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A wireless sensor network (WSN) consists of several sensor nodes; all these nodes can sense physical events, including light, heat, and pressure. These networks are essential in smart homes, smart agriculture, and smart water management, swelling with the concept of the Internet of Things. However, WSN needs to address the challenges of energy issues; thus, energy-conserving techniques have been pursued for communication. Optimization of energy is normally solved using the Particle Swarm Optimization (PSO) algorithm since it offers high accuracy but is prone to local optima, thus resulting in early convergence. To tackle this challenge, this paper proposes the development of an enhanced particle swarm optimization for the node power estimation (EPSO-NPE) model. EPSO-NPE calculates distinct transmission powers for each node, preventing the formation of isolated areas within a sensor cluster. Unlike the original PSO, the EPSO algorithm enhances exploration capabilities by avoiding stagnation on search space boundaries. A comparative analysis with the original PSO-based model (PSO-NPE), where nodes adopt maximum power for connectivity, reveals superior performance by EPSO-NPE. The enhanced model exhibits heightened energy-saving capabilities, ultimately extending the network’s lifetime.

Spin Transfer Torque Random Access Memory (STT-RAM) is an emerging Non-Volatile Memory (NVM) technology that has garnered attention to overcome the drawbacks of conventional CMOS-based technologies. However, such technologies must be evaluated before deployment under real workloads and architecture. But there is a lack of available open-source STT-RAM-based system evaluation framework, which hampers research and experimentation and impacts the adoption of STT-RAM in a system. This paper proposes a novel, extendable STT-RAM memory controller design integrated inside the gem5 simulator. Our framework enables understanding various aspects of STT-RAM, i.e., power, delay, clock cycles, energy, and system throughput. We will open-source our HOPE framework, which will fuel research and aid in accelerating the development of future system architectures based on STT-RAM. It will also facilitate the user for further tool enhancement.

A systematic simulation model is proposed in this research paper to estimate the energy consumption of electric vehicles. The main advantage of this model is that it is made in a generic and simplified way in order to be adaptable to different electric vehicles. The overall electrical power corresponding to the performed maneuver is estimated considering: a tabular form of electric motor efficiency, mechanical power losses, a generalized efficiency map of the power electronics, the auxiliary power losses, and an electro-thermal Lithium-Ion battery pack model. The battery model was developed in a previous work, which simulates the open circuit voltage curves at different temperatures and the alteration in the internal resistance of the battery cells. The proposed model is validated with experimental data from the maneuver tests. The battery model proved high accuracy in estimating the voltage values relevant to the WLTP2 driving cycle on the chassis roller test bench. Furthermore, the mechanical and electrical power were estimated with excellent matching compared to actual test field driving test measurements, giving only the measured vehicle speed and auxiliary power losses. Finally, the state of charge change is predicted accurately along the performed test field dynamic maneuver. [ABSTRACT FROM AUTHOR]

The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems. [ABSTRACT FROM AUTHOR]

Weather disturbances pose a significant challenge when estimating the energy production of photovoltaic panel systems. Energy production and forecasting models have recently been used to improve energy estimations and maintenance tasks. However, these models often rely on environmental measurements from meteorological units far from the photovoltaic systems. To enhance the accuracy of the developed model, a measurement Internet of Things (IoT) prototype was developed in this study, which collects on-site voltage and current measurements from the panel, as well as the environmental factors of lighting, temperature, and humidity in the system's proximity. The measurements were then subjected to correlation analysis, and various artificial neural networks (ANNs) were implemented to develop energy estimations and forecasting models. The most effective model utilizes lighting, temperature, and humidity. The model achieves a root mean squared error (RMSE) of 0.255326464. The ANN models are compared to an MLR model using the same data. Using previous power measurements and actual weather data, a non-autoregressive neural network (Non-AR-NN) model forecasts future output power values. The best Non-AR-NN model produces an RMSE of 0.1160, resulting in accurate predictions based on the IoT device. [ABSTRACT FROM AUTHOR]

