore, the score can be used as a selection criterion for artificial recharge sites. It is concluded that the possibility of artificial recharge is relatively low if the score of the ANN model is less than about 1.5. Further studies and field surveys on the other regions in Korea will lead to draw out a more applicable ANN model.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.18-25
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• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.