In spite of the many research contributions that have been introduced in the recent literature, maximum power point trackers (MPPT) still carry important issues related to convergence speed, computation load, accuracy...
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In spite of the many research contributions that have been introduced in the recent literature, maximum power point trackers (MPPT) still carry important issues related to convergence speed, computation load, accuracy, ease of implementation, and so on. The fuzzylogic based MPPT presents high steady-state performances in terms of speed of convergence and accuracy. However, the complexity of implementation is the first drawback of this MPPT type especially for embedded systems. In addition, fuzzyalgorithms show relatively a large computation time compared to most of conventional MPPTs. In this way, the aim of this paper is to benefit from fuzzylogic advantages along with M5P model tree based modeling. Indeed, a new fast MPPT algorithm is established by modeling the fuzzy logic algorithm using M5P model tree that translates the model on simple if-then instructions. The new model has been trained based on simulation based dataset recorded for 500 W/m(2) irradiation. The obtained model presents a good agreement with the fuzzyalgorithm even for different irradiations and weather conditions. Furthermore, the developed algorithm shows improved performances compared to P&O and InCon algorithms. Moreover, satisfactory results have been obtained when the new algorithm has been subjected to one-day profile of real measurement.
In order to avoid accidents due to aircraft icing, an algorithm for identifying supercooled water was studied. Specifically, a threshold method based on millimeter wave radar, lidar, and radiosonde was used to retriev...
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In order to avoid accidents due to aircraft icing, an algorithm for identifying supercooled water was studied. Specifically, a threshold method based on millimeter wave radar, lidar, and radiosonde was used to retrieve the coverage area of supercooled water and a fuzzy logic algorithm was used to classify the observed meteorological targets. The macrophysical characteristics of supercooled water could be accurately identified by combing the threshold method with the fuzzy logic algorithm. In order to acquire microphysical characteristics of supercooled water in a mixed phase, the unimodal spectral distribution of supercooled water was extracted from a bimodal or trimodal spectral distribution of a mixed phase cloud, which was then used to retrieve the effective radius and liquid water content of supercooled water by using an empirical formula. These retrieved macro- and micro-physical characteristics of supercooled water can be used to guide aircrafts during takeoff, flight, and landing to avoid dangerous areas.
Braking force distribution (BFD) for electrified vehicles with the aim of maximizing energy regeneration has been a challenging research topic, due to the complex operating conditions and tradeoff among different vehi...
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Braking force distribution (BFD) for electrified vehicles with the aim of maximizing energy regeneration has been a challenging research topic, due to the complex operating conditions and tradeoff among different vehicle performance measures. It is known that the level of tire-road friction has a significant impact on the braking force boundaries that define the locking conditions of front and rear wheels, and therefore, on the allowable set of BFD's where the vehicle's stability and controllability are maintained. However, previously developed BFD strategies for regenerative braking have not considered the changing boundaries of braking limits due to varying tireroad friction levels and introduce conservative BFD constraints to ensure stability and controllability. This paper proposes a BFD strategy for an all-wheel-drive electrified vehicle with a single electric motor, based on the estimation of the tire-road friction coefficient (mu) using a fuzzylogic estimation approach. The proposed strategy takes into consideration the motor efficiency and available speed reduction ratios in order to find the optimal BFD, which maximizes the regenerative power during braking, for a given vehicle speed and deceleration demand. Simulation analyses demonstrate the effectiveness of the proposed tireroad friction estimation-based BFD optimization strategy that significantly improves braking energy recovery. Preliminary test results with a prototype vehicle provide additional validation of the benefits of the proposed method.
Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as ...
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Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzylogic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses. The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and "reshape" the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system.
A wheeled ground robot was designed and built for better understanding of the challenges involved in utilization of accelerometer based intelligent tires for mobility improvements. Since robot traction forces depend o...
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A wheeled ground robot was designed and built for better understanding of the challenges involved in utilization of accelerometer based intelligent tires for mobility improvements. Since robot traction forces depend on the surface type and the friction associated with the tire-road interaction, the measured acceleration signals were used for terrain classification and surface characterization. To accomplish this, the robot was instrumented with appropriate sensors (a tri-axial accelerometer attached to the tire innerliner, a single axis accelerometer attached to the robot chassis and wheel speed sensors) and a data acquisition system. Wheel slip was measured accurately using encoders attached to driven and non-driven wheels. A fuzzy logic algorithm was developed and used for terrain classification. This algorithm uses the power of the acceleration signal and wheel slip ratio as inputs and classifies all different surfaces into four main categories;asphalt, concrete, grass, and sand. The performance of the algorithm was evaluated using experimental data and good agreements were observed between the surface types and estimated ones. (C) 2017 ISTVS. Published by Elsevier Ltd. All rights reserved.
Hydrometeor classification for dual polarization Doppler weather radar echo is a procedure that identifies hydrometeor types based on the scattering properties of precipitation particles to polarized electromagnetic w...
