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Wireless Sensor Network (WSN) has inexpensive, small, and less energy sensor nodes, which are allocated in random ways in particular areas for measuring the phenomenon or events in that field. In recent days, WSN has played a vital role in various applications, like industrial monitoring, medical treatments, agricultural monitoring, and military operations. However, the security challenges and network lifetime are the main issues in the existing methods. In order to overcome these issues, the Taylor-Spotted Cat Optimization (Taylor-SCO) approach is devised in this paper. Here, the Cluster Heads (CHs) are selected based on the developed optimization method, named Taylor CSO. Moreover, the delay, distance, and energy parameters are considered for effective Cluster Head Selection (CHS). Here, route maintenance is also done for increasing network lifetime and reducing complexities. In addition, the Modified K-Vertex Disjoint Paths Routing (KVDPR) model is established for routing. The modification of KVDPR is carried out using several factors, such as link reliability, throughput, and various trust factors. Moreover, the developed Taylor-SCO algorithm is developed by combining the Spotted Hyena Optimizer (SHO), Cat Swarm Optimization (CSO) algorithm, and Taylor series. The Taylor-SCO achieved better performance with energy consumption, trust, and throughput of 0.00037 J, 0.51, and 793160 kbps. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G communication and wireless Internet of Things technology, the application of intelligent wearable devices based on wireless data transmission technology is becoming more and more popular. However, due to the bandwidth of wireless data transmission nodes and the power consumption of the device system, the use time and data storage of wearable devices are severely restricted. The compressed sensing (CS) technology has become an effective method to tackle this problem. CS technology includes data compressive sensing at the transmission end and data reconstruction at the receiving end. In this paper, we consider the reconstruction of nonnegative sparse vectors, an important problem in the area of CS for wireless data transmission. It is known that the interval-passing (IP) algorithm is a low-complexity message-passing type method for the problem. However, the reconstruction performance of the IP algorithm is inferior to that of the other state-of-the-art CS reconstruction algorithms such as the orthogonal matching pursuit (OMP) algorithm. In order to address the problem, we propose a two-stage reconstruction algorithm in this paper. The proposed algorithm applies the IP algorithm for the first stage of reconstruction. If the reconstruction fails, the OMP algorithm is then used on the basis of the results in the first stage. The proposed algorithm is evaluated and compared with other state-of-the-art algorithms by the probability of perfect reconstruction under the given sparsity order value. Simulation results suggest that the proposed two-stage algorithm can greatly improve the reconstruction performance of the IP algorithm and can even outperform the OMP algorithm. In addition, the low-complexity advantages of the IP algorithm are maintained in the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal design of different VLSI circuits, namely, the CMOS voltage reference circuit and the CMOS ring oscillator (RO). The optimization technique used here is the multiobjective differential evolution algorithm (MDEA). All the circuits are designed for 90 nm technology. The main objective of the CMOS voltage reference circuit is to minimize the voltage variation at the output. The targeted value of the reference voltage is 550 mV. A CMOS ring oscillator (RO) is designed depending on the performance parameters such as power consumption and phase noise. The optimal transistor sizing of each circuit is obtained from MDEA. Each circuit is implemented in SPICE by taking the optimal dimensions of the transistors, and the performance parameters are achieved. The designed voltage reference circuit achieves a reference voltage of 550 mV with 600 nW power dissipation. The reference voltage variation of 8.18% is observed due to temperature variation from −40°C to + 125°C. The MDEA-based optimal design of RO oscillates at 2.001 GHz frequency, has a phase noise of −87 dBc/Hz at 1 MHz offset frequency, and consumes 71 μW power. This work mainly aims to optimize the MOS transistors' sizes using MDEA for better circuit performance parameters. SPICE simulation has been carried out by using the optimal values of MOS transistor sizes to exhibit the performance parameters of the circuit. Simulation results establish that design specifications are closely met. SPICE results show that MDEA is a better technique for the optimal design of the above-mentioned VLSI circuits. [ABSTRACT FROM AUTHOR]

