• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.451-465
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• 2017

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• 한국유체기계학회 논문집
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• 제18권5호
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• pp.33-41
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• 2015

Organic Rankine cycle (ORC) has been used to generate electrical or mechanical power from low-grade thermal energy. Usually, this thermal energy is not supplied continuously at the constant thermal energy level. In order to optimally utilize fluctuating thermal energy, an axial-type turbine was applied to the expander of ORC and two supersonic nozzle were used to control the mass flow rate. Experiment was conducted with various turbine inlet temperatures (TIT) with the partial admission rate of 16.7 %. The tip diameter of rotor was to be 80 mm. In the cycle analysis, the output power of ORC was predicted with considering the load dissipating the output power produced from the ORC as well as the turbine efficiency. The predicted results showed the same trend as the experimental results, and the experimental results showed that the system efficiency of 2 % was obtained at the TIT of $100^{\circ}C$.

• Asian Pacific Journal of Cancer Prevention
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• 제15권18호
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• pp.7775-7780
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• 2014

This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2006년도 전력전자학술대회 논문집
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• pp.488-491
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• 2006

In this paper proposed method of maximum power point tracking using boost converter for a connected single phase inverter with photovoltaic system. The maximum power point tracking control is based on generated circuit control MOSFET switch of boost converter and single phase inverter uses predicted current control to control four IGBT's switch in full bridge. The predicted current control provide current with sinusoidal wave shape and inphase with voltage. The generation control circuit allows each photovoltaic module to operate independently at peak capacity, simply by detecting of the output power of the system. Furthermore, the generation control circuit attenuates low-frequency ripple voltage, which is caused by the full-bridge inverter, across the photovoltaic modules. Consequently, the output power of system is increased due to the increase in average power generated by the photovoltaic modules. The effectiveness of the proposed inverter system is confirmed experimentally and by means of simulation.

• Advances in concrete construction
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• 제3권2호
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• pp.91-102
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• 2015

In this paper, the load-deflection analysis of the Carbon Fiber Reinforced Polymer (CFRP) strengthened Reinforced Concrete (RC) slab using Recurrent Neural Network (RNN) is investigated. Six reinforced concrete slabs having dimension $1800{\times}400{\times}120mm$ with similar steel bar of 2T10 and strengthened using different length and width of CFRP were tested and compared with similar samples without CFRP. The experimental load-deflection results were normalized and then uploaded in MATLAB software. Loading, CFRP length and width were as neurons in input layer and mid-span deflection was as neuron in output layer. The network was generated using feed-forward network and a internal nonlinear condition space model to memorize the input data while training process. From 122 load-deflection data, 111 data utilized for network generation and 11 data for the network testing. The results of model on the testing stage showed that the generated RNN predicted the load-deflection analysis of the slabs in acceptable technique with a correlation of determination of 0.99. The ratio between predicted deflection by RNN and experimental output was in the range of 0.99 to 1.11.

• Smart Structures and Systems
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• 제24권5호
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• Journal of Biosystems Engineering
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• 제35권2호
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• pp.77-84
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• 2010

In this study, we tried to predict tractor power output, fuel consumption rate and work performance indirectly in order to develop an eco driving system. Firstly, we developed equations which could predict tractor power output and fuel consumption rate using characteristic curves of tractor power output. Secondly, with actual engine rpm determined by initial engine rpm and work load, tractor power output and fuel consumption rate were forecasted. Thirdly, with speed signals of GPS sensor system, it was possible to foresee tractor work performance and fuel consumption rate. Lastly, precision of the eco driving system was evaluated through tractor PTO test, and effects of the eco driving system were investigated in the plowing and rotary tilling operations. Engine rpm, power output, fuel consumption rate, work performance and fuel consumption rate per plot area were displayed in the eco driving system. Predicted tractor power outputs in the full load curve were well coincided with the actual power output of prototype, but small differences, 1 to 6 ㎾, were found in the part load curve. Error of the fuel consumption rate was 0.5 L/h, 4.5%, the greatest, and 1 to 3 L/h at the part load curve. It was shown that 69% and 53% of fuel consumption rates could be reduced in plowing and rotary tilling operations, respectively, when the eco driving system was installed in tractor.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.82-85
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• 2004

This paper proposes the development of delay compensator to minimize performance degradation caused by time delays in network-based real-time control systems. The delay compensator uses the time-stamp method as a direct delay measuring method to measure time delays generated between network nodes. The delay compensator predicts the network time delays of next period in the views point of time delays and minimizes performance degradation from network through considering predicted time delays. Control output considering network time delays is generated by the defuzzification of probable time delays of next period. The time delays considered in the delay compensator are modeled by using a timed Petri net model. The proposed delay prediction mechanism for the delay compensator is evaluated through some simulation tests by measuring deviation of the predicted delays from simulated delays.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2012년도 추계 학술논문 발표대회
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• pp.253-254
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• 2012

The purpose of this study is to suggest and verify high-strength concrete mix design model applying neural network theory, in order to minimize effort and time wasted by using trial and error method utill now. There are 7 input and 2 output to predict mix design. 40 data of mix design were learned with back-propagation algorithm. Then they are repeatedly learned back-propagation in neural network theory. Also, to verify predicted model, we analyzed and compared value predicted from 60MPa mix design with value measured by actual compressive strength test.