Evaluation Algorithm of Skilled Talents’ Quality Based on Deep Belief Network Model

In order to solve the problem that the evaluation algorithm is easy to fall into local extremum, which leads to slow convergence speed, a skilled talent quality evaluation algorithm based on a deep belief network model was designed. Establish an evaluation set with 4 first level indicators and 14 second level indicators, and calculate the corresponding weights to complete the construction of the evaluation index system. A DBN structure composed of several RBMs and a BP network is constructed. Based on the DBN, a quality evaluation algorithm is designed. The algorithm training is used to evaluate the test data and output the evaluation level. The experimental results show that the convergence speed of DBN based evaluation algorithm is significantly better than that of BP neural network and SVM based evaluation algorithm under the same number of iterations, which is suitable for the accurate evaluation of talent quality.

Prediction of PM2.5 concentration based on BP neural network optimized by bee colony algorithm

According to the ambient air pollutants data and meteorological conditions data of Mianyang City in 2017, the BP neural network model based on MATLAB is established to predict the daily average PM2.5 concentration of Mianyang City in the next two days. However, the traditional BP network has the disadvantages of slow convergence speed and easy to fall into local optimum. In order to improve the prediction accuracy of the model, an optimization algorithm is added to the prediction model to avoid the model falling into local minimum. In this paper, the bee colony algorithm is added to the prediction model to improve the accuracy of BP neural network prediction model. The data from January to November are used for training, and the data from December are used as the verification results. The results show that the optimization model can accurately predict the daily average PM2.5 concentration of Mianyang City in the next two days, which provides a new idea for the prediction of PM2.5 concentration of the city, provides a theoretical basis for the early warning and decision-making of air pollution, and also provides more reliable prediction services for people’s daily travel.

Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

The traditional BP neural network has the disadvantages of easy falling into local minimum and slow convergence speed. Aiming at the shortcomings of BP neural network (BP neural network), an artificial bee colony algorithm (ABC) is proposed to cross-optimize the weight and threshold of BP network parameters. This study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. It includes the application of BP neural network algorithm in English course recommendation technology, English course teaching design mode, the application of BP neural network algorithm in English course, and the optimal combination of bee colony algorithm and BP neural network. After 4690 iterations, the neural network reaches the target accuracy, and the training is completed. At the same time, the prediction error of the model is less than 10%, which further shows that the performance of the prediction model is good. Therefore, the combination model is recommended in this paper. The results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network.

Towards a Diagnostic Tool for Diagnosing Joint Pathologies: Supervised Learning of Acoustic Emission Signals

Acoustic emission (AE) testing detects the onset and progression of mechanical flaws. AE as a diagnostic tool is gaining traction for providing a tribological assessment of human joints and orthopaedic implants. There is potential for using AE as a tool for diagnosing joint pathologies such as osteoarthritis and implant failure, but the signal analysis must differentiate between wear mechanisms—a challenging problem! In this study, we use supervised learning to classify AE signals from adhesive and abrasive wear under controlled joint conditions. Uncorrelated AE features were derived using principal component analysis and classified using three methods, logistic regression, k-nearest neighbours (KNN), and back propagation (BP) neural network. The BP network performed best, with a classification accuracy of 98%, representing an exciting development for the clustering and supervised classification of AE signals as a bio-tribological diagnostic tool.

A Prediction Method for Evolution Effects of Software Architecture

Software architecture evolution may lead to architecture erosion, resulting in the increase of software maintenance cost, the deterioration of software quality, the decline of software performance, and so on. In order to avoid software architecture erosion, we should evaluate the evolution effect of software architecture in time. This paper proposes a prediction method for the evolution effects of software architecture based on BP network. Firstly, this method proposes four evolution principles and evaluates the overall evolution effects based on the combined measurements. Then, we extract the evolutionary activities from release notes. Finally, we establish a prediction model for evolution effect based on BP network. Experimental results show that the proposed method can be used to predict the evolution effect.

Early Assessment of Acute Ischemic Stroke in Rabbits Based on Multi-Parameter Near-Field Coupling Sensing

Background: Maintaining normal supply of cerebral blood flow (CBF) and preventing secondary damage caused by acute ischemic stroke (AIS) are essential to the treatment of cerebrovascular diseases. Nevertheless, there hasn’t been fully accepted method targeting continuous assessment of AIS in clinical. Methods: Near-field coupling (NFC) sensing can obtain the electromagnetic properties related to the volume of intracranial components with advantages of noninvasiveness, strong penetrability and real-time monitoring. In this work, we built a multi-parameter monitoring system that is able to measure the phase and amplitude changes in electromagnetic wave reflection and transmission. For investigating its feasibility in AIS detection, sixteen rabbits were chosen to establish AIS models by bilateral common carotid artery ligation and then were enrolled for monitoring experiments.Results: During the six hours after AIS, the reflection amplitude (RA) shows a decline trend with a range of 0.69dB and reflection phase (RP) has an increased variation of 6.48°. Meanwhile, transmission amplitude (TA) and transmission phase (TP) decrease 2.14dB and 24.29° respectively. The statistical analysis illustrates that before ligation, three hours after ligation and six hours after ligation can be effectively distinguished by the four parameters individually. When all those parameters are regarded as recognition features in BP network, the classification accuracy of the three different periods reaches almost 100%.Conclusion: These results prove the feasibility of multi-parameter NFC sensing to assess AIS, which is promised to become an outstanding point-of-care testing method in the future.

Intrusion Detection Method of Electric Power Information Network in Cloud Computing Environment

With the widespread application of power grid systems, the information security problems faced by power grids have become more obvious. Various internal and external intrusion attacks that occur frequently have become an important issue affecting the normal operation of power generation and operations. The purpose of this paper is to study the intrusion detection method of electric power information(PI) network in the cloud computing environment. With the help of the cloud platform’s ability to process big data, and based on the analysis of the PI network structure, a DBN optimized BP network algorithm is proposed, and the optimized BP neural network is used as a runtime classification program. Experimental results show that MR-DBN-BP has a detection rate of 96.7% for intrusion detection of PI networks, which can effectively detect intrusions and effectively protect the power dispatch system network.

Wind power short-term power forecasting based on Improved Grey Wolf algorithm and Optimized Generalized Regression Neural Network

Aiming at the problem of wind turbine output prediction, a wind power prediction method based on Improved Gray Wolf algorithm and optimized generalized regression neural network is proposed in this paper. Firstly, according to the daily similarity of wind speed and wind power, cluster analysis is used to classify the data. Considering that the degree of each factor affecting wind power output changes, based on the selection of similar days, an improved gray wolf algorithm is introduced to optimize the weight of each influencing factor. The two models of the first mock exam are selected to input the radial single mode function RBF and the back propagation (BP) network to predict the output of the wind turbine separately. The prediction results of the two models are input to the generalized regression neural network optimized by the Wolf Wolf algorithm and the nonlinear combination forecasting is carried out. The basis models are used to predict the output of the wind turbine. The example analysis of an area shows that the model can be closer to the real value in the peak and valley of the prediction curve and has higher prediction accuracy than the combined prediction model of single BP, RBF and non optimized general regression neural network (GRNN).