Weather disturbances pose a significant challenge when estimating the energy production of photovoltaic panel systems. Energy production and forecasting models have recently been used to improve energy estimations and maintenance tasks. However, these models often rely on environmental measurements from meteorological units far from the photovoltaic systems. To enhance the accuracy of the developed model, a measurement Internet of Things (IoT) prototype was developed in this study, which collects on-site voltage and current measurements from the panel, as well as the environmental factors of lighting, temperature, and humidity in the system’s proximity. The measurements were then subjected to correlation analysis, and various artificial neural networks (ANNs) were implemented to develop energy estimations and forecasting models. The most effective model utilizes lighting, temperature, and humidity. The model achieves a root mean squared error (RMSE) of 0.255326464. The ANN models are compared to an MLR model using the same data. Using previous power measurements and actual weather data, a non-autoregressive neural network (Non-AR-NN) model forecasts future output power values. The best Non-AR-NN model produces an RMSE of 0.1160, resulting in accurate predictions based on the IoT device.

This article introduces a novel approach to voltage regulation in a DC/DC boost converter. The approach leverages two advanced control techniques, including learning-based nonlinear control. By combining the backstepping (BSC) algorithm with artificial neural network (ANN)-based control techniques, the proposed approach aims to achieve accurate voltage tracking. This is accomplished by employing the nonlinear distortion observer (NDO) technique, which enables a fast dynamic response through load power estimation. The process involves training a neural network using data from the BSC controller. The trained network is subsequently utilized in the voltage regulation controller. Extensive simulations are conducted to evaluate the performance of the proposed control strategy, and the results are compared to those obtained using conventional BSC and model predictive control (MPC) controllers. The simulation results clearly demonstrate the effectiveness and superiority of the suggested control strategy over BSC and MPC. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this research work, different parameters have been studied which affect the energy consumption of belt conveyors. The effect of conveying distance and vertical lift on energy consumption was studied in detail by collecting energy consumption and other associated data of belt conveyors from different cement plants and analyzed with the help of different statistical analysis. The highest specific energy consumption was found to be 0.0011 kWh/Ton‐m in the smallest belt conveyor of 15 m and the lowest specific energy consumption was found to be 0.0007 kWh/Ton‐m in the longest belt conveyor of 100 m. Specific energy consumption is reduced up to 36% per ton‐meter of material conveying in the longest belt conveyor compared with the smallest belt conveyor. On the contrary, the specific energy consumption for different vertical lifts is found almost the same. Hence, it can be suggested that for horizontal conveying, the series of belt conveyors should be replaced with a single long conveyor to achieve maximum conveying efficiency and to lower specific energy consumption. The above study will be helpful for heavy industries such as cement plants, steel plants, and so forth to reduce their energy consumption on belt conveyors and achieve the environmental sustainability goals. At the last part of the study an equation is derived for estimating energy consumption in belt conveyors by applying multiple regression analysis. The proposed equation will be helpful for estimation of energy consumption of belt conveyors according to which the decision for conveyor length can be taken. [ABSTRACT FROM AUTHOR]

Cloud datacenters are compute facilities formed by hundreds or even thousands of servers. With the increasing demand of cloud services, energy efficiency of servers in data center has become a significant issue. The knowledge of the energy consumption corresponding to hardware and software configuration is important for operators to optimize energy efficiency of a data center. We are currently working on a predictive model for energy consumption of a server, with inputs as service provided, hardware material equipped (type and quantity of processor, memory and hard drive) and technical environment (energy conversion and cooling). In this article, we characterize some potential factors on the power variation of the servers, such as: original fabrication, position in the rack, voltage variation and temperature of components on motherboard. The results show that certain factors, such as original fabrication, ambient temperature and CPU temperature, have noticeable effects on the power consumption of servers. The experimental results emphasize the importance of adding these external factors into the metric, so as to build an energy predictive model adaptable in real situations. [ABSTRACT FROM AUTHOR]

