• 한국컴퓨터정보학회논문지
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• 제25권4호
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• pp.55-61
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• 2020

본 논문에서는 사용자의 경로를 기반으로 가중치 맵을 계산하고, 이를 이용하여 게임이나 가상현실과 같은 인터랙티브 가상 환경에서 논플레이어 캐릭터(Non-playable characters, NPC)의 위치를 효율적으로 제어할 수 있는 방법을 제시한다. 우리의 방법은 사용자의 움직임을 참조하여 NPC에 새로운 경로를 제공하는 네비게이션 메쉬를 자동으로 구성한다. 우리의 방법은 시간이 지남에 따라 사용자가 주로 다니는 길목을 정확하게 찾아내기 때문에 가상환경에 적응형으로 자동 변경이 가능하고, 사용자가 선호하는 경로를 가중치로 스마트 에이전트와 같은 움직임을 만들어 낼 수 있다. 우리는 비디오 게임이나 가상현실과 같은 인터랙티브 환경의 몇 가지 예제 시나리오를 통해 본 논문에서 제안하는 알고리즘의 유용성 테스트를 실험했다. 실제로 우리의 프레임워크는 인터랙티브 환경을 활용하는 모든 유형의 탐색에 쉽게 적용 할 수 있다.

• 한국산학기술학회논문지
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• 제22권2호
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• pp.714-720
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• 2021

머신 러닝 및 신호처리에 활용되고 있는 정보이론적 학습법(ITL, information theoretic learning)은 커널 사이즈(σ) 설정이 매우 민감한 어려움을 지닌다. ITL의 성능지표중 하나인 코렌트로피 함수를 최대화하는 성능지표에 대해, 기울기에 존재하는 1/σ2를 제거한 뒤 남은 커널 사이즈에 대해 적응적으로 조절하는 방법들이 연구되었다. 이 논문에서는, 1/σ2의 커널 사이즈가 실제 시스템의 민감성이나 불안정에 큰 역할을 하고 있으며 남은 부분에 존재하는 커널 사이즈에 대한 최적해는 오차의 절대값 근방에 수렴함에 따라 오히려 수렴 후 가중치 갱신을 멈추게 하는 부작용이 나타남을 밝혔다. 이에 적응적 커널 사이즈 조절 대신 적절한 상수를 선택하는 것이 보다 효과적이라는 것을 제안하였고, 실험결과에서 동일한 수렴 속도에 약 2dB 향상된 정상상태 MSE를 보였다. 제안한 방식을 더욱 열악한 다경로 채널환경에 적용하여 실험한 결과 4dB 이상의 성능향상을 보여 제안한 방식은 열악한 상황일수록 더욱 향상된 성능을 보임을 알 수 있다.

• 한국컴퓨터정보학회논문지
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• 제26권3호
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• pp.1-7
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• 2021

본 논문에서는 Hybrid CDN/P2P 구조를 기반으로 최적화된 미디어 데이터 탐색과 전송을 수행하며 사용자의 요청 가능성 예측을 통한 선별적 저장을 통해 사용자로의 끊김없는 데이터 전송과 불필요한 트래픽의 감소를 가능하게 한다. 또한 전송지연 및 패킷 손실의 가능성을 최소화하여 실시간으로 미디어를 활용할 수 있도록 하는 새로운 미디어 관리 기법을 제안한다. 이를 위해 각 미디어를 논리적인 세그먼트로 나누어 구성하고 각 세그먼트에 대한 가중치를 지속적으로 계산하며 계산된 가중치에 따라 세그먼트 데이터의 저장 여부를 결정하도록 한다. 또한 네트워크상에 산재되어 있는 컴퓨팅 노드들을 거리에 따라 지역적 그룹으로 지정하고 해당 그룹 내에서 저장 공간을 효율적으로 공유하고 활용하도록 한다. 제안하는 기법의 효율성을 검증하기 위해 수행된 실험을 통해 제안하는 방식이 기존의 방법들에 비해 비교적 좋은 성능 평가가 도출되는 것을 확인하였으며 이는 전송과정에서 발생되는 초기 지연시간 감소와 끊김 없는 전송 모두를 가능하게 할 수 있음을 알 수 있다.

• International Journal of Knowledge Content Development & Technology
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• 제10권4호
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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.

• 한국수자원학회논문집
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• 제55권1호
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• pp.11-21
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• 2022

지난 2019년 말 발생한 COVID-19는 2020년을 기점으로 국내에 본격적으로 확산되기 시작하여, 사회 전반에 커다란 영향을 미치고 있다. COVID-19 확산을 억제하기 위한 방역수칙들은 인간 생활에 많은 변화를 가져왔으며, 사회적 거리두기 등 사회활동 제한에 따른 다양한 영향이 사회 전반에 걸쳐 나타나고 있다. 본 연구에서는 물 분야 COVID-19 위기 대응의 일환으로, COVID-19 확산에 따른 국내 상수도 사용량 변화를 분석하고, 상수도 사용량의 변화가 공급 서비스에 미치는 위협을 알아보고자 하였다. 국내 중소규모 도시인 S시 주거지역을 대상으로 COVID-19 확산 전후 일정기간 동안의 1시간 단위 용수 사용량 자료를 수집하였으며, 먼저 수집 데이터를 분석 목적에 따라 정제하고 전체 용수 사용량의 변화 및 사용 비중 변화, 그리고 시간별 용수 사용 패턴 변화 등을 분석하였다. 분석 결과, 가정용수 및 영업용수 사용량 및 이용패턴이 COVID-19 확산 이후 뚜렷한 변화를 보였으며, 일부 사용량 변화는 상수도 운영관리 차원에서의 검토가 필요한 것으로 나타났다.

• 대한한의학방제학회지
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• 제31권1호
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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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• 제33권3호
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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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• 제31권2호
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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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• 제51권1호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권9호
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• pp.2605-2625
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• 2024

Siamese-based segmentation and tracking algorithms improve accuracy and stability for video object segmentation and tracking tasks simultaneously. Although effective, variability in target appearance and background clutter can still affect segmentation accuracy and further influence the performance of tracking. In this paper, we present a memory propagation-based target-aware and mask-attention decision network for robust object segmentation and tracking. Firstly, a mask propagation-based attention module (MPAM) is constructed to explore the inherent correlation among image frames, which can mine mask information of the historical frames. By retrieving a memory bank (MB) that stores features and binary masks of historical frames, target attention maps are generated to highlight the target region on backbone features, thus suppressing the adverse effects of background clutter. Secondly, an attention refinement pathway (ARP) is designed to further refine the segmentation profile in the process of mask generation. A lightweight attention mechanism is introduced to calculate the weight of low-level features, paying more attention to low-level features sensitive to edge detail so as to obtain segmentation results. Finally, a mask fusion mechanism (MFM) is proposed to enhance the accuracy of the mask. By utilizing a mask quality assessment decision network, the corresponding quality scores of the "initial mask" and the "previous mask" can be obtained adaptively, thus achieving the assignment of weights and the fusion of masks. Therefore, the final mask enjoys higher accuracy and stability. Experimental results on multiple benchmarks demonstrate that our algorithm performs outstanding performance in a variety of challenging tracking tasks.