In this paper, we tackle the decentralized-output feedback sampled-data disturbance rejection control for a dual-drive H-Gantry (DDHG) system with a symmetrical structure. For the DDHG system with disturbances, only the position information of the sy...
This paper focuses on the design of a continuous distributed observer for linear systems under multirate sampled output measurements involving multiple delays. It is mathematically proved that the continuous distributed observer can achieve estimatio...
The design of fractional order-holds (FROH) of correcting gains β ∈ [ − 1 , 1 ](potentially and possibly including zero-order-holds, ZOH with β=0, and first-order-holds,FROH with β=1) is discussed related to achieving output deviations being close wi...
The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the shor...
In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integra...
A nonlinear reduced-order state observer is applied to estimate the degree of polymerization in a series of polycondensation reactors. The finishing stage of polyethylene terephthalate synthesis is considered in this work. This process has a special...
Currently, deep reinforcement learning has been widely applied to energy system optimization and scheduling, and the DRL method relies more heavily on historical data. The lack of historical operation data in new integrated energy systems leads to in...
This research studies a class of linear, hybrid, time-varying, continuous time-systems with time-varying delayed dynamics and non-necessarily bounded, time-varying, time-differentiable delay. The considered class of systems also involves a contributi...
This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transm...
The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significan...
Digital control in UPS systems is currently the only reasonable way of controlling a voltage source inverter (VSI). The control frequency range is restricted to up to about 1 kHz owing to the output low-pass LC filter, which should also maintain the...
Hybrid thermoelectric generators (HTEGs) play a pivotal role in sustainable energy conversion by harnessing waste heat through the Seebeck effect, contributing to global efforts in energy efficiency and environmental sustainability. In practical sust...
This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optim...
In this paper a number of LT Spice simulations have been carried out on an Electrical Impedance Tomography (EIT) system, which includes the whole analog and digital circuitry as well as the subject to be examined (phantom model). The aim of this stud...
In this paper, a forward converter with multiple outputs is employed to build up a circuit system with full-digital control without any analog-to-digital (ADC) converter adopted. In this circuit, all the output voltages can be regulated by individual...
Based on the application of the conditional mean rule, a sampling-recovery algorithm is studied for a Gaussian two-dimensional process. The components of such a process are the input and output processes of an arbitrary linear system, which are chara...
This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE). With the increasing penetration of renewable energy sources (RESs) into power systems, several probabil...
This paper presents new architectures and implementations of original open-loop Class-BD audio amplifiers with balanced Common-Mode output. The output stage of each proposed amplifier includes the typical H-bridge with four MOSFETs and four additiona...
In this paper, we consider the calibration of measuring bridges for non-conventional instrument transformers with digital output. In this context, the main challenge is represented by the necessity of synchronization between analog and digital output...
In order to reduce the hardware cost and data acquisition time in near-field scenarios, such as airport security imaging systems, this paper discusses the layout of a multiple-input multiple-output (MIMO) radar array. In view of the existing multi-in...
The use of hierarchical Bayesian small area models, which take survey estimates along with auxiliary data as input to produce official statistics, has increased in recent years. Survey estimates for small domains are usually unreliable due to small s...
Minimization of the Euclidean distance between output distribution and Dirac delta functions as a performance criterion is known to match the distribution of system output with delta functions. In the analysis of the algorithm developed based on that...
Data-driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input-output matching model identified by data sensed from interaction with target physical objects. In data-driven modeling...
In this study, Ni–Co–P–BN(h)–Al2O3 binary nanocomposite coatings were fabricated on steel C1045 substrates by jet electrodeposition. The samples were then processed using self-made laser processing equipment to investigate the...
In the present study, a data-driven ripple-free design is proposed for a dual-rate sampled-data control system in which the sampling interval of the plant output is longer than the holding interval of the control input. The objective of the present s...
Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifie...
We have developed circular defects in 2-dimensional photonic crystal lasers that allow current injection for interconnected optical communications. However, when cleaving the sample to measure the output light, the output light intensity changes due...