The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for currentmodels and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective power assessment is only possible in a limited sample of pre-selected experiments. Thus, a key objective is to find the experiments that might give the worst situations again for testing power. It offers a machine-based circuit power estimation (MLCPE) system for the selection of exams. Two distinct techniques of predicting are utilized. Firstly, to find testings with power dissipation, it forecasts the behavior of testing. Secondly, the changemovement and energy data are linked to the semiconductor design, identifying small problem areas. Several types of algorithms are utilized. In particular, the methods compared. The findings show great accuracy and efficiency in forecasting. That enables such methods suitable for selecting the worst scenario. [ABSTRACT FROM AUTHOR]

Power optimization has become a major concern for most digital hardware designers, particularly in early design phases and especially in limited power budget systems (battery-operated hand-held devices, electro-optical pluggable modules, IoT and green energy systems, etc.). Subsequently, early power consumption estimation at design time is crucial for power optimization. The aim of this paper is to present an overview of high-level power estimation techniques currently available along with a comprehensive comparison between different methodologies and their applications on estimated models. When high speed and high performance are key features of a specific embedded system, increase in energy consumption becomes the main hurdle to be tackled while keeping speed/performance v/s power consumption trade-off at a minimum. This paper provides designers, interested in power consumption modeling, with knowledge on how to select best power estimation techniques applied to designated target models. [ABSTRACT FROM AUTHOR]

A systematic simulation model is proposed in this research paper to estimate the energy consumption of electric vehicles. The main advantage of this model is that it is made in a generic and simplified way in order to be adaptable to different electric vehicles. The overall electrical power corresponding to the performed maneuver is estimated considering: a tabular form of electric motor efficiency, mechanical power losses, a generalized efficiency map of the power electronics, the auxiliary power losses, and an electro-thermal Lithium-Ion battery pack model. The battery model was developed in a previous work, which simulates the open circuit voltage curves at different temperatures and the alteration in the internal resistance of the battery cells. The proposed model is validated with experimental data from the maneuver tests. The battery model proved high accuracy in estimating the voltage values relevant to the WLTP2 driving cycle on the chassis roller test bench. Furthermore, the mechanical and electrical power were estimated with excellent matching compared to actual test field driving test measurements, giving only the measured vehicle speed and auxiliary power losses. Finally, the state of charge change is predicted accurately along the performed test field dynamic maneuver.

The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems.

The propose of this research is to improve the composite power system for electric vehicle. The proposed architecture is used lead-acid battery, lithium battery, and supercapacitor as a composite power supply pack as well as design a power source decision Algorithm based on electric power estimation method to improve the performance of composite power system. An electric forklift is used to test the proposed method, and the performance is compared in last step. The driving model is first established using pre-recorded electrical driving data such as current, and the discharge current estimation model is established using the MLP method. The discharge current estimation model established by the multilayer perceptron (MLP) method can predict the current trend and has only a slight delay. Using the regression method to estimate the accuracy of the estimated model, the regression equation between the true value and the estimated value has a slope of up to 0.9695 and an intercept of only 0.1751. The second step is the practical test. The performance test at this stage is divided into the original design, the composite power system without estimation function and the composite power system with estimation function. The last step is performance comparison, the weight of the forklift was reduced by 25% from 315 to 267 kg. With the appropriate control of the composite power pack combined with the estimation model, the maximum discharge current can be increased by 30%, and the power can be saved by 25% under the same test conditions. [ABSTRACT FROM AUTHOR]

Based on a single‐phase cascaded H‐bridge (CHB) multilevel converter, this paper investigates a control strategy combining power estimation and natural frame control (NFC) with the omission of grid voltage sensors. Besides, this control method, without phase‐locked loop (PLL), can meet the continuous operation of the system and also eliminates grid voltage feedforward. First, natural frame control is introduced to derive the implementation process of control strategy without PLL, and the calculation formula of AC reference currents is given. Furthermore, the separate control of active and reactive currents is achieved. Second, the principle of proposed control algorithm is presented, and the sinusoidal voltage unit vector for synchronization can be accurately estimated. Next, considering the errors, the article corrects the method through parameter compensation. In addition, the balanced control method stabilized the DC side voltage of the converter. Finally, the effectiveness of this method is verified by computer simulation and experimental prototype. [ABSTRACT FROM AUTHOR]

