The complexity of physiological system can be well represented by the complexity of time series. In this paper, complexity analysis was performed on the inter-beat-interval(IBI) of apnea syndrome and normal sleep popu...
The complexity of physiological system can be well represented by the complexity of time series. In this paper, complexity analysis was performed on the inter-beat-interval(IBI) of apnea syndrome and normal sleep population. Hurst exponent, sample entropy and fractal dimension were used as three indicators of complexity measurement. The results show that optimal combination of statistical and nonlinear features with the Support Vector Machine (SVM) can achieve the apnea recognition accuracy of up to 75.51%. In the complexity measurement, t-test shows that the average of Hurst exponent, sample entropy or fractal dimension of the normal sleep group was significantly larger than that of the apnea group. The results show that long-term apnea syndrome will cause certain damage to the autonomic nerve system.
Prediction over heterogeneous data attracts much attention in urban computing. Recently, satellite imagery provides a new chance for urban perception but raises the problem of how to fuse visual and non-visual feature...
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With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an indust...
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We consider distributed Kalman filter for dynamic state estimation over wireless sensor networks. It is promising but challenging when network is under cyber attacks. Since the information exchange between nodes, the ...
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In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the...
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In this paper, a flower species recognition system combining object detection and Attention Mechanism is proposed. In order to strengthen the ability of the model to process images under complex backgrounds, we apply ...
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Solid oxide fuel cell is a device that can convert chemical energy directly into electricity. Its advantages, such as high efficiency, low emission, quiet operation, fuel flexibility, bring about the broad application...
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Solid oxide fuel cell is a device that can convert chemical energy directly into electricity. Its advantages, such as high efficiency, low emission, quiet operation, fuel flexibility, bring about the broad application prospect. However, it is difficult to obtain the temperature distribution in the existing planar cross flow solid oxide fuel cell through the experiment. In this paper, a control orient two-dimensional differential equation model is established for a planar cross flow solid oxide fuel cell based on the finite node method, and an iterative algorithm for calculating the real time voltage of the fuel cell is proposed for the model. Based on the model, the temperature distribution of the fuel cell in the test and system configuration is simulated. The simulation results show that the model can reflect the thermoelectric characteristics of the planar cross flow solid oxide fuel cell, especially the temperature distribution of the fuel cell. The SOFC temperature distribution modeling in this paper is helpful for the development of temperature distribution observers and design related control methods in later studies.
In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric...
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In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric load forecasting are not proper enough. A short-term electric load forecasting method based on grey neural network based on snap-drift cuckoo search optimization algorithm (SDCS-GNN) is proposed in this paper. Parameters of gray neural network (GNN) are selected randomly which is similar to the initial spatial position of birds' eggs in the parasitic nest of cuckoo. The SDCS is utilized to search the better weight and threshold of the conventional gray neural network (GNN), which improves the stability and accuracy of the prediction model. To validate the superior performance of the proposed method, several well-known evolutionary algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), moth-fire suppression optimization (MFO) and cuckoo search optimization (CS) are employed to constitute the contrast experiment of the prediction of short-term power load. The mean squared error predicted by the SDCS-GNN model is the smallest, which compared with GNN, PSO-GNN, GWO-GNN, MFO-GNN, and CS-GNN is 0.36, 1.79, 15.23, 4.53, 2.93, respectively. The Average prediction accuracy of SDCS-GNN model is better than other models which is 7.1592, 1.427, 15.1516, 11.5438, 10.5202, respectively. The simulation results show that the SDCS-GNN model has better approximation ability and higher prediction accuracy than the conventional GNN and other evolutionary algorithms in the short-term electric load forecasting. The experiments above indicates that the prediction method is effective and feasible.
In order to more accurately predict the impact of solid oxide fuel cells to stack life on standby and shutdown, this paper proposes a modeling method based on experimental data. Which is based on Elman neural network(...
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In order to more accurately predict the impact of solid oxide fuel cells to stack life on standby and shutdown, this paper proposes a modeling method based on experimental data. Which is based on Elman neural network(NN). At the same time, a main factor is considered in the modeling process which is the effect of cooling rate on the stack. In the process of modeling, the most obvious cooling rate is used to modeling. There are two different influencing factors on classify, the training set and the verification set respectively. After the reliability of the model, the Solid Oxide Fuel cell (SOFC) stack life prediction is carried out. From the predict results and the experimental results, it is found that the prediction results are good and the high precision.
Image classification is one of the most important tasks in image analysis and computer vision. BP neural network is a successful classifier for the task. However, with regard to the low study efficiency and the slow c...
Image classification is one of the most important tasks in image analysis and computer vision. BP neural network is a successful classifier for the task. However, with regard to the low study efficiency and the slow convergence speed in BP algorithm, some optimization algorithms have been proposed for achieving better results. Among all these methods, BP neural network improved by particle swarm optimization (PSO) and genetic algorithm (GA) may be the most successful and classical ones. Nevertheless, both GA and PSO are easy to fall into the local optimal solution, which has a great impact on the precision of classification. As a result, a novel optimization algorithm called sine cosine algorithm (SCA) is presented to improve the classification performance. The experimental results manifest that the proposed method has good performances, and the classification accuracy is better than BP neural network optimized by GA, PSO or other algorithms.
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