Article of the Year 2020
A Novel Image Classification Approach via Dense-MobileNet ModelsRead the full article
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Software-Defined Network Resource Optimization of the Data Center Based on P4 Programming Language
This paper makes use of the new architecture software-defined network (SDN) in the cloud data center based on P4 language to realize the flexible management and configuration of the network equipment to achieve (a) data center virtualization management and (b) data center resource optimization based on the P4 programming language. Furthermore, error tolerance of dynamic network optimization depends on the virtual machine (VM) online migration technology, and the load balancing mechanism has a very good flexibility. At the same time, the paper proposed a multipath VM migration strategy based on a quality of service (QoS) mechanism, which divides the VM migration resources into different QoS flows by network dynamic transmission and then selects valid forwarding for each flow path to migrate VMs. This ensures to improve the overall migration performance of VMs and ultimately the dynamic optimization of the network resources and their management. Our experimental evaluations show that the proposed model is approximately 13% and 17% better than the traditional state-of-the-art methods in terms of minimum migration time and the least downtime, respectively.
Evaluation Model of Educational Curriculum in Higher Schools Based on Deep Neural Networks
Classroom teaching quality evaluation system can enable the school’s functional departments to accurately assess the performance of the teaching staff and current teaching operations. As per the requirements for cultivating high-quality talents, planned teaching staff construction and teaching reforms need to be carried out to promote teachers’ appointments. Improving the system makes the appointment process more scientific by giving due attention to the individual characteristics of all types of teachers while hiring them for related jobs. The system motivates the love of teaching, high academic level, high teaching level, and competitive teaching. In recent years, the rapid development of artificial intelligence and deep learning caused many colleges and universities to put forward the target of campus digitization and education informatization. The state of the classroom is a critical reference factor throughout the teaching and learning process for evaluating students’ acceptance of the course and the quality of the teaching. However, at present, the analysis of the classroom status is mainly conducted manually, which distracts teachers and is also not much precise. Therefore, finding a method that can improve the efficiency of classroom status analysis has great research significance. This study uses the deep neural network method to read each class’s video recording and analyze it from the aspects of students’ behavior and attendance. The system can realize class behavior and eventually evaluate the course quality employed to motivate teachers to improve teaching and overall quality of education.
Analysis of the Movement Characteristics of the Pump Valve of the Mine Emulsion Pump Based on the Internet of Things and Cellular Automata
Studying the movement characteristics of the coalmine emulsion pump valve is of great significance for optimizing the dynamic response characteristics of the pump valve, reducing the hysteresis effect, and improving the volumetric efficiency. This article combines the Internet of Things (IoT) and cellular automata techniques to investigate the movement characteristics of the valve of the emulsion pump. Based on Adolf’s exact differential equation and Runge–Kutta iterative method, the movement displacement and movement of the pump valve spool speed curve are computed using Scilab software. We employ cellular automata and AMESim to establish the hydraulic system model of emulsion pump and analyze the movement characteristics of pump valve movement displacement, speed, stability, and closing hysteresis through simulation. Finally, the IoT techniques and a test device are used to evaluate the movement displacement of the pump valve. The experimental results verify the feasibility of using the proposed method to study the pump valve motion characteristics, greatly reduce the cost of testing and parameterized design, and contribute to the development of highly reliable and efficient emulsion pump valves.
An Improved Channel Estimation Algorithm Based on WD-DDA in OFDM System
Channel estimation is the key technology to ensure reliable transmission in orthogonal frequency division multiplexing (OFDM) system. In order to improve the accuracy of the channel estimation algorithm in a low signal-to-noise ratio (SNR) channel environment, in this paper, we proposed an improved channel estimation algorithm based on the transform domain. The improved algorithm with wavelet denoising (WD) and distance decision analysis (DDA) to perform secondary denoising on the channel estimation algorithm based on the transform domain is proposed. First, after the least-squares (LS) algorithm, WD is used to denoise for the first time, then the DDA is used to further suppress the residual noise in the transform domain, and the important channel taps are screened out. Simulation results show that the proposed algorithm can improve the detection performance of existing channel estimation algorithms based on transform domain in low SNR.
Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit
Power quality disturbance (PQD) is essential for devices consuming electricity and meeting today’s energy trends. This study contains an effective artificial intelligence (AI) framework for analyzing single or composite defects in power quality. A convolutional neural network (CNN) architecture, which has an output powered by a gated recurrent unit (GRU), is designed for this purpose. The proposed framework first obtains a matrix using a short-time Fourier transform (STFT) of PQD signals. This matrix contains the representation of the signal in the time and frequency domains, suitable for CNN input. Features are automatically extracted from these matrices using the proposed CNN architecture without preprocessing. These features are classified using the GRU. The performance of the proposed framework is tested using a dataset containing a total of seven single and composite defects. The amount of noise in these examples varies between 20 and 50 dB. The performance of the proposed method is higher than current state-of-the-art methods. The proposed method obtained 98.44% ACC, 98.45% SEN, 99.74% SPE, 98.45% PRE, 98.45% F1-score, 98.19% MCC, and 93.64% kappa metric. A novel power quality disturbance (PQD) system has been proposed, and its application has been represented in our study. The proposed system could be used in the industry and factory.
Cellular Traffic Prediction Based on an Intelligent Model
The evolution of cellular technology development has led to explosive growth in cellular network traffic. Accurate time-series models to predict cellular mobile traffic have become very important for increasing the quality of service (QoS) with a network. The modelling and forecasting of cellular network loading play an important role in achieving the greatest favourable resource allocation by convenient bandwidth provisioning and simultaneously preserve the highest network utilization. The novelty of the proposed research is to develop a model that can help intelligently predict load traffic in a cellular network. In this paper, a model that combines single-exponential smoothing with long short-term memory (SES-LSTM) is proposed to predict cellular traffic. A min-max normalization model was used to scale the network loading. The single-exponential smoothing method was applied to adjust the volumes of network traffic, due to network traffic being very complex and having different forms. The output from a single-exponential model was processed by using an LSTM model to predict the network load. The intelligent system was evaluated by using real cellular network traffic that had been collected in a kaggle dataset. The results of the experiment revealed that the proposed method had superior accuracy, achieving R-square metric values of 88.21%, 92.20%, and 89.81% for three one-month time intervals, respectively. It was observed that the prediction values were very close to the observations. A comparison of the prediction results between the existing LSTM model and our proposed system is presented. The proposed system achieved superior performance for predicting cellular network traffic.