Power and area estimation in the early stage of designing is very critical for a system. This paper presents the neural network-based early area and power estimation model. The flow starts with the training of the neural network model from the selected behavioral level parameters, which imposes to provide accurate estimations. The model accuracy is validated against ITC99 benchmark programs. The run-times are faster than the synthesis run-times. For the ASIC-based designs, the proposed model took 5 seconds, while Synopsys Design Compiler took 5 minutes. In terms of timing, the estimation speed is more than the order of magnitude faster than the conventional synthesis-based approach. The modeling methodology provides a better, accurate, and fast area and power estimations, at an early stage of the Very-Large-Scale Integration (VLSI) design. In addition, the model eliminates the need for synthesis-based exploration and provides the design picking before synthesis.

In order to solve the problem of severely decreased performance under the situation of rapid moving sources and unstable array platforms, a null broadening robust adaptive beamforming algorithm based on power estimation is proposed in this paper. First of all, we estimate the interference signal power according to the characteristic subspace theory. Then, the correspondence between the signal power and steering vector (SV) is obtained based on the orthogonal property, and the interference covariance matrix (ICM) is reconstructed. Finally, with the aim of setting virtual interference sources, null broadening can be carried out. The proposed algorithm results in a deeper null, lower side lobes and higher tolerance of the desired signal steering vector mismatch under the condition of low complexity. The simulation results show that the algorithm also has stronger robustness. [ABSTRACT FROM AUTHOR]

This paper presents a methodology used to estimate the energy generated during one year by a photovoltaic module (PVM) operating under partial shading conditions. The methodology starts by calculating the solar paths and contours of nearby objects that produce shadows. Then, a method was proposed to estimate the shading factors of each submodule. Afterwards, the solar resource data and the calculated shading factors were used to feed a detailed PVM model to calculate the power–voltage curves for each hour, which were used to obtain a power profile and estimate the energy generated by the PVM in one year. The procedure was validated through simulation and experimental results. The simulation results consider a case study available in the literature, which was simulated to evaluate the effect on the PVM energy estimation considering and disregarding the partial shading conditions. The experimental results illustrate the capacity of the proposed methodology to predict the shaded and unshaded submodules and the module power–voltage curve. The results show that the proposed method avoids the energy overestimation introduced by classical estimation methods, which affects the sizing of a photovoltaic generator. [ABSTRACT FROM AUTHOR]

Regarding to the issue that there is no automatic data acquisition for low-voltage distributed photovoltaic generation, a power estimation method for low-voltage distributed photovoltaic generation based on similarity aggregation is proposed in this paper. Firstly, based on the time series power curve data characteristics of middle-voltage distributed photovoltaic generation and L2 norm distance, a middle-voltage distributed photovoltaic aggregation model is established, and a solution method of middle-voltage distributed photovoltaic aggregation based on K-means clustering is proposed; Secondly, based on the aggregation results of middle-voltage distributed photovoltaic, a parameter calculation method for the power estimation of low-voltage distributed photovoltaic based on geographical distribution and electricity data is proposed; Thirdly, considering the time series data characteristics of distributed photovoltaic changing with date, a strategy to determine whether to re-calculate the middle-voltage distributed photovoltaic aggregation and the power estimation parameters according to the cumulative error is proposed. Finally case studies are accomplished and the effectiveness of the proposed method are verified. The aggregation of middle-voltage distributed photovoltaic and the power estimation of low-voltage distributed photovoltaic, which is helpful to master the overall power generation of regional distributed photovoltaic, realize the observability of low-voltage distributed photovoltaic power generation, improve the safe operation level of distribution network, and has important theoretical and practical significance for serving the orderly development of distributed photovoltaic generations.

