• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.618-633
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• 2014

Relay technology is becoming more important for mobile communications and wireless internet of things (IoT) networking because of the extended access network coverage range and reliable quality of service (QoS) it can provide at low power consumption levels. Existing mobile multihop relay (MMR) technology uses fixed-point stationary relay stations (RSs) and a divided time-frame (or frequency-band) to support the relay operation. This approach has limitations when a local fixed-point stationary RS does not exist. In addition, since the time-frame (or frequency-band) channel resources are pre-divided for the relay operation, there is no way to achieve high channel utilization using intelligent opportunistic techniques. In this paper, a different approach is considered, where the use of mobile/IoT devices as RSs is considered. In applications that use mobile/IoT devices as relay systems, due to the very limited battery energy of a mobile/IoT device and unequal channel conditions to and from the RS, both minimum energy consumption and QoS support must be considered simultaneously in the selection and configuration of RSs. Therefore, in this paper, a mobile RS is selected and configured with the objective of minimizing power consumption while satisfying end-to-end data rate and bit error rate (BER) requirements. For the RS, both downlink (DL) to the destination system (DS) (i.e., IoT device or user equipment (UE)) and uplink (UL) to the base station (BS) need to be adaptively configured (using adaptive modulation and power control) to minimize power consumption while satisfying the end-to-end QoS constraints. This paper proposes a minimum transmission power consuming RS selection and configuration (MPRSC) scheme, where the RS uses cognitive radio (CR) sub-channels when communicating with the DS, and therefore the scheme is named MPRSC-CR. The proposed MPRSC-CR scheme is activated when a DS moves out of the BS's QoS supportive coverage range. In this case, data transmissions between the RS and BS use the assigned primary channel that the DS had been using, and data transmissions between the RS and DS use CR sub-channels. The simulation results demonstrate that the proposed MPRSC-CR scheme extends the coverage range of the BS and minimizes the power consumption of the RS through optimal selection and configuration of a RS.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.136-143
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• 2013

In this paper, a algorithm to find joints of a small animal like a lizard from the multiple-view silhouette images is presented. The proposed algorithm is able to calculate the 3D coordinates so that the locomotion of the lizard is markerlessly reconstructed. The silhouette images of the lizard was obtained by a adaptive threshold algorithm. The skeleton image of the silhouette image was obtained by Zhang-Suen method. The back-bone line, head and tail point were detected with the A* search algorithm and the elimination of the ortho-diagonal connection algorithm. Shoulder joints and hip joints of a lizard were found by $3{\times}3$ masking of the thicked back-bone line. Foot points were obtained by morphology calculation. Finally elbow and knee joint were calculated by the ortho distance from the lines of foot points and shoulder/hip joint. The performance of the suggested algorithm was evaluated through the experiment of detecting joints of a small lizard.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Economic and Environmental Geology
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• v.43 no.3
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• pp.259-267
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• 2010

Resistivity logging instruments were designed to measure electrical resistivity of formation, which can be directly interpreted to provide water-saturation profile. Short and long normal logging measurements are made under groundwater level. In some investigation sites, groundwater level reaches to a depth of a few meters. It has come to attention that the proximity of groundwater level might distort short and long normal logging readings, when the measurements are made near groundwater level, owing to the proximity of an insulating air. This study investigates the effects of the proximity of groundwater level (and also the proximity of earth surface) on the normal by simulating normal logging measurements near groundwater level. In the simulation, we consider all the details of real logging situation, i.e., the presence of wellbore, the tool mandrel with current and potential electrodes, and currentreturn and reference-potential electrodes. We also model the air to include the earth’'s surface in the simulation rather than the customary choice of imposing a boundary condition. To obtain apparent resistivity, we compute the voltage, i.e., potential difference between monitoring and reference electrodes. For the simulation, we use a twodimensional, goal-oriented and high-order self-adaptive hp finite element refinement strategy (h denotes the element size and p the polynomial order of approximation within each element) to obtain accurate simulation results. Numerical results indicate that distortion on the normal logging is greater when the reference potential electrode is closer to the borehole and distortions on long normal logging are larger than those on short normal logging.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.51-59
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• 2017

