The broadband properties of impedance in power line communication (PLC) networks would enable smart PLC systems to implement fault detection and channel health monitoring in smart grid. In this paper, a novel real-time impedance estimation method for power line communication networks is proposed. We study the relationship between the channel frequency response (CFR) and the impedance behavior, CFR can be treated as the already known variable which is normally calculated by channel estimation algorithms in PLC devices, and find that the variation of certain key factor of the CFR curves including the frequency characteristics and values of Peak-Valley Difference and so on, could be used to tracking the impedance. A VMD (Variational Mode Decomposition) algorithm is used to obtain the useful frequency properties information in CFR waveform and machine learning method is used to estimate the parameters. Proposed impedance estimation method is verified by the computer simulations and the results show that it is a feasible solution for impedance estimation without any additional hardware and costs.
The failure prediction method of virtual machines (VM) guarantees reliability to cloud platforms. However, the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud platform. In this Study, a failure prediction method of VM based on AdaBoost-Hidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud platforms. This method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model, proved the influence of the AdaBoost algorithm on the hidden Markov model (HMM), and realized the prediction of the VM failure state. Results show that the proposed method adapts to the complex dynamic cloud platform environment, can effectively predict the failure state of VMs, and improve the predictive ability of VM security state.
On the basis of studying the data characteristics of soybean meal option, in this article, we changed Dumas Linear Polynomial model, by enhancing the power of polynomial of parameter tau, to obtain several models. And based on the market data, we tested these models with different perspectives and the consequences are effective. From the result of the experiments, we can conclude four principles as followed. Firstly, with the complexity of model increased, the model can outperform on the side of data explanation. Secondly, it was going to present over-fitting if the model gets more complicated, which weakened the model's prediction ability. Thirdly, although the more complex models outperformed, the basic one with power of polynomial of parameter tau equaled one also conducted well in prediction. Fourthly, we developed that as the power of polynomial of parameter tau equaled two, the algorithm presented better ranging from fitness to prediction, which is adapted to establish the model of soybean meal options with intraday quotation.
This paper presents a study of the changes of the main function index of the improvement and new model development of the main producing model of CIVIC, from 2011 to 2015. Using the collection and analysis of the data and the calculation of the index of service sensitivity, it was evaluated that the influence of the development and the sale of the new product from the service sensitivity under the service-leading supply chain condition.
In 2020, China will fully realize a well-off society. To achieve this goal, China's poverty alleviation work has entered a crucial stage. Under the background of the Internet era, the "Internet + Order Agriculture + Precision Poverty Alleviation" model has been formed, and the order agriculture has played an important role in promoting the development of precision poverty alleviation. Based on 1000 questionnaires in Hubei Province, this paper quantitatively studied the impact of contract farming on farmers' income levels using multiple linear regression methods. The research shows that there is a positive correlation between farmers' participation in contract farming and farmers' income, and the correlation coefficient is high, indicating that farmers' participation in contract farming can significantly increase their income. Therefore, the promotion of contract farming is conducive to the in-depth development of regional precision poverty alleviation, and can help farmers solve poverty problems.
According to the hazardous characteristics of LPG storage tanks and their types and hazards of fire and explosion accidents, the impact evaluation method, the shock wave overpressure criterion and the heat flux criterion are applied to the standard of human injury. Fire shock wave and heat radiation generated by three different accident types were calculated. The quantitative estimation model of safety distance and LPG reserves was studied, which provided a supplementary basis for the personnel evacuation and firefighter command and decision on LPG fire scene.
Big data has already occupied a lot in the information society. The application of big data to intelligent agriculture is the core development direction for maximizing the utilization of agricultural data information, and the deep learning method can more effectively extract abstract information from big data and convert it into useful knowledge, thus supporting the development of intelligent agriculture from different dimensions. In this paper, a CNN-RNN model is constructed based on cloud computing technology, and the parallel neural network model divided by training set is adopted to design the batch gradient descent algorithm based on deep unsupervised learning and BP algorithm based on Map-Reduce. An experiment verifies the feasibility of deep unsupervised learning neural network based on cloud computing and verifies that the optimize algorithm proposed in this paper can better increase the training efficiency of neural network.
In this paper, the power load data is increasing exponentially and the traditional forecasting model is fatigued and difficult to achieve high efficiency when dealing with massive data. A XGBoost load forecasting model based on similar days is proposed. This model analyzes the common laws of meteorological and daily types on the load, The XGBoost model with the second-order Taylor expansion and loss function is added to the regular term to control the complexity and over-fitting. The real charge data and temperature data in a certain area are taken as samples. The simulation results show that the XGBoost model based on similar days can predict the load in short-term load forecasting effectively.