This work presents an accurate, scalable, and efficient hybrid machine learning (ML) and neural network (NN) model based, cross‐platform application to analyze and estimate crucial device parameters like scattering (S‐) parameter, gain, power, device‐width and traps of GaN high electron mobility devices. A hybrid multilayer perceptron random forest (RF) model with 13 input parameters was trained on Technology Computer‐Aided Design generated data. An inclusive view of device physics using various device parameters has been presented, which helps to develop insights into structural architecture, DC, and RF performance of the device, and its trade‐off as a function of frequency and biases, all in one go. Moreover, a cross‐platform graphical user interface application is developed to ease the complete process, which further makes the platform scalable to other devices as well. The extremely fast prediction time of 3–7 s and 35–40 ms in regression and classification, respectively, depict the excellent time efficiency of the proposed approach. The results show superior performance with the global mean absolute error (MAE) ranging from 8e−5 to 0.010 and mean squared error (MSE) ranging from 5e−7 to 0.072 accounts for the overall accuracy of 99% and above for the S‐parameter and current gain predictions. A maximum 0.06% variation in the prediction of device width over 140 iterations depicts the excellent generalization capability of the model. The evaluated results thus direct the usefulness of the cross‐platform applications in accelerating accurate device parameter estimations and efficient optimization in device design. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we solve the problem of synthesizing a power regulator in a traction electric drive system using artificial neural networks. To control the vehicle and obtain the desired quality of transients, neural network observers have been developed that allow the measurement of indirect parameters to determine the immutable coordinates of the system. For this purpose, this paper uses dynamic neural networks. When developing the neural network observer, experimental data obtained by the authors on an operating vehicle in real operating conditions are used. To test the effectiveness of using the created artificial neural network, an object is simulated with a random nature of the supply voltage change. A comparative analysis of transients in a system with a power neuroregulator and classical regulators in a subordinate control system shows a fairly high convergence of the results.

Nowadays, high-end Field-Programmable Gate Arrays (FPGAs) are capable of implementing relatively high-performance systems in the field of Digital Signal Processing (DSP). Due to the abundant application of multipliers, their implementation efficiency and performance have become a critical issue in designing the DSP systems. On the other hand, FPGAs consume a large amount of power due to their complex circuitry. So, the power estimation of FPGA implementations at an early design stage has become a critical design metric. Various models are available in the literature based on Look-up Tables (LUTs), but not much literature is available on speed-optimized multiplier design using DSP slices only. In this paper, an embedded multiplier (12.0 IP core) has been analyzed and customized for different Input/Output (I/O) configurations to estimate the power using Vivado Design Suite (2014.4) targeted to the Zynq-family FPGA device (Zynq evolution and development kit). The embedded multiplier IP has been optimized for performance using two different approaches, i.e., Mults (DSP)-based and LUTs-based. Post-synthesis attributes have been used for formulating the power estimation models based on Artificial Neural Network (ANN) and curve fitting and regression technique. The power values estimated from the proposed models have been authenticated with reference to those assessed from the commercial tool. Based on the results obtained, ANN-based model provides average errors of 0.73% and 0.88% for the LUTs and DSP-based designs, respectively. Whereas, the model based on curve fitting and regression technique provides average errors of 3.61% and 1.59% for the LUTs and DSP-based designs, respectively. The timing analysis has been done to get the design performance and time complexity of the proposed models. Area analysis of the design has also been performed in order to report the resource utilization. [ABSTRACT FROM AUTHOR]

Power output is one of the most accurate methods for measuring exercise intensity during outdoor endurance sports, since it records the actual effect of the work performed by the muscles over time. However, power meters are expensive and are limited to activity forms where it is possible to embed sensors in the propulsion system such as in cycling. We investigate using breathing to estimate power output during exercise, in order to create a portable method for tracking physical effort that is universally applicable in many activity forms. Breathing can be quantified through respiratory inductive plethysmography (RIP), which entails recording the movement of the rib cage and abdomen caused by breathing, and it enables us to have a portable, non‐invasive device for measuring breathing. RIP signals, heart rate and power output were recorded during a N‐of‐1 study of a person performing a set of workouts on a stationary bike. The recorded data were used to build predictive models through deep learning algorithms. A convolutional neural network (CNN) trained on features derived from RIP signals and heart rate obtained a mean absolute percentage error (MAPE) of 0.20 (ie, 20% average error). The model showed promising capability of estimating correct power levels and reactivity to changes in power output, but the accuracy is significantly lower than that of cycling power meters. [ABSTRACT FROM AUTHOR]