With the growth of Cloud computing, it is important to consider resource management techniques to minimize the overall costs of management. In cloud environments, each host's utilization and virtual machine's request based on user preferences are dynamic in nature. To solve this problem, efficient allocation method of virtual machines to hosts where the classification of virtual machines and hosts is undetermined should be studied. In reducing the number of active hosts to reduce energy consumption, thresholds can be implemented to migrate VMs to other hosts. By using Fuzzy logic in classifying resource requests of virtual machines and resource utilization of hosts, we proposed an adaptive VM allocation and migration approach. The allocation strategy classifies the VMs according to their resource request, then assigns it to the host with the lowest resource utilization. In migrating VMs from overutilized hosts, the resource utilization of each host was used to create an upper threshold. In selecting candidate VMs for migration, virtual machines that contributed to the high resource utilization in the host were chosen to be migrated. We evaluated our work through simulations and results show that our approach was significantly better compared to other VM allocation and Migration strategies.

• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.2
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• pp.196-206
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• 2012

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability, compared with mechanically scanned array radar. AESA radar brings a new challenges, radar resource management(RRM), which is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed stochastic radar beam scheduling algorithm using simulated annealing(SA), and evaluated the performance on the multi-function radar scenario. As a result, we showed that our proposed algorithm is superior to previous dispatching rule based scheduling algorithm from the viewpoint of beam processing latency and the number of scheduled beams, with real time capability.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1714-1728
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• 2016

This paper presents improved harmonics extraction based on Adaptive Linear Neuron (ADALINE) algorithm for single phase photovoltaic (PV) shunt active power filter (SAPF). The proposed algorithm, named later as Improved ADALINE, contributes to better performance by removing cosine factor and sum of element that are considered as unnecessary features inside the existing algorithm, known as Modified Widrow-Hoff (W-H) ADALINE. A new updating technique, named as Fundamental Active Current, is introduced to replace the role of the weight factor inside the previous updating technique. For evaluation and comparison purposes, both proposed and existing algorithms have been developed. The PV SAPF with both algorithms was simulated in MATLAB-Simulink respectively, with and without operation or connection of PV. For hardware implementation, laboratory prototype has been developed and the proposed algorithm was programmed in TMS320F28335 DSP board. Steady state operation and three critical dynamic operations, which involve change of nonlinear loads, off-on operation between PV and SAPF, and change of irradiances, were carried out for performance evaluation. From the results and analysis, the Improved ADALINE algorithm shows the best performances with low total harmonic distortion, fast response time and high source power reduction. It performs well in both steady state and dynamic operations as compared to the Modified W-H ADALINE algorithm.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.383-401
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• 2016

In this paper, we described the statistical modeling of crop yields using satellite images, climatic datasets, soil property maps, and fertilizer data for the Midwestern United States during 2001-2012. Satellite images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic datasets were provided by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group. Soil property maps were derived from the Harmonized World Soil Database (HWSD). Our multivariate regression models produced quite good prediction accuracies, with differences of approximately 8-15% from the governmental statistics of corn and soybean yields. The unfavorable conditions of climate and vegetation in 2012 could have resulted in a decrease in yields according to the regression models, but the actual yields were greater than predicted. It can be interpreted that factors other than climate, vegetation, soil, and fertilizer may be involved in the negative biases. Also, we found that soybean yield was more affected by minimum temperature conditions while corn yield was more associated with photosynthetic activities. These two crops can have different potential impacts regarding climate change, and it is important to quantify the degree of the crop sensitivities to climatic variations to help adaptation by humans. Considering the yield decreases during the drought event, we can assume that climatic effect may be stronger than human adaptive capacity. Thus, further studies are demanded particularly by enhancing the data regarding human activities such as tillage, fertilization, irrigation, and comprehensive agricultural technologies.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.12
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• pp.1190-1197
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• 2013

AESA(Active Electronically Scanned Array radar) radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability. For this reason, radar resource management(RRM) becomes new challenging issue. RRM is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. This paper deals with a design of radar resource management algorithms and simulator implemented main algorithms for development of airborne AESA radar. In addition, evaluation results show that developed radar system satisfies a main requirement about simultaneous multiple target tracking and detection by adopting proposed algorithms.