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As a widely used network security defense technology, network intrusion detection has more deep learning methods used to improve the performance of intrusion detection. However, this method requires a large-scale network traffic data set for training, increasing privacy leakage risk. In this paper, a network intrusion detection algorithm based on Gaussian differential privacy federated learning (NIDS-FLGDP) is proposed. NIDS-FLGDP adopts the client–server architecture of federated learning, introduces the differential privacy of the Gaussian mechanism to ensure the security of the calculation process, uses the improved FedAvg algorithm to reduce communication overhead, and uses the improved 1D CNN to participate in collaborative training for the local model. Optimal parameters for Gaussian differential privacy and the optimal number of participating clients were determined from experiments. Model accuracy rates for binary classification and multi-classification training NIDS-FLGDP are 0.97, 0.975, 0.97 and 0.97, 0.985, 0.96, respectively, for KDD CUP99, NSL_KDD, and UNSW_NB15 network intrusion detection datasets. The results show that NIDS-FLGDP improves intrusion detection performance while protecting network traffic privacy compared with the previous methods. Its applicability and effectiveness have been fully verified, which provides a practical reference for the safe processing and analysis of a large number of diversified network traffic data in the future. [ABSTRACT FROM AUTHOR]

This paper investigates the overflow oscillation elimination problem in the fixed-point two-dimensional (2D) digital filter (DF) based on Roesser model subjected to two's complement overflow (TCO) nonlinearities. By utilizing the system information and behavior of TCO nonlinearities more effectively, a new global asymptotic stability (GAS) criterion for 2D DFs is presented. A comparison of the obtained criterion with existing criteria is made with the help of examples. [ABSTRACT FROM AUTHOR]

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at t and t − 1. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method. [ABSTRACT FROM AUTHOR]

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users' sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user's accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]

This paper presents the realization of binary and floating-point comparators on FPGA. The implementation is done by exploiting the primitive instantiation of FPGA resources which has enabled a significant improvement in resource utilization in terms of Look-Up Table (LUT) usage and overall combinational path delay when compared to the conventional inference approach. The comparator architectures are implemented using Vivado 2020.1 and ISE Design Suite 14.7 environment on multiple Xilinx FPGA platforms and are compared with the existing designs. The results indicate an improvement of 33.33% and 45.45% in LUT utilization, 14.41% and 30.73% in delay for 32-bit and 64-bit binary comparators respectively, compared to the existing architectures. The proposed floating-point comparator requires 88.57% and 285.29% lesser LUTs for single precision and double precision representation respectively, compared to the existing design. [ABSTRACT FROM AUTHOR]

This paper introduces a Multi-Level Cascade Inverter (MLI) based on Enhanced Quasi-Z Source Inverter (MQZSI) to connect photovoltaic (PV) systems based on the proposed method. Usually the interface among PV power supply and load is completed with MQZS-CMLI. In this paper, the proposed control scheme is comprehensive implementation of Recalling Enhanced Recurrent Neural Network (RERNN) and Quasi-Opposite Chemical Reaction Optimization (QOCRO) named as RENCO. The main objective of proposed method is to decide the efficiency of the PV system by the maximal power extraction. Here, MQZS-CMLI's modeling design contains suitable number of components, other than their capacitors and semiconductors comply with low-voltage stress. It is improved for providing that maximum power of the PV power generation system. At first, the goal function is described according to the parameters and limitations of controller (like voltage, current, power, modulation index, so on). These parameters apply to recommend RENCO technology input. The proposed RENCO technology improves voltage distribution, power transmission, and minimizes power fluctuations while sharing power with load. The proposed MPPT-based technology ensures that the maximum power is provided to load. The proposed method adjust the duty cycle of MQZS-CMLI and reduces the modulation load. Finally, the proposed technology is executed on the MATLAB/Simulink platform, and its output efficiency is compared to existing systems under dissimilar load circumstances. [ABSTRACT FROM AUTHOR]

Microwave hyperthermia is a new method of treating cancer, where the therapeutic effect is determined by the heating temperature. Traditional active temperature sensors are interfered by high frequency so that the accuracy of temperature measurement cannot be guaranteed. It is of great significance to study the high-precision fluorescent optical fiber temperature sensor with complete insulation. This paper has realized a compact and practical fluorescent optical fiber temperature sensor after studying the optical path, circuit, data processing algorithm. In order to improve the accuracy of the system, the weighted linear least-squares fitting algorithm is improved in this paper. Through experimental tests, compared with the standard linear least-squares fitting algorithm and the unimproved weighted linear least-squares fitting algorithm, the accuracy of the algorithm is improved by about 98% and 65.5%, respectively. In addition, the response time is reduced by about 36.5%, compared with the unimproved weighted linear least-squares fitting algorithm. This algorithm fully meets the precision requirements of microwave hyperthermia. [ABSTRACT FROM AUTHOR]

The fast warning for financial risk of enterprises has always been a realistic demand for their managers. Currently, this mainly relies on expert experience to make comprehensive analysis from massive business data. Benefitting from the strong computational performance of deep learning, this paper proposes a fuzzy neural network (FNN)-based intelligent warning method for financial risk of enterprises. An improved FNN structure with time-varying coefficients and time-varying time lags is established to extract features of enterprises from complex financial context. The algorithm of fuzzy C-means and fuzzy clustering based on sample data are studied. In this paper, the fuzzy C-means algorithm is used to cluster the samples, the input sample set is preprocessed, a new set of learning samples is formed, and then the neural network is trained. The enterprise financial risk sample and its modular FNN model are established, and the evaluation of the enterprise financial risk sample is simulated. Then, a decision part is added following the FNN part to output the warning results. After that, we have also conducted a case study as simulation experiments to evaluate the proposed technical framework. The obtained results show that it can perform well in the fast warning of financial risk for enterprises. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods. [ABSTRACT FROM AUTHOR]

