• Journal of Aerospace System Engineering
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• v.15 no.5
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• pp.33-42
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• 2021

Modern aircraft fighters improve the maneuverability and performance with the RSS (Relaxed Static Stability) concept and therefore these aircrafts are susceptible to abrupt pitch-up in the transonic and moderate Angle-of-Attack (AoA) flight region where the shock wave is formed and the mean aerodynamic center is moved forward during deceleration. Also, the modeling of the aircraft flying in this flight region is very difficult due to complex flow filed and unpredictable dynamic characteristics and the model-based control design technique does not fully cover this problem. In this paper, we analyzed the performance of the TPMC (Transonic Pitching Moment Compensation) control based on the model-based IDI (Incremental Dynamic Inversion) and the Hybrid IDI based on the model and sensor based IDI during the SDT (Slow Down Turn) in transonic region. As the result, the Hybrid IDI had quicker response and the same maximum g suppression performance and provided the predictable flying qualities compared to the TPMC control. The Hybrid IDI improved the performance of the Over-G protection controller in the transonic and moderate AoA region

• Journal of the Korea Computer Graphics Society
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• v.27 no.3
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• pp.65-74
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• 2021

3D reconstruction means that reconstructing the 3D shape of the object in an image and a video. We proposed a progressive occupancy network architecture that can recover not only the overall shape of the object but also the local details. Unlike the original occupancy network, which uses a feature vector embedding information of the whole image, we extract and utilize the different levels of image features depending on the receptive field size. We also propose a novel network architecture that applies the image features sequentially to the decoder blocks in the decoder and improves the quality of the reconstructed 3D shape progressively. In addition, we design a novel decoder block structure that combines the different levels of image features properly and uses them for updating the input point feature. We trained our progressive occupancy network with ShapeNet. We compare its representation power with two prior methods, including prior occupancy network(ONet) and the recent work(DISN) that used different levels of image features like ours. From the perspective of evaluation metrics, our network shows better performance than ONet for all the metrics, and it achieved a little better or a compatible score with DISN. For visualization results, we found that our method successfully reconstructs the local details that ONet misses. Also, compare with DISN that fails to reconstruct the thin parts or occluded parts of the object, our progressive occupancy network successfully catches the parts. These results validate the usefulness of the proposed network architecture.

• Journal of Platform Technology
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• v.12 no.3
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• pp.16-26
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• 2024

Videos shot underwater are known to have significant color distortion. Typical causes are backscattering by floating objects and attenuation of red colors in proportion to the depth of the water. In this paper, we aim to analyze color correction performance and color distortion patterns for images taken underwater. Backscattering and attenuation caused by suspended matter will be discussed in the next study. In this study, based on the DeepSeeColor model proposed by Jamieson et al., we verify color correction performance and analyze the pattern of color distortion according to changes in water depth. The input images were taken in the US Virgin Islands by Jamieson et al., and out of 1,190 images, 330 images including color charts were used. Color correction performance was expressed as angular error using the input image and the correction image using the DeepSeeColor model. Jamieson et al. calculated the angular error using only black and white patches among the color charts, so they were unable to provide an accurate analysis of overall color distortion. In this paper, the color correction error was calculated targeting the entire color chart patch, so an appropriate degree of color distortion can be suggested. Since the input image of the DeepSeeColor model has a depth of 1 to 8, color distortion patterns according to depth changes can be analyzed. In general, the deeper the depth, the greater the attenuation of red colors. Color distortion due to depth changes was modeled in the form of scale and offset movement to predict distortion due to depth changes. As the depth increases, the scale for color correction increases and the offset decreases. The color correction performance using the proposed method was improved by 41.5% compared to the conventional method.

• The Journal of the Korea institute of electronic communication sciences
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• v.1 no.2
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• pp.120-126
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• 2006

In this paper, we propose for improving QoS in hospital wireless network using Cellular-IP/PRC(Paging Route Cache) with Paging Cache and Route Cache in Cellular-IP. Although the Cellular-IP/PRC technology is devised for mobile internet communication, it has its vulnerability in frequent handoff environment. This handoff state machine using differentiated handoff improves quality of services in Cellular-IP/PRC. Suggested algorithm shows better performance than existing technology in wireless mobile internet communication environment. When speech quality is secured considering increment of interference to receive in case of suppose that proposed acceptance method grooves base radio station capacity of transfer node is plenty, and most of contiguity cell transfer node was accepted at groove base radio station with a blow, groove base radio station new trench lake acceptance method based on transmission of a message electric power estimate of transfer node be. Do it so that may apply composing PC(Paging Cache) and RC(Routing Cache) that was used to manage paging and router in radio Internet network in integral management and all nodes as one PRC(Paging Router Cache), and add hand off state machine in transfer node so that can manage hand off of transfer node and Roaming state efficiently, and studies so that achieve connection function at node. Analyze benevolent person who influence on telephone traffic in system environment and forecasts each link currency rank and imbalance degree, forecast most close and important lake interception probability and lake falling off probability, GoS(Grade of Service), efficiency of cell capacity in QoS because applies algorithm proposing based on algorithm use gun send-receive electric power that judge by looking downward link whether currency book was limited and accepts or intercept lake and handles and displays QoS performance improvement.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.3
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• pp.243-254
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• 2014