With the development of computer technology, digital image processing, as an emerging field, has become an indispensable tool in the information society. The action interference effect refers to the phenomenon that an individual reacts to a dangerous object longer than a safe object in the state of preparation for action. Currently, there is no effective anti-jamming technology to recognize dance movements. In order to solve these problems, this paper proposes a dance movement interference suppression algorithm based on contour model and AdaBoost algorithm, which aims to realize the application of gesture-based behavior recognition technology in the field of dance steps and complete the recognition of some dance steps. The method of this paper is to study the contour model based on the image and then propose to use the AdaBoost algorithm to detect body movements and finally use the radio frequency interference suppression algorithm to ensure the smooth operation of the technology. The function of the method is to propose a dance action recognition and anti-interference algorithm based on contour model and AdaBoost algorithm. This article describes the contour model and then uses the AdaBoost algorithm to recognize the action and adjust the action interference effect. The results show that the algorithm proposed in this paper can accurately identify dance movements, with an average recognition accuracy rate of 99.2%. [ABSTRACT FROM AUTHOR]

To address the phenomenon of serious psychological stress among college students, there are problems of high cost and subjectivity in assessing psychological stress by collecting physiological data, and this paper proposes a stress assessment method (Improved SMOTE + XGBoost) based on intelligent data collection, which divides stress levels into five levels. In the process of processing a large amount of data, there will be too little data. Therefore, this paper applies the improved SMOTE method to the data preprocessing, which can reduce the difficulty of collecting psychological stress test data while ensuring the amount of data. Firstly, we extracted features from cell phone data to generate samples, processed the samples by SMOTE, and then filtered features by XGBoost algorithm to filter features; meanwhile, we trained RF, SVM, BP, and KNN with the data before and after sampling and before and after feature screening, and the results showed that Improved SMOTE + XGBoost outperformed other methods. [ABSTRACT FROM AUTHOR]

Most of the existing resource allocation algorithms do not take into account the restriction conditions such as the delay limitation of different services and the service volume requirements of different STAs. Moreover, the surge in the number of smart terminal devices has brought great challenges to the access effect of WLAN systems. Therefore, 6G technology will surely replace the current technology as the main means of future communication technology. This paper combines 6G technology to study the smart system resource coordination algorithm and analyzes the calculation process and actual effect of the algorithm. Moreover, this paper analyzes the coordination of smart system resources and verifies the effect of 6G technology in the coordination of smart system resources through experimental research. The results of experimental research show that the smart system resource coordination algorithm based on 6G technology can effectively improve the resource coordination effect of smart systems and has certain anti-interference, which will promote the construction of smart systems in the future. [ABSTRACT FROM AUTHOR]

The installation of automotive electrical switches is a complex three-dimensional space assembly project which has high requirements for installation accuracy. In order to improve the installation effect of automotive electrical switches, this paper applies the PSO-BP neural network algorithm to automotive electrical switches and integrates PSO and ELM algorithms. The training speed of the ELM model is fast, the model generalizes the data well, and the noise data have little effect on the model. Moreover, this article combines simulation research to evaluate the effect of this algorithm. After confirming the performance of the effect, this paper uses a case study to study the effect of the application of the PSO-BP neural network algorithm to the automotive electrical switch. The research results show that the CAD-assisted 3D assembly system of automobile electrical switch considering PSO-BP neural network algorithm has a good effect. [ABSTRACT FROM AUTHOR]

In order to explore the optimal control method of tennis racket string diameter, this paper applies the Kalman filter algorithm to the collection and processing of tennis sports parameters. When the Kalman filter uses the optimal gain (the Kalman filter enters the steady state), the corresponding cost function is established based on the noncorrelated nature of its residual sequence, which is used as the judgment condition for the sampling strategy switch to improve the stability of the system. In addition, this paper improves the real-time performance and calculation accuracy of data transformation through adaptive sampling strategy and adaptive scale factor, improves the stability and estimation accuracy of the system as a whole, and builds an intelligent monitoring system. Finally, this paper systematically studied the optimization control method of tennis racket string diameter and verified that the Kalman filter algorithm can play a certain role in the optimization control of tennis racket string diameter. [ABSTRACT FROM AUTHOR]

From the perspective of practical application, information popularity prediction is of positive significance for corporate marketing, advertising, traffic control, and risk management. This paper combines the fast K-nearest neighbor algorithm to predict and analyze the popularity of multimedia network information and improves the nonindependent and identically distributed KNN classification algorithm. Moreover, this paper proves that it is a superior measurement method when considering the nonindependent and identical distribution among data objects to measure similarity and the improved CS_KNN algorithm can greatly improve the classification performance. Finally, this paper constructs a prediction model of multimedia network information popularity based on the fast K neighbor algorithm. Through the experimental research results, it can be seen that the prediction effect of the multimedia network information popularity prediction system based on the fast K neighbor algorithm proposed in this study is very good. [ABSTRACT FROM AUTHOR]