As a widely used equation in electrostatics, the Poisson equation has significant research value in numerical solution. The basic principle of existing methods is to divide the solution domain into various grids and solve the numerical solutions at each grid node. Therefore, the accuracy of the solution is strongly correlated with the grid density divided. Based on this, this paper proposes a grid-free numerical calculation method that requires far fewer model parameters than traditional methods, and can ignore the order of the equation to solve high-dimensional Poisson equations. Given a Poisson equation, which has a certain type of boundary condition. A certain number of coordinate points are selected on the solution space and its boundary to construct a dataset. Using automatic differentiation technique to fit the differential operator in the equation, a loss function is constructed by incorporating the given boundary conditions or initial conditions, and the final numerical solution is obtained through iterative optimization algorithms. In the numerical experiment section, the algorithm proposed in this paper was used to solve the two-dimensional and three-dimensional Poisson equations with given exact solutions. The relative errors between the numerical solution and the true solution were 8. 5 3 e − 4 and 6. 4 0 e − 2 , which are within the acceptable range. This proves that the proposed algorithm is feasible for solving the two-dimensional and three-dimensional Poisson equations with precise solutions. Secondly, the proposed algorithm is used to solve the four-dimensional Poisson equation with first-type boundary conditions, and the relative error range of the solution was within [0,0.56], which successfully extends the algorithm to solve high-dimensional Poisson equations and verifies its feasibility and efficiency in solving high-dimensional Poisson equations regardless of the dimension restriction. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, subway construction has become a key part of urban development. However, shield construction often needs to cross existing railway lines, which requires accurate prediction of ground surface settlement of railway operation facilities to ensure railway safety and smooth construction. The existing Internet of Things (IOT) ground surface settlement prediction mainly relies on deep learning algorithms, but these algorithms are often difficult to take into account the extraction of local features and the capture of global features when processing long time sequence data, resulting in the prediction effect is affected when processing long-term dependent ground surface settlement data. In order to solve the above problems, this paper proposes a ground surface settlement prediction method based on T-BiGRU Mask Multi-Head Self-Attention Informer (TM-Informer). First, T-BiGRU is introduced into the model to effectively cover a wide range of time scales and extract the local feature of the settlement sequence data. Second, Mask Multi-Head Self-Attention (MMSA) is used to effectively solve the long-term dependence problem in long time sequence settlement data prediction and enhance the global feature capture ability of the model. In this paper, TM-Informer is compared with Informer, Transformer and LSTM by four evaluation indexes MSE, MAE, RMSE and MAPE. The experimental results show that the MSE, MAE, RMSE and MAPE of TM-Informer are 0.301, 0.432, 0.548 and 2.617, respectively, which are 0.08, 0.04, 0.06 and 0.26 higher than those of Informer, which proves the effectiveness of TM-Informer in railway ground surface settlement monitoring. [ABSTRACT FROM AUTHOR]

When the sources of renewable energy and numerous power electronic gadgets are introduced into the power system, which is related to the negative resistance features of power electronic gadgets, the power system with high power electronics often exhibits weak inertia. Also the stability of multi-converter grid-connected systems is adversely affected. To address these issues, a process of small disturbance stability analysis is proposed in this paper for the grid-connected systems of multiple grid-forming converters based on impedance ratio criterion. The Nyquist trajectory map of the system ratio of impedance in the complex plane is used in the suggested analytical method to estimate the quantity of RHPs in the right half-plane of the system, and the stability at the common points of the system is evaluated against the Nyquist criterion. In addition, the instability of grid-forming converters in grid-connected systems is reproduced by using grid-forming converters state space equation. Unlike the traditional methods of analysis, the method proposed in this paper can be applied to specifically judge the key equipment or control link of instability of the system, which provides guidance on optimizing the parameters of the system. Finally, simulation analysis is conducted to confirm that the model is accurate and that the proposed approach is applicable. [ABSTRACT FROM AUTHOR]

The widespread adoption of e-commerce has rendered online reviews an indispensable source for acquiring knowledge about product features. The Kano model serves as a classic framework for categorizing customer requirements. It was proposed by the Japanese scholar Noriaki Kano in 1984 as a tool primarily used to help businesses better understand and categorize customer needs, guiding product development and service quality improvement. However, existing research on mining online review information often relies predominantly on static analysis, lacking comprehensive categorization of user demands. To address this gap, this paper introduces an enhanced Kano model for the identification and dynamic analysis of user demands. This model classifies review data based on emotional polarity and user attention, leveraging the characteristics inherent in online reviews. Furthermore, it constructs a user demand evolution model using time series analysis and utilizes SnowNLP for emotional calculations. By conducting word frequency statistics and employing LDA models to analyze product features, this paper identifies the evolving trends in user demands. Additionally, the exponential smoothing method is used to forecast the trend in users’ emotional values towards the product. The findings demonstrate that this model effectively extracts valuable insights regarding product feature information from user reviews, thereby offering novel perspectives and methodologies for related fields. Consequently, this contributes significantly to advancing the identification and dynamic analysis of user demands. [ABSTRACT FROM AUTHOR]

This paper takes the wind turbine of the new energy pumping unit as the study subject, which mainly analyzes realistic wind speed estimation on the windward side of the wind turbine and then proposes a wind turbine speed control method based on wind speed estimation. The support vector regression (SVR) model is built first in this study, and the parameters of the SVR model are optimized by the particle swarm algorithm. The SVR II wind speed prediction model is constructed and the working characteristic curve of the wind turbine is generated by gathering wind speed and output power data from the wind turbine at the assigned test location. The prediction model can compensate for the disturbance of system inertia and make the output wind speed more accurate. The effective wind speed acting on the turbine surface can be obtained by substituting the predicted wind speed into the optimal blade tip velocity ratio equation. Suppose the wind turbine is in maximum power tracking control. In that case, the optimal speed can be given directly by the model SCR III, and the speed can be tracked in real-time by the controller to accomplish the largest wind power capture. The optimal speed tracking control uses the calculated effective wind speed if the wind turbine is under constant power output control. In this paper, 14,000 sets of characteristic samples were extracted within the range of wind speed variation of 4–20 m/s, and model verification was carried out for models SVR-I, SVR-II and SVR-III. Finally, the predicted effective wind speed value and the actual value are within ±0.3 m/s of each other. In practical engineering applications, the forecasted effective wind speed value can entirely substitute the actual value. This work put forward a maximum power tracking control approach that can satisfy new energy pumping units' energy conservation needs. [ABSTRACT FROM AUTHOR]

