Double Input Research Articles

ROBUST OPTIMAL DESIGN BASED ON STORY DUCTILITY RATIO OF ELASTIC-PLASTIC BUILDING STRUCTURES WITH UNCERTAIN OIL DAMPERS SUBJECTED TO CRITICAL DOUBLE IMPULSE

A new robustness evaluation method based on the specified story ductility ratio is proposed for a building model with uncertain oil dampers. Since the influence of input motion uncertainty on the response variation can be simply taken into account, the critical double impulse input is used instead of input ground motions. In the proposed method, the robustness function is derived as a function of the input velocity amplitude for a specified ductility ratio. This procedure is suitable for the performance-based design. A robust optimal design method on damper placement is also presented based on the proposed new robustness function. Numerical examples of robust optimal design of dampers are presented for a 12-story elasto-plastic building model with linear oil dampers.

Double input DC-DC converter for highly flexible and reliable Battery Storage Systems

Battery storage systems are fundamental in UPS applications. UPSs are exploited when high reliability is required. A DC-DC converter is typically used to interface the battery to the inverter to match the different voltage levels. In normal operation, the battery of the UPS is not used and it intervenes only during grid blackout. However, the battery is subjected to deterioration and UPS intervention could fail. In medium and high power UPS, more battery modules are connected in series. If one battery is damaged, all the series is affected. To prevent this issue, a new double-input DC-DC converter is presented in this paper. The two DC sources can be controlled separately, resulting in a system reliability improvement. In addition, the damaged battery is not bypassed; hence the overall system performance can be maximized, since the deteriorated battery can provide energy at a limited rate. Additionally, the proposed converter allows batteries based on different technologies to be mixed together, achieving the best performances from each technology.

Performance investigation of double input enhanced converter for hybrid renewable energy

Nowadays, power shortage turned into a massive issue in numerous countries because of increasing load demand, which cannot be solved by traditional energy power generation. These problematic circumstances motivate analysts to extract the most extreme electric power by concentrating on non-conventional energy sources. In order to extract the electric power, new extendable multi-input DC-DC converters are utilized to maximize the Power Conversion efficiency. The efficient power conversion is accomplished with minimum voltage stress across the semiconductor devices. This topology gives maximum voltage gains by expanding the quantity of input sources, thereby being appropriate for various requirements from minimum to maximum voltage/power in Hybrid Energy Systems (HES). This paper evaluates the present and the future trend of non-isolated DC-DC converters with different parameters investigated utilizing MATLAB Software. Because of the simulation result, the performances of various non-isolated converters are assessed and help to establish the appropriate converter with a specific power rating for hybrid renewable energy-based requirements.

Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning

A major challenge in artificial intelligence based ECG diagnosis lies that it is difficult to obtain sufficient annotated training samples for each rhythm type, especially for rare diseases, which makes many approaches fail to achieve the desired performance with limited ECG records. In this paper, we propose a Meta Siamese Network (MSN) based on metric learning to achieve high accuracy for automatic ECG arrhythmias diagnosis with limited ECG records. First, the ECG signals from three different ECG datasets are preprocessed through resampling, wavelet denoising, R-wave localization, heartbeat segmentation and Z-score normalization. Then, an ECG dataset with limited records is constructed to verify the performance of the proposed model and explore variation of model performance with the sample size. Second, a metric-based meta-learning framework is proposed to address the challenge of few-shot learning for automatic ECG diagnosis of cardiac arrhythmia, and siamese network is employed to achieve arrhythmia diagnosis based on similarity metric. Finally, the N-way K-shot meta-testing strategy is proposed based on the siamese network with double inputs, and the experimental results demonstrate that the proposed strategy can effectively improve the robustness of the proposed model.

Model reference input shaped neuro-adaptive sliding mode control of gantry crane

This article proposes a hybrid control strategy that integrates the merits of both zero-vibration double derivative input shaping control and neuro-adaptive sliding mode control. The approximation ability of the radial basis function neural network is utilized to estimate the unknown dynamics of the gantry crane system. The input shaping control is used to shape the trolley position, velocity, and acceleration references. These reference trajectories are generated as the state trajectories of a closed-loop system obtained by controlling a nominal system model with a sliding mode control constructed based on the nominal model. The shaped trajectories are sent to the proposed neuro-adaptive sliding mode control as references to be tracked. In comparison with the conventional sliding mode control, the proposed control has higher tracking accuracy and robustness to external disturbances, and effectively reduces the sway angle without suffering from the chattering phenomenon. Moreover, the proposed control has a very high insensitivity to parameter uncertainties. The adaptation mechanisms of the radial basis function neural networks are established based on the Lyapunov stability theory. The effectiveness of the proposed controller is verified through some numerical simulations.

