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In order to improve the effect of intelligent sports prediction and analysis, this paper analyzes the traditional high-speed model and proposes a reliable sports action recognition algorithm on this basis. With this algorithm as the core algorithm, sports action recognition can be performed. The paper mainly constructs a standard database structure, inputs a reliable standardized movement model into the database structure, and compares the identified sports movement model with the standard movement model to judge the accuracy of the sports movement. Moreover, this paper uses statistical analysis to study sports training and sports competitions, and obtains prediction results through multi-factor analysis and comparison. In addition, this paper uses statistical methods to process experimental data to draw graphs and tables that can visually observe the results of sports predictive analysis. From the experimental analysis, it can be seen that the algorithm model constructed in this paper has a significant effect.

For multi-attribute decision-making problems with interval numbers whose attribute weights are completely unknown, it is difficult to compare the value of interval numbers. So this paper proposes a method based on TOPSIS and the weighted parameter to deal with it. Firstly, this paper transforms the interval number matrix into two exact number matrices which reduces sorting complexity. Secondly, a parameter is given when determining the weights with entropy weight in order to reflect all the information of the interval numbers. Then TOPSIS is used to determine the order of each scheme. In addition, the average value of the ranking number is used to reflect the actual situation better. Finally, the analysis of the car purchase case shows the proposed method is feasible and practical. And the comparative analysis with the other method based on practical application dataset demonstrates the proposed method is stable and effective.

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.

The paper deals with the performance checking of an optimally designed LCL-filter connected along the grid-side converter (GSC) in a doubly fed induction generator (DFIG) based wind energy conversion system (WECS) in attenuating harmonics. A repetitive approach is required for designing the parameters of the LCL-filter. The reasons being the explicitness of design parameters and design requirements of the proposed filter like IEEE-519 Std. for harmonic current attenuation, maximum voltage difference allowed in between two sides of the filter to limit switching losses, and limit of reactive power compensation. The proposed filter is compared with the conventional LCL-filter. Based on the proposed method, a minimal inductance and optimal capacitance-based filter is obtained in this paper which is validated using a 2.2 kW DFIG based WECS. Comparative stability analysis of DFIG system with and without proposed filter is performed using small signal stability analysis. The system under study is simulated using MATLAB/Simulink and OPAL-RT 4510, simultaneously. Simulation results validates the effective use of proposed filter design method into DFIG system in terms of harmonic attenuation, total harmonic distortion and reactive power compensation.

With the continuous use of various new radar systems and complex radar systems, the electromagnetic environment is extremely deteriorated. The traditional emitter recognition methods have been difficult to meet the requirements of recognition performance in the rapidly changing battlefield environment. To solve this problem, a radar electronic signal recognition algorithm based on wavelet transform and deep learning is proposed in this paper. Starting from the radar reconnaissance system, the causes of signal preprocessing are analyzed, and the methods of signal denoising, signal normalization, signal intra pulse modulation recognition, multipath signal detection and suppression are deeply studied. In particular, the denoising algorithm based on threshold wavelet transform is proposed, which significantly improves the reliability of the algorithm. Aiming at the individual feature extraction of emitter signal, the extraction methods of emitter signal time domain feature, frequency domain feature, fuzzy function slice feature and cyclic spectrum feature based on wavelet transform are studied and analyzed, which provides stable and reliable classification features for emitter signal recognition. According to the characteristics of radar emitter signal, an optimized convolution neural network is designed, and the feature fusion processing is carried out at the decision-making level, which greatly improves the recognition effect and enhances the robustness of the recognition system. Experiments on radar data show that the fusion recognition rate is higher than any single feature and has strong robustness. In addition, compared with the traditional SVM and elm networks, the CNN network proposed in this paper can extract detailed features more effectively and improve the recognition rate of electronic radar.

In order to build a smart campus teaching system with good teaching efficiency, this paper improves the traditional ZigBee algorithm and builds a smart campus teaching system based on ZigBee wireless sensors. Moreover, this paper improves the estimation algorithm of students' specific location and direction on campus, and analyzes the network structure of the smart teaching system based on the actual needs of smart teaching, and builds a three-tier teaching structure system. In addition, on the basis of demand analysis, this paper sets the system function modules, analyzes the data processing effect of the network nodes of this smart campus through experimental research, and evaluates the system teaching effect. Finally, this paper uses simulation technology to analyze 100 sets of data to evaluate the data processing effect of ZigBee network. Through the experimental research results, we can see that the smart campus teaching system constructed in this paper has good results.

With the development of science and technology, the power source of the automobile industry has gradually changed from non-renewable energy sources such as petroleum to electric energy, a clean and renewable energy source. By the end of 2020, scientists have found that the global demand for batteries has greatly increased. The demand for power batteries has reached 50% of all battery demand, occupying half of the market. In addition, scientists also boldly predict that by 2025, China's consumption of batteries in the automobile industry will reach more than 100,000 tons per year. Faced with so much battery consumption, how to dispose of and recycle LiFePO4 used batteries has become the key to restricting the development of the industry. Failure to dispose of used batteries will not only hinder the development of the industry, but also cause serious pollution to the environment. The application of sensors today has been extended to various fields, and the emergence of satellite navigation and positioning technology is one of the driving forces to promote its development. However, the sensor cannot be directly applied to various environments. When in a complex environment, the obstruction of various objects in the environment will seriously affect the stability of the sensor. In order to ensure the normal operation of the sensor, positioning technology began to be applied to the sensor. The industrial production process of solid-phase regeneration and electrochemical performance of waste lithium iron phosphate materials discussed in this article is based on this perspective, through the use of computer vision positioning to reduce the impact of environmental factors on the accuracy of the sensor, thereby improving the quality of the battery. In order to match the image point of the waste lithium iron phosphate material with the space point, this paper creatively uses the image point and the point in the corresponding space field. In addition, the author also studied the camera's rear rendezvous method and proposed a new rear rendezvous method. In addition, in order to strengthen the detection of waste lithium iron phosphate materials, this paper strengthened the construction of the sensor hardware system and software system, and completed the upgrade of the waste lithium iron phosphate material detection system.

Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

Color medical images hold noteworthy impact in clinical conclusion and treatment. Medical image segmentation reduces the uncertainty by providing detailed information about the shape, size, or location characteristics. However, traditional methods suffer from low accuracy, high complexity, and are less robust. To overcome these drawbacks, this paper proposes an efficient metaheuristic algorithm, exchange market algorithm (EMA) for multilevel thresholding (MLT) of distinct medical images. Optimal threshold is effectively obtained through the most promising objective functions such as Kapur, Otsu and minimum cross entropy (MCE) aided with EMA. The EMA involves exchange of shares among the investors in stable and unstable market situations to achieve profit. Exploration and exploitation are achieved by second and third groups of stable and unstable modes of EMA. Moreover, the execution time is reduced by the highly competent shareholders retaining their top rank without any changes in their shares. The efficacy of the proposed paper is evaluated on three distinct medical images at 4, 5, 6 and 7th threshold levels and compared with the recent algorithms such as Krill herd (KHA), Teaching-learning based optimization (TLBO) and Cuckoo search algorithm (CSA). Quantitative and qualitative validation by metrics such as computational time, Peak signal to noise ratio (PSNR), Structural similarity index (SSIM) and Wilcoxon rank sum test affirm that the EMA is superior to other algorithms. On the other hand, Otsu based EMA method is found to be more accurate and robust for improved clinical decision making and diagnosis.

Oil price forecasting is one of the most challenging issues since it is noisy, non-stationary, and chaotic. In this paper, we design a Bayesian Nonlinear Quantile method consisting of a Threshold Improved model and an Adaptive MCMC model to improve predicting performance. Specifically, the threshold improve model is introduced to solve the problems caused by the completely asymmetric distribution, and the Adaptive MCMC model is used to get the optimal threshold. Besides, the two-stage framework is applied to improve traditional methods' performance, including the Indirect GARCH model and Asymmetric Slope model. The experimental results show that our approach provides a promising alternative to oil price prediction, and the framework also improve the performance of the traditional methods. The contribution of this paper is to improve the accuracy of the oil price forecasting model, and the framework applies to other energy prices as well.

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters. The main purpose is to explore the relationship between sports biomechanical parameters. The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training. Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed. The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system. In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement. The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability.

A novel passive frequency beam-steering linear antenna array is proposed in this paper to be utilized at the transmit side of a long-range wireless power transfer (WPT) system. To assure high power handling for a long-range transfer, the proposed antenna is designed based on stripline technology. Moreover, unlike other reported designs, the antenna array in this paper uses parallel feed while frequency beam-steering. This permits to apply a special synthesis at the radiating elements to minimize the energy wasted at the side lobes. The antenna array uses common-fed dipoles as radiating elements to eliminate the surface current at the ground planes that is considered a critical issue in high power applications. The antenna covers the frequency band from 1.1 to 1.5 GHz with a reflection coefficient better than −10 dB (VSWR

As the most basic travel mode of residents, walking trip takes place more frequently in residential districts. It is of great significance to study the land use characteristics of the residential district suitable for walking in order to create a good walking environment and land use model. In this paper, 18 residential blocks in Xi'an are taken as the research object, based on the relevant data of the pedestrian flow rate and land use of residential blocks, this paper analyzes the interactive relationship between the non-commuting hours during the week, the commuting hours during the week and the weekends by using the multiple linear regression model, and the land use characteristics of residential blocks under the guidance of walking trip are revealed. The results show that the land use factors related to pedestrian flow rate are mainly 9 factors, such as building density, buildings mixing degree, mixing degree of business functions and density of functional formats. In different hours, walking trips have different demands on various elements of land use. During the non-commuting hours during the week, the demand for walking trips for building density, mixing degree of business functions, density of functional formats and footpath area ratio is large. During the commuting hours during the week, the demand for walking trips for buildings mixing degree, density of functional formats, road network connectivity, and green area ratio of footpath is large. During the weekends, the demand for building density, density of functional formats, road network density, and footpath density is large. Through the above analysis, the optimization strategy is proposed for residential blocks in Xi 'an, which provides a theoretical basis for the optimization and transformation of the space environment of residential blocks to promote walking trip.

This paper exhibits a novel approach for solving fully intuitionistic fuzzy multi-objective fractional transportation problem (FIF-MOFTP). Because of the change of market policies, we expect that the transportation cost, the shipped quantity, the source, and the destination parameters are not generally exact. In this paper, a theorem which demonstrate that FIF-MOFTP is ever solvable was presented. In our approach, the problem is converted into a linear one based on some transformations. Then the linearized model is reduced to a crisp multi-objective transportation problem utilizing the accuracy function for each objective. Moreover, various theorems that set up the relation among the FIF-MOFTP and its equivalent crisp model using linear, hyperbolic, and parabolic membership functions are also proofed. To approve the proposed approach, a numerical example is incorporated. Deductions and future exploration of this paper are depicted at end.

Cyber-physical system (CPS) is the core technology of Industry 4.0. The deterministic behaviors of the CPS require real-time deterministic guarantee. Therefore, this paper improves the ant colony optimization (ACO) into a scheduling algorithm for time-triggered flows in time-sensitive network (TSN), a standard developed by the IEEE 802.1 Working Group that fully satisfies the strict end-to-end delay requirements of industrial applications. Simulation results show that the improved ACO (IACO) can schedule the time-triggered flows in the TSN excellently, and outperform the traditional ACO in convergence speed, optimization ability and the proneness to local optimum trap. To sum up, this paper provides an effective real-time guarantee for the TSNs.

