• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.55-61
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• 2020

In this paper, we present a novel approach to efficiently control the location of NPC(Non-playable characters) in the interactive virtual world such as game, virtual reality. To control the NPC's movement path, we first calculate the main trajectory based on the user's path, and then move the NPC based on the weight map. Our method constructs automatically a navigation mesh that provides new paths for NPC by referencing the user trajectories. Our method enables adaptive changes to the virtual world over time and provides user-preferred path weights for smartagent path planning. We have tested the usefulness of our algorithm with several example scenarios from interactive worlds such as video games, virtual reality. In practice, our framework can be applied easily to any type of navigation in an interactive world.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.714-720
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• 2021

The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.1-7
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• 2021

In this paper, optimized media data retrieval and transmission based on the Hybrid CDN/P2P architecture and selective storage through user's prediction of requestability enable seamless data transfer to users and reduction of unnecessary traffic. We also propose a new media management method to minimize the possibility of transmission delay and packet loss so that media can be utilized in real time. To this end, we construct each media into logical segments, continuously compute weights for each segment, and determine whether to store segment data based on the calculated weights. We also designate scattered computing nodes on the network as local groups by distance and ensure that storage space is efficiently shared and utilized within those groups. Experiments conducted to verify the efficiency of the proposed technique have shown that the proposed method yields a relatively good performance evaluation compared to the existing methods, which can enable both initial latency reduction and seamless transmission.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.4
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• pp.29-46
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• 2020

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.11-21
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• 2022

The COVID-19 started to spread at early 2020 in South Korea and has been threatening our life in many aspects. Countermeasures such as social distancing to prevent COVID-19 spread have brought many changes in our society an human life. In this study, as a part of the COVID-19 pandemic management, drinking water usage change is analyzed to evaluate potential risks on water supply service. We collected hourly water use data of residential area in S city, which is a mid-size city in South Korea, before and after the COVID-19 pandemic. The collected data were analyzed to reveal the changes in total water consumption, water usage weight, and hourly water-demand pattern caused by the COVID-19 pandemic. The case study revealed the noticeable changes in water consumption caused by the COVID-19 pandemic and required more secured and adaptive operation of drinking water system under the pandemic situation caused by infectious disease.

• Herbal Formula Science
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• v.31 no.1
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• pp.21-28
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• 2023

Objective : Scolopendra, a dried body of Scolopendra subspinipes mutilans, is one of Korean medicine. Several reports revealed that Scolopendra has therapeutic effects for arthritis, neuroinflammatory diseases and neuropathic pain. However, the fetal adaptive response or teratogenicity associated with administration of Scolopendra is unclear. Therefore, this study aimed to investigate the fetal toxicity effects that were induced following oral administration of Scolopendra water extract (SWE) in pregnant mice. Methods : The pregnant mice were administrated SWE at dosed of 0, 100, 500 and 1000 mg/kg/day during gestation day 0-18. The mortality, body weight and clinical signs of pregnant mice were observed throughout experimental period. Also, the mortality and malformations in foetus were examined. Results : No meaningful changes were observed in the mortality and clinical signs of pregnant mice between the normal control group and SWE administrated groups. Additionally, there are no significant changes in fetal mortalities, and malformations by SWE administration. conclusion : These results suggest that oral exposure to SWE during pregnancy at oral dosages up to 1000 mg/kg/day did not induce teratogenic toxicity in regard to fetal mortality and morphology.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Steel and Composite Structures
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• v.51 no.1
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• pp.25-41
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• 2024

The measurement of pile bearing capacity is crucial for the design of pile foundations, where in-situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy-based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems (ANFIS) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer (FHO) and equilibrium optimizer (EO) with the ANFIS, referred to as ANFIS_FHO and ANFIS_EO, respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANFIS_FHO and ANFIS_EO both have amazing potential for precisely calculating pile bearing capacity. The R² values obtained for ANFIS_FHO were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANFIS_FHO system had less uncertainty than the ANFIS_EO model. The research found that the ANFIS_FHO model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.

• Korean Journal of Veterinary Research
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• v.1 no.1
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• pp.1-29
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• 1961

A routine hematological observation in the course of starvation was carried out on eight experimentel1y starved rabbits. They were strictly selected and restricted all of food intake with the exception of optional water intake until death. The body weight of each rabbit on the day before starvation was about 2 kilograms. The results are summarized as follows. 1. The average decrememt ratio of body weight on the terminal day before death was $34.3{\pm}7.5$ per cent with the range from 24.5 to 46.3 per cent. The average life duration until death was $10.25{\pm}2.6$ days, the range being from 6 to 14 days. 2. The decrease in number of reticulocytes with a parallel disappearance of polychromatic erythrocytes in peripheral blood in the course of starvation Was the most remarkable change in erythrocytic series, an evidence suggesting marked restriction of the erythropoietic function on 3rd to 4th day and almost complete suspension in about a week of starvation. 3. Erythrocyte count, hemoglobin content and haematocrit value of peripheral blood, were normal or indicative of slight hemoconcentration. 4. Mean Corpuscular Hemogloin Concentration was slightly higher than normal and Mean Corpuscular Volume tended to be low and no appreciable shifts were observed in Mean Corpuscular Diameter and Price-Jones curve of erythrocytes, while fewer macrocytes than normal were seen. These changes were considered to have resulted from a marked decrease in young erythrocytes in peripheral blood in the course of starvation. 5. Neither poikilccytoses or anisosytosis was observed. 6. Leukopenia was observed in all of 8 starved rabbits. The decrement ratio on the terminal day of starvation was between 13 to 64 per cent. The leukopenia was mainly due to fall of lymphocytes in 6 cases and to fall of neutrophilic leukocytes in the other 2 cases. In many cases, irregular fluctuation of neutrophilic leukocytes in its biological curve were seen in contrast to the relatively smooth changes of lymphocytes. Eosinophilic leukocytes tended to decrease in absolute number especially in later stage of starvation. Little significance in regard to monocytes and basophilic leukocytes in this study was discussed. 7. Proplasma cells, rarely plasma cells, appeared with a tendency to increase in number at later stage of starvation. 8. The most characteristic changes on circulating blood cells in complete starvation of rabbits were the leukoponia and failure of regeneration of erythroctes. These changes were considered as adaptive phenomena in response to the catabolic consumption of body constituents.