A biaxial compression Hopkinson bar set-up bas been designed. It consists in a projectile, an input bar and two co-axial output bars. After the projectile impact on the input bar, the internal output bar measures the axial loading of the cross sample...
We investigated the effect of Ti/Al and ITO/Ag n-type contacts on the emission uniformity and light output of different chip-size vertical-geometry light-emitting diodes (VLEDs) for vehicle headlamp application. The forward voltage of the Ti/Al-based...
This paper addresses the new method for output voltage regulation of DC–DC buck converter nonlinear systems by a sampled-data linear parameter varying (LPV) controller. For this purpose, an output-error state-space affine LPV model is presented...
The variable selection problem in general, and specifically for the ordinary linear regression model, is considered in the setup in which the number of covariates is large enough to prevent the exploration of all possible models. In this context, Gib...
A flat circular transmission line-based 300 MHz resonator was implemented for the non-contact assessment of burn depths in biological tissues. Used as a transmit-and-receive sensor, it was placed at a 2 mm distance from organic material test samples...
In this paper, we focus on the redesign of the output layer for the weighted regularized extreme learning machine (WRELM). For multi-classification problems, the conventional method of the output layer setting, named “one-hot method”, is...
This paper introduces an approach that applies a digital sampling technique for a sinusoidal pulse width modulation (SPWM) multilevel inverter modulation that reduces the total harmonic contents in the output voltage compared to that of classical reg...
Multi-output Gaussian process regression measures the similarity between samples based on Euclidean distance and assigns the same weight to each feature. However, there are significant differences in the aerodynamic performance of plane cascades comp...
Mode collapse has always been a fundamental problem in generative adversarial networks. The recently proposed Zero Gradient Penalty (0GP) regularization can alleviate the mode collapse, but it will exacerbate a discriminator’s misjudgment problem, th...
This study investigates an event-triggered disturbance estimation approach for the inner-formation system. An extended state observer is designed using an event-based sampling scheme, which offers advantages over traditional estimation methods by red...
We analyze several factors that affect the linear output range of CMOS image sensors, including charge transfer time, reset transistor supply voltage, the capacitance of integration capacitor, the n-well doping of the pinned photodiode (PPD) and the...
The importance of energy supply and demand has been emphasized over the past few years. Renewable energy without regional bias continues to attract attention. The improvement of the economic feasibility of renewable energy leads to the expansion of t...
We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the d...
Amongst the various topologies of multi-input multi-output (MIMO) DC-DC converters, single-inductor MIMO (SI-MIMO) converters have the advantages of a reduced component count, a simpler structure, and low cost. These converters are suitable in electr...
Cortisol is an important marker of hypothalamic-pituitary-adrenal function and follows robust circadian and diurnal rhythms. However, biomarker sampling protocols can be labor-intensive and cost-prohibitive. Objectives: Explore analytical approaches...
We consider the compression of a continuous real-valued source X using scalar quantizers and average squared error distortion D. Using lossless compression of the quantizer’s output, Gish and Pierce showed that uniform quantizing yields the smallest...
The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of clus...
This paper presents an ultra-low power CMOS voltage reference which operates in the subthreshold region. Modified from the conventional 2T circuit, the proposed circuit is capable of generating higher output voltage by using the resistor subdivision....
This paper discusses the possible use of 3D fused deposition modeling (FDM) to fabricate capillaries for low-cost chemiresistive gas sensors that are often used in various applications. The disadvantage of these sensors is low selectivity, but 3D pri...
Practical experience in the use of high power impulse magnetron sputtering (HiPIMS) technology has revealed that output bias current depends on the total energy output of the cathodes, which means that bias voltage settings do not necessarily match t...
Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning...
This paper presents the Improved Grey Prediction Model, also called IGM (1,1) model, to increase the prediction accuracy of the Grey Prediction Model (GM) model that performs the GHPS output temperature prediction. This was based on correcting the cu...
Data envelopment analysis (DEA) is a leading approach in performance analysis and discovering newer benchmarks, and the traditional DEA models cannot forecast the future efficiency of decision-making units (DMUs). Machine learning, such as the artifi...
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