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. A power estimation approach for calculating the power of a flapping-wing air vehicle (FWAV) in forward flight is proposed in this paper. One of the challenges and essential points of FWAVs is endurance. In order to optimize FWAVs, it is necessary to analyze power required for flight in addition to kinematic and aerodynamic analyses of the prototype. Previously, calculating the power of birds was limited to calculating their average power, which assumed the lift was usually the same as the gravitational force. However, the lift varies with the flapping angle during flight. As a result, the power required for forward flight of FWAVs is determined in this work by using a kinematic model of the drive element and wing flapping, along with the aerodynamic model, which varies with the flapping angle during the flapping cycle. Experiments were performed with two prototypes with wingspans of 1.6 and 1.8 m, utilizing a wind tunnel platform. The correlations between power and angle of attack, flapping frequency, and incoming flow velocity were discovered, and recommendations for FWAVs and flying mode design were provided. However, several challenges are highlighted in the application of the model to practical design efforts. [ABSTRACT FROM AUTHOR]

Combining the auxiliary DAB converter and the bidirectional LLC converter operating at a fixed frequency in an input-series output-parallel (ISOP) structure can achieve bidirectional power regulation and high efficiency in DC-DC transformer (DCX) applications. However, for bidirectional DCX applications, the synchronous rectification (SR) operation becomes challenging since that the instantaneous-transferred power is difficult to obtain, and LLC-DCX will operate in discontinuous current mode (DCM) under light load conditions. In addition, the rectifier-bridge has to be swapped under different power flows. To achieve on-line SR modulation for LLC-DCX under bidirectional power application, a new solution to achieve accurate SR operation, based on instantaneous-transferred power estimation is proposed under full load range. The proposed method uses the added auxiliary DAB converter as a power detector to estimate the power value without input voltage sampling and any auxiliary current sensor or SR detection circuit. Besides, the mathematical derivation of power estimation, DCM operation conditions and the SR conduction period for the rectifier-bridge, are analyzed in depth. Finally, the effectiveness of the proposed method and the correctness of analysis are verified by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Power and area estimation in the early stage of designing is very critical for a system. This paper presents the neural network-based early area and power estimation model. The flow starts with the training of the neural network model from the selected behavioral level parameters, which imposes to provide accurate estimations. The model accuracy is validated against ITC99 benchmark programs. The run-times are faster than the synthesis run-times. For the ASICbased designs, the proposed model took 5 seconds, while Synopsys Design Compiler took 5 minutes. In terms of timing, the estimation speed is more than the order of magnitude faster than the conventional synthesisbased approach. The modeling methodology provides a better, accurate, and fast area and power estimations, at an early stage of the Very-Large-Scale Integration (VLSI) design. In addition, the model eliminates the need for synthesis-based exploration and provides the design picking before synthesis. of the design metrics such as performance, power, area, etc [1]. However, taking decisions at this level is very difficult, since the design space is extremely wide. Efficient system-level estimation methods are therefore necessary for designspace exploration. The designs are getting complex to incorporate more functions, thereby increasing the area and power dissipation. Therefore, a quick and accurate estimation of power and area characteristics is of a paramount importance to guide the decision-making process. [ABSTRACT FROM AUTHOR]

In this paper, the design and implementation of a novel recursive method for the power measurement ac-cording to the IEEE Standard 1459-2010 have been described. The most important parts are adaptive band and low-pass FIR filters that extract fundamental and dc components, respectively. In addition, by using oversampling techniques and decimation filters, coefficient sensitivity problems of the large-order FIR comb cascade structure are overridden and the parameter estimation accuracy is improved. The symmetrical components are estimated through a transformation matrix of adaptive phase shifters. The effectiveness of the proposed techniques is demonstrated by simulation results.