Comparative Study between Physics-Informed CNN and PCA in Induction Motor Broken Bars MCSA Detection

In this article, two methods for broken bar detection in induction motors are considered and tested using data collected from the LIAS laboratory at the University of Poitiers. The first approach is Motor Current Signature Analysis (MCSA) with Convolutional Neural Networks (CNN), in which measurements have to be processed in the frequency domain before training the CNN to ensure that the resulting model is physically informed. A double input CNN has been introduced to perform a 100% detection regardless of the speed and load torque value. A second approach is the Principal Components Analysis (PCA), in which the processing is undertaken in the time domain. The PCA is applied on the induction motor currents to eventually calculate the Q statistic that serves as a threshold for detecting anomalies/faults. Even if obtained results show that both approaches work very well, there are major differences that need to be pointed out, and this is the aim of the current paper.

Design of double-integral sliding-mode controller for fourth-order double input DC - DC boost converter with a voltage multiplier cell

ABSTRACT A voltage multiplier cell (VMC)-integrated dual-input boost converter (DIBC) with an inductor at the two inputs for dual boost, and working at a duty cycle greater than 0.5 is a requirement for achieving a high voltage gain from low-voltage energy sources. The main focus of this work is to design a suitable control to effectuate the voltage regulation during input voltage and load disturbances, as well as to reduce the start-up transient. The pulse-width-modulation-based double-integral sliding-mode controller was investigated for desirable voltage regulation. Two input inductor current references are incorporated in the controller design to facilitate the decoupling between the interacting loops, which render the design complex with two sliding surfaces for each input inductor current. Also, a comparative analysis with dual-loop proportional-integral control for DIBC with single VMC is presented to highlight the robustness of the proposed controller. A converter model of 200 W is developed, and the controller is carried out using a field-programmable gate array. Under various disturbance scenarios, MATLAB simulation and experimental results clearly showed the satisfactory transient and steady-state output voltage regulation with the proposed controller.

Single and Double Input DC Sources Multilevel Inverter Topologies With Reduced Components Counts and Voltage Boosting Property for Grid-Connected Photovoltaic Converters

This article proposes a single-source nine-level multilevel inverter topology that handles a reduced number of components. Moreover, it utilizes the virtual dc sources in terms of the switched capacitors to reduce the number of the active dc source. Those capacitors have a self-charging with naturally balancing capability. The proposed topology can be upgraded to a 19-level configuration. This modification is performed by adding a single supply and a pair of switches. To highlight the good features of the proposed structures, a set of comparative studies are held with other structures based on some of the performance evaluating parameters. The comparison results prove the superiority of the proposed configurations over the other comparative references. A simple method is presented to estimate the boundary for the capacitance of capacitors. The performance of the proposed configurations is validated through the practical prototypes for these structures, which are implemented and tested based on the dSPACE (DS-1103) hardware. For further validation, the proposed topology is integrated with a photovoltaic (PV) system to study its performance with a high output power system.

Novel Three and Four Switch Inverters With Wide Input and Output Voltage Range for Renewable Energy Systems

The proposed inverters receive input dc voltage in a wide range and generate output ac voltage in a wide range due to their buck–boost ability. They require only three or four active switches and two inductors. Therefore, the circuit topologies and their operation are simple, and control is easy. The output current of the proposed inverters is continuous, which lowers the output harmonics and filtering capacitor. In the proposed inverters, the output and input voltage sources or capacitors share the same ground, which results in a constant common-mode voltage and negligible leakage current. Of the proposed inverters, an inverter with four active switches and double input voltage sources is analyzed and tested in detail. The circuit operation is discussed, a control strategy is developed, a common-mode voltage analysis is presented, and design guidelines for circuit components selection are given. To verify the theoretical analysis, a 500-W, 40-kHz prototype of the proposed four-switch inverter is designed, fabricated, and tested. An output ac voltage of 110 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{V}_{\mathrm {rms}}$ </tex-math></inline-formula> is generated from an input dc voltage of 100–200 V. A peak efficiency of 97.5% is obtained. The proposed inverter obtains high efficiency due to simple circuit operation, few circuit components in the conduction path, and switching of only one switch at high frequency.

Multivariable Decoupling and Nonlinear Correction Method for Image-Based Closed-Loop Tracking of the Risley Prisms System.