The timely access to accurate traffic data is essential to the development of intelligent traffic systems. However, the existing traffic prediction methods cannot achieve satisfactory results, mainly because of three factors: the structure is too simple to extract deep features; many external factors are overlooks, such as weather and traffic incidents; the nonlinearity of traffic flow is not well handled. To solve the problem, this paper improves the deep belief network (DBN), a deep learning method, for accurate traffic prediction under poor weather. Firstly, the data of poor weather and traffic data were collected from IoV, rather than induction coils in traditional methods. Next, the support vector regression (SVR) was introduced to improve the classic DBN. In the improved DBN, the underlying structure is a traditional DBN that learns the key features of traffic data in an unsupervised manner, and the top layer is an SVR that performs supervised traffic prediction. To verify its effectiveness, the improved DBN was applied to predict the traffic data based on the traffic data from the control center of an expressway and the weather data from local monitoring stations, in comparison with the autoregressive integrated moving average (ARIMA) model and the traditional neural network. The experimental results show that the improved DBN controlled the traffic prediction error within 9%, and maintained good robustness despite the extension of the time interval. To sum up, this paper provides an effective way to predict traffic flow under poor weather, shedding new light on the application of deep learning in traffic prediction.

Due to the absence of power demands and yet effective performances, the semi-active tuned mass dampers (STMDs) have been broadly investigated. However, there is still a gap between the theoretical simulations and practical implementation of controlled tuned mass dampers. Moreover, using displacement and velocity sensors make such control systems less cost-effective, less reliable, and more complex. In this paper, the acceleration-based ground-hook semi-active control (ABGH) was proposed for an electromagnetic regenerative tuned mass damper (ERTMD). In this manner, a prototype of a compact back iron-based ERTMD was manufactured. The concept of a controllable system with switched transistor was utilized for the proposed ERTMD. Comparative simulations between the controlled and passive ERTMD was conducted and then validated by experimental results of the ERTMD prototype implemented in a protected mass test-bench similar to a typical vehicle unsprung mass. This paper concluded that the proposed control policy can be utilized for the ERTMD to increase the regenerative ability of the fabricated ERTMD under random excitations.

In this paper, the application of spatial partial differential equation in user intent oriented information mining is proposed. The hybrid mining algorithm is used to avoid the defects of a single algorithm, which makes the discovery and identification of user intent efficient and practical. The v-svc algorithm can suppress the classification of errors, the hierarchical generation based on attributes can easily get the focus of services, and the fuzzy clustering method can get the service level or service quality of users. This method can be used for any complex object research similar to user intention and the evaluation mechanism of a research object. In view of the problems of traditional methods, this paper puts forward the technology of association mining based on the mathematical model of partial differential classification, and makes an experimental comparative analysis. The results show that the convergence, stability and performance of this method are good.

Based on the stability theory of nonlinear differential equation, this paper analyzes the stability of the evolutionary game supply chain, and obtains the strategies that both sides should adopt in different situations. In this paper, aiming at the problem of various random interference factors in the attack and defense game system, the concept of Gauss white noise is used for reference, and it o random differential equation is used to establish the random attack and defense evolution game model. The influence of various random interference factors such as system environment and strategy change on the selection and evolution of attack and defense strategy is analyzed. According to the stability discrimination theorem of stochastic differential equation, the stability of attack and defense strategy is analyzed. In addition, a security strategy selection algorithm based on evolutionary game model of random attack and defense is designed. The results show that under different conditions, there is a unique evolutionary stable strategy in the attack and defense game system.

IEEE 802.15.4 protocol is intended to accomplish the characteristics of low power and low rate Wireless Sensor Network. It employs carrier sense multiple access with collision avoidance (CSMA/CA) protocol to access the network using either unslotted or slotted mode. This paper concentrates on the unslotted CSMA/CA where the node tends to wait for a very limited backoff exponent value, and cannot perform clear channel assessment until backoff procedure is completed. Therefore, the collision probability and the average delay are high. To enhance the unslotted CSMA/CA performance, this paper proposes a modified unslotted CSMA/CA that divides the backoff delay into Main Backoff and Secondary Backoff. The Markov model for the modified CSMA/CA is presented to assess a single hop IEEE 802.15.4 behavior, and derive expressions for average delay, energy consumption and reliability. An optimization problem is also presented to minimize the delay while guaranteeing energy consumption and reliability constraints for the modified CSMA/CA. OPNET simulation tool is applied to compare the modified to the standard unslotted CSMA, and to validate the Markov model proposed. The results demonstrate that the modified CSMA improves the reliability, while reducing the average delay significantly.

This is a review on the published paper (Mirzaee and Hoseini, 2013), where there are some major scientific errors. After considering these errors, we attempt to correct them by presenting an accurate formula. In addition, to illustrate how the method works in practice after our correction, we introduce some numerical examples and present the corresponding results. Finally, we have a short review on the paper (Mirzaee and Hoseini, 2014) by the same authors. The same scientific errors are occurred in this paper, motivating us to correct them, as well. Keywords: Integral equations, Block-Pulse functions, Taylor polynomials

The process of selecting new uses for heritage buildings needs to consider a number of crucial criteria in order to determine and select the optimum option. The inappropriate selected use of a heritage building can lead to lose its value. This paper aims to introduce an appropriate evaluation tool to support the efficiency in selecting the optimum solution. The integrated multi criteria decision-making tools are used to support decision making in such cases (problems involving multiple criteria). Several MCDM tools have been developed for choosing the optimal probable options. Each tool has its own characteristics in finding the best alternatives. The Analytic Hierarchy Process, (AHP) that was developed by Professor Thomas Saaty in the late 1970s, was the most suitable tool for this study. The software selected and used in this work was Microsoft Excel. Aziza Fahmy Palace was selected as the case study in this paper. There are four suggested alternatives; which are: hotel, museum, office building and mixed uses. Using the application of AHP, the result showed that the best use is mixed uses with the highest value, which is 0.30 (30%), followed in sequential order by museum, office building and hotel. Keywords: Heritage buildings, Multi-criteria decision making, Analytic hierarchy process, Re-use selection

