• 디지털정책학회지
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• 제3권1호
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• pp.27-32
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• 2024

본 논문은 초해상도 결과의 품질을 향상시키기 위해 질감 특징을 세분화하여 각각을 대조하고, 그 결과를 가중치로 이용하는 초해상도 방법을 제안하였다. 초해상도에서 중요한 평가 기준인 품질의 향상을 위해서는 경계 영역과 같은 세부사항에서의 정확하고 명확한 복원 결과가 필요하며, 인공물과 같은 불필요한 잡음을 최소화하는 것이 중요하다. 제안하는 방법은 품질 향상을 위해 기존 CNN(Convolutional Neural Network) 기반의 초해상도 방법에서 특징 추정을 위해 다중 경로의 잔차 블록 구조와 skip-connection을 구성하였다. 추가적인 질감 분석을 위한 선명 및 흐림 이미지 결과를 추가로 학습하였다. 이를 활용하여 초해상도 수행 결과 또한 각각을 대조하여 가중치를 할당하는 방법을 이용해 영상의 세부사항 영역과 평활화 영역에 대해 개선된 품질을 얻을 수 있도록 하였다. 제안하는 방법의 실험 결과 평가 기준으로 활용되는 PSNR과 SSIM 값이 기존 알고리즘 대비 높은 결과 값을 얻어 품질이 개선됨을 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• 한국산학기술학회논문지
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• 제10권4호
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• pp.736-739
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• 2009

영상 내의 움직임 적응적인 효과적인 무손실 영상 압축 알고리즘을 제안한다. 이 알고리즘은 비선형 예측기를 토대로 움직임에 적응하는 단계와, 예측기에 의한 차분 데이터를 압축하는 단계로 구성된다. 제안한 비선형 예측기는 과거의 예측 오차로부터 화면간 혹은 화면내 예측치를 선택하며, 움직임 적응 단계를 진행되면서 주변 화소들의 예측 오차를 고려하여, 현재 화소에 대한 예측 오차를 줄이는 능력을 가진다. 예측 오차는 기존의 문맥 적응적인 코딩 기법에 의해서 압축된다. 실험결과는 제안한 알고리즘이 FELICS, CALC, JPEG-LS와 같은 문맥 모델링에 기반을 둔 무손실 압축 기법보다 우수한 압축률을 보여준다.

• Steel and Composite Structures
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• 제24권5호
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• pp.635-641
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• 2017

Safety service improvement and development of efficient maintenance strategies for corroded steel structures are undeniably essential. Therefore, understanding the influence of damage caused by corrosion on the remaining load-carrying capacities such as tensile strength is required. In this study, artificial neural network (ANN) approach is proposed in order to produce a simple, accurate, and inexpensive method developed by using tensile test results, material properties and finite element method (FEM) results to train the ANN model. Initially in reproducing corroded model process, FEM was used to obtain tensile strength of artificial corroded plates, for which surface is developed by a spatial autocorrelation model. By using the corroded surface data and material properties as input data, with tensile strength as the output data, the ANN model could be trained. The accuracy of the ANN result was then verified by using leave-one-out cross-validation (LOOCV). As a result, it was confirmed that the accuracy of the ANN approach and the final output equation was developed for predicting tensile strength without tensile test results and FEM in further work. Though previous studies have been conducted, the accuracy results are still lower than the proposed ANN approach. Hence, the proposed ANN model now enables us to have a simple, rapid, and inexpensive method to predict residual tensile strength more accurately due to corrosion in steel structures.

• ETRI Journal
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• 제42권2호
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• pp.230-238
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• 2020

Human-object interaction (HOI) detection is a popular computer vision task that detects interactions between humans and objects. This task can be useful in many applications that require a deeper understanding of semantic scenes. Current HOI detection networks typically consist of a feature extractor followed by detection layers comprising small filters (eg, 1 × 1 or 3 × 3). Although small filters can capture local spatial features with a few parameters, they fail to capture larger context information relevant for recognizing interactions between humans and distant objects owing to their small receptive regions. Hence, we herein propose a three-stream HOI detection network that employs a context convolution module (CCM) in each stream branch. The CCM can capture larger contexts from input feature maps by adopting combinations of large separable convolution layers and residual-based convolution layers without increasing the number of parameters by using fewer large separable filters. We evaluate our HOI detection method using two benchmark datasets, V-COCO and HICO-DET, and demonstrate its state-of-the-art performance.