It is essential to predict the coil temperature under over load and over speed conditions for reliability in high speed low torque surface mounted PM synchronous motors(SPM). In the present study, the losses and coil temperature are measured under rated condition and calculated under over speed and over load conditions in the three different motors with 35PN440, 25PN250 and 15HTH1000. The heat transfer modeling has been performed based on acquired losses and temperature. The difference of coil temperature between heat transfer modeling and experiment is less than 6.4% under no load, over speed and over load conditions. Subsequently, the coil temperature of the motor with 15HTH1000 is 84.4% of the coil temperature of the motor with 35PN440 when speed is 0.9 and load is 3.0. The output of motor with 15HTH1000 is 85.2% greater than the output of the motor with 35PN440 when the dimensionless coil temperature is 1.0.

• Journal of the Korea Computer Graphics Society
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• v.9 no.3
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• pp.23-29
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• 2003

There is the temporal correlation of the video sequence between the motion vector of current block and the motion vector of the previous block. If we can obtain useful and enough information from the motion vector of the same coordinate block of the previous frame, the total number of search points used to find the motion vector of the current block may be reduced significantly. In this paper, we propose the block-matching motion estimation using an adaptive initial search point by the predicted motion information from the same block of the previous frame. And the first search point of the proposed algorithm is moved an initial point on the location of being possibility and the searching process after moving the first search point is processed according to the fast search pattern. Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved UP to the 1.05dB as depend on the image sequences and improved about 0.33~0.37dB on an average. Search times are reduced about 29~97% than the other fast search algorithms. Simulation results also show that the performance of the proposed scheme gives better subjective picture quality than the other fast search algorithms and is closer to that of the FS(Full Search) algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.127-134
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• 2022

In this study, RCPTs (Rapid Chloride Penetration Test) were performed for fly ash concrete with curing age of 4 ~ 6 years. The concrete mixtures were prepared with 3 levels of water to binder ratio (0.37, 0.42, and 0.47) and 2 levels of substitution ratio of fly ash (0 and 30%), and the improved passed charges of chloride ion behavior were quantitatively analyzed. Additionally, the results were trained through the univariate time series models consisted of GRU (Gated Recurrent Unit) algorithm and those from the models were evaluated. As the result of the RCPT, fly ash concrete showed the reduced passed charges with period and an more improved resistance to chloride penetration than OPC concrete. At the final evaluation period (6 years), fly ash concrete showed 'Very low' grade in all W/B (water to binder) ratio, however OPC concrete showed 'Moderate' grade in the condition with the highest W/B ratio (0.47). The adopted algorithm of GRU for this study can analyze time series data and has the advantage like operation efficiency. The deep learning model with 4 hidden layers was designed, and it provided a reasonable prediction results of passed charge. The deep learning model from this study has a limitation of single consideration of a univariate time series characteristic, but it is in the developing process of providing various characteristics of concrete like strength and diffusion coefficient through additional studies.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.29-42
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• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• Korean Journal of Mineralogy and Petrology
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• v.37 no.2
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• pp.23-34
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• 2024

It is essential to know the mineral composition of core samples to assess the possibility of gas hydrate (GH) in sediments. During the exploration of gas hydrates (GH), mineral composition values were obtained from each core sample collected in the Ulleung Basin using X-ray diffraction (XRD). Based on this data, machine learning was performed with 3100 input values representing XRD peak intensities and 12 output values representing mineral compositions. The 488 data points were divided into 307 training samples, 132 validation samples, and 49 test samples. The random forest (RF) algorithm was utilized to obtain results. The machine learning results, compared with expert-predicted mineral compositions, revealed a Mean Absolute Error (MAE) of 1.35%. To enhance the performance of the developed model, principal component analysis (PCA) was employed to extract the key features of XRD peaks, reducing the dimensionality of input data. Subsequent machine learning with the refined data resulted in a decreased MAE, reaching a maximum of 1.23%. Additionally, the efficiency of the learning process improved over time, as confirmed from a temporal perspective.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.