This paper proposes a new dielectrically modulated (DM) twin cavity nanotube biosensor and its sensitivity evaluation suitability by the transit time (τ) and device efficiency (g m / I ds ). In addition, the vertical structure of the device also helps the uniform spreading of biomolecules within the nanogap cavity region. The inner cavity of the proposed biosensor offers more space for the stabilization of biomolecules and uses the benefits of material solubility. In this paper, the sensing ability of the biosensor is examined in terms of τ and g m / I ds for five neutral biomolecules. Furthermore, the charged biomolecule and deoxyribonucleic acid (DNA) are separately investigated for positive as well as negative charge densities. Also, various optimizations on the device are analyzed for different biomolecules in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the linear active disturbance rejection control (LADRC) is applied to the position control of a dicing saw chuck table, which is used to improve the steady-state accuracy and anti-interference ability of the control system. Aiming at the problem that LADRC parameters are difficult to adjust, a parameter adjustment method based on an improved sand cat swarm algorithm is proposed. The nonlinear convergence factor strategy and multiple chaotic map optimization strategy are used to improve the algorithm. In order to verify the performance of the proposed method, the improved sand cat swarm optimization algorithm is compared with the standard sand cat swarm optimization algorithm, grey wolf optimization algorithm and salp swarm optimization algorithm. The final experimental results show that using the improved sand cat swarm optimization algorithm to optimize the LADRC controller has higher steady-state accuracy, stronger anti-interference ability, and better optimization results. [ABSTRACT FROM AUTHOR]

In this paper, an example of an intelligent security system based on deep learning approach has been analyzed and described. Security is an important element in many aspects of human life. Depending on customer requirements, different levels of security may be required and implemented. Nowadays, artificial intelligence is becoming an indispensable part in many areas of human life and accordingly security systems are also based on the new achievements in this field. In this paper, an intelligent security system based on two methods for identifying persons, gait recognition and face recognition, was described. The two methods mentioned above can be used to give persons access to different buildings, facilities, institutions, etc. or to different indoor or outdoor areas. In light of the above, the aforementioned system uses face and gait recognition to identify persons who should be granted access to a facility or a specific area within the facility. Accordingly, two deep learning models for gait and face recognition have been developed and described. The analysis, the settings defined and the results obtained in relation to the models developed were also described. [ABSTRACT FROM AUTHOR]

The increasing demand for low voltage, power efficient, high-speed analog-to-digital converters (ADCs) results in the improvement of speed and power of regenerative dynamic comparator. In this paper, a dual-tail dynamic comparator is used with two extra transistors in the latch stage. These extra transistors help in the increase of transconductance of the latch stage, which helps decrease the delay of the proposed comparator. Mathematical analysis is done for the proposed architecture; this gives the idea of reducing the delay of the comparator with an increase in the transconductance of the comparator. The simulation and layout of the proposed comparator are done on the Cadence software with 90 nm CMOS technology. This proposed design is simulated with a 2 GHz clock frequency at supply voltage of 1 V. The proposed architecture consumes a power of 39.19 μ W and a delay of 143.12 ps at 1 V supply voltage, 5 mV input difference voltage and 0.9 V common mode voltage. The Monte Carlo simulation of the proposed architecture for power, delay, power delay product (PDP) and offset is also demonstrated in this paper. Process corner analysis is done for power, delay and PDP. [ABSTRACT FROM AUTHOR]

Due to the complex underground environment of coal mines, the unsafe behaviors of miners are likely to lead safety accidents. Therefore, research on underground abnormal behavior recognition methods based on video images is gradually gaining attention. This paper proposes an underground abnormal behavior recognition method based on an optimized Alphapose-ST-GCN. First, an image set captured in underground monitoring video is defogged and enhanced by the CycleGAN. Second, the Alphapose target detection is optimized using the LTWOA-Tiny-YOLOv3 model. Third, the ST-GCN is used for abnormal behavior recognition. The image quality of the dataset before and after a CycleGAN enhancement is compared, the convergence curves of LTWOA under four test functions are compared, and the mean average accuracy mAP of the LTWOA-Tiny-YOLOv3 model is evaluated. Finally, the performance of the proposed method is compared with other detection algorithms. The results show that CycleGAN significantly improves the quality of the dataset images. The whale optimization algorithm improved by the logistic-tent chaos mapping has a more significant convergence effect than the other optimization algorithms, and the LTWOA-Tiny-YOLOv3 model has a better recognition accuracy of 9.1% in mAP compared with the unoptimized model. The underground abnormal detection model proposed in this paper achieves an 82.3% accuracy on the coal mine underground behavior dataset. [ABSTRACT FROM AUTHOR]

With the change of social structure and management mode, emergency social security incidents have become the focus of social security management. In order to solve the problems of insufficient overall performance and low security in the practical application of cloud management mode in traditional social security emergencies. With the use of improved pick-KX load balancing algorithm and RSA encryption algorithm, this paper optimizes the cloud computing model and applies it to the cloud management mode of emergent social security events. Then the optimized cloud management model is simulated and evaluated by the multi-level fuzzy comprehensive evaluation model. The simulation results reflect that the proposed cloud management model in this paper is able to effectively improve the management effect of social security events. [ABSTRACT FROM AUTHOR]

In order to address the problems of traditional inpainting algorithm models, such as the inability to automatically identify the specific location of the area to be restored, the cost of inpainting and the difficulty of inpainting, and the problems of structural and texture discontinuity and poor model stability in deep learning-based image inpainting, this paper proposes an image inpainting based on a contextual coherent attention. This paper designs a network model based on generative adversarial networks. First, to improve the global semantic continuity and local semantic continuity of images in image inpainting, a contextual coherent attention layer is added to the network; second, to solve the problems of slow convergence and insufficient training stability of the model, a cross-entropy loss function is used; finally, the trained generator is used to repair images. The experimental results are compared using PSNR and SSIM metrics, compared with the traditional GAN model, our model has a 3.782dB improvement in peak signal-to-noise ratio and a 0.025% improvement in structural similarity. The experimental results show that the image inpainting method in this paper has better performance in terms of image edge processing, pixel continuity and overall image structure. [ABSTRACT FROM AUTHOR]

The uninterrupted power supply (UPS) plays a dominant role in domestic appliances and also the growth rate of solar roof top system implementation is massively increasing day by day. To minimize the cost on the converters, solar PV power is directly connected with existing UPS with separate MPPT controller. However, due to the presence of nominal frequency transformer, the size and cost of UPS becomes more. In addition, it consumes the power with poor power quality to charge the battery. Apart this, the current supplied from the battery is much higher due to the lower voltage level of battery, which makes voltage dip during peak load condition and resulting to shorten the battery life and degrades its performances. In order to overcome all these drawbacks, this particular paper proposes a transformer-less novel hybrid converter based solar PV fed UPS system. In the hybrid structure, the gain can be achieved higher with minimum components counts. The novelty of the proposed transformer-less hybrid converter is that the regeneration of switched capacitor voltage is applied to input of switched inductor network in DC–DC converter, due to this hybrid structure the gain has been extended drastically with minimum number of devices. Also the coordinated control algorithm is implemented in solar PV fed UPS to take the decision according to the situation of generation, load and battery back-up. The detailed operation of the proposed coordinated control algorithm has been addressed in this paper. Moreover, the detailed simulation study is carried out for each mode of operation and measured results are obtained and presented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of online education course recommendations through a knowledge graph combined with reinforcement learning, and proposes a deep learning-based joint extraction method of course knowledge entities and relations in the education domain. This joint extraction method can extract both course knowledge entities and their relationships from the unstructured text of online courses, thus alleviating the problem of error propagation. On the other hand, since some parameters in the joint model can be shared by the entity identification task and the relationship classification task, this helps the model to capture the interaction between the two subtasks. Similar courses are judged based on the extracted course knowledge points, while course knowledge chains are generated based on the relationships between course knowledge points. In terms of user learning behavior, by analyzing user online learning behavior data, this paper uses five variables, namely the number of learning hours, the number of discussions, the number of visits, the number of task points completed, and the number of learning courses, to judge and cluster user similarity using an information entropy-based learner behavior weight assignment method. Based on the course knowledge map, this paper firstly constructs a learner model with four dimensions of basic learner profile, cognitive level, learning style, and historical learning records. Secondly, it predicts the target knowledge points of learners based on their learning data using the Armorial algorithm and maps them in the knowledge map, then uses natural language processing related techniques to find the conceptual similarity between knowledge points and proposes a deep recommendation strategy based on the knowledge graph correlations. At the same time, the recommended courses based on learners' behavioral data are more relevant and accurate, which greatly improves learners' efficiency and satisfaction in the learning process. [ABSTRACT FROM AUTHOR]

Using logic gates is the traditional way of designing logic circuits. However, in many cases, the use of modules is advantageous as the module is considered a uniform structure composed of multiple gates. In this paper, a nonlinear approach is proposed for designing logic circuits for use as modules multiplexers (MUXs) or Reed–Muller universal blocks (RMs). The experimental results show that the method gives better results compared to other methods available in the literature. The main advantages of the method are that it guarantees minimality and it can also handle Boolean functions for incompletely specified functions. The method is general enough and can be used for any kind of modules. [ABSTRACT FROM AUTHOR]

A miniature and high-efficiency interface circuit based on parallel synchronous switch harvesting on capacitors (P-SSHC) for piezoelectric energy harvesting (PEH) is proposed in this paper. This interface circuit consists of a two-stage synchronous rectifier and the P-SSHC circuit. The two-stage synchronous rectifier, composed of a negative voltage converter (NVC) and an active diode (AD), achieves higher efficiency compared with the full-bridge rectifier (FBR). In addition, the two-stage synchronous rectifier detects the zero-crossing moment of the input current; therefore, an extra current zero-crossing detection circuit is eliminated, which simplifies the structure of the interface circuit, reduces power consumption and improves peak converting efficiency. The P-SSHC circuit aims to improve the power extraction capability of the rectifier. The P-SSHC achieves considerable voltage flipping efficiency with very small volume compared to the parallel synchronized switch harvesting on inductor (P-SSHI), which is more suitable for volume sensitive applications. The proposed interface circuit is designed in SMIC 0.35 μ m CMOS process. Simulation results show that it achieves a 2. 9 7 × output power improvement compared with FBR for the case of a 3.4 V open-circuit voltage, the voltage flipping efficiency is as high as 83.6% and the peak power converting efficiency is up to 91.5%. The overall volume of the capacitors used in this paper is only 0.6 mm3, which is much smaller than the inductor used by conventional P-SSHI interface circuit. [ABSTRACT FROM AUTHOR]