In order to improve the cost control effect of the supply chain of intelligent manufacturing enterprises, this paper improves the IQC algorithm. In order to overcome the problem of multitype data processing in the supply chain, this paper describes the high-frequency NCS as a delta arithmetic system with input time-varying delay, high-frequency constraints, and actuator saturation. Moreover, through a double closed-loop feedback configuration, this paper combines the loop structure of the supply chain to analyze the stability of the actuator saturated high-frequency NCS and combines the experiments to verify the reliability of the improved algorithm. In addition, on the basis of the improved algorithm, this paper constructs an intelligent supply chain cost control system, evaluates its functional modules, sets up the system architecture, and analyzes the performance of the system in combination with experimental research. The experimental research results show that the supply chain cost control method of intelligent manufacturing enterprises based on the improved IQC algorithm proposed in this paper has good results and meets the operational needs of intelligent manufacturing enterprises. [ABSTRACT FROM AUTHOR]

An association rule mining algorithm is an algorithm that mines the association between things and is often used to mine the association knowledge between things. Association rule mining algorithms can find potential connections between different qualities of college students from the data of college students' life and learning, which can help teachers discover the problems and their own strengths of different students and achieve teaching according to their aptitude. The purpose of this paper is to solve some problems related to the associative rule extraction algorithm and to investigate the impact of applying the associative rule extraction algorithm in a college student quality assessment system. Based on the algorithm, a quality assessment system for college students has been developed. A modified script-based associative rule extraction algorithm is used to find the correlation between the quality and the ability of college students. The quality assessment data of college students are analyzed and studied. The results show that the use of associative rule extraction algorithms to assess the quality and ability of college students can improve the efficiency of the test by 24% and the accuracy of the test score by 33% and reduce the probability of outliers in the scoring process by 27%. It can be seen that the association rule extraction algorithm can be applied to college students' quality assessment system and also reduces the probability of encountering obstacles in accuracy and performance assessment. At the same time, this experiment also proves the robustness and feasibility of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

In order to improve the effect of automatic intelligent control, this paper improves the intelligent vector control algorithm, analyzes the performance of the algorithm with common power pulses as an example, and obtains an improved intelligent vector control algorithm suitable for modern intelligent systems. By making two mutually adjacent vectors nonidentical combination in time, the two vectors have the difference between zero and nonzero, and the vector can be rotated into a certain area. In addition, with the support of improved algorithms, this paper gets an automated intelligent control system based on intelligent control algorithms. Finally, this paper verifies the effect of the intelligent control system through experimental research. The analysis of the test results shows that the automated intelligent control system proposed in this paper has a better effect in intelligent control. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. First, based on the four modules of data collection, data preprocessing, feature selection, and intrusion detection, the overall framework of the intrusion detection model is constructed by designing corresponding functions. Then, by introducing the decision tree algorithm, the overfitting is reduced and the data processing speed of the model is improved, and on this basis, the feature selection is carried out through the "gain rate" optimization method, which reduces the redundant information of the feature vector. Finally, by introducing the Particle Swarm Optimization (PSO) algorithm into the optimization of the initial weights and thresholds of the BP neural network, the BP neural network is improved based on the momentum factor and adaptive learning rate, and the high detection rate and low false detection rate are realized. Through simulation experiments, the proposed intrusion detection method and the other three methods are compared and analyzed under the same conditions. The results show that the detection rate and false detection rate of the method proposed in this paper are the best under five different types of sample data, the highest detection rate reaches 95.72%, and the lowest false detection rate drops to 2.03%. The performance of the proposed algorithm is better than that of the other two comparison algorithms. [ABSTRACT FROM AUTHOR]

A smart city's vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The "Eigen-trick" method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle's score, and the CH is selected based on the maximum vehicle's score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay. [ABSTRACT FROM AUTHOR]