With the continuous improvement of railway operating speed, the efficient and secure transmission of balise message is a key problem in the present study. The decoding process of balise message has nothing to do with the secondary sequence of coding symbol reaching decoding end. This paper uses an optimized degree distribution fountain code to realize unequal error protection of data. Data with different levels of importance are protected by different mechanisms. Window W1 where MIB is stored adopts truncated degree distribution and window W2 LIB uses RSD degree distribution. Matlab simulation is used to obtain the decoding failure size under different code lengths. Simulation results show that: Using the unequal error protection of optimization degree distribution can ensure that the high priority data can be better protected. The theoretical feasibility of this method is verified.
The centrifugal pump can not meet the technical requirements of deep-sea mining in performance and structure. Moreover, coarse particles and water was regarded as a research object. In order to study the performance of deep sea mining, this article uses a new type of electric pump as a model. At the same time, there are different diameter particles in deep-sea mining. The force of the coarse particles in the quasi-fluid is analyzed by CFturbo for 3D modeling and extracting the flow path of the quasi-fluid. The ICEM is used for the meshing, and the quasi-fluid at the concentration of 5%, 8% and 10% was selected. The Particle model was used for numerical simulation in CFX to predict the electric pump performance from the results obtained. Using two-stage electric pump to do the water test, the data obtained with the numerical simulation results are compared. It shows that when the particle concentration is 10% in the deep sea mining, the system is more stable and the efficiency and the head are more reasonable.
In order to solve the problem of low parallel processing efficiency of current deep learning algorithm data, a parallel optimization method of deep learning algorithm based on behavior recognition is proposed. The behavior recognition method is used to extract learning morphological features, and the clustering specification parameters of deep learning features are calculated. The learning state feature values and clustering specification parameters are compared according to the calculation results, so as to effectively check out the heterogeneous data hidden in the parallel processing process. The parallel processing execution steps are optimized according to the parameters of the depth learning feature clustering specification, and the control flow of the parallel processing of the behavior depth learning is improved by planning the parallel rules of the depth learning algorithm, so as to finally achieve the research goal of the parallel optimization of the depth learning algorithm based on the behavior recognition. Finally, experiments show that the parallel optimization efficiency of the depth learning algorithm based on behavior recognition is significantly higher than that of the traditional depth learning algorithm.
The convex 11 -regularization problem has been received special attentions in the last two decades because of its properties of improving model interpretation. So far, there exists plentiful optimization methods to solve this problem. In this paper, we review the existing optimization methods and compare them to help users choose the most proper solver in their own scenarios.
In order to overcome the difficulties in measurement data collecting of the performance level of discipline construction in local universities, GM (1,1) grey prediction model was used to predict and analyze the performance level of discipline construction in local universities, which combined with the general performance indicator system of discipline construction. The research shows that to improve the performance level of discipline construction, we must strengthen the construction of teaching staff, ensure sufficient and high-quality teaching resources, optimize the process of personnel training, promote the development of students, strengthen quality supervision and guarantee, enhance the scientific research ability and awareness of teachers and students, clarify the orientation and goals of universities and actively promote the economic and social development of the country and the region.
In order to realize the fire risk assessment of A stadium, this paper establishes the fire scene model of the stadium, and takes the fire model simulation analysis to the stadium. The composition and structure of the stadium, the division of safety zones, evacuation and other aspects are analyzed. The model is established and the fire simulation experiment is carried out. The design requirements are given. Finally, the simulation data are obtained, and improvement suggestions for A venues are given through data. Fire risk assessment plays an important guiding role in improving the fire safety level of the stadium.
In view of the insufficiency in the education service quality in colleges and universities, a kind of evaluation model of the education service quality satisfaction in the colleges and universities that is dependent on the classification attribute big data feature selection algorithm is put forward in this paper based on the existing work. On the basis of detailed description of the model components, further study on the evaluation method of the proposed model for the education service quality satisfaction in the colleges and universities is carried out. Under the guidance of the evaluation model of the education service quality satisfaction in the colleges and universities, the method for the construction of the evaluation model of the education service quality satisfaction in the colleges and universities is studied with the orientation to the education service resources in the colleges and universities under the open big data environment. In addition, experimental verification is carried out on the basis of the evaluation data in the 360 Encyclopedia on the education service quality satisfaction in the colleges and universities. The experimental results show that the model and method put forward in this paper can effectively evaluate the quality of the education service in the colleges and universities.