• 로봇학회논문지
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• 제18권3호
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• pp.293-298
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• 2023

The military is facing a continuous decrease in personnel, and in order to cope with potential accidents and challenges in operations, efforts are being made to reduce the direct involvement of personnel by utilizing the latest technologies. Recently, the use of various sensors related to Manned-Unmanned Teaming and artificial intelligence technologies has gained attention, emphasizing the need for flexible utilization methods. In this paper, we propose four dataset construction methods that can be used for effective training of robots that can be deployed in military operations, utilizing not only RGB image data but also data acquired from IR image sensors. Since there is no publicly available dataset that combines RGB and IR image data, we directly acquired the dataset within buildings. The input values were constructed by combining RGB and IR image sensor data, taking into account the field of view, resolution, and channel values of both sensors. We compared the proposed method with conventional RGB image data classification training using the same learning model. By employing the proposed image data fusion method, we observed improved stability in training loss and approximately 3% higher accuracy.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.75-77
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• 2021

딥러닝 기술을 통해 영상 내의 객체를 찾아내기 위해서는 학습을 위한 영상 데이터셋이 필요하다. 객체의 인식률을 높이기 위해서는 많은 양의 영상 학습 데이터가 필요하다. 많은 양의 데이터셋을 구축하는 데에는 많은 비용이 들기 때문에 개인이 구축하기에 어려움이 있다. 본 논문에서는 회전 영상을 촬영하여 객체의 여러 면을 포함하는 영상 데이터셋을 보다 손쉽게 구축하는 방법을 소개한다. 회전판 위에 객체를 올려둔 뒤 촬영하고 촬영된 영상을 필요에 맞게 분할, 합성하여 데이터셋을 구축하는 방법을 제안한다.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.167-176
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• 2024

Lip-reading is the task of inferring the speaker's utterance from silent video based on learning of lip movements. It is very challenging due to the inherent ambiguities present in the lip movement such as different characters that produce the same lip appearances. Recent advances in deep learning models such as Transformer and Temporal Convolutional Network have led to improve the performance of lip-reading. However, most previous works deal with English lip-reading which has limitations in directly applying to Korean lip-reading, and moreover, there is no a large scale Korean lip-reading dataset. In this paper, we introduce the first large-scale Korean lip-reading dataset with more than 120 k utterances collected from TV broadcasts containing news, documentary and drama. We also present a preprocessing method which uniformly extracts a facial region of interest and propose a transformer-based model based on grapheme unit for sentence-level Korean lip-reading. We demonstrate that our dataset and model are appropriate for Korean lip-reading through statistics of the dataset and experimental results.

• 드라이브 ㆍ 컨트롤
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• 제19권3호
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• pp.9-15
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• 2022

This paper proposes a method for constructing and verifying datasets used in deep learning technology, to prevent safety accidents in automated construction machinery or autonomous vehicles. Although open datasets for developing image recognition technologies are challenging to meet requirements desired by users, this study proposes the interface of virtual simulators to facilitate the creation of training datasets desired by users. The pixel-level training image dataset was verified by creating scenarios, including various road types and objects in a virtual environment. Detecting an object from an image may interfere with the accurate path determination due to occlusion areas covered by another object. Thus, we construct a database, for developing an occlusion area detection algorithm in a virtual environment. Additionally, we present the possibility of its use as a deep learning dataset to calculate a grid map, that enables path search considering occlusion areas. Custom datasets are built using the RDBMS system.

• Wind and Structures
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• 제36권6호
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• pp.423-434
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• 2023

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

• 방송공학회논문지
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• 제27권1호
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• pp.124-132
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• 2022

최근 코로나바이러스감염증-19(COVID-19)로 인해 온라인 원격 수업과 비대면 시험으로 인해 수업 태도 및 시험 부정행위에 대한 관리가 어려움을 겪고 있다. 따라서 온라인으로 학생들의 행동을 자동으로 인식하고 검출하는 시스템이 필요하다. 사람의 행동을 인식하는 행동 인식의 경우 컴퓨터 비전에서 많이 연구되는 기술 중 하나이다. 이러한 시스템을 개발하기 위해서는 온라인 수업 및 시험에서 주요 정보가 될 수 있는 사람의 팔 움직임 정보와 주변 물체에 대한 정보를 포함하는 데이터가 필요하다. 기존 데이터 세트는 여러 분야에 대해 분류를 하거나 일상생활 행동으로 구성되어 있어 본 시스템에 적용시키기에 어려움이 있다. 본 논문에서는 실시간으로 진행되는 온라인 시험 및 수업에서 태도를 분류할 수 있는 데이터 세트를 제시한다. 또한, 기존의 행동 인식 데이터 세트와의 비교를 통해 제안된 데이터 세트가 올바르게 구성되었는지를 보여준다.

• Journal of Construction Engineering and Project Management
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• 제4권4호
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• pp.9-16
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• 2014

The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.361-369
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• 2022

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

• 한국건설관리학회논문집
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• 제21권6호
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• pp.125-134
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• 2020

시공 서류에서 접하는 자연어는 당국에서 권장하는 언어와 크게 다르다. 일관성이 부족한 이러한 관행은 자동화를 통한 통합 연구를 방해하고 장기적으로 업계의 생산성을 저하시킬 것이다. 이 연구는 여러 문자열 유사성(문자열 일치) 알고리즘을 비교하여 여러 다른 방법으로 작성된 동일한 작업 이름을 인식하는 각 알고리즘의 성능을 비교하는 것을 목표로 한다. 우리는 또한 앞서 언급 한 편차가 얼마나 널리 퍼져 있는지에 대한 토론을 시작하는 것을 목표로 한다. 마지막으로, 우리는 실제로 발견된 시공 작업 이름을 형식에 비해 덜 복잡한 해당 작업 이름과 연결하는 작은 데이터 세트를 구성했다. 이 데이터 세트를 사용하여 미래의 자연어 처리 접근방식을 검증 할 수 있을 것으로 기대한다.

• 전기학회논문지
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• 제60권2호
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.