Abstract Power output is one of the most accurate methods for measuring exercise intensity during outdoor endurance sports, since it records the actual effect of the work performed by the muscles over time. However, power meters are expensive and are limited to activity forms where it is possible to embed sensors in the propulsion system such as in cycling. We investigate using breathing to estimate power output during exercise, in order to create a portable method for tracking physical effort that is universally applicable in many activity forms. Breathing can be quantified through respiratory inductive plethysmography (RIP), which entails recording the movement of the rib cage and abdomen caused by breathing, and it enables us to have a portable, non‐invasive device for measuring breathing. RIP signals, heart rate and power output were recorded during a N‐of‐1 study of a person performing a set of workouts on a stationary bike. The recorded data were used to build predictive models through deep learning algorithms. A convolutional neural network (CNN) trained on features derived from RIP signals and heart rate obtained a mean absolute percentage error (MAPE) of 0.20 (ie, 20% average error). The model showed promising capability of estimating correct power levels and reactivity to changes in power output, but the accuracy is significantly lower than that of cycling power meters.

This paper presents a novel method to estimate the power consumption of distinct active components on an electronic carrier board by using thermal imaging. The components and the board can be made of heterogeneous material such as plastic, coated microchips, and metal bonds or wires, where a special coating for high emissivity is not required. The thermal images are recorded when the components on the board are dissipating power. In order to enable reliable estimates, a segmentation of the thermal image must be available that can be obtained by manual labeling, object detection methods, or exploiting layout information. Evaluations show that with low-resolution consumer infrared cameras and dissipated powers larger than 300 mW, mean estimation errors of 10% can be achieved. [ABSTRACT FROM AUTHOR]

This study focuses on the effect of environmental factors on the power generation in a solar photovoltaic (PV) panel and the estimation of the power generated from this panel. Artificial Neural Network (ANN) and Multiple Linear Regression (MLR), as two different data-driven models, were used to assess the power of solar PV panels. Six factors are determined as input parameters in these models such as irradiance (W/m2), panel temperature (°C), ambient temperature (°C), wind speed (m/s), wind chill (°C), and humidity data (RH%). The output parameter of these models is the electrical power (W) generated instantly by the PV panel. In the development of both models, 550 experimental data sets (6x550 input data and 1 × 550 output data) were used, which were obtained from the position of the PV panel at different times throughout the year. In the light of these data, eight different training algorithms of ANN algorithms were tested and their success results were compared. According to the study results, the R2 value was found 98.9% in ANN trained with Levenberg-Marquardt algorithm (trainlm), which showed the highest success. In the MLR analysis, the solar panel's power estimation was found to be 94.8% (R2). Also, the weight of each environmental factor on the output power was determined by MLR analysis. The results indicate that the ANN model can successfully estimate the power generated under variable environmental conditions without extract a solar PV panel's model-physical parameters. Also, the effects of environmental factors on power were determined by correlation and MLR analysis. When the presented methodology is generalized for PV plants, accurate energy planning can be made and a relationship can be established between demand and installed power capacity that prevents unrequited investment. [ABSTRACT FROM AUTHOR]

The penetration of distributed generation (DG) in the distribution network has become a necessity and a significant solution to improve power grid quality, and solve power losses issue. To reach these targets, integrating these DGs in an optimal placement with an optimal sizing should be investigated and taken into consideration. This paper focuses on obtaining the optimal allocation and size of a photovoltaic (PV) distributed generation (PVDG) in order to reduce the total power losses and enhance voltage and frequency profiles of a modified IEEE 14 node distribution network (Hooshmand in J Appl Sci 8(16):2788–2800, 2008, https://doi.org/10.3923/jas.2008.2788.2800). An objective function is used in this paper aims to reduce grid power losses, and two optimization algorithms are applied to solve this function which are the particle swarm optimization (PSO) and the genetic algorithm (GA). Added to that, two scenarios are discussed in this paper in order to analyze the effects of variable PV penetration level, hourly load consumption profile variation and atmospheric condition change on the sizing optimization resolution. The obtained simulation results prove that the PSO algorithm has better performance compared to the GA in terms of speed convergence, power loss reduction and grid quality improvement (voltage and frequency profiles). Then, it shows that variable load consumption curve and weather condition change can affect not only the determination of the PVDG optimal position and capacity but also grid security. [ABSTRACT FROM AUTHOR]