Image-based closed-loop tracking (IBCLT) is an important part of the process of target tracking. The Risley prism system has a unique advantage in improving the target tracking ability because of its compact and lightweight structure. Compared with traditional target tracking equipment, the Risley prism system has two difficulties in the process of IBCLT. First, the Risley prism is a complex coupling system of double input and double output. Second, the Risley prism itself is a nonlinear system. These problems lead to decrease in dynamic response and inconsistent target tracking capabilities. Thus, this paper proposes a method to implement multivariable decoupling and reduce the nonlinear effect. First, the boresight error of IBCLT is decoupled to the azimuth and elevation directions by the rotation matrix error-decoupling (RMED) method. Second, the gains of IBCLT in azimuth and elevation directions are independent variables that comes from two functions of the target elevation angle. The experimental results show that the IBCLT error deviation of different static targets in the field of view is within 0.025 arcsec, which is 70% lower compared with the fixed gain method. Furthermore, the steady-state error deviation of moving targets is controlled within 2.5 arcsec. These experimental results prove the feasibility and effectiveness of the proposed method.

Strong Responses of Soil Greenhouse Gas Fluxes to Litter Manipulation in a Boreal Larch Forest, Northeastern China

Alterations in plant litter inputs into the soil are expected to significantly affect soil greenhouse gas (GHG) emissions. However, the influence on boreal forest soils is not clear, given the large amount of accumulated soil organic matter that may buffer the impacts from the input of fresh litter. In this study, we conducted a litter manipulation experiment to explore the effects of the litter layer on soil GHG fluxes in a Dahurian larch (Larix gmelinii) forest ecosystem in northeastern China. Three litter treatments were implemented, namely aboveground litter removal (LR), litter double (LD), and unchanged litter input (CK). The associated microclimate, litter characteristics, and soil properties were also measured. The results showed that this larch forest soil acts as a source of CO2 and N2O but acts as a sink for CH4 for all litter manipulation treatments. LD increased the soil CO2 and N2O fluxes by 15% and 34%, while LR decreased them by 8% and 21%, respectively. However, soil CH4 uptake decreased by 34% in LD treatment and increased by 22% in LR treatment, respectively. Litter manipulation treatments can not only affect soil GHG fluxes directly but also, via their effects on soil MBC, NH4+−N, and NO3−−N content, indirectly affect variations in soil CO2, CH4 and N2O fluxes, respectively. Our study highlights the importance of the plant litter layer in regulating soil GHG between the atmosphere and soil in a Dahurian larch forest ecosystem, especially for litter addition. Considering the natural increase in litter quantity over time, this important regulatory function is essential for an accurate estimation of the role of boreal forests in mitigating future climate change.

Optimal PID Controllers for AVR Systems Using Hybrid Simulated Annealing and Gorilla Troops Optimization

In the literature, all investigations dealing with regulator design in the AVR loop observe the AVR system as a single input single output (SISO) system, where the input is the generator reference voltage, while the output is the generator voltage. Besides, the regulator parameters are determined by analyzing the terminal generator voltage response for a step change from zero to the rated value of the generator voltage reference. Unlike literature approaches, in this study, tuning of the AVR controllers is conducted while modeling the AVR system as a double input single output (DISO) system, where the inputs are the setpoint of the generator voltage and the step disturbance on the excitation voltage, while the output is the generator voltage. The transfer functions of the generator voltage dependence on the generator voltage reference value and the excitation voltage change were derived in the developed DISO-AVR model. A novel objective function for estimating DISO-AVR regulator parameters is proposed. Also, a novel metaheuristic algorithm named hybrid simulated annealing and gorilla troops optimization is employed to solve the optimization problem. Many literature approaches are compared using different regulator structures and practical limitations. Furthermore, the experimental results of 120 MVA synchronous generators in HPP Piva (Montenegro) are presented to show the drawbacks of the literature approaches that observe generator setpoint voltage change from zero to the rated value. Based on the presented results, the proposed procedure is efficient and strongly applicable in practice.

The Need to Implement Change in Educational Leadership

Abstract This article is inspired by the practical approach to education reforms, within education for all. These reforms have greatly fueled political dialogue and exchange of experience. Given the number of countries and regions of the world concerned, these reforms take place in a wide variety of contexts that underlie the specificity of each. Consequently, any possibility of classification and typology may encounter theoretical and practical obstacles that are difficult to overcome. Although we do not ignore them, observing certain dominant common features of these reforms allows us to try it through a double input: the expectations and the conduct of change, especially those determined by the globalization of reforms through the frameworks of successive education assessments and international assessments (PISA, TIMSS, PASEC, SAQMEC). The challenge is threefold. It is primarily a matter of categorizing expectations and strategic choices that make it possible to distinguish policies in the field of educational reform. This opens up a deeper understanding of the nature and impact of each of these different types of reform to better inform the analysis of the options that have guided the unfolding of change. The second challenge is to identify the major challenges faced by change management. Such challenges, by their scale and complexity, often explain the discrepancies between expectations for change and the results of reforms. One of the implications is also that addressing these challenges creates the critical conditions for successful reforms. The reflection on the lessons learned then opens the way to new perspectives for the development of reforms to which current and future trends in the evolution of education systems are articulated.