Previous studies indicate that floating structures developed through the last decades concluding that it’s the most sustainable solution against sea level rise (SLR), preferring it to land reclamation approaches regarding sustainability, lifespan, and cost-effectiveness, that Egyptian researches seem to disregard. This paper guides architects and urban planners when designing floating structures by introducing new approach of floating community model and architectural design guidelines for floating structures for tackling SLR impacts on Abu-Qir. To illustrate such approach, this paper initially conducts analysis on Abu-Qir and coastal management solutions practiced as their properties provide the required knowledge for selecting the best mitigation solution for such area. Second, it conducts exploratory analysis to investigate floating structures as a more sustainable and long-term solution. Third, applying conclusions from previous sections of site analysis, mitigation solutions, and floating structures to define a floating community concept model for Abu-Qir. Findings support key arguments that traditional mitigation methods aren’t sustainable or long-term solutions. Analysis demonstrate Abu-Qir Bay as the most vulnerable area to SLR. Although floating structures have demonstrated not to be the optimum solution in every case regarding cost-effectiveness and/or site suitability, however upon site analysis, it presents itself as the ideal solution for Abu-Qir bay. Keywords: Sea level rise, Land subsidence, Adaptation approaches, Architectural design of floating structures, Sustainability, Egypt

Parametric CAD modelling systems requires the successful CAD model creation and modification in the product design phase. It demands persistent identification of topological entities, retrieving faces, edges, and vertexes are done with those entities specific unique names called persistent IDs. Those names should contain their unique properties which would be unambiguous among topological entities. In this paper, we are going to present reviews of existing persistent identification mechanisms and discuss their merits and demerits based on topology based approach, geometry based approach, or a combination of both ones. This review paper tries to define persistent identification mechanism not simply by topology and geometry but by their unique properties like feature orientation based persistent identification, faceIDGraph based persistent identification etc. Previous papers classified the persistent identification mechanism by only the topological or geometrical properties of entities which are too simple to understand the inside merits and demerits of that method. This paper also identifies the advantages and limitations of the current persistent identification approaches while suggesting a new approach should be developed consisting of both topology and geometry based approach. This paper also suggests the future direction of persistent identification of parametric CAD models and their entities. Keywords: Feature based CAD, Persistent identification, Topology, Geometry, Topological entity

This paper presents state of the art reconfiguration techniques used for service restoration in distribution systems under different practical considerations. The different formulations of the problem together with the different solution methods are presented to show the advantages and disadvantages of each formulation and each solution. Other aspects that enhance the solution practicability such as load variations, load priority, cold load pickup, network connectivity representation, and existence of distributed generation are considered with directions for future research to convert current distribution system into an automatic self-healing system, which is the main pillar of the smart grid. The control method and the communication techniques used in the execution of the reconfiguration problem solution are highlighted and the new research directions in each issue are emphasized. The conclusion about the reconfiguration problem the formulation, the solution method, and the guidelines about unexplored research areas in this topic are presented at the end of the paper. Keywords: Distributed generation, IEC 61850, Network reconfiguration, Restoration, Smart grid

More issues in construction management were found especially for decision making that related to the Arabian construction management office requirements. Operation research especially linear programming models considered one of the most important tool used in optimization applications at many fields of production engineering and mass production, also linear programming applications was developed to construction engineering field. This paper presents a linear programming technique to spotlight decision making application for optimizing competitive bidding strategy to select best tender as shown in real case study. Therefore, project manager or decision maker can use this concept for getting the best project cost. This paper give linear programming concepts that are reviewed to describe recent linear programming component which had large focus on related time-cost and time problems for studied project. Linear programming models are formulated to solve various cost and time problems by using LINDO software. The developed models had many limitations and restrictions for studied project. Construction managers can use it to explore more possible opportunities to predict influence of decision for construction to facilitate preferred different management objectives. Linear programming implementation shows the practice of wide variety for construction problems especially cost with time issues and it is more applicable to generate a shortest computational effort and time with low cost. Keywords: Linear programming, Construction management, Cost optimization, Tendering strategy, Optimization model, Contractor selection and decision making

The Internet and the Internet of Things (IoT) connect the world inside and outside the network through online and offline communication, which forms the original single network virtual platform. The integration of IoT, virtual reality and artificial intelligence can provide intelligent WebVR interaction for various application fields. However, we need to solve the problems of data lightweight, real-time data transmission based on the IoT, and visualization of scene data on the Web. This paper uses the IoT and virtual reality technology to propose a virtual reality scene modeling method based on the IoT platform, as well as the database dynamic loading management method and device of the virtual reality system, which includes building the database, storing the involved data structures for various scenes and functions in the virtual reality system. The time–space IoT engine is connected with the integrated service sharing center. The IoT platform collects data through the IoT sensor system and provides support for the application platform through the integrated service sharing center. The proposed method can meet the needs of large-scale data acquisition and modeling, shorten the loading time, improve the efficiency, and save the wasted computing resources and memory.