The activation function is crucial in the Hopfield neural network (HNN) to restrict the input–output relation of each neuron. The physical realizability and simplicity of the hardware circuit of activation function are beneficial to promote the practical engineering application of the HNN. However, the HNN commonly used hyperbolic tangent activation function involves a complex hardware circuit implementation. This paper discusses a piecewise-linear activation function (PWL-AF) with simplified circuit implementation and a tri-neuron small-world HNN is built as a paradigm. The hardware implementation circuit of the HNN is greatly simplified, benefited from the PWL-AF with a simple analog circuit. Meanwhile, the dynamics related to the PWL-AF and initial conditions are numerically explored. The numerical results demonstrate that the PWL-AF-based HNN can produce dynamical behaviors like the HNN based on the hyperbolic tangent activation function. Nevertheless, the multistability with up to six kinds of coexisting multiple attractors emerged because of the PWL-AF breakpoint. This can give more flexible and potential aspects in multistability-based engineering applications. Especially, the PWL-AF breakpoint value simultaneously acts as the offset booster and amplitude controller in regulating the offset boosting and amplitude rescaling of neuron states. Afterwards, an analog circuit with three straightforward operational amplifiers (op-amp)-based circuit modules is designed for the PWL-AF, and a PCB-based analog circuit is thereby implemented for the tri-neuron small-world HNN. The hardware experiments agree with the numerical simulations, implying the feasibility of the PWL-AF simplification for the HNN. [ABSTRACT FROM AUTHOR]

Given the difficulties in measuring the effective wind speed of wind turbine during the maximum power point tracking (MPPT) process and the unknown nature of the system, this paper proposes a neural network super-twisted sliding mode control (NNST-SMC) method with echo state network (ESN) wind speed estimation. The rotor speed and electromagnetic power are taken as ESN inputs, and the effective wind speed is estimated through the inverse model of wind turbine dynamics. The super-twisted algorithm (STA) can effectively improve the chattering problem of the traditional sliding mode control (SMC) system. The RBF neural network is introduced to compensate for disturbance and uncertainty characteristics of the wind turbine. Results show that compared with the neural network first-order sliding mode control (NNSMC) and super-twisted sliding mode control (ST-SMC), the proposed method can improve the efficiency of wind energy utilization. [ABSTRACT FROM AUTHOR]

To achieve low power consumption and high performance, an approximate multiplier is a commonly used operation. A new approximate computation method has been used for Error Tolerant Image Processing applications. This operation is popular in error tolerance applications. Inexact computing is applied to error-tolerant digital signal processing applications where error can be tolerated. Multiplier is the basic unit of arithmetic logic unit of any computer computational unit. In this paper, a new approximate compressor computation method is proposed for error-tolerant image processing applications. An approximate compressor has been designed for better output power with an improved figure of merit. A comparison of the proposed compressor with the previous 4:2 compressor design has been investigated and a reduction in area, delay and power consumption has been achieved. A similar algorithm has been applied to 8-bit multiplier applications which have considerable error performance. With the newly designed multiplier using a compressor, the simulation results compared the best power utilization. The results of designing the multiplier indicate that the proposed design of a multiplier using an approximate compressor achieves a reduction in power by 129 mw. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) is the process of extracting sources from mixed data without or with limited awareness of the sources. This paper uses field programmable gate array (FPGA) to create an effective version of the Blind source separation algorithm (ICA) with a single Multiply Accumulate (MAC) adaptive filter and to optimize it. Recently, space research has paid a lot of attention to this technique. We address this problem in two sections. The first approach is ICA, which seeks a linear revolution that can enhance the mutual independence of the mixture to distinguish the source signals from mixed signals. The second is a powerful flexible finite impulse response (FIR) filter construction that makes use of a MAC core and is adaptable. The adjustable coefficient filters have been used in the proposed study to determine the undiscovered system utilizing an optimal least mean square (LMS) technique. The filter tap under consideration in this paper includes 32 taps, and hardware description language (HDL) and FPGA devices were used to carry out the analysis and synthesis of it. When compared to the described architecture, the executed filter architecture uses 80% fewer resources and increases clock frequency by nearly five times, and speed is increased up to 32%. [ABSTRACT FROM AUTHOR]

This paper has the aim of solving problems in research studies on the analysis tasks of text emotion; the problems are the low utilization of text, the difficulty of effective information extraction, the failure of recognizing word polysemy with effectiveness. Thus, based on LSTM and Bert, the method of sentiment analysis on text is adopted. To be precise, word embedding of dataset in view of the skip-gram model is used for training course. In each sample, the word embeddings combine matric with the two-dimensional feature to be neural network input. Next, construction of analysis model for text sentiment combines Bert pre-training language model and long short-term memory (LSTM) network, using the word vector pre-trained by Bert instead of that trained in the traditional way to dynamically generate the semantic vector according to the word context. Finally, the semantic representation of words from text is improved by effectively identifying the polysemy of words, and the semantic vector is input into the LSTM to capture the semantic dependencies, thereby enhancing the ability to extract valid information. The Accuracy, Precision, Recall and F-Measure for the method of Bert–LSTM based on analysis of text sentiment are 0.89, 0.9, 0.84 and 0.87, indicating high value than the compared ones. Thus, the proposed method significantly outperforms the comparison methods in text sentiment analysis. [ABSTRACT FROM AUTHOR]

In the field of Internet of Things, mobile communication will be deeply integrated with industrial construction, biomedicine, agricultural production, transportation and other industries to fully realize the "Internet of Everything" and realize smart city and sustainable development. This paper intends to solve problems in increasing latency and consumption of energy and security after deploying Mobile Edge Computing (MEC) servers in single cell multi-user network model, and has advocated a strategy of allocating the resources of 5G communication in regards to the protection mechanism for privacy in the environment of edge computing. First of all, solution model for the problem is characterized as a model with time delay and energy consumption. Second, privacy is used to measure the uncertainty of data in order to quantify privacy data while computing the allocation of tasks and facilitate the design of objective function. Finally, the task migration problem of minimizing energy consumption under the limitation of completion time is constructed as a nonlinear 0–1 programming problem. Besides, we design the optimal resource allocation strategy of discrete algorithm of binary particle swarm optimization (BPSO) for problem solution. Results indicate that while the quantity of users is 100, the total user expenditure of proposed method is 18.3J, which is lower than 20.0J and 30.7J of the compared methods. Moreover, the proposed method has lower latency and energy consumption, which can well balance the load of MEC servers. [ABSTRACT FROM AUTHOR]