DISNet: A sequential learning framework to handle occlusion in human action recognition with video acquisition sensors

Human action recognition (HAR) requires a clear line of sight for the video acquisition system to properly collect the data. However, the data acquisition is hampered due to presence of the obstructions in front of the action happening in real time environment. In recently reported HAR techniques, the widely used multi-stream sequential networks are having two major limitations. First, the performance of the existing algorithms degrades comprehensively in the presence of partial loss of action data due to obstruction. Second, the performance of the existing networks is restricted by not exploiting the dependency relationship between different streams of a multi-stream network. To handle these problems, a novel double input sequential network (DISNet) is proposed which takes care the HAR algorithm in the presence of partial loss of action data. The DISNet, which learns inter-stream information, is jointly trained on the normal data and the artificially created obstructed data of a single video to provide immunity to the HAR network against obstructions. The proposed DISNet is evaluated on publicly available datasets like KTH, UCF-sports, JHMDB and UCF101, which provides performance accuracy of 83.57%, 82.27%, 50.24% and 54.96% respectively.

Simultaneous actuator and sensor fault reconstruction of singular delayed linear parameter varying systems in the presence of unknown time varying delays and inexact parameters

SummaryIn this article, robust fault diagnosis of a class of singular delayed linear parameter varying systems is considered. The considered system has delayed dynamics with unknown time varying delays and also it is affected by noise, disturbance and faults in both actuators and sensors. Moreover, in addition to the aforementioned unknown inputs and uncertainty, another source of uncertainty related to inexact measures of the scheduling parameters is present in the system. Making use of the descriptor system approach, sensor faults in the system are added as additional states into the original state vector to obtain an augmented system. Then, by designing a suitable proportional double integral unknown input observer (PDIUIO), the states, actuator, and sensor faults are estimated. The uncertainty due to the mismatch between the inexact parameters that schedule the observer and the real parameters that schedule the original system is formulated with an uncertain system approach. In the PDIUIO, the uncertainty induced by unknown inputs (disturbance, noise and actuator, and sensor faults), unknown delays, and inexact parameter measures are attenuated in H∞ sense with different weights. The constraints regarding the existence and the robust stability of the designed PDIUIO are formulated using linear matrix inequalities. The efficiency of the proposed method is verified using an application example based on an electrical circuit.

Design and optimization of nanooptical couplers based on photonic crystals involving dielectric rods of varying lengths

This study presents design and optimization of compact and efficient nanooptical couplers involving photonic crystals. Nanooptical couplers that have single and double input ports are designed to obtain efficient transmission of electromagnetic waves in desired directions. In addition, these nanooptical couplers are cascaded by adding one after another to realize electromagnetic transmission systems. In the design and optimization of all these nanooptical couplers, the multilevel fast multipole algorithm, which is an efficient full-wave solution method, is used to perform electromagnetic analyses and simulations. A heuristic optimization method based on genetic algorithms is employed to obtain effective designs that provide the highest efficiency values. Two types of optimization strategies are applied using nanorods with a fixed length and using nanorods with varying lengths. This way, photonic crystals consisting of irregular arrays of both identical and nonidentical dielectric elements are designed for the realization of nanooptical couplers. The designs and their numerical results show that it is possible to design and further improve efficient nanooptical couplers with simple and compact geometries based on the principles of photonic crystals. Using relatively simple geometries and a single material, the designed nanooptical couplers are more preferable than the available designs in the literature.