• 대한산업공학회지
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• 제42권4호
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• pp.263-269
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• 2016

The purpose of this study is to challenge a computational regression-type problem, that is handling large-size data, in which conventional metamodeling techniques often fail in a practical sense. To solve such problems, regression-type boosting, one of ensemble model techniques, together with bootstrapping-based re-sampling is a reasonable choice. This study suggests weight updates by the amount of the residual itself and a new error decision criterion which constructs an ensemble model of models selectively chosen by rejection limits. Through these ideas, we propose AdaBoost.RMU.R as a metamodeling technique suitable for handling large-size data. To assess the performance of the proposed method in comparison to some existing methods, we used 6 mathematical problems. For each problem, we computed the average and the standard deviation of residuals between real response values and predicted response values. Results revealed that the average and the standard deviation of AdaBoost.RMU.R were improved than those of other algorithms.

• Journal of information and communication convergence engineering
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• 제20권4호
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• pp.259-264
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• 2022

In wireless sensor networks, sensor nodes are often deployed in large numbers in places that are difficult for humans to access. However, the energy of the sensor node is limited. Therefore, one of the most important considerations when designing routing protocols in wireless sensor networks is minimizing the energy consumption of each sensor node. When the energy of a wireless sensor node is exhausted, the node can no longer be used. Various protocols are being designed to minimize energy consumption and maintain long-term network life. Therefore, we proposed KOCED, an optimal cluster K-means algorithm that considers the distances between cluster centers, nodes, and residual energies. I would like to perform a performance evaluation on the KOCED protocol. This is a study for energy efficiency and validation. The purpose of this study is to present performance evaluation factors by comparing the K-means algorithm and the K-medoids algorithm, one of the recently introduced machine learning techniques, with the KOCED protocol.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.109-110
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• 2022

Currently, interest in the quality of concrete is increasing. Among the important factors for evaluating the quality of concrete, interest in unit-water content is also increasing. Currently, the air-meter method, the microwave oven drying method, the capacitance method, and the microwave penetration method are used to measure the unit-water content of concrete.. Among the above methods, except for the microwave method, the measurement method is complicated, portability is reduced, and economic efficiency is reduced. This research aims to measure a unit-water content by using a Frequency Domain Reflectometry(FDR) sensor that is economical, simple to measure, and portable among microwave methods. In addition, it is an experimental study to determine the accuracy of unit-water content using a single input residual model during deep learning to solve the limitations of the FDR sensor.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.481-483
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• 2022

With the advent of the era of artificial intelligence, more and more fields have begun to use artificial intelligence technology, especially the medical field. Cancer is one of the biggest problems in the medical field. [1] If it can be detected early and treated early, the possibility of cure will be greatly increased. Malignant skin cancer, as one of the types of cancer with the highest fatality rate in recent years has problems such as relying on the experience of doctors and being unable to be detected and detected in time. Therefore, if artificial intelligence technology can be used to help doctors in early detection of skin cancer, or to allow everyone to detect skin lesions or spots anytime, anywhere, it will have great practical significance. In this paper we used attention residual learning convolutional neural network (ARL-CNN) model [2] to classify skin cancer pictures.

• 대한환경공학회지
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• 제38권2호
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• pp.87-95
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• 2016

탁도는 송 배수 관로의 부식 등에 의해 발생되는 것으로 알려진 'Discolored Water'현상을 수용가의 물 사용자가 인지할 수 있는 주요 지표로서 활용되고 있다. 즉, 'Discolored Water'는 수돗물 사용자가 육안으로 인지할 수 있는 정도의 탁도를 가진 상태로 정의할 수 있으며, 사용자는 수돗물에 존재하는 불특정의 용존 물질보다는 미세한 입자들에 대한 시각적인 인지인 탁도를 통해서 'Discolored Water'를 인식하게 된다. 이에 본 연구에서는 실제 국내 상수도 시스템 내에서 관측된 다항목의 수질데이터(탁도, pH 및 잔류염소)를 대상으로 하여 탁도 이외의 수질데이터들을 예측모형의 설명변수로 설정한 후 데이터 마이닝 기법(data mining)을 통해 기계학습(machine learning)을 수행하여, 상수도 시스템 내에서의 탁도 변화를 예측하는 모형을 수립하고자 하였다. 수집된 수질 데이터를 대상으로 데이터 마이닝 기법인 Decision Tree를 이용해 탁도 예측모형을 구축한 결과 pH 및 잔류염소를 설명변수로 적용한 모형이 가장 높은 예측결과를 나타내었다. 하지만 예측모형들은 peak 관측치에 대해서는 예측오차가 다소 증가하였는데 이를 보완하기 위해 고주파통과필터를 이용한 전처리 과정을 적용하였다. 그 결과 탁도 데이터의 시계열변화 및 peak 관측치에 대한 예측오차가 감소하는 것으로 나타났다.