The 5G networks promise to use advanced access techniques and modulation formats to achieve new requirements of the huge number of users such as higher data, high speed, and low latency. Millimeter-wave (MMW) signal and new modulation schemes such as universal filtered multicarrier (UFMC) and filtered bank multicarrier (FBMC) are playing a fundamental role to accomplish these requirements. UFMC and FBMC are classified under multicarrier modulation formats which are considered as a suitable modulation scheme in 5G and replacing a well-known modulation format of filtered orthogonal frequency division multiplexing (OFDM) which was used in 4G. This interest in using UFMC and FBMC in 5G wireless systems is the reduction of out-of-band (OOB) spectrum in UFMC and reduction of sidelobe suppression in FBMC. The contribution of this paper is to propose a model of wavelength conversion using a semiconductor optical amplifier (SOA) for both schemes UFMC and FBMC- off-set quadrature amplitude (OQAM). In addition, we analyze and compare their performance in terms of bit error rate (BER) and error vector magnitude (EVM) for a wavelength conversion application. Finally, we investigate the main disadvantage of OFDM which is the high Peak to Average Power Ratio (PAPR) for both schemes, and compare between them. Photonic switching of a 4 Gbps 16-QAM UFMC and FBMC-OQAM signal centered at 50 GHz is performed. The optical single-sideband (OSSB) signal is generated with 18.85 dB of sideband suppression ratio (SSR). The results show that the best values of BER and EVM can be obtained when the injection current (IC) is 0.7–0.9A. For converted signal using FBMC-OQAM, it has power penalty ∼ 2.5 dB at BER threshold 1 × 10 -3 and has also ∼ 2 dB power penalty compared to the UFMC scheme at EVM threshold 5.6%. In addition, the wavelength switching system with FBMC and UFMC modulation schemes achieves the same PAPR.

This paper proposes a design for a modified distribution static compensator (DSTATCOM) with a proposed current controller for microgrid (MG) applications. The modified DSTATCOM is incorporated in a three-phase four-wire network system to deal accurately at grid connected mode with balanced, unbalanced and excursion load for both nonlinear and inductive loads. Moreover, it handles the stability issues of the MG at fault caused islanding incidents. The design criteria of the modified DSTATCOM parameters is presented and discussed. The hysteresis current controller is introduced to drive the voltage source converter (VSC) and its reference voltage signal is generated based on the instantaneous symmetrical components theory (ISCT). Matlab/Simulink software is used to verify the performance of the modified DSTATCOM with the proposed controller under different load types and loading conditions for grid connected mode and islanded mode of MG operation. Based on the simulation results, the power factor is improved and the non-linear harmonics currents are compensated therefore the harmonic distortion of the supply currents and voltage are reduced, moreover the stability operation at fault cause islanding is maintained. The results show the superiority of the proposed modification of DSTATCOM in dealing with unbalanced, distorted and bad power factor system in comparing with conventional DSTATCOM.

In the present scenario, there are prime challenges that need to be addressed in modern wireless communication systems. These challenges consist of implementation of techniques which are meant for increasing the capacity and enhancing the data rate for providing the better quality of service. Frequency Selective Surfaces (FSSs) help to address these challenges by offering spatial filtering techniques in wireless communication systems. In this paper, a detailed survey on the FSSs is presented with a focus on the fundamental principles, geometries, and fabrication techniques employed for manufacturing. An extensive review is presented for understanding the journey from traditional elements utilized for making FSSs structures up to three-dimensional FSS geometries. In addition to the shapes of the FSS structures, the focus has also been on the design and analysis of the geometries for numerous applications. An inherent property of FSSs is their capability to act as spatial filters when substituted in the electromagnetic radiation environment. Also, the spatial filtering mechanism exhibited by the FSSs is reported indicating the differentiation on the basis of applications and frequency ranges on which these surfaces are exposed.

In order to improve the operation performance and environmental adaptability of the manipulator, it is essential to control the position and force of the manipulator simultaneously. This paper proposed a hybrid control method of auto-dynamic bit based on bit control to realize flexible grasping of manipulator in unknown environment. The constraint conditions and control types of the hybrid force/bit control are analyzed firstly, and the dynamic model when contacting with the environment is established. Then, an improved position controller based on conventional fuzzy-PID control strategy is introduced to realize the hybrid force/bit control based on the position control by combining constraint estimation method and impedance force control. Finally, the control performance of the force/bit hybrid controller is verified by simulation experiments. The results show that the designed control strategy has relatively ideal force control precision and higher trajectory tracking ability, and the manipulator model has better compliance.

The continuous speed ratio of the continuously variable transmission (CVT) changed rapidly, which made the quantity of oil intake and discharge of hydraulic system vary frequently, resulting in large energy loss and reduction in vehicle economy. In view of this issue, in this paper, taking a plug-in hybrid electric vehicles (PHEV) equipped with CVT as the research object, and a CVT discretely variable speed ratio optimization method based on genetic algorithm (DVROM-GA), which could reduce the energy loss and improve the system transmission efficiency, was proposed. Firstly, five driving modes of PHEV were studied to obtain the system transmission efficiency under different modes. Subsequently, the continuously variable speed ratio of CVT was divided into discretely variable speed ratios, and a genetic algorithm, which aimed at optimizing system transmission efficiency, was adopted to simultaneously optimize the number of gears and the discretely variable speed ratio to obtain the shifting rules under different driving modes. Ultimately, to verify the effectiveness of the proposed method, compared with the energy consumption of continuously variable transmission, the proposed method could improve the economy of PHEV by 2.2%.

Asymmetrical six-phase induction machines are widely used in high-power safety-critical applications. The so-called pseudo six-phase winding layout has recently been proposed as a promising contender for high power medium voltage applications in terms of torque density, phase current quality, simple single-layer-based stator winding design, and fault-tolerant capability. This winding layout employs quadruple three-phase stator winding sets connected to provide only six terminals, while being fed from two three-phase inverters. Therefore, the same conventional six-phase-based control structure can still be employed. Among the different control techniques, model predictive current control is considered as one of the cutting-edge technologies for multiphase drive systems, thanks to its simplicity and flexibility in defining new control objectives. This paper investigates how the available 64 voltage vectors of a six-phase inverter are classified among different subspaces when employing a pseudo six-phase stator winding. Moreover, the required modifications to classical predictive current control (PCC) are introduced in order to properly control the machine in a manner similar to conventional asymmetrical six-phase machines while ensuring minimum circulating xy current components. The concept of virtual voltage vectors (VVVs) has also been employed to further enhance the stator current quality. The proposed controller is validated using a 2Hp prototype pseudo six-phase machine under different operating conditions. An experimental comparative study with an asymmetrical six-phase machines is also carried out to verify the claimed equivalence.