Drivers of soil respiration and nitrogen mineralization change after litter management at a subtropical Chinese sweetgum tree plantation

AbstractLeaf litter decomposition transfers elements from litter to soils that are essential for regulating nutrient cycles in plantation ecosystems, especially carbon and nitrogen. However, soil carbon and nitrogen dynamics in response to tree litter management remains insufficiently researched. We conducted a one‐year field experiment at a fast‐growing sweetgum tree plantation to evaluate the effects of leaf litter management on soil available nutrients, respiration rate and nitrogen mineralization rate. Three leaf litter treatments were applied, which were: (1) natural input (control); (2) double input and (3) non‐input. It was found that the double input treatment increased soil inorganic nitrogen and microbial biomass nitrogen, but had little effect on microbial biomass carbon, dissolved organic carbon or dissolved organic nitrogen compared with natural input. The non‐input treatment caused dissolved organic carbon to decrease compared with natural input. The respiration rate increased in the double input treatment, with a positive priming effect observed. Soil net ammonification, nitrification and mineralization rates also increased in the double input treatment in specific seasons. Meanwhile, positive linear relationships between respiration rate and all nitrogen transformation rates were observed for all treatments. Soil temperature was found to be an important prediction factor for predicting the respiration rate and mineralization as seasonal variations, but not for litter‐induced fluctuations. Soil water content and mineral nitrogen were the primary drivers of litter‐induced change to the respiration rate, whereas mineral nitrogen and microbial biomass were primary drivers of mineralization change. These results suggest that changes in soil nitrogen mineralization rate are strongly associated with the soil respiratory process, resulting in a potentially strong plant–soil feedback mechanism.

Improving vegetation segmentation with shadow effects based on double input networks using polarization images

Fractional vegetation cover (FVC) plays an important role in the study of vegetation growth state, and the key issue is accurately segmenting and extracting green vegetation from the background. However, the shadows generated by natural lights produce extreme illuminance differences in images, which greatly reduces the vegetation extraction accuracy. The polarization information for ground objects is independent of the physical state of the reflectivity of ground objects, and it can be used to eliminate the influence of strong reflections in images to a certain extent, reduce the illuminance differences under extreme sunlight conditions, and help improve the vegetation recognition effect under shadow conditions. To improve the accuracy of vegetation segmentation under shadow conditions, this study introduces polarized reflection information for vegetation and an improved semantic segmentation network, notably a double input residual network based on DeepLabv3plus (DIR_DeepLabv3plus), with fusion strategies based on concatenation and addition is proposed. The network extracts low-level features and high-level features at different spatial scales from both light intensity (red-greenblue (RGB)) images and degree of linear polarization (DoLP) images independently by a deep residual network and atrous spatial pyramid pooling (ASPP) structure, effectively improving the accuracy of vegetation segmentation in shadow situations. The results show that the mean intersection over union (mIoU) values of vegetation without shadows, with light shadows and with shadows are 94.01%, 92.508% and 90.969%, respectively. Compared with the color index method and green fractional vegetation cover extraction from digital images using a shadow-resistant algorithm (SHAR-LABFVC), the proposed method provides a greatly improved extraction accuracy, and it has 0.18%, 1.00% and 1.49% higher mIoU values for vegetation under different shadow conditions than the method without polarization information. This study provides a new approach for vegetation segmentation and improves the accuracy of FVC calculations under shadow conditions.

Multi-input interleaved DC-DC converter for hybrid renewable energy applications

The increasing demand for hybrid energy systems based on renewable energy sources has enabled the new dimension for multi-input converter (MIC). Various topologies have been introduced over the last decade. However, most of these topologies have several drawbacks in terms of design complexity or efficiency. Therefore, this research aims to introduce a multi-input DC-DC converter for hybrid renewable energy applications. The proposed multi-input converter is able to hybridize different sources such as solar PV array and PEMFC. Analysis and simulation have been carried out for the double input two-phase interleaved converter in operating the boost mode. The proposed converter is designed in matlab simulink by using interleaved boost converter method to achieve a boosted and smoothened output. The proposed topology has shown a remarkable performance in terms of output voltage boosting, voltage ripple reduction as well as enhanced efficiency through interleaved boosting technique. From the simulation results, it can be observed that the proposed converter can gain high efficiency which is higher than 97%. The obtained results have been validated with previously published works and the proposed technique has been proven to yield compatible and improved outcomes.

Design of Performance Evaluation System of College Students' Innovation and Entrepreneurship Education Based on AHP Method

Based on the interviews with experts and scholars, entrepreneurs, and the personnel in charge of the application-oriented college enrollment and employment department, this paper makes a preliminary determination of the Performance Evaluation Index of Innovation and Entrepreneurship education for college students. On this basis, through combing the characteristics of innovative entrepreneurship education in universities in the applied, using the analytic hierarchy process (AHP) to determine index weight, from double gen environment and double input and double the output of a three-level of constructing applied universities entrepreneurship education innovation performance evaluation index system, the performance evaluation of innovation and entrepreneurship education mechanism to boost the development of entrepreneurship education innovation.