• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.252-262
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• 2020

In Remote Sensing, a machine learning based SVM model is typically utilized for land cover classification. And study using neural network models is also being carried out continuously. But study using high-resolution imagery of KOMPSAT is insufficient. Therefore, the purpose of this study is to assess the accuracy of land cover classification by neural network models using high-resolution KOMPSAT-3 satellite imagery. After acquiring satellite imagery of coastal areas near Gyeongju City, training data were produced. And land cover was classified with the SVM, ANN and DNN models for the three items of water, vegetation and land. Then, the accuracy of the classification results was quantitatively assessed through error matrix: the result using DNN model showed the best with 92.0% accuracy. It is necessary to supplement the training data through future multi-temporal satellite imagery, and to carry out classifications for various items.

• Computers and Concrete
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• v.22 no.2
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.27-35
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• 2021

In this paper, we propose a new stacking ensemble framework for deep learning models which reflects the distribution of label embeddings. Our ensemble framework consists of two phases: training the baseline deep learning classifier, and training the sub-classifiers based on the clustering results of label embeddings. Our framework aims to divide a multi-class classification problem into small sub-problems based on the clustering results. The clustering is conducted on the label embeddings obtained from the weight of the last layer of the baseline classifier. After clustering, sub-classifiers are constructed to classify the sub-classes in each cluster. From the experimental results, we found that the label embeddings well reflect the relationships between classification labels, and our ensemble framework can improve the classification performance on a CIFAR 100 dataset.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• Physical Therapy Korea
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• v.15 no.1
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• pp.30-37
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• 2008

This study was designed to examine the effects of temporary immobilization of the ankle and knee joints on standing in healthy young adults with the use of a postural control mechanism. The subjects were twenty-four college students (12 males and 12 females, aged between 20 and 28). A Biodex balance system SD 950-302 and its software were used to measure indirect balance parameters in standing. Each subject underwent postural stability tests in 4-different joint conditions: free joints, ankle immobilization only, knee immobilization only, and ankle and knee immobilization. In addition, the postural stability test was conducted once with the subject's eyes open and once with the eyes closed conditions. For data analysis of the postural stability tests, the overall stability index, antero-posterior stability index, and medio-lateral stability index were recorded. The overall stability index (p=.000) and medial-lateral index (p=.003) were significantly greater different conditions with eyes closed in postural stability. Therefore, the eyes closed condition is expected to be used as an effective postural stability training for treatment planning in patients with unstable postures. In addition, training based on the dynamic multi-segment model can improve postural stability and is available to therapeutic programs, helping people with unstable balance to reduce their risk of falling.

• Asia pacific journal of information systems
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• v.10 no.4
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• pp.1-20
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• 2000

Electronic Data Interchange(EDI) has the potential to improve business operations by expediting the exchange of business documents. It will also provide substantive operational and strategic benefits to the trading firms. However, the successful implementation of EDI systems requires the mutual trust and cooperation between the trading firms. The extent of EDI diffusion and performance depends on inter-organizational, intra-organizational, as well as innovation factors. Researches based on the sociopolitical process framework in the use of IT, organizational theory, resource dependence theory, and innovation diffusion theory have identified 3 inter-organizational variables(transaction climate, dependence, external IS expert support) and 4 intra-organizational variables(strategic IS planning, infrastructure, top management support, education/training,), and 3 innovation variables(compatibility, relative advantage, cost) that affect EDI diffusion. In this study, a multi-dimensional measure on EDI diffusion has been developed to capture the external and internal integration. Then, the influence of these 10 variables on the extent to which the EDI adopting firms pursue diffusion has been examined. Whether more diffusion leads to superior performance has also been studied. International trade managers from 107 firms in the trade industry participated in a field survey. The results based on a structural equation model(SEM), developed using AMOS, provide quite a strong support for the hypothesized relations. Both education/training and IT infrastructure influenced external and internal diffusion of EDI systems. Internal diffusion of EDI enables the adopting firms to improve operational and strategic performance, whereas external diffusion contributes only to operational performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1434-1449
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• 2019

Video object segmentation is a significant task in computer vision, but its performance is not very satisfactory. A method of video object segmentation using weakly temporal information is presented in this paper. Motivated by the phenomenon in reality that the motion of the object is a continuous and smooth process and the appearance of the object does not change much between adjacent frames in the video sequences, we use a feed-forward architecture with motion estimation to predict the mask of the current frame. We extend an additional mask channel for the previous frame segmentation result. The mask of the previous frame is treated as the input of the expanded channel after processing, and then we extract the temporal feature of the object and fuse it with other feature maps to generate the final mask. In addition, we introduce multi-mask guidance to improve the stability of the model. Moreover, we enhance segmentation performance by further training with the masks already obtained. Experiments show that our method achieves competitive results on DAVIS-2016 on single object segmentation compared to some state-of-the-art algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1065-1072
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• 2024

Previous approaches to 3D excavator pose estimation via synthetic data training utilized a single virtual excavator model, low polygon objects, relatively poor textures, and few background objects, which led to reduced accuracy when the resulting models were tested on differing excavator types and more complex backgrounds. To address these limitations, the authors present a realism-centric synthetization and labeling approach that synthesizes results with improved image quality, more detailed excavator models, additional excavator types, and complex background conditions. Additionally, the data generated includes dense pose labels and depth maps for the excavator models. Utilizing the realism-centric generation method, the authors achieved significantly greater image detail, excavator variety, and background complexity for potentially improved labeling accuracy. The dense pose labels, featuring fifty points instead of the conventional four to six, could allow inferences to be made from unclear excavator pose estimates. The synthesized depth maps could be utilized in a variety of DNN applications, including multi-modal data integration and object detection. Our next step involves training and testing DNN models that would quantify the degree of accuracy enhancement achieved by increased image quality, excavator diversity, and background complexity, helping lay the groundwork for broader application of synthetic models in construction robotics and automated project management.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.6
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• pp.628-638
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• 2017

In response to difficult employment conditions in the maritime industry and a desire to expand their career options, domestic mates are persuing DP operator training at institutions both domestically and abroad based on their shipboard experience. However, since the offshore plant service industry has not yet been established in Korea, those seeking to enter this field have difficulty acquiring qualifications and most seek work overseas for offshore shipping companies. Individuals wishing to work as DP operators are likely to face more conservative recruitment processes with overseas offshore shipping companies, focusing on career language restrictions as they will be non-native speakers relative to the foreign company, difficulty living in a multi-cultural environment, and lack of systematic information on essential job requirements. For these reasons, domestic mates have difficulty seeking jobs. Therefore, this study analyzes the capabilities and qualification required to be a DP operator to provide basic data for developing NCS standards representing a minimum level of competency. These standards can be applied by the government to develop plans for professional training for DP operators. In study, job classifications, competency standards and career development paths for DP operators have been proposed along with joint use of DP training vessels, to train specialized DP instructors. An NCS export model led by the government to activate professional training for DP operators is also presented.