This paper presents a high-efficiency operational amplifier (op-amp) used in the readout circuit of micro-sensor. The chopping and dynamic element matching (DEM) techniques are used in the proposed design, which greatly reduce the offset voltage and flicker noise (1 / f noise) of the op-amp. By optimizing the circuit structure, the maximum chopping frequency is increased to 2 MHz when the offset voltage is less than 2 0 μ V. Therefore, the maximum bandwidth of signal processing can be up to 1MHz. The proposed circuit is designed and fabricated using TSMC 0.18- μ m 1P5M CMOS technology. It occupies an area of 0.16 mm2 and consumes 7 5 μ A from a 1.8-V supply. When the chopping frequency is 100 kHz, the input-referred offset voltage is 4.21 μ V and the input-referred noise is 42 nV / √ Hz. It achieves a noise efficiency factor of 7.82 and a power efficiency factor of 110.07. [ABSTRACT FROM AUTHOR]

This paper investigates a novel projection neurodynamic approach for solving the basis pursuit denoising (BPDN) in a distributed manner. First, by using the distributed consensus theorem over undirected graph and supplementary variables method, the distributed version of BPDN is obtained. Second, with the help of projection operators, primal-dual dynamical system and derivative feedback terms, a novel distributed neurodynamic approach is proposed to deal with the distributed version of BPDN for sparse recovery. Moreover, the optimality and convergence properties of the proposed distributed projection neurodynamic approach (DPNA) are analyzed rigorously. Finally, we apply DPNA to sparse signal reconstruction which demonstrates the effectiveness of DPNA through numerical experiments. In addition, inspired by the role of image reconstruction technology in the field of defense against adversarial attack, we use DPNA as a preprocessing method to enhance the robustness of the deep model. Compared with known defense schemes such as JEPG, ComDefend, and OMP, our DPNA is better than them. [ABSTRACT FROM AUTHOR]

This paper presents a low-ripple and high-efficiency buck–boost DC–DC converter with delta-sigma modulator controller that converts 1.8 V to 1.2 V and 3.3 V. A current and voltage closed-loop controller has been designed to achieve better performance simultaneously such as lower settling-time, steady-state error and over shoot and under shoot. The converter is started in the current controller mode initially, when the output voltage approaches the desired value, the power stage is controlled with the voltage controller mode. PI compensator and a third-order delta-sigma modulator are utilized in the voltage mode. Also, the closed-loop stability of the designed converter has been analyzed with Lyapunov method using converter nonlinear model. This work has been simulated in MATLAB-SIMULINK environment. The performance results show that the maximum efficiency of the closed-loop DC–DC converter is 90% at 5 MHz switching frequency, and the output voltage ripple is 0.5% and 0.6% for the buck and boost modes, respectively. Also, the simulation results demonstrate the closed-loop stability of the converter in the presence of 10% load variation. [ABSTRACT FROM AUTHOR]

The new emerging non-volatile memory (NVM) devices known as memristors could be the promising candidate for future digital architecture, owing to their nanoscale size and its ability to integrate with the existing CMOS technology. The device has involved in various applications from memory design to analog and digital circuit design. In this paper, a combination of memristor devices and CMOS transistors is working together to form a hybrid CMOS-memristor circuit for XAX- Module, a core element used as digital circuit for elliptic curve cryptography. The proposed design was implemented using Pt/TaOx/Ta memristor device and simulated in Cadence Virtuoso. The simulation results demonstrate the design functionality. The proposed module appears to be efficient in terms of layout area, delay and power consumption since the design utilizes the hybrid CMOS/memristor gates. [ABSTRACT FROM AUTHOR]

The Dadda algorithm is a parallel structured multiplier, which is quite faster as compared to array multipliers, i.e., Booth, Braun, Baugh-Wooley, etc. However, it consumes more power and needs a larger number of gates for hardware implementation. In this paper, a modified-Dadda algorithm-based multiplier is designed using a proposed half-adder-based carry-select adder with a binary to excess-1 converter and an improved ripple-carry adder (RCA). The proposed design is simulated in different technologies, i.e., Taiwan Semiconductor Manufacturing Company (TSMC) 50 nm, 90 nm, and 120 nm, and on different GHz frequencies, i.e., 0.5, 1, 2, and 3.33 GHz. Specifically, the 4-bit circuit of the proposed design in TSMC's 50 nm technology consumes 25 uW of power at 3.33 GHz with 76 ps of delay. The simulation results reveal that the design is faster, more power-energy-efficient, and requires a smaller number of transistors for implementation as compared to some closely related works. The proposed design can be a promising candidate for low-power and low-cost digital controllers. In the end, the design has been compared with recent relevant works in the literature. [ABSTRACT FROM AUTHOR]

In order to improve the decision-making and control effect of autonomous vehicles, in this paper, combined with literature research and process analysis, the control algorithm of autopilot vehicle is analyzed, and the driving process is analyzed combined with the flow method. In order to improve the effect of autonomous driving, with the support of improved algorithms, an integrated decision-making control system for autonomous vehicles under multi-task constraints in intelligent traffic scenarios is constructed, and system performance is improved by simulating autonomous driving decisions in a variety of complex situations. Moreover, this paper designs the road driving model according to actual needs, sets the functional modules of the entire system, and build the overall framework of the system. Finally, in order to study the integrated decision-making effect of this system, this paper conducts test research by designing a simulation test method. From the simulation test results, it can be seen that the intelligent decision-making system for autonomous vehicles constructed in this paper has certain effects. [ABSTRACT FROM AUTHOR]

