• Advances in Computational Design
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• v.4 no.1
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• pp.53-72
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• 2019

Safety measures for tower cranes are extremely important among the seismic countermeasures at high-rise building construction sites. In particular, the collapse of a tower crane from a high position is a very serious catastrophe. An example of such an accident due to an earthquake is the case of the Taipei 101 Building (the author was the project director), which occurred on March 31, 2002. Failure of the bolted joints of the tower-crane mast was the direct cause of the collapse. Therefore, it is necessary to design for this eventuality and to take the necessary measures on construction sites. This can only be done by understanding the precise dynamic behavior of mast joints during an earthquake. Consequently, we created a new hybrid-element model (using beam, shell, and solid elements) that not only expressed the detailed behavior of the site joints of a tower-crane mast during an earthquake but also suppressed any increase in the total calculation time and revealed its behavior through computer simulations. Using the proposed structural model and simulation method, effective information for designing safe joints during earthquakes can be provided by considering workability (control of the bolt pretension axial force and other factors) and less construction cost. Notably, this analysis showed that the joint behavior of the initial pretension axial force of a bolt is considerably reduced after the axial force of the bolt exceeds the yield strength. A maximum decrease of 50% in the initial pretension axial force under the El Centro N-S Wave ($v_{max}=100cm/s$) was observed. Furthermore, this method can be applied to analyze the seismic responses of general temporary structures in construction sites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.495-507
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• 2019

Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.11
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• pp.911-920
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• 2018

In this study, we performed a study to track the angle between the guided weapon and the target by using the strap-down image seeker, and constructed a test bed that can simulate it visually. This paper describes a method to maintain high-performance feature distribution in the implementation of sparse feature tracking algorithm such as Lucas Kanade's optical flow algorithm for target tracking using image information. We have extended the feature tracking problem to the concept of feature management. To realize this, we constructed visual environment using Unity3D engine and developed image processing simulation using OpenCV. For the co-simulation, dynamic system modeling was performed with Matlab Simulink, the visual environment using Unity3D was constructed, and computer vision work using OpenCV was performed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.5
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• pp.326-334
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• 2019

Development of Small UAV using HILS (Hardware In the Loop Simulation) can be effectively used to improve the reliability of UAV (Unmanned Aerial Vehicle) while reducing cost and time. It is also possible to reduce the damage to people or property by simulating the malfunction of the Flight Control Computer (FCC) that may occur during the actual flight. For applying such HILS, a real-time simulation environment capable of providing an environment similar to an actual flight condition is required. In this paper, we constructed a real - time HILS environment for Small UAV using 6 D.O.F motion table. In order to link the 6 D.O.F motion table developed in the previous research with the HILS environment in real time, the motion algorithm was changed from the position control method to the velocity control method. Also, we implemented modeling of inverse kinematics model for command transmission in Matlab $Simulink^{(R)}$ and verified the action of motion table according to the simulation model.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.877-886
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• 2019

The energy band gaps and optical constants of zincblende $In_yGa_{1-y}As_{1-x}N_x$ on the variation of temperature and composition are determined by using band anticrossing method. The energy band gaps are decreasing continuously in $In_yGa_{1-y}As_{1-x}N_x$ ($0{\leq}x{\leq}0.05$, $0{\leq}y{\leq}1.0$, 300K) and the bowing parameter is calculated as 0.522eV. The calculation results of energy band gaps are consistent with those of other studies. A refractive index n and a high-frequency dielectric constant ${\varepsilon}$ are calculated by a proposed modeling equation using the results of energy band gaps.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.87-92
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• 2021

Bias, variance, error and learning are important factors for performance in modeling a big data based recommendation system. The recommendation model in this system must reduce complexity while maintaining the explanatory diagram. In addition, the sparsity of the dataset and the prediction of the system are more likely to be inversely proportional to each other. Therefore, a product recommendation model has been proposed through learning the similarity between products by using a factorization method of the sparsity of the dataset. In this paper, the generalization ability of the model is improved by applying the max-norm regularization as an optimization method for the loss function of this model. The solution is to apply a stochastic projection gradient descent method that projects a gradient. The sparser data became, it was confirmed that the propsed regularization method was relatively effective compared to the existing method through lots of experiment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.108-122
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• 2021

With the development of 3D graphics software and the speed of computer hardware, it is an era that can be realistically expressed not only in movie visual effects but also in console games. In the production of such realistic 3D models, 3D scans are increasingly used because they can obtain hyper-realistic results with relatively little effort. Among the various 3D scanning methods, photogrammetry can be used only with a camera. Therefore, no additional hardware is required, so its demand is rapidly increasing. Most 3D artists shoot as many images as possible with a video camera, etc., and then calculate using all of those images. Therefore, the photogrammetry method is recognized as a task that requires a lot of memory and long hardware operation. However, research on how to obtain precise results with 3D photogrammetry scans is insufficient, and a large number of photos is being utilized, which leads to increased production time and data capacity and decreased productivity. In this study, point cloud data generated according to changes in the number and resolution of photographic images were produced, and an experiment was conducted to compare them with original data. Then, the precision was measured using the average distance value and standard deviation of each vertex of the point cloud. By comparing and analyzing the difference in the precision of the 3D photogrammetry scans according to the number and resolution of images, this paper presents a direction for obtaining the most precise and effective results to 3D artists.

• Korean Journal of Metals and Materials
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• v.48 no.5
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• pp.469-475
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• 2010

In this study, the interfacial bond strength of a squeeze infiltrated $Al_{18}B_{4}O_{33}$/AS52 Mg composite was investigated by using a finite element method. Three types of Mg composites with volume fractions of 15, 25 and 35% were fabricated. Three-dimensional models of the composite were developed by using a unit cell model in order to determine the effective elastic modulus of the metal matrix composite and the interfacial bond strength between the whisker and magnesium matrix. After modeling, numerical results were compared with the experimental tensile test results of $Al_{18}B_{4}O_{33}$/AS52 Mg composites. The results showed that the effective modulus of the composite strongly depended on the interfacial strength between the matrix and reinforcement. Based on the numerical and experimental findings, it was found that the strong interfacial bond was achieved by the interfacial reaction product of 30 nm thick MgO, which led to an improvement in the mechanical properties of the $Al_{18}B_{4}O_{33}$/AS52 Mg composites.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.70-78
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• 2021

Estimating 3D information from a single image is one of the essential problems in numerous applications. Since a 2D image inherently might originate from an infinite number of different 3D scenes, thus 3D reconstruction from a single image is notoriously challenging. This challenge has been overcame by the advent of recent deep convolutional neural networks (CNNs), by modeling the mapping function between 2D image and 3D information. However, to train such deep CNNs, a massive training data is demanded, but such data is difficult to achieve or even impossible to build. Recent trends thus aim to present deep learning techniques that can be trained in a weakly-supervised manner, with a meta-data without relying on the ground-truth depth data. In this article, we introduce recent developments of weakly-supervised deep learning technique, especially categorized as scene 3D reconstruction and object 3D reconstruction, and discuss limitations and further directions.