Control of multi-phase machine in both healthy and post-fault operations has recently gained considerable interest. Finite control set model predictive control (FCS-MPC), in particular, has been proposed to drive multi-phase machines to avoid sophisticated pulse width modulation (PWM) methods. Nevertheless, FCS-MPC generally experiences some technical limitations. One of the widely reported challenges related to FCS-MPC is the weighting factor (WF) design, which is empirically designed and found to be an operating point dependent factor. Thus, assuming a constant WF can result in poor performance. In this paper, the prediction stage of FCS-MPC for asymmetrical six-phase (A6P) induction motor (IM) has been reformulated based on double dq (2dq) modeling approach. Unlike the reported vector space decomposing (VSD)-based FCS-MPC algorithms, the proposed approach results in four control variables (the stator sequence currents i s α 1 , i s β 1 , i s α 2 , and i s β 2 ) which have the same priority. Thus, equal weighting factors could be used in the cost function, which will inherently minimize the effect of the circulating x-y current components. Performance comparison of the proposed method against VSD-based FCS-MPC is investigated. The theoretical findings are experimentally validated using a 1 kW prototype six-phase IM.

The poor acoustic environment of the waiting hall of the high-speed railway station will result in poor passenger waiting process experience, and there is currently no research related to reducing the poor acoustic environment of the waiting hall. Based on this, this study studied the factors affecting the acoustic environment of the high-speed rail waiting hall from various aspects and used the image simulation technology to carry out the simulation analysis of the actual situation and focused on the analysis of the sound absorption materials in the waiting hall. At the same time, this study obtained the actual noise situation of the high-speed railway station through data collection and compared and analyzed the passenger experience of the high-speed rail hall under the influence of different factors. In addition to this, the reverberation time of the hall was controlled by establishing a corresponding model for analysis and selection and arrangement of materials. Then, the reverberation time was calculated, and the software is used to simulate the test. Therefore, before the project was completed, the reverberation time was theoretically controlled to a more appropriate range. The research shows that the method provided in this paper has certain effects and can provide theoretical reference for subsequent related research.

Nowadays, the piezoelectric transduction mechanism has a great concern to be used in the (micro-electromechanical systems) MEMS microphones. In piezoelectric microphones, the thickness, length, width of the piezoelectric and electrode materials are key parameters that need to be optimized in the design loop. The sensitivity is also another vital design factor for MEMS microphones. One common scenario in modeling the sensitivity is to build an electrical equivalent model from lumped components in any simulator. This approach generally requires specialist design expertise and substantial time to build a complete equivalent model. In this paper, a powerful simulation platform to design high-performance cantilever piezoelectric MEMS microphones with sensitivity estimation has been presented. This simulation platform, called MEMS microphone optimizer platform (MMOP), can predict a wide range of key issues related to the successful design of a MEMS Microphone such as the optimum values of piezoelectric material thickness, electrode material thickness, and the length of a cantilever. MMOP offers also the capability to simulate sensitivity directly from the input parameters of the designed model. To validate the proposed simulation platform, a real model of a cantilever MEMS microphone has been studied. In the performed simulations and analysis, sweeping dimensions in micrometer have been considered to predict the best performance. In the proposed model, Aluminum nitride (AlN) and molybdenum (Mo) were utilized as the piezoelectric material and electrode materials, respectively. A high agreement has been found between the theoretical results and the output of the MMOP platform. The platform opens the door for a fast optimized design with accurate results. Finally, MMOP enables a designer to simulate key issues that are specific to cantilever MEMS microphones, including optimized thickness values and predicted sensitivity.

Advanced Video Coding (H.264/AVC) is one of the most important video coding standards which was developed by the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG). Due to the complex procedures needed to find the optimal mode in intra prediction mode stage of H.264/AVC, an efficient mode decision is suggested in this paper. The main objectives of the proposed work are improving the compression efficiency of video coding and minimizing the degradation of video quality. The proposed algorithm is based on the gradient method for edge detection using the Prewitt filter to determine the texture direction of a block to be predicted in the luminance component of a video frame. To decide either implemented the DC mode or the edge-detection based gradient method to predict block under consideration, a predefined threshold value is used for the homogeneity test of each macro-block. To reduce the Bit Rate of the compressed video, Gaussian pulse modulation is also suggested to the Discrete Cosine Transformation (DCT) components of each macro-block during the coding process. The proposed algorithm is carried out on a luminance component of a set of different YUV video sequences with different resolutions and different quantization parameters using MATLAB. The suggested algorithm is evaluated using different metrics which are Peak Signal to Noise Ratio (PSNR) and bit rate measured by Bjontegaard Delta method. The simulation results show the ability of the proposed algorithm in enhancing the BDPSNR averagely by 1.14 dB and reducing the BDBR averagely by 11.34 %for QCIF sequences compared to the existing state-of-the-art fast intra-prediction mode decision algorithms.

The point of this paper is to analyze and investigate the analytic-approximate solutions for fractional system of Volterra integro-differential equations in framework of Caputo-Fabrizio operator. The methodology relies on creating the reproducing kernel functions to gain analytical solutions in a uniform form of a rapidly convergent series in the Hilbert space. Using the Gram-Schmidt orthonomalization process, the orthonormal basis system is constructed in a dense compact domain to encompass the Fourier series expansion in view of reproducing kernel properties. Besides, convergence and error analysis of the proposed technique are discussed. For this purpose, several numerical examples are tested to demonstrate the great feasibility and efficiency of the present method and to support theoretical aspect as well. From a numerical point of view, the acquired solutions simulation indicates that the methodology used is sound, straightforward, and appropriate to deal with many physical issues in light of Caputo-Fabrizio derivatives.

