Generation Prototype Research Articles

Semantic Guided prototype learning for Cross-Domain Few-Shot hyperspectral image classification

Hyperspectral image(HSI) classification has found extensive application in the field of remote sensing. However, traditional HSI classification methods typically rely on a large number of labeled samples, incurring significant costs and requiring considerable time and effort for acquisition. To address this issue, this paper proposes a novel cross-domain few-shot learning method termed Semantic Guided Prototype Learning (SGPL), which aims to optimize prototype learning through the incorporation semantic information, thereby enhancing the classification accuracy of hyperspectral images in few-shot scenarios. The core idea of the SGPL method is to integrate semantic information into the prototype learning framework, thereby enhancing the expressive capacity of each category prototype. Specifically, HSI category labels are encoded via one-hot encoding, and the corresponding HSI features are concatenated with these encoded vectors to construct an augmented feature representation. These augmented feature vectors are subsequently fed into a prototype generator, which is designed to learn the generation of category prototypes from the concatenated features. Through the application of one-hot encoding and feature concatenation, SGPL effectively captures and leverages discriminative information between classes. Moreover, the proposed model exhibits superior performance on three different HSI datasets: Salinas, the University of Pavia(UP), and Indian Pines(IP) with overall classification accuracies of 91.27%, 85.84%, and 69.33%, respectively, outperforming other methods. The source code is available at https://github.com/AIYAU/SGPL.

Generation of multi-photon Fock states at telecommunication wavelength using picosecond pulsed light

Multi-photon Fock states have diverse applications such as optical quantum information processing. For the implementation of quantum information processing, Fock states should be generated within the telecommunication wavelength band, particularly in the C-band (1530-1565 nm). This is because mature optical communication technologies can be leveraged for transmission, manipulation, and detection. Additionally, to achieve high-speed quantum information processing, Fock states should be generated in optical pulses with as short a duration as possible, as this allows embedding lots of information in the time domain. In this paper, we successfully generate picosecond pulsed multi-photon Fock states (single-photon and two-photon states) in the C-band with Wigner negativities for the first time, which are verified by pulsed homodyne tomography. In our experimental setup, we utilize a single-pixel superconducting nanostrip photon-number-resolving detector (SNSPD), which is expected to facilitate the high-rate generation of various quantum states. This capability stems from the high temporal resolution of SNSPDs (several tens of picoseconds in our case and also in general) allowing us to increase the repetition frequency of pulsed light from the conventional MHz range to the GHz range, although in this experiment the repetition frequency is limited to 10 MHz due to the bandwidth of the homodyne detector. Consequently, our experimental setup is anticipated to serve as a prototype of a high-speed optical quantum state generator for ultrafast quantum information processing at telecommunication wavelength.

Experimental study on the power generation characteristics of a free piston engine generator prototype with dual linear generator modules

The dual-piston dual-cylinder free piston engine generator (DD-FPEG) has garnered significant attention for its high thermal efficiency, power density, and suitability for various fuels. To improve performance and stability, this paper proposes a DD-FPEG prototype integrating dual linear generator (LG) modules. The structure and working process of the prototype is introduced, followed by testing its operating characteristics and power generation performance from the cold start to power generation process. When LG1 operates in motor mode, and LG2 operates in generation mode with no load, the motor thrust must exceed 126 N to ensure piston movement. As the motor thrust decreases from 221 N to 0 N and then switches to generation mode, the peak cylinder pressure, operating frequency, output voltage and power show a decreasing trend. The coefficients of variation for peak cylinder pressure position, top and bottom dead center positions are lower than that of peak cylinder pressure, and their values are below 3 %. When the load is 50 Ω, the prototype operates at about 31.2 Hz, with an average indicated power of 1422.8 W and a peak output power of about 1002.5 W. These results demonstrate that the proposed control strategy enables the designed prototype to achieve stable operation and smooth switching.

Design and Construction and Energy Consumption Study of a New Electrolyzed Water Cell Generator Prototype for Food Disinfection

This study explores the feasibility of producing electrolyzed water (EW) for post-harvest treatment of fruits and vegetables as a new substitute for current chemical products. A prototype generator using tap water and NaCl solution was tested for EW’s sanitization efficiency, energy, and economic costs. In vitro tests on Penicillium e., Aspergillus n., Botrytis c., and Alternaria a. assessed EW pH, chlorine concentration, electro-oxidative potential, pathogen contact time, and energy consumption. Optimal results were achieved with a pH of 4.6, electro-oxidative potential of 188 mV, active chlorine concentration of 3.4 mg/L, and a contact time of 1–2 min. The prototype produced 10.0 L of EW in 1 h, consuming 0.11 kWh of electricity. Real-scale energy consumption was 545 kWh/m3 EW, costing 12.51 euro/m3. The study concludes that optimizing EW production can reduce energy consumption, making it a viable alternative for industrial sanitization of fruits and vegetables.

Getting prepared for the fresh COVID19 wave:

Regular supply of pure oxygen is the need of the hour to be prepared for the fresh waves of COVID-19. The cost of such oxygen supplies should be economical to reach out to the masses & avoid unwelcome burden on the national economy. A requirement has always been felt to identify an alternate source of oxygen generation with readily available raw materials which can ensure its cost effectiveness and last mile oxygen availability through independent generation capabilities.Chemical generation of oxygen has been the go-to technology for portable oxygen generation. However, the cost of production and controlling the reaction has generally been uneconomical for commercial applications. The authors undertook a task to build up a prototype of chemical oxygen generator which did not require compressed gas. It on the principle of dissociation of Sodium per carbonate (SPC) into sodium carbonate and hydrogen peroxide. Hydrogen peroxide so produced interacts with catalyst MnO which further decomposes to produce oxygen. A cast iron tank with a reaction chamber at the bottom is fabricated where upper tank consists of water and space for storing gases. The flow of the water is regulated to control the speed of the reaction.The chemical mixture of sodium percarbonate and manganese dioxide is tightly packed in a cloth bag.Oxygen release valve is kept at the top of the tank which is connected via a pipe to the standard to check the quantity of oxygen as well as the oxygen mask for the patient.This equipment, has a to be the in the fight against COVID especially in remote and difficult to access areas as well as areas without electricity.

Accurate numerical prototypes of microfluidic droplet generators with open source tools

Droplet-based microfluidics gained significant attention for its high technological impact in various fields like (bio)analysis and (bio)synthesis. Precise and controlled droplet size is critical, for the encapsulated products, or the yield of chemical reactions. In a broad range of experimental parameters, the understanding of how droplets form, interact and move with accurate predictive models is crucial. In this work, numerical prototypes of droplet generators were made with Basilisk, an open source software for solving partial differential equations on adaptive Cartesian meshes including grid adaptation and scalability for High-Performance Computing (HPC). This research aims to analyze and compare the obtained droplets against existing experimental data. The evaluation involves qualitative and quantitative comparisons, considering various channel geometries, flow rates, and rheological conditions. The validation of the proposed tool in terms of accuracy and computational performance, enable us to offer to the microfluidics community a reliable tool to design and optimize droplet generators.

A comprehensive study on electromagnetic torque and rotor UMP based on an improved 3D static air-gap eccentricity model

Static air-gap eccentricity (SAGE) is a common fault in generators. Current researches primarily focus on the parallel SAