Double Input Research Articles

Correction to: Dynamic vibration characteristics analysis of double input face gear split-parallel transmission system

Correction to: Dynamic vibration characteristics analysis of double input face gear split-parallel transmission system

Dynamic vibration characteristics analysis of double input face gear split-parallel transmission system

Dynamic vibration characteristics analysis of double input face gear split-parallel transmission system

Improved Similarity Law for Scaling Dynamic Responses of Stiffened Plates with Distorted Stiffener Configurations

Experimental analysis on small-scale models is widely used to predict the dynamic responses of full-scale structures subjected to impact loads. However, due to manufacturing constraints, achieving a perfectly scaled model under a large scaling factor is challenging, leading to the use of distorted scaled models as a compromise. This paper introduces an improved similarity law that ensures distorted scaled models accurately replicate the dynamic responses of prototype stiffened plates under impact loads. The proposed similarity law meticulously considers both the distorted attached plate thickness and variations in stiffener configuration. Double input parameters involving the load case and geometry are formulated to govern the dynamic responses of integrated stiffeners. Additionally, an approximate method rooted in elastic–plastic theory is developed to assess the dominant behaviors of stiffened plates during impact. Consequently, the distorted stiffener configuration of scaled models is designed through a methodology primarily centered on capturing dominant behaviors. Comprehensive numerical simulations are conducted to evaluate the behavior of stiffened plates subjected to impact loads. The results compellingly demonstrate that the proposed similarity law adeptly compensates for geometric distortions, ensuring reliable predictions of dynamic responses in distorted scaled models.

Bidirectional Optical Neural Networks Based on Free-Space Optics Using Lens Arrays and Spatial Light Modulator.

This paper introduces a novel architecture-bidirectional optical neural network (BONN)-for providing backward connections alongside forward connections in artificial neural networks (ANNs). BONN incorporates laser diodes and photodiodes and exploits the properties of Köhler illumination to establish optical channels for backward directions. Thus, it has bidirectional functionality that is crucial for algorithms such as the backpropagation algorithm. BONN has a scaling limit of 96 × 96 for input and output arrays, and a throughput of 8.5 × 1015 MAC/s. While BONN's throughput may rise with additional layers for continuous input, limitations emerge in the backpropagation algorithm, as its throughput does not scale with layer count. The successful BONN-based implementation of the backpropagation algorithm requires the development of a fast spatial light modulator to accommodate frequent data flow changes. A two-mirror-like BONN and its cascaded extension are alternatives for multilayer emulation, and they help save hardware space and increase the parallel throughput for inference. An investigation into the application of the clustering technique to BONN revealed its potential to help overcome scaling limits and to provide full interconnections for backward directions between doubled input and output ports. BONN's bidirectional nature holds promise for enhancing supervised learning in ANNs and increasing hardware compactness.

Uniaxial and multiaxial cyclic deformation behavior prediction of Z2CN18.10 austenitic stainless steel based on Transformer deep learning method

In this study, the experimental study of cyclic deformation behavior of Z2CN18.10 austenitic stainless steel under uniaxial, cross, rhombus and circular paths is investigated. Based on Transformer deep learning method, three models are built to predict the cyclic deformation behavior of the material. One is the single input–single output model, which is used to predict the uniaxial cyclic deformation behavior. The second is the double input–single output model, which is used to predict the biaxial cyclic deformation behavior. The third is the time series multi-step model, which is used to predict the stress evolution under all paths. The Cross-Entropy loss function is adopted to train the models and the rationality of its application in the regression model is deduced from the theoretical point of view. Transformer deep learning method is based on attention mechanism, which can focus on the historical effect of Z2CN18.10 stainless steel cyclic deformation and accurately describe the uniaxial and multiaxial cyclic deformation behavior of the material.

Comparative Analysis of SMC and INC MPPT Algorithm for Dual Input Single Output System

Many MPPT algorithms present to date have maximum output power from solar PV. This paper compares the SMC and INC algorithm results for the double input single output (DISO) system simulated on MATLAB/Simulink and verified with hardware. As the system is DISO, power is fed to the load, one from solar PV and another from the battery, with the help of a boost converter and bidirectional chopper. A control technique is designed to have constant voltage across the load and extract maximum power from solar PV under variable irradiance. In this project, the PI controller is used on the battery side, which helps to decide whether the battery works in undercharging or discharging mode. Different modes of operation are also shown in the paper, and the MPPT algorithm is used to extract maximum power from solar PV.

Iron dissolution from Patagonian dust in the Southern Ocean: under present and future conditions

Although the input of desert dust as a key source of trace metals in the Southern Ocean (SO) has been previously studied, the dissolution process of metals in surface waters, particularly iron (Fe), remain poorly understood. Given the crucial role of Fe in primary production and the biological carbon pump in the SO, we focused on experimental estimations of Fe dissolution from Patagonian dust, the primary natural dust source in the SO. Our study considered both current and projected future conditions, encompassing sea-surface warming, acidification, increased photosynthetically active radiation, and doubled dust inputs. Through controlled laboratory experiments using filtered SO seawater, conducted over 7 days, we assessed changes in particulate Fe (pFe) concentrations, Fe redox speciation (Fe(II)/Fe(III)), and in the mineralogy of Fe-bearing dust in abiotic condition. The predominant minerals in the dust included quartz and aluminosilicates, with silicon (Si), aluminum (Al), and Fe as the major elements. No significant alterations in the mineralogy and the elemental composition of the dust were recorded during the dissolution experiments, neither under present nor under projected future conditions. The particulate Fe(II)/Fe(III) ratio remained consistently at 0.25 during the experiments, unaffected by changed conditions. Consequently, changes in environmental conditions in the SO would therefore not significantly alter the mineralogy and redox speciation of pFe in the Patagonian dust. On the contrary, pFe exhibited a dissolution rate of 3.8% and 1.6% per day under present and future conditions, respectively. The environmental changes anticipated for 2100 in the SO will likely to result in a decrease in the dissolution rate of pFe. Thus, even though a doubling of dust input by 2100 is anticipated, it will unlikely provide significantly more dissolved Fe (dFe) in seawater in the SO. Consequently, the future intensification of Patagonian dust inputs may not alleviate the Fe limitation in the SO.

Design of 2DOF control system fused with artificial intelligence for power enhancement and mitigation of degradation in fuel cell systems

The commercialization of Fuel Cell Systems (FCSs) faces a key obstacle in the form of limited cell lifespan and resultant cell degradation. This paper introduces a new approach, leveraging an artificial intelligence-based 2DOF control system to regulate the Oxygen Excess Ratio (OER) with two main goals: mitigating cell degradation due to oxygen starvation and optimizing net power output. The proposed control system incorporates a data-driven feedforward controller in conjunction with a feedback controller, facilitating the tracking of desired OER values generated by a data-driven reference generator. We develop fuzzy models and neural networks as the reference generator and feedforward controller to capture the complex FCS behavior by processing the stack current w/wo the temperature of FCS (i.e. Single input or Double input models). By exploring the effects of the structural settings of the models, this study provides a comprehensive understanding of their impact on the representation performance of the FCS characteristics. Although the fitting performances of all models are quite satisfactory, their actual performance gain is evaluated on a realistic FCS model at various operation points. The findings and comparative analysis emphasize the efficacy of incorporating stack temperature in fuzzy-model-based 2DOF control systems, showcasing their potential to maximize net power output through enhanced OER control loop performance while also extending the lifespan of FCSs, as confirmed by results from a developed degradation model.

An innovative fuzzy logic based controller for solid oxide fuel cells

The solid oxide fuel cell (SOFC) stands out as a vital component in sustainable distributed generation systems, thanks to its reliability and adaptable power generation capabilities. This adaptability allows the SOFC to efficiently balance grid power by adjusting its power output. Nevertheless, maintaining the desired fuel usage rate and achieving rapid load tracking pose considerable hardships in SOFC control. To deal with these difficulties, this study introduces a novel approach, the Cascaded Double Input Interval Type-2 Fuzzy Logic Controller (C-DIT2-FLC), designed for SOFC control. In this innovative model, the Improved Salp Swarm Algorithm (ISSA) is applied to determine the crucial parameters of Proportional Integral-Proportional Derivative (PI-PD) cascade controllers. The C-DIT2-FLC dynamically adjusts the controller gains, which are optimized using ISSA, to enhance the effectiveness of SOFC operations. The research results substantiate the superior efficacy of the C-DIT2-FLC over conventional controllers, thereby providing enhanced control and performance capabilities for SOFC systems.

Soil extracellular enzyme stoichiometry reveals the nutrient limitations in soil microbial metabolism under different carbon input manipulations

Changes in the quality and quantity of litter and root inputs due to climate change and human activities can influence below-ground biogeochemical processes in forest ecosystems. However, it is unclear whether and how much aboveground litter and root inputs affect soil microbial metabolism and nutrient limitation mechanisms. In this study, according to a 4-years field manipulation experiment, litter and root manipulations (control (CK), double litter input (DL), no litter (NL), no root (NR), and no inputs (NI)) were set up to analyze the extracellular enzyme activities and stoichiometric ratios characteristics of 0–10 cm and 10–20 cm soils, explore the metabolic limitations of microorganisms, and clarify the main driving factors restricting nutrient limitation. The results showed that the enzyme activities associated with the C cycling (β-1,4-glucosidase (BG), cellulose disaccharide hydrolase (CBH)) and N cycling (β-1,4-N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP)) in DL treatment were significantly higher than those in NR treatment. Moreover, enzyme activities related to P cycling are significantly higher in comparison to other treatments. The acid phosphatase (AP), which is related to the P cycle, showed the highest activity under NR treatment. In addition, there was no significant difference in soil microbial metabolic limitation by the different carbon inputs, which did not change the original nutrient limitation pattern. The main drivers of microbial nutrient metabolic limitation included soil physicochemical properties, soil total nutrients, and available nutrients, among which soil SWC and pH presented the greatest influence on microbial C limitation and soil total nutrients showed the greatest influence on microbial N limitation. Changes in soil carbon input altered soil extracellular enzyme activities and their stoichiometric ratios by affecting soil physicochemical properties, total nutrients. This study provides data for the understanding of material cycling in forest ecosystems under environmental change.

Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes

Accurate prediction of biogas yield is crucial for optimizing waste-to-energy conversion systems in anaerobic co-digestion processes. In this study, a double input and single output (DISO) fuzzy mamdani model (FMM) was developed for the prediction of biogas yield in a pilot scale of 105-L mesophilic anaerobic sludge bio-digester. The input variables considered are the combination of cow dung and pig waste and the retention time (RT), while the output variable is the experimental biogas yield. Triangular Fuzzy Membership Functions (TFMF) were utilized to define the input and output datasets, and rules were derived from de-fuzzification. Comparative analysis between the FMM's predicted results and experimental values showcased its effectiveness in forecasting biogas yield during the anaerobic co-digestion of the hybrid wastes. Significantly, the FMM consistently produced results with low error values for the sample dataset, underscoring its accuracy even under stochastic conditions. This study emphasizes the FMM's ability to generate predictions with minimal deviations, offering superior results. As a prospect for future research, the implementation of hybrid algorithms may further enhance biogas yield prediction accuracy within waste-to-energy systems.

A Chromo-fluorogenic Probe for Selective Detection of Picric Acid Alongside Its Recovery by Aliphatic Amines and Construction of Molecular Logic Gates.

Nitroaromatic compounds are illicit explosive chemicals. For environmental security and homeland safety, selective and sensitive identification of these secondary-class explosives has been a reason for the exhaustive research arena of chemists for about a decade. We introduced a sensitive optical sensor with desalted neutral red (NR) dye. After ingressing picric acid (PA) in acetonitrile, the probe becomes non-fluorescent, displaying a colorimetric changefrom yellow to pink. The quenched phenomena and the changed color were re-established with aliphatic amine, trimethylamine (TEA). The reversibility is produced cyclically, both in fluorimetrically and spectrophotometrically. The detection limit for PA with our probe comes out as 0.639µM; this value is significantly lower than many chemosensors available in the literature. Also, NR-stained filter paper strips-based test kit analysis has been deployed as a displayable photonic device for in-situ detection of PA. Furthermore, the whole system was conceptualized to produce single input, single output, and double input single output logic gates, which can be applied to digital devices. The chronological input manner as NTP (NR- TEA-PA) pushed us to configure a molecular keypad lock system, the basis of digital locking devices. The repeatable & reversible detection system exhibits "Write read- Erase-read Write-read' type memory devices.

A Decoupling Control Method for Converting DIDO Sequence Admittance Model of Grid-Tied Converter System Into SISO

This paper proposes a decoupling control method to eliminate frequency coupling in three-phase grid-tied system. Firstly, the admittance model of three-phase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -type three-phase grid-tied converter (GTC) considering DC-bus voltage control (DVC) loop and phase-locked loop (PLL) is established in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -domain frame, and the influence of asymmetrical DVC and PLL control structure on frequency coupling oscillation is analyzed. Then decoupling factors are added to the DVC loop and current control (CC) loop to achieve that the off-diagonal elements of the sequence admittance matrix of the GTC are 0, which means the frequency coupling in the GTC is eliminated. Therefore, the GTC system can be regarded as a single input single output (SISO) system, and the stability can be analyzed by the classic Nyquist criterion in the sequence-domain frame. Compared with the double input double output (DIDO) system, the resulted SISO system convenient for calculating the stability margin of the system and guiding parameter design. In addition, the SISO system is easier to be analyzed on the oscillation and resonance mechanism, and the analysis results can be used to optimize the design of the converter controller, grid planning and operation. Finally, simulation and experiment verify the feasibility and effectiveness of the proposed decoupling control method.

Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validation

This research was done to build computational models for estimating solubility of hydrogen (S) in a given system based on the inputs of temperature (T) and pressure (P). In fact, multiples models were built considering double inputs and single output. To achieve this, three different regression algorithms were trained and tested on a comprehensive dataset using the optimization technique of Grey Wolf Optimization (GWO). The three models used were Elastic Net, Support Vector Regression (SVR), and Automatic Relevance Determination (ARD) Regression. R2 score, root mean square error (RMSE), and mean absolute error were just few of the measures used to assess the models' overall efficacy (MAE). The Elastic Net model achieved the highest R2 score of 0.965 with an RMSE of 8.84×10−2 and an MAE of 7.79×10−2. The SVR technique also performed well, indicated R2 of 0.949, an RMSE of 9.81×10−2, and an MAE of 7.79×10−2. The ARD Regression model revealed an R2 of 0.905 with an RMSE of 1.53×10−1 and an MAE of 1.32×10−1. These findings highlight the potential of machine learning models and metaheuristic algorithms such as GWO in accurately estimating gas solubility in oil, which can have significant implications for various industrial applications.

Nonreciprocal double-carrier frequency combs in cavity magnonics

We propose a scheme for realizing strongly nonreciprocal double-carrier frequency combs in three-mode cavity magnonic system consisting of two microwave cavities and a yttrium iron garnet sphere with magnon Kerr nonlinearity. The nonreciprocal microwave frequency comb (MWFC) and magnonic frequency comb (MFC) can be simultaneously implemented due to the asymmetrical nonlinear response in two different directions. We first show high tunability of nonreciprocal MWFC and MFC based on independent sideband in the situation of single input probe field. By properly operating the input fields, the perfect nonreciprocity of comb in a broad frequency range (nonreciprocity of number of comb) and ultrahigh isolation ratio (nonreciprocity of intensity of comb) are obtained. We prove that the tooth spacing of frequency comb can be flexibly modulated by adjusting the beat frequency between the probe field and the control field, the fraction-order comb thereby is generated, which unlocks the full potential for producing high resolution frequency comb. More importantly, it is find that the sideband mixing emerges for the situation of double input probe fields. The hybridized MWFC and the hybridized MFC further are formed, which enhances greatly nonreciprocity of fraction-order comb. These consequently give rise to the simultaneous achievement of high resolution and strong nonreciprocity of double-carrier frequency combs. Our proposal provides an effective avenue for the manipulation of the nonreciprocity and resolution of MWFC and MFC, and has potentially practical applications in precision measurement and integrated quantum technology.

A General Double-Input Synchronous Signal Processor for Imbalanced Vibration Mitigation in AMB-Rotor Systems

Imbalanced vibration is an urgent yet challenging problem in active magnetic bearings (AMBs) rotor manufacturing due to the rotor mass imbalance effect. The virtue of active control in AMB systems lies in enabling substantial online mitigation of imbalanced vibrations. However, in practice, due to the lack of speed sensors in most of the existing AMB-rotor systems, efficient rotational speed feedback is still on the way. As a remedy, this article proposes a rotational speed sensor-free synchronous signal processor (SSP) with the double inputs: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$</tex-math> </inline-formula> -and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$</tex-math> </inline-formula> -axes direction displacement measurements of radial AMBs. The proposed SSP is capable of estimating the rotational speed and accordingly generating synchronous signals of the imbalanced vibrations by filtering noise in both directions. Such signals are afterward implemented as a feedforward compensator for eliminating the periodical imbalance effects. With the assistance of the Lyapunov theory, the conditions of the proposed SSP method together with the feedforward imbalance compensator are derived to guarantee the stability of the closed-loop AMB-rotor system. Extensive experimental results substantiate the effectiveness and superiority of the proposed SSP method in terms of imbalanced vibration suppression.

Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.

Design of a floating raft system by exploiting the nonlinear damping

In the present study, the optimization design of the cubic nonlinear damping is conducted for a floating raft isolation system, which can be simplified as a multiple degree-of-freedom system with double parallel freedom inputs. The direct relationship between the power transmissibility and cubic nonlinear damping parameters is derived by taking advantage of the output frequency response function (OFRF) approach. The design requirements are proposed in order to achieve low resonant peak values, low power transmissibility over the high frequency range. The detailed step-by-step design process of the floating raft isolation system applied in a practical vessel is provided. A polynomial function, in terms of the required frequency range, is developed and validated from the simulation data. The case application indicates that the design method determined by the OFRF approach can effectively realize the design requirements of a floating raft isolation system, which can deal with the relationship of the power transmissibility and nonlinear damping parameters.

A Novel Single Coupled-Inductor Boost TPC With Two Inductively Interfaced Ports Suitable for Renewable Energy Integration

Three Port Converters (TPCs) are extensively used for renewable energy systems (RES) catering to stand-alone load and/or grid. Such systems require an energy storage element (ESE) which necessitates the TPCs to operate in both Single Input Double Output (SIDO) as well as Double Input Single Output (DISO) modes. Further, the ports connecting RES and ESE should preferably be inductively interfaced facilitating low ripple current for enhancing power extraction from RES, and for extending the lifetime of ESE. Single Inductor-based TPCs, despite offering high power density, lack inductively interfaced ports. To address the aforesaid issue, this paper proposes a single coupled inductor-based TPC in which RES and ESE are inductively interfaced. This solution is named Coupled Inductor Based Three-Port Converter (CIBTPC) and is derived by coupling the inductors of an existing three-switch boost TPC. CIBTPC offers the following advantages over its uncoupled counterpart: (a) reduced ripple current with the same core size and (b) improvement in overall efficiency. The operation of CIBTPC is illustrated by implementing a solar Photovoltaic (PV) based DC stand-alone and nano-grid scheme with battery backup. Validation and performance analysis of the overall system is carried out utilizing a 400W experimental prototype.

A scalable optical computer based on free-space optics using lens arrays and a spatial light modulator

A scalable optical computer based on free-space optics and Koehler illumination was proposed to obtain optical parallelism in an artificial neural network. The Koehler illumination scheme can provide more uniform illumination and less crosstalk compared with the previous systems based on Abbe illumination. The proposed design was realised in hardware with 2 × 2 inputs and 2 × 2 outputs using light-emitting diodes, a liquid crystal display, lens arrays, and a detector array assembled in an optical cage system with subsidiary electronics. The feasibility of the constructed optical computer was demonstrated by achieving the expected results of the NAND and NOR logics in parallel in the nondifference mode. Additionally, a simple pattern-checking function in the difference mode was shown. In addition, the limit of the scalability and the effect of geometric aberration was discussed. Furthermore, a clustering technique was suggested to overcome the limitations of scalability. Clustering can continue to increase the array size and throughput by creating a new block of doubled input and output arrays from multistacks of basic optical modules. The proposed architecture and clustering method have the potential to realise massive optical parallelism if large-array components and high-speed electronics are used in the future.