Virtualization technology, as the technical support for cloud computing, frees users from the limitations of hardware resources. Dynamic migration technology enables virtual machines to be moved across hosts, and services running on the virtual machines are basically uninterrupted during the migration process, which greatly facilitates the maintenance work in the data center. However, dynamic migration needs to transmit a large amount of repeated data. In order to improve the migration efficiency, this paper conducts the following research on the original pre-copy algorithm. First, we introduce a high dirty page determination algorithm based on the locality principle to classify memory pages and propose an improved memory migration pre-copy algorithm on this basis. Second, in order to solve the problem that the pre-copy algorithm cannot converge under a high load, an improved VM downtime determination algorithm is proposed. Third, the paper also introduces the LZ4 compression algorithm to further optimize the memory migration process. Finally, this paper implements the above optimization scheme on a KVM platform and tests its performance. It is compared with the native pre-copy algorithm and Three-Phase Optimization algorithm (TPO). The result shows that the optimized scheme significantly reduces the total migration time and downtime. [ABSTRACT FROM AUTHOR]

The importance of Phasor Manipulation Unit (PMU) in the smart grid makes it a target for attackers who can create PMU Data Manipulation Attacks (PDMA) by adding a small constant to change the magnitude and angle of the voltage and current captured by the PMU. To prevent the attack result from being detected by PDMA detection based on the properties of equivalent impedance, this paper proposes a collaborative step attack. In this attack, the equivalent impedance's value on the end of the transmission line is equal whether before or after been attack, which is taken as the constraint condition. The objective function of it is to minimize the number of the elements which is not 0 in attack vector but this number is not 0. Turn a vector construction problem into an optimization problem by building objective functions and constraints and then we use the Alternating Direction Method of Multipliers (ADMM) and Convex Relaxation (CR) to solve. The experiment verifies the feasibility of using the CR-ADMM algorithm to construct attack vectors from two aspects of attack vector construction time and vector sparsity. Further, it uses the constructed attack vectors to carry out attacks on PMU. The experimental results show that the measurement value of PMU will change after the attack, but the equivalent impedance value at both ends of the transmission line remains the same. The attack vector successfully bypasses the PDMA detection method based on the property of equivalent impedance and the attack model constructed based on this method was more covert than the original model. [ABSTRACT FROM AUTHOR]

A novel bipolar photon-controlled generalized memristor model with an avalanche photodiode (APD) passive quenching circuit is presented in this paper. The SPICE model of the circuit is established and its fingerprints are analyzed by the pinched hysteresis loops with different bipolar periodic stimuli. The dynamical characteristics of the proposed circuit model are investigated both theoretically and simulatively. The results verified by Cadence Spectre circuit simulator demonstrate that the proposed circuit model is a simple bipolar photon-controlled generalized memristor. Compared with the previously published memristor models, the biggest innovation of this paper is to propose a bipolar generalized memristor model instead of the traditional model, which can easily form the pinched hysteresis loop. Another highlight is that the generalized memristor model in this paper is controlled by photons while conventional memristors are charge-controlled/flux-controlled. Furthermore, the circuit level models are more stable, more reliable and more resistant to interference than the device level models. The topological structure of the proposed circuit model in this paper is much more simpler. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of various error compensation techniques for fixed-width multiplier design along with its applications. In this paper, we have studied different error compensation circuits and their complexities in the fixed-width multipliers. Further, we present the experimental results of error metrics, including normalized maximum absolute error , normalized mean error and normalized mean-square error to evaluate the accuracy of fixed-width multipliers. This survey is intended to serve as a suitable guideline and reference for future work in fixed-width multiplier design and its related research. [ABSTRACT FROM AUTHOR]

With the use of Convolutional Neural Network (CNN) in the application of sensor signal processing system, it usually faces the urgent requirements of system integration, high throughput, hardware resource and energy efficiency. This paper introduces a resource efficient accelerator with general two-dimensional multiply-add array operator to focus on the characteristic of sensor signal processing, which can be applied to standard CNN, depth-wise CNN, Fully Connected (FC) layer for varied networks. Meanwhile, resource estimation model is also constructed to provide the exploration of parallel parameters for computing efficiency. Finally, a board-level verification is implemented to demonstrate the efficiency of proposed accelerator with common scene of LeNet and complex scene of MobileNetV1. Experimental results show that the Inferences Per Second (IPS) of 332225 and 1498 is realized with 100 MHz frequency. The corresponding efficiency is 88.84% and 61.09%, which outperforms other related works about CNN accelerator design in terms of signal processing. This paper is also applicable and scalable to other fields about effective acceleration research. [ABSTRACT FROM AUTHOR]

At present, the informatization of basic education quality assessment has become a hot topic in the field of education and is playing an increasingly important role. Based on the theory of deep convolutional neural network, this paper adopts the methods of mathematical analysis and experimental research to construct a regional basic education quality assessment model. The model solves the data informatization problem of education quality assessment. In the simulation process, two key modules of data self-assessment and expert assessment of the deep convolutional neural network are realized by ASENET+SQL SERVER, and the assessment results are integrated by using the weighted average method and the fuzzy comprehensive assessment method. The experimental results show that the quantitative analysis of the quality assessment is carried out by using the logic and support relationship, and the results of comprehensive qualitative analysis and quantitative analysis are realized and segmented when the threshold level is 9, the MIOU obtains the highest value of 0.7501, and the MIOU of the multi-stage method of the quality evaluation model proposed in this paper is 0.8116, which is 6.15% points higher than the traditional multi-stage algorithm, which effectively improves the current stage area quality of basic education. [ABSTRACT FROM AUTHOR]