This paper proposes an improved marine predators’ optimization algorithm (IMPOA) for solving the combined heat and power (CHP) economic dispatch problem. This problem provides optimal scheduling of heat and power generation supplies and pursues to minimize the overall fuel cost (OFC) supply of cogeneration units considering their operational constraints. Four test systems are considered to check the performance of both the MPOA and the proposed IMPOA. The first test system is small sized which involve 5-unit, whereas the second system is medium sized which contains 48-unit system. The third and fourth test systems are large sized systems. The third test system includes 84-unit, which are divided into 40 power-only units, 20 heat only units, and 24 CHP units. The fourth test system includes 96-unit, which are divided into 52 power-only units, 20 heat-only units, and 24 CHP units. The obtained results clearly show the capability, efficiency, and feasibility of the IMPOA with respect to other relevant optimization techniques for optimal solutions of small, medium and large-scale systems. Additionally, the convergence characteristics of the proposed IMPOA are stable and the arrival of the optimal solution is faster than the conventional MPOA.

Over the last decade, there has been a constant development in control techniques for DC-DC power converters which can be classified as linear and nonlinear. Researchers focus on obtaining maximum efficiency using linear control techniques to avoid complexity although nonlinear control techniques may achieve full dynamic capabilities of the converter. This paper has a similar purpose in which a novel hybrid metaheuristic optimization algorithm (AEONM) is proposed to design an optimal PID controller for DC-DC buck converter’s output voltage regulation. The AEONM employs artificial ecosystem-based optimization (AEO) algorithm with Nelder-Mead (NM) simplex method to ensure optimal PID controller parameters are efficiently tuned to control output voltage of the buck converter. Initial evaluations are performed on benchmark functions. Then, the performance of AEONM-based PID is validated through comparative results of statistical boxplot, non-parametric test, transient response, frequency response, time-domain integral-error-performance indices, disturbance rejection and robustness using AEO, particle swarm optimization and differential evolution. A comparative performance analysis of transient and frequency responses is also performed against simulated annealing, whale optimization and genetic algorithms for further performance assessment. The comparisons have shown the proposed hybrid AEONM algorithm to be superior in terms of enhancing the buck converter’s transient and frequency responses.

Time cost and quality are three important factors in planning and controlling construction projects. Getting these factors in balance, which minimizes the project duration, the total project cost, and maximizes the total quality could define the success of a construction project. Aside from planning and estimating the project properly, a consideration of uncertainty while implementing the project is needed to represent a more realistic outcome for time, cost, and quality trade-off (TCQT) problem in construction projects. In this paper, fuzzy logic is utilized to model the uncertainty embedding α-cut approach to see the effect of the uncertainty on the time, cost, and quality of the project. Then multi-objective Symbiotic Organism Search (SOS) algorithm is applied to find a set of the optimal solution in different uncertainty levels and provide the project manager several possible actions to implement the project. Two numerical case studies of a repetitive construction project were analyzed to see the effectiveness of the model and its capability to solve the TCQT problem in the construction project. The results showed that the proposed model is powerful to explore the solution for the shortest project duration with minimum incurred cost and high overall quality in the construction project. In comparison to the other widely used methods and other algorithms, the proposed model is proven to be effective and competitive in solving the TCQT problem.

This paper reviews the safety monitoring systems, bridge safety assessment methods, and hesitant fuzzy set theories at home and abroad, and presents a novel bridge safety monitoring and evaluation method based on hesitant fuzzy set, which has greatly significance to scientific research. Through multi-scale estimation and Butter Worth (BWorth) filtering, the structural response signals were identified and extracted under car load and crowd load. Then, a mathematical model was established for bridge safety monitoring and evaluation based on hesitant fuzzy set. Drawing on hesitant fuzzy similarity theory, the authors designed damage recognition algorithms based on single index, dual-index linkage, and multi-index linkage. Finally, example analysis was conducted to confirm the feasibility of multi-index linkage, coupled with multi-period dynamic recognition, in the evaluation of bridge structural damages.

Robot simulation is an important way to verify the correctness of model and feasibility of design. Taking the Delta parallel mechanism as the effector of a weeding robot, this paper designs the workspace for the weeding mechanism in the field of corn seedlings, when each corn seedling has three to five leaves, and then carries out a simulation analysis on the workspace of the weeding mechanism. To realize variable rate precision spraying, the authors planned a three-segment gate-shaped trajectory for the end effector, detailed the trajectory under the joint coordinate system, and analyzed the motion law of S-curve motion profile. Then, the law of each kinematics parameter was solved on MATLAB, and plotted into graphs. Based on the simulation results, a prototype was developed for the proposed weeding mechanism, and subject to weeding experiments to obtain the end effector trajectory. Compared with the simulated results, the maximum deviation in the end effector trajectory during actual operation was 11.58%; the prototype could complete a weeding action every 2.8 s, while advancing at the speed of 0.4 m/s. The results fully demonstrate the feasibility of applying Delta parallel mechanism in weeding, and the reasonability of our workspace design and trajectory planning.

The ever increasing active and reactive power demands, along with limited sources of generation and delays in transmission expansion projects, have led many power systems to operate near their voltage stability limits. In this context, voltage stability monitoring methodologies have become an important topic in power systems research. This paper presents a novel methodology for long-term voltage stability monitoring in power systems that exploits the feasibility of phasor-type information in order to estimate the long-term voltage stability status. The information regarding the current system condition is acquired through synchronized phasor measurements and the power system is divided in sub-areas for improving its supervision; then, an artificial intelligence approach based on kernel extreme learning machine is used for long-term voltage stability assessment. The proposed scheme allows foreseeing the voltage instability caused by limitations in reactive power transmission, and it also permits alerting when a system area experiences a deficit of reactive power from supply sources. The validation of the proposed method is performed on the 39-bus test system, obtaining feasible results. The tests confirmed that the proposed method works properly under different scenarios and system conditions, always ensuring proper voltage stability status results independently of its cause.

