• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.129-141
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• 2010

In this paper, we present a wearable intelligent device based on multi-sensor for monitoring human activity. In order to recognize multiple activities, we developed activity recognition algorithms utilizing an image sensor and a 3-axis accelerometer sensor. We proposed a grid?based optical flow method and used a SVM classifier to analyze data acquired from multi-sensor. We used the direction and the magnitude of motion vectors extracted from the image sensor. We computed the correlation between axes and the magnitude of the FFT with data extracted from the 3-axis accelerometer sensor. In the experimental results, we showed that the accuracy of activity recognition based on the only image sensor, the only 3-axis accelerometer sensor, and the proposed multi-sensor method was 55.57%, 89.97%, and 89.97% respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.352-354
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• 2010

Recently, the efficient management system for large-scale sensor data has been required due to the increasing deployment of large-scale sensor networks. In previous studies, sensor data was managed by distributed database system which built in a single server or a grid server. Thus, it has disadvantages such as low scalability, and high cost of building or managing the system. In this paper, we propose a cloud-based sensor data management system with low cast, high scalability, and efficiency. The proposed system can be work with the application of a variety of platforms, because processed results are provided through REST-based web service.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• Journal of Power Electronics
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• v.19 no.1
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• pp.44-57
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• 2019

Recently, the use of LLCL filters for grid inverters has been suggested to give better harmonic attenuation than the commonly used L and LCL filters, particularly around the switching frequency. Nevertheless, this filter is mainly designed for constant switching frequency pulse width modulation (CSF PWM) methods. In variable switching frequency PWM (VSF PWM), the harmonic components are distributed across a wide frequency band which complicates the use of a high order filter, including LCL and LLCL filters. Recently, a confined band variable switching frequency (CB-VSF) PWM method has been proposed and demonstrated to be superior to the conventional constant switching frequency (CSF) PWM in terms of switching losses. However, the applicability of LLCL filters for this type of CB-VSF PWM has not been discussed. In this paper, the authors study the suitability of an LLCL filter for CB-VSF PWM and propose design guidelines for the filter parameters. Using simulation and experimental results, it is demonstrated that the effectiveness of an LLCL filter with CB-VSF PWM depends on the parameters of the filters as well as the designed variable frequency band of the PWM. Simulation results confirm the performance of the suggested LLCL design, which is further validated using a lab scale prototype.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.96-98
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• 2014

Wireless Sensor Network(WSN) is a wireless real-time information(Acquired from the sensor nodes that have the computing power and wireless communication capabilities.) collected, and to take advantage of processing techniques. Currently it is very diverse, such as environmental monitoring, health care, security, smart home, smart grid applications is that. Thus it is required in the wireless sensor network, the algorithm for the efficient use of the limited energy capacity. Suggested by the algorithm for selecting a cluster head node for a hybrid type and clustered, by comparing the amount of energy remaining and a connection between the nodes In this paper, we aim to increase efficiency and accuracy of the wireless sensor network.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.65-74
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• 2021

A key factor that governs the wave interferences of a submerged body is the dimensionless Froude number. Computational Fluid Dynamics (CFD) is used to describe the resistance force coefficients and the generated waves of two SUBOFF submarine models. Grid independence studies are performed on two cases, totally and shallowly submerged cases, with four sets of computing meshes. The highest peaks are marked by red points at given wavelengths, a line is fitted to those points with a least-squares approximation, and the half wake angle at multiple Froude numbers is defined between the fitted line and the centerline of the free surface. The results show that when the depth of the target is 1.1D, constructive interferences occur at Fn = 0.3 and 0.5, while destructive interference occurs at Fn = 0.35 with distortion of the waveform. The half wake angle is less than 19.47° because of the interference between the bow and stern wave systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2606-2626
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• 2022

In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station. First, the UAV-adapted Harris Hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot. Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users. Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs). The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period. Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average. Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• Geophysics and Geophysical Exploration
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• v.15 no.2
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• pp.75-84
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• 2012

The sea layer in marine Controlled-Source Electromagnetic (mCSEM) survey changes the conventional definition of apparent resistivity which is used in the land CSEM survey. Thus, the development of a new algorithm, which computes apparent resistivity for mCSEM survey, can be an initiative of mCSEM data interpretation. First, we compared and analyzed electromagnetic responses of the 1D stratified gas hydrate model and the half-space model below the sea layer. Amplitude and phase components showed proper results for computing apparent resistivity than real and imaginary components. Next, the amplitude component is more sensitive to the subsurface resistivity than the phase component in far offset range and vice versa. We suggested the induction number as a selection criteria of amplitude or phase component to calculate apparent resistivity. Based on our study, we have developed a numerical algorithm, which computes appropriate apparent resistivity corresponding to measured mCSEM data using grid search method. In addition, we verified the validity of the developed algorithm by applying it to the stratified gas hydrate models with various model parameters. Finally, by constructing apparent resistivity pseudo-section from the mCSEM responses with 2D numerical models simulating gas hydrate deposits in the Ulleung Basin, we confirmed that the apparent resistivity can provide the information on the geometric distribution of the gas hydrate deposit.

• Journal of Korea Water Resources Association
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• v.53 no.1
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• pp.1-13
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• 2020

The most common approach for computing engineering flow problems at high Reynolds number is still the Reynolds-averaged Navier-Stokes (RANS) computations based on turbulence models with wall functions. The recently developed generalized wall functions blending between the wall-limiting viscous and the outer logarithmic relations ensure a smooth transition of flow quantities across two regions. The performances and convergence properties of widely used turbulence models with wall functions that are applicable for turbulence kinetic energy (TKE), turbulent and specific dissipation rates, and eddy viscosity are presented through a series of near wall flow simulations. The present results show that RNG k-𝜖 model should be carefully applied with small tolerance to get the stable solution when the first grid lies in the buffer layer. The standard k-𝜖 and RNG k-𝜖 models are not sensitive to the selection of wall functions for both TKE and eddy viscosity, while the k-ω SST model should be applied together with kL-wall function for TKE and nutUB-wall functions for eddy viscosity to ensure accurate and stable boundary conditions. The applications to a backward-facing step flow at Re=155,000 reveal that the reattachment length is reasonably well predicted on appropriately refined mesh by all turbulence models, except the standard k-𝜖 model which about 13% underestimates the reattachment length regardless of the grid refinement.