• Land and Housing Review
• /
• v.4 no.2
• /
• pp.185-192
• /
• 2013

This study presents application effects of hybrid seismic isolation system to realize high seismic performance for low-rise lightweight buildings through a non-linear analysis and onsite experiments. The complex seismic isolation system applied in this study is a method of mixing sliding bearing and laminated rubber bearing in order to overcome limitation of laminated rubber bearing in increasing natural period of the whole seismic isolation system. As a result of the non-linear analysis, seismic isolation buildings designed with complex seismic isolation system are safe because its maximum response displacement is within allowable design displacement even for a strong earthquake which rarely occurs and its maximum response shear is less than design seismic force. As a result of the onsite experiment, the rigidity of seismic isolation stories corresponds to approximately 95.8% of the design equivalent stiffness value. This indicates that actual properties of the whole seismic isolation system correspond to design values.

• Journal of Korea Water Resources Association
• /
• v.31 no.3
• /
• pp.347-360
• /
• 1998

Numerical simulations for fluid flow and solute transport in a fracture rock masses are performed by using a transient flow model, which is based on the three-dimensional stochastic and discrete fracture network model (DFN model) and is coupled hydraulic model with mechanical model. In the numerical simulations of the solute transport, we used to the particle following algorithm which is similar to an advective biased random walk. The purpose of this study is to predict the response of the tracer test between two deep bore holes (GPK1 and GPK2) implanted at Soultz sous Foret in France, in the context of the geothermal researches.l The data sets used are obtained from in situcirculating experiments during 1995. As the result of the transport simulation, the mean transit time for the non reactive particles is about 5 days between two bore holes.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.16-21
• /
• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.4
• /
• pp.147-152
• /
• 2018

Underwater transient signals are complex, variable and nonlinear, resulting in a difficulty in accurate modeling with reference patterns. We analyze one type of underwater transient signals, in the form of whale sounds, using the MFCC(Mel-Frequency Cepstral Constant) and synthesize them from the MFCC and the weighted $L_2$-norm minimization techniques. The whales in this experiments are Humpback whales, Right whales, Blue whales, Gray whales, Minke whales. The 20th MFCC coefficients are extracted from the original signals using the MATLAB programming and reconstructed using the weighted $L_2$-norm minimization with the inverse MFCC. Finally, we could find the optimum weighted factor, 3~4 for reconstruction of whale sounds.

• Nuclear Engineering and Technology
• /
• v.51 no.3
• /
• pp.807-817
• /
• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Korean Chemical Engineering Research
• /
• v.56 no.6
• /
• pp.784-791
• /
• 2018

UiO-66 particles were synthesized under various synthesis conditions to study the adsorption of carbon dioxide for hydrogen purification. For the purpose, the design and analysis of experiments was performed using statistical design of experiment method. As the synthesis time, temperature and acetic acid amount increased, the crystallinity of UiO-66 particles increased. Especially, the amount of acetic acid was confirmed as an important factor in determining the crystallinity of the particles. The specific surface area of the particles measured by the nitrogen adsorption method also showed a similar tendency. Using the general factor analysis in the experimental design method, the main effects and interactions of major factors were analyzed. In addition, the carbon dioxide adsorption capacity was predicted using a nonlinear regression method. Then, the adsorption performance was shown through surface and contour maps for all ranges.

• Journal of Ocean Engineering and Technology
• /
• v.33 no.3
• /
• pp.214-221
• /
• 2019

A series of model tests was performed to evaluate the survivability of an articulated tower-type buoy structure under harsh environmental conditions. The buoy structure consisted of three long pipes, a buoyancy module, and top equipment. The scale model was made of acrylic pipe and plastic with a scale ratio of 1/22. The experiments were carried out at the ocean engineering basin of KRISO. The performance of the buoy structure was investigated under waves only and under combined environmental conditions from sea state (SS) 5 to 7. A nonlinear time-domain numerical simulation was conducted using the mooring analysis program OrcaFlex. The survivability of the buoy was analyzed based on three factors: the pitch motion, submergence of the top structure, and anchor reaction force. The model test results were directly compared to the results of numerical simulations. The effects of the sea state and combined environment on the performance of the buoy structure were investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.7
• /
• pp.2496-2512
• /
• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.2
• /
• pp.103-108
• /
• 2022

In this paper, we propose a new image analysis method based on regression manifold 3-D PCA. The proposed method is a new image analysis method consisting of a regression analysis algorithm with a structure designed based on an autoencoder capable of nonlinear expansion of manifold 3-D PCA and PCA for efficient dimension reduction when entering large-capacity image data. With the configuration of an autoencoder, a regression manifold 3-DPCA, which derives the best hyperplane through three-dimensional rotation of image pixel values, and a Bayesian rule structure similar to a deep learning structure, are applied. Experiments are performed to verify performance. The image is improved by utilizing the fine dust image, and accuracy performance evaluation is performed through the classification model. As a result, it can be confirmed that it is effective for deep learning performance.

• Geophysics and Geophysical Exploration
• /
• v.24 no.4
• /
• pp.164-170
• /
• 2021

Frequency-domain and time-domain induced polarization methods can provide spectral information about subsurface media. Analysis of spectral characteristics has been studied mainly in the frequency-domain, however, time-domain induced polarization research has recently become popular. In this study, assuming a homogeneous half-space model, an inversion method was developed to extract Cole-Cole parameters from the measured secondary potential or electrical resistivity. Since the Cole-Cole parameters of chargeability, time constant, and frequency index are not independent of each other, various problems, such as slow convergence rate, initial model problem, local minimum problem, and divergence, frequently occur when conventional nonlinear inversion is applied. In this study, we developed an effective inversion method using the initial model close to the true model by introducing a grid search method. Finally, the validity of the developed inversion method was verified using inversion experiments.