This paper proposes an improved binary bat algorithm (IBBA) for solving static and dynamic transmission network expansion planning (TNEP) problems for standard and realistic networks considering different objective functions (OFs). The proposed IBBA has two modifications to enhance the solution quality based on multi V-shaped transfer function and adaptive search space (ASS). The IBBA is applied to solve the static TNEP problem. A two-stage procedure is employed to solve the dynamic TNEP problem. In stage-1, the adaptive neuro-fuzzy inference system (ANFIS) is utilized to find the long-term load forecasting (LTLF) up to 2039. In stage-2, the IBBA is used to solve the dynamic TNEP problem. Two OFs are considered for solving TNEP problems. The first OF achieves the investment cost reduction. The second OF aims to minimize the total costs, which include the investment cost and the total costs of energy losses and reactive power compensation (RPC). The proposed procedure is applied to Garver’s 6-bus system and the West Delta Network (WDN) as a part of the Unified Egyptian Transmission Network (UETN) to solve TNEP problems. The obtained results are compared with other methods to show the robustness of the proposed procedure for solving TNEP problems.

Lightweight constrained application protocol (CoAP) has emerged as a common communication protocol for resource-constrained equipment in distribution internet of things (IoT). CoAP introduces two modes for data transmission, i.e., non-confirmed mode for reducing transmission delay and confirmed mode for reducing packet-loss ratio, which can be dynamically selected to satisfy the service requirements. However, there are still some challenges in dynamic CoAP mode selection, including incomplete information and differentiated quality of service (QoS) requirements of distributed IoT services. In this paper, we propose a upper confidence bound (UCB)-based dynamic CoAP mode selection algorithm for data transmission to address these challenges. The simulation results validate that, compared with the fixed mode selection algorithm, the proposed algorithm can flexibly balance the tradeoff between packet-loss ratio and transmission delay as well as satisfy the differentiated QoS in distribution IoT.

Underwater imaging is a crucial and challenging problem. The backscattering caused by particles in turbid water can severely degrade the image. In this paper, A novel image dehazing method is devised, which combines optics with image processing technology. This method can directly remove part of the backscattering through polarization technology, and then quickly solve the dehazing image through our algorithm. Experimental results show that our method is valid and robust for targets of diverse materials, grayscale images and color images in diverse scattering environments. Furthermore, our method takes only 2 percent of the time of the fast dark channel prior (DCP) method when processing 1 K resolution images, which meets the requirements of real-time.

Digital transformation of manufacturing is a hot topic among strategic managers of manufacturing companies. The crux of digital transformation lies in the digitalization of manufacturing supply chain (MSC). However, the digital transformation of the MSC is highly uncertain, owing to the dynamic and complex changes of its nodes and structure in response to growing customer demand and fierce market competition. To propel the MSC digital transformation, it is crucial to effectively identify and predict the risk factors in the course of digital transformation. Therefore, this paper attempts to help manufacturing companies in China to successfully switch to a digital MSC. Firstly, the risk sources of the MSC digitization were identified, and complied into an evaluation index system for the digital transformation of the MSC. Next, the principal component analysis (PCA) was performed to reduce the dimension of the original data by revealing the three key principal components, and then the characteristic parameters of risk prediction are selected, so as to simplify the structure of neural network and improve the speed and efficiency of network training. On this basis, a backpropagation neural network (BPNN) was constructed for predicting the risks in MSC digitization. The results of training the model based on some data show that the proposed BPNN model has a good predictive effect. Furthermore, our model was compared with the traditional artificial neural network (ANN) model on a test set. The comparison demonstrates that our model achieved better effect than the traditional model in risk prediction. The results also show that the selected three principal components are reasonable, and the evaluation index system is valuable. The research results provide new insights to the smooth digital transformation of the MSC.

Execution time, reliability, and energy consumption are the three main performance parameters of processor scheduling for real-time embedded systems. It is very meaningful to optimize the energy optimization of processor scheduling to satisfy the requirements on time limit and reliability. This paper tries to optimize the energy consumption of the processor under three constraints: the partial ordering relations between task modules, the time limit, and the reliability. Based on directed acrylic graph (DAG) and quantum particle swarm optimization (QPSO), two scheduling algorithms were developed for the problem, namely, DAG_QPSO_I and DAG_QPSO_II. The two algorithms were compared with each other, and with other intelligent algorithms. The results show that the proposed algorithms are superior in optimization effect and efficiency, DAG_QPSO_I makes energy consumption more efficient than DAG_QPSO_II, and DAG_QPSO_II meets stricter requirements on time limit and reliability than DAG_QPSO_I.

We consider a model of wavelet shrinkage with two normal distributions where the variance of the noise is estimated using exponential distribution. Instead of normal and double exponential distributions on the parameters domain of wavelet coefficients, which is usually the case in the existing literature, the ∊ -contamination prior distribution is involved that is particularly attractive to work with when one seeks robust priors in Bayesian analysis. The proposed method starts with computing the posterior mean and the posterior mean under the mass point. This article covers the major topic of theoretical approaches. Wavelet methods are applied, involving decimated wavelet transform with a single parameter for each level. The proposed method is illustrated on the standard test functions of Donoho and Johnstone, where the level of noise corrupts these functions. This procedure can be explained as a type of inverse problem. We compared some standard wavelet-based methods with the existing process. Extensive simulation is implemented to perform the proposed method and improve signal’s reconstruction using average mean-squared error to compare different approaches. As a practical illustration, we present an application to a real-life data set. The main interest of the paper is to compare two different likelihoods-the first is the normal distribution, and the other is the double exponential distribution.