• ETRI Journal
• /
• 제44권4호
• /
• pp.641-653
• /
• 2022

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane-change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane-change trajectories for autonomous vehicles. When comparing this generated trajectory with a man-generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane-change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane-change trajectory.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2023년도 학술발표회
• /
• pp.26-26
• /
• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• Nuclear Engineering and Technology
• /
• 제55권9호
• /
• pp.3359-3366
• /
• 2023

In this study, a spiking cortical model (SCM) based n-g discrimination method is proposed. The SCM-based algorithm is compared with three other methods, namely: (i) the pulse-coupled neural network (PCNN), (ii) the charge comparison, and (iii) the zero-crossing. The objective evaluation criteria used for the comparison are the FoM-value and the time consumption of discrimination. Experimental results demonstrated that our proposed method outperforms the other methods significantly with the highest FoM-value. Specifically, the proposed method exhibits a 34.81% improvement compared with the PCNN, a 50.29% improvement compared with the charge comparison, and a 110.02% improvement compared with the zero-crossing. Additionally, the proposed method features the second-fastest discrimination time, where it is 75.67% faster than the PCNN, 70.65% faster than the charge comparison and 38.4% slower than the zero-crossing. Our study also discusses the role and change pattern of each parameter of the SCM to guide the selection process. It concludes that the SCM's outstanding ability to recognize the dynamic information in the pulse signal, improved accuracy when compared to the PCNN, and better computational complexity enables the SCM to exhibit excellent n-γ discrimination performance while consuming less time.

• 국제학술발표논문집
• /
• The 7th International Conference on Construction Engineering and Project Management Summit Forum on Sustainable Construction and Management
• /
• pp.80-88
• /
• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• 로봇학회논문지
• /
• 제18권4호
• /
• pp.436-443
• /
• 2023

With the advancement of robot intelligence, the concept of man and unmanned teaming (MUM-T) has garnered considerable attention in military research. In this paper, we present a transformer-based architecture for predicting the health status of agents, with the help of multi-head attention mechanism to effectively capture the dynamic interaction between friendly and enemy forces. To this end, we first introduce a framework for generating a dataset of battlefield situations. These situations are simulated on a virtual simulator, allowing for a wide range of scenarios without any restrictions on the number of agents, their missions, or their actions. Then, we define the crucial elements for identifying the battlefield, with a specific emphasis on agents' status. The battlefield data is fed into the transformer architecture, with classification headers on top of the transformer encoding layers to categorize health status of agent. We conduct ablation tests to assess the significance of various factors in determining agents' health status in battlefield scenarios. We conduct 3-Fold corss validation and the experimental results demonstrate that our model achieves a prediction accuracy of over 98%. In addition, the performance of our model are compared with that of other models such as convolutional neural network (CNN) and multi layer perceptron (MLP), and the results establish the superiority of our model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권1호
• /
• pp.105-125
• /
• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• 한국정보처리학회논문지
• /
• 제4권12호
• /
• pp.3033-3044
• /
• 1997

본 논문에서는 가상현실의 기본 요소중의 하나인 사용자 인터페이스 분야에서 동적 손 제스처를 실시간으로 인식하는 시스템의 구현에 관하여 상술한다. 사람의 손과 손가락은 사람마다 같은 동작이라도 데이터의 변화가 다양하며 같은 동작을 반복해서 할 때에도 다른 데이터를 얻게되는등 시간에따른 변화도 존재한다. 또한, 손가락의 외형 및 물리적 구조가 사람마다 다르기 때문에 다른 두사람에 의해 만들어진 같은 손 모양도 일반적인 센싱장비에의해 측정될 때 다른 측정값을 나타낸다. 또한 동적 손제스처에서 동작의 시작과 끝을 명확히 구분하기가 매우 힘들다. 본 논문에서는 동적 손 제스처에 대해 각각의 의미있는 동작을 구분하기위해 상태 오토마타를 이용하였고, 인식 범위의 확장성을 고려하여 동적 손 제스처를 퍼지 이론을 도입한 특징 해석에의해 기본 요소인 손의 운동을 분류하고 퍼지 최대－최소 신경망을 적용하여 손의 모양을 분류함으로써 전체 손 제스처를 인식하는 시스템을 제안한다.

• 전자공학회논문지CI
• /
• 제45권6호
• /
• pp.154-161
• /
• 2008

본 논문에서는 컬러와 패턴 정보를 이용하여 텍스타일 영상에 포함된 감성을 자동으로 인식할 수 있는 방법을 제안한다. 이때, 감성을 표현하기 위해 고바야시의 10가지 감성 그룹 - {romantic, clear, natural, casual, elegant chic, dynamic, classic, dandy, modern}- 을 이용한다. 제안된 시스템은 특징 추출과 분류로 구성된다. 특징 추출 단계에서는 주관적인 감성을 물리적인 영상 특징으로 표현하기 위해 텍스타일을 구성하는 대표 컬러와 패턴을 추출 한다. 이 때 대표 컬러를 추출하기 위해서 양자화 기법을 이용하고, 패턴정보를 표현하기 위해서는 웨이블릿 변환 후의 통계적인 정보를 이용한다 추출된 컬러와 패턴 특징은 신경망을 이용한 분류기의 입력으로 사용되고, 분류기를 통해 입력 텍스타일이 임의의 감성을 가지는지 여부가 결정된다. 제안된 감성인식 방법의 효율성을 증명하기 위해서 인위적인 도메인, 패션 도메인, 인테리어 도메인에서 얻어진 389장의 텍스타일 영상에서 실험하였다. 다양한 도메인의 영상에 대해 사용된 결과 제안된 방법은 100%의 정확도와 99%의 재현율을 보였다. 이러한 실험 결과는 제안된 감성인식 방법이 다양한 텍스타일 관련 산업분야에 일반화되어 사용될 수 있음을 보여주었다.

• 한국구조물진단유지관리공학회 논문집
• /
• 제22권2호
• /
• pp.76-82
• /
• 2018

본 논문에서는 지점부 경계조건을 고려하여 단순보의 유한요소모델을 개선하는 기법을 제안하였다. 기존의 유한요소모델개선 기법은 주로 가속도 응답으로부터 추정된 동특성(고유진동수, 모드형상)을 이용하여 유한요소모델을 개선하였다. 이렇게 개선된 유한요소모델은 실제 구조물의 정적응답을 예측하기 어렵고, 잘못된 구조물의 물성치를 추정하는 문제가 발생한다. 제안된 기법은 먼저, 구조물의 처짐과 지점부 회전변위를 계측하여 지점부 경계조건을 간략화한 유한요소모델의 회전 스프링 강성을 정량적으로 추정한다. 회전 스프링 강성이 개선된 유한요소모델과 구조물의 동특성을 사용하여 구조물의 물성치를 추정함으로써 최종 개선된 유한요소모델을 구축된다. 제안된 유한요소모델 개선 기법과 기존 유한요소모델개선 기법을 수치해석 시뮬레이션을 통하여 비교 및 검증하였다.

• Geomechanics and Engineering
• /
• 제31권2호
• /
• pp.129-147
• /
• 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.