Quantitative training virtualization can be applied in the fields of human–computer interaction, virtual reality and motion analysis, and has attracted much attention. Based on the virtual reality theory, this paper constructs a training quantitative evaluation method, evaluates the trainer's ergonomics, and finally compares the evaluation method with the simulation results. According to the collected training data, this paper uses the methods of model reuse and feature parameter adjustment in the Jack software to quickly generate a 3D training model with the required percentages, and performs data reorganization and analysis on the captured training data, which solves the problem of training quantitative evaluation and the problem of uncertainty of results. During the simulation process, according to its reorganized data, a large number of comparative experiments and evaluations were performed on the performance of the model proposed in this paper on multiple indicators of multiple public data sets. The experimental results show that the cycle is reduced by 10 times, the mini-batch is 32, and the sequence length is 16. In this way, the spatial characteristics of the channel coupling relationship can be better analyzed, and the effect of spatial cognitive training can be effectively evaluated. The training pose and shape estimation model and the corresponding data set and multiple indicators have reached the performance of the existing state-of-the-art models. The integrated model of accurate training pose and shape sequence in the camera coordinate system can be reconstructed, which effectively enhances the effectiveness of the virtual scene parameter adjustment strategy. [ABSTRACT FROM AUTHOR]

The aim of this paper is to solve the problem of a large amount of redundant data in the process of large-scale acquisition of Internet of Things sensors. This paper proposes a frequency adaptive data sensing method based on revolving gate algorithm for STM32 power safety data acquisition system. The method realizes the intelligent control of current protection terminal through three-stage current protection algorithm and frequency adaptive data acquisition algorithm. First, the three-stage current protection algorithm is used to protect the circuit, which can trip quickly in case of overload, and reduce the peak current caused by some equipment when starting up, so as to avoid damage to the equipment. Second, the arithmetic mean of each statistic is compared with the last value reported to the server. If the absolute value of the difference between the two exceeds the specified threshold range, the reported value is updated. Otherwise, it is filtered out, and the data smoothness is calculated according to the rules. The data collection interval is dynamically adjusted according to the data smoothness, which can greatly reduce the collection of redundant data and the traffic consumption of Internet of Things devices. On the server side, the inverse algorithm is used for interpolation and reconstruction to recover the collected data. This method is applied to the developed electricity safety equipment, and the electricity consumption is monitored and collected continuously for 24 h. After filtering by the frequency adaptive algorithm, only 108 pieces of data records are reported for 28,713 pieces of original data, and the compression ratio reaches 99.49 %. Compared with other data collection strategies, this method significantly reduces the amount of redundant data collection and the energy consumption of terminal nodes. Furthermore, this method realizes the real-time perception of line data and environmental data through current, residual current, temperature sensor and electric energy metering chip, and controls the opening and closing of wire controlled micro circuit breaker through PC817 optocoupler to protect the safety of the circuit. [ABSTRACT FROM AUTHOR]

The price of the product has a great impact on the market competitiveness of the product. The manager can adjust the operation strategy according to the cost information of the product, cut costs, adjust the market decision and make the product more competitive in the market. Therefore, the research of product costing method has a very important significance. Considering that the physical resources in the processing of the virtual network mapping (VNM) fragment embedded in the request are rejected, and the problem of physical resource usage is weakened, a VNM algorithm based on the optimal subnet edge matching algorithm is proposed. After the VNM request is coarsened to the subnet, the breadth-first search algorithm is used to create the physical subnet candidate set, and the constraints of the virtual node and the coarsening network topology are complied with. The simulation results show that the proposed algorithm reduces the number of link mapping hops and can improve the acceptance rate and cost-benefit ratio of virtual networks. First of all, this paper analyzes the research background of manufacturing cost calculation and the research situation at home and abroad, discusses the advantages and disadvantages of standard cost calculation and studies and analyzes the principles, advantages and disadvantages of standard cost calculation in daily business activities. Second, design a new mathematical accounting model based on the mathematical model of standard cost calculation in daily business activities. This combines the advantages of standard costing and time-based activity costing, and provides theoretical support for manufacturing companies to use this accounting method. Finally, the research results of this paper are summarized, and the shortcomings of this paper are analyzed. Cost accounting method needs to be further studied. [ABSTRACT FROM AUTHOR]

Achieving higher voltage at load side is essential in many applications, especially in small-scale solar PV systems. Traditional boost converter fails to lift the voltage level beyond five times due to saturation in the passive elements. In order to attain high voltage gain ratio, a novel switched-inductor and switched-capacitor networks-based high-gain step-up converter is proposed in this paper. It can attain higher voltage gain at lower duty ratios, thereby reducing the losses associated with the converter and enhancing the efficiency. The detailed analysis of the proposed converter has been made under the CCM and DCM conditions. Moreover, investigation on device stresses is carried out and the results are compared with recently derived converters. Also, the performance comparison of the proposed converter with other published topologies is presented in this paper. In order to validate the operation details, the measured results are presented and the same have been confirmed with the theoretical approach. Furthermore, experimental works have been carried out and the results are presented to confirm the feasibility of the operation. [ABSTRACT FROM AUTHOR]

Symmetry is not only used to simplify the analysis of three-phase electrical systems, but it is also used to define its voltages, currents and loads. When the loads are symmetrical, the currents of a three-phase AC system are expected to be symmetrical as well. Given the proper conditions, in converters such as the matrix converter (MC), the output voltages and currents are expected to be sinusoidal with periodic symmetry. However, in some cases this symmetry is broken so that, there appears nonlinear behaviors such as quasiperiodicity and so on. Based on simulations and experiments, this paper focuses on an analysis of a quasiperiodic behavior and the presence of a DC component in the output currents of a Venturini modulated MC. The presence of such behaviors in the output currents indicates that the symmetry in a period is broken. The broken symmetries appear when the input and output frequencies are mismatched. In addition, this paper shows the possibility to recover the symmetry of the output currents of the MC. The method for symmetry recovery is based on a time-delayed feedback control. The simulation and experimental results indicate the possibility of attenuating the quasiperiodic behavior and DC component. [ABSTRACT FROM AUTHOR]