This paper describes the integration of a photovoltaic (PV) renewable energy source with a superconducting magnetic energy storage (SMES) system. The integrated system can improve the voltage stability of the utility grid and achieve power leveling. The control schemes employ model predictive control (MPC), which has gained significant attention in recent years because of its advantages such as fast response and simple implementation. The PV system provides maximum power at various irradiation levels using the incremental conductance technique (INC). The interfaced grid side converter of the SMES can control the grid voltage by regulating its injected reactive power to the grid, while the charge and discharge operation of the SMES coil can be managed by the system operator to inject/absorb active power to/from the grid to achieve the power leveling strategy. Simulation results based on MATLAB/Simulink® software prove the fast response of the system control objectives in tracking the setpoints at different loading scenarios and PV irradiance levels, while the SMES injects/absorbs active and reactive power to/from the grid during various events to improve the voltage response and achieve power leveling strategy. 相似文献
The discovery of eco-friendly, rapid, and cost-effective compounds to control diseases caused by microbes and insects are the main challenges. Herein, the magnesium oxide nanoparticles (MgO-NPs) are successfully fabricated by harnessing the metabolites secreted by Penicillium chrysogenum. The fabricated MgO-NPs were characterized using UV-Vis, XRD, TEM, DLS, EDX, FT-IR, and XPS analyses. Data showed the successful formation of crystallographic, spherical, well-dispersed MgO-NPs with sizes of 7–40 nm at a maximum wavelength of 250 nm. The EDX analysis confirms the presence of Mg and O ions as the main components with weight percentages of 13.62% and 7.76%, respectively. The activity of MgO-NPs as an antimicrobial agent was investigated against pathogens Staphylococcus aureus, Bacillus subtilis, Pseudomonas aeruginosa, Escherichia coli, and Candida albicans, and exhibited zone of inhibitions of 12.0 ± 0.0, 12.7 ± 0.9, 23.3 ± 0.8, 17.7 ± 1.6, and 14.7 ± 0.6 mm respectively, at 200 µg mL−1. The activity is decreased by decreasing the MgO-NPs concentration. The biogenic MgO-NPs exhibit high efficacy against different larvae instar and pupa of Anopheles stephensi, with LC50 values of 12.5–15.5 ppm for I–IV larvae instar and 16.5 ppm for the pupa. Additionally, 5 mg/cm2 of MgO-NPs showed the highest protection percentages against adults of Anopheles stephensi, with values of 100% for 150 min and 67.6% ± 1.4% for 210 min. 相似文献
Multimedia Tools and Applications - This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop... 相似文献
Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.
The term Internet of Things (IoT) represents all communicating countless heterogeneous devices to share data and resources via the internet. The speedy advance of IoT devices proposes limitless benefits, but it also brings new challenges regarding security and forensics. Likewise, IoT devices can generate a massive amount of data that desires integrity and security during its handling and processing in an efficient way. IoT devices and data can be vulnerable to various types of cyber-crimes at each IoT layer. For combating these cyber-crimes in IoT infrastructure, IoT forensic term has shown up. The IoT forensic is the process of performing digital forensic investigation in the IoT environment in a forensically sound and timely fashion manner. Sundry challenges face the IoT forensics that requires urgent solutions and mitigation methods; digital evidence needs to be collected, preserved, analyzed, processed, and reported in a trusted manner to be acceptable for presenting in the court of law. Preserving the evidence unchanged or tampered with is the most critical challenge in digital forensics. Authentication is another challenge facing digital forensics; who is allowed to deal with the evidence? One of the most recent solutions for supporting IoT forensics is the use of Blockchain. Using Blockchain in digital forensics guarantees data integrity, immutability, scalability, and security. Therefore, this paper presents a comprehensive review of IoT security and forensics with the integration with Blockchain technology. It begins by providing an inclusive discussion of IoT security, as well as the need for IoT forensics, and the concepts of Blockchain. Then, a review of Blockchain-based IoT security and forensics issues is presented. Finally, a discussion of open research directions is provided.
Copper slag (CS) is a by-product of the copper extraction process, which can be used as coarse and/or fine aggregate in hot mix asphalt (HMA) pavements. This study used CS as a replacement of the fine aggregate with a percentage of up to 40% by total aggregate weight. The objective of this study was to evaluate the effect of CS on the rutting potential of the asphalt concrete mix using two methods. One method is based on the Dynamic modulus |E*| testing result. Actual pavement temperature data from a test section were used with the developed |E*| master curves. EverStressFE finite element program was used to perform a linear elastic load-deformation analysis for a pavement section and to determine the vertical resilient strain in a 40-mm HMA surface layer. The M-E PDG permanent deformation model was used with and Excel Visual Basic for Applications code to predict the accumulated rutting for different CS mixes for 10 million ESALs. The other method used the data from the flow number (FN) test. Based on the |E*| approach, the results indicated that adding 5% CS in the mix increased the predicted rutting from 0.59 to 0.98 mm at 10 million ESALs (increase by 68%). When 40% CS was used, rutting increased by more than 700% compared with the control mix. After analysing the FN results with the Francken model, the results indicated a decrease in FN as CS content is increased, indicating higher rutting potential. The decrease in FN ranged from 9% for 5% CS to 95% for 40% CS. The mixes containing up to 10% CS satisfied the minimum FN criteria for rutting. A calibration process for the M-E PDG distress prediction models that allows the use of waste and by-product materials such as CS should be considered in the future. 相似文献
While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach. 相似文献
The performance of a model-based control system depends strongly on the accuracy of the process model used. LS-SVM is a powerful method for modeling nonlinear systems. The main objective of this paper is to implement a conventional controller based on LS-SVM model for hydraulic motor. An off-line model is first identified based on LS-SVM, then via simulation tests the parameters of the discrete PI-Controller and its velocity-form are obtained then the controller parameters are applied experimentally for the hydraulic motor as a speed controller. The system performance has been evaluated; results show good performance over a wide range of operating conditions and load disturbances.相似文献