Accurate wind power forecasting can help reduce disturbance to the grid in wind power integration. In this paper, a short-term power forecasting model is established by using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and nonlinear fitting characteristics of support vector machines (SVM) to accurately predict wind power. First, the wind power data are preprocessed and decomposed to 6 stable power components using CEEMDAN, thus reducing the impact of excessive forecasting errors of oscillatory points at peaks and valleys. Then, particle swarm optimization (PSO) based on improved empirical mode decomposition is designed to optimize the kernel function and penalty factor of the SVM. It establishes a new short-term power forecasting CEEMDAN-combined model. Finally, each stable component data is processed using the power forecasting model, and then, the results are combined to get the final power forecasting value. Analysis of test results and comparative studies show that the RMSE and MAPE of the new model are only one-third of that of the traditional SVM algorithm. The forecasting accuracy and speed meet the requirements for safe operation of wind farms. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) has become a very popular technology with a wide range of applications. It consists of several spatially distributed sensors that work collaboratively to monitor a given region of interest (ROI). The limited energy available for each sensor node is a crucial restriction that affects the overall performance of the network. Therefore, energy efficiency is a major concern in WSNs. Over the years, many techniques have been developed and used to reduce energy consumption in WSNs. Clustering is one of the most effective energy-saving techniques that significantly can improve the efficiency of WSNs in terms of the network lifetime, energy consumption, and the number of received packets. In this paper, an energy-efficient algorithm for cluster head (CH) selection based on a newly formulated fitness function and using the manta ray foraging optimization (MRFO) is proposed. The objective function for the proposed formulation takes into account different network parameters such as the average distance between the CH and the sensors in its cluster, the distance between CHs and the base station (BS), residual energy, and CH balancing. The proposed algorithm is tested by running many simulations under a variety of conditions. The simulation results showed that the proposed algorithm has a better performance than that of some other algorithms reported in the literature in terms of energy consumption, networks lifetime, and the number of received packets. [ABSTRACT FROM AUTHOR]

This paper proposes an interphase power control algorithm (IPCA) and submodule power control algorithm (SPCA) based on state transition PWM (ST-PWM) for cascaded H-bridge inverter with unbalanced DC sources. In this method, the correction value helps control the interphase power distribution and submodule power distribution by directly modifying the duty cycle of each switching state. In order to achieve linear modulation, an in-depth analysis of ST-PWM is conducted. Combining ST-PWM and IPCA, balanced AC currents and accurate power control on DC side can be achieved with an unbalanced DC source. The proposed strategies are simulated using MATLAB/Simulink and tested on a five-level laboratory prototype. Simulation and experimental results demonstrate the advantage and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The accuracy of English pronunciation is the key index to evaluate the quality of English teaching. Correct pronunciation and smooth language flow are the expectations of every student for English learning. Aiming at the poor effect and slow speed of the original SSD (Single Shot MultiBox Detector) algorithm in English teaching pronunciation detection, this paper proposes a clustering and improved SSD algorithm for English teaching pronunciation detection and recognition. The algorithm improves the concept module to enhance the feature extraction ability of the network and improve the detection speed. Meanwhile, it integrates multiscale features to realize multilayer multiplexing and equalization of features, so as to improve the detection effect of small target sound. This algorithm extracts more features by introducing channel attention mechanism, which increases the detection accuracy while reducing computation. In order to optimize the network's ability to extract target location information, K-means clustering method is used to set the default parameters that are more in line with the characteristics of target samples. The experimental results showed that the proposed algorithm can accurately evaluate the pronunciation quality of reading aloud, so as to effectively reflect the oral English level of the reader. [ABSTRACT FROM AUTHOR]

In order to solve the problem of complex event pattern in big data and strengthen research on key technologies of the Internet of Things and computer time matching algorithms, this paper studies the problem based on Hadoop clustering algorithm. Firstly, based on the subtype attribute of event type, the maximum value is selected as the final attribute value of the event after weighting the event. Secondly, cluster analysis is conducted on the Internet of Things flow dataset through the relationship between complex events. Finally, the simulation test is carried out on the simulation dataset of complex event relationship, and the simulation test of clustering algorithm comparison is carried out. The experimental results show that the clustering accuracy of different datasets is above 85%, and the clustering accuracy of causality reaches 96.07% when the dataset is 5G. Therefore, the algorithm proposed has high feasibility, good stability, and high speed and effectiveness for complex event processing. This study has certain practical significance for solving the problem of complex event pattern in big data. [ABSTRACT FROM AUTHOR]

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%. [ABSTRACT FROM AUTHOR]

Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implementing multistrategy grey wolf optimization. This multistrategy optimization includes three components, a population diversity initialization strategy, a nonlinear adjustment strategy of the convergence factor, and a generalized opposition-based learning strategy. They can enhance the population diversity, better balance exploration and exploitation, and further enhance the global search capability, respectively. In order to evaluate the clustering performance of our clustering algorithm, UCI datasets are selected for experiments. Experimental results show that this algorithm can achieve higher accuracy and stronger robustness. [ABSTRACT FROM AUTHOR]