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Hydrometeor classification for dual polarization Doppler weather radar echo is a procedure that identifies hydrometeor types based on the scattering properties of precipitation particles to polarized electromagnetic waves. The difference in shape, size, or spatial orientation among different types of hydrometeor will produce different scattering characteristics for the electromagnetic waves in a certain polarization state. Moreover, the polarimetric measurements, which are calculated from the radar data and closely associated with these characteristics, are also different. The comprehensive utilization of these polarimetric measurements can effectively improve the identification accuracy of the phase of various hydrometeors. In this paper, a new identification method of the hydrometeor type based on deep learning (DL) and fuzzy logic algorithm is proposed: firstly, the feature extraction method based on deep learning is used for training the correlation among multiple parameters and extracting the relatively independent features. Secondly, the Softmax classifier is applied to classify the precipitation patterns, including rain, snow, and hail, and it is based on the features extracted by deep learning algorithm. Finally, the fuzzy logic algorithm is adopted to identify the hydrometeor types in various precipitation patterns. In order to test the accuracy of the classification results, the hydrometeor classifier has been applied to a stratiform cloud precipitation process, and it is found that the classification results agree well with the other polarimetric products.
A new intelligent Automatic Generation Control (AGC) scheme based on Evolutionary algorithms (EAs) and fuzzylogic concept is developed for a multi-area power system. EAs i.e. Genetic algorithm-Simulated Annealing (GA...
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A new intelligent Automatic Generation Control (AGC) scheme based on Evolutionary algorithms (EAs) and fuzzylogic concept is developed for a multi-area power system. EAs i.e. Genetic algorithm-Simulated Annealing (GA-SA) are used to optimize the gains of fuzzy logic algorithm (FLA)-based AGC regulators for interconnected power systems. The multi-area power system model has three different types of plants i.e. reheat, non-reheat and hydro, and are interconnected via Extra High Voltage Alternate Current transmission links. The dynamic model of the system is developed considering one of the most important Governor Dead Band (GDB) non-linearity. The designed AGC regulators are implemented in the wake of 1% load perturbation in one of the control areas and the dynamic response plots are obtained for various system states. The investigations carried out in the study reveal that the system dynamic performance with hybrid GA-SA-tuned fuzzy technique (GASATF)-based AGC controller is appreciably superior as compared to that of integral and FLA-based AGC controllers. It is also observed that the incorporation of GDB non-linearity in the system dynamic model has resulted in degraded system dynamic performance.
An early-warning and explosion-proof monitoring system for coal mine environment is designed based on Zig Bee, in view of the underground special environment. The underground system, with CC2530 + CC2401 chip as the c...
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An early-warning and explosion-proof monitoring system for coal mine environment is designed based on Zig Bee, in view of the underground special environment. The underground system, with CC2530 + CC2401 chip as the core of ZigB ee node, realizes communication with upper computer through CAN bus by the central controller. Upper computer uses the Labview software to program and realize real-time display of the coal mine parameters including CH, CO, O concentration, temperature and humidity and personnel coordinates. The early warning technology based on fuzzy logic algorithm, explosion-proof technology based on physical isolation are adopted to this system. A kind of RSSI weighted centroid algorithm mixed with LQI was put forward. Experiment proved that this system runs stably, improving the personnel positioning accuracy and has a good prospect of application.
This study aims to secure medical data by combining them into one file format using steganographic methods. The electroencephalogram (EEG) is selected as hidden data, and magnetic resonance (MR) images are also used a...
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This study aims to secure medical data by combining them into one file format using steganographic methods. The electroencephalogram (EEG) is selected as hidden data, and magnetic resonance (MR) images are also used as the cover image. In addition to the EEG, the message is composed of the doctor's comments and patient information in the file header of images. Two new image steganography methods that are based on fuzzy-logic and similarity are proposed to select the non-sequential least significant bits (LSB) of image pixels. The similarity values of the gray levels in the pixels are used to hide the message. The message is secured to prevent attacks by using lossless compression and symmetric encryption algorithms. The performance of stego image quality is measured by mean square of error (MSE), peak signal-to-noise ratio (PSNR), structural similarity measure (SSIM), universal quality index (UQI), and correlation coefficient (R). According to the obtained result, the proposed method ensures the confidentiality of the patient information, and increases data repository and transmission capacity of both MR images and EEG signals. (C) 2015 Elsevier Ltd. All rights reserved.
There are many open source and commercially available Learning Management System (LMS) on the Internet and one of the important problems in this field is how to choose an LMS that will be the most effective one and th...
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There are many open source and commercially available Learning Management System (LMS) on the Internet and one of the important problems in this field is how to choose an LMS that will be the most effective one and that will satisfy the requirements. In order to help in the solution of this problem, the author has developed a computer program to aid in the selection of an LMS. The developed system is web-based and can easily be used over the Internet any where over the world at any time. The developed system is basically a web-based decision support system used to evaluate LMSs by using a flexible and smart algorithm derived from artificial intelligent concepts with fuzzylogic values. The paper describes the development of the LMS evaluation system. The individuals who are most likely to be interested in the LMS evaluation process are teachers, students, and any educational organizations such as: universities, schools, institutes, and anyone else who seeks to have a LMS. (C) 2009 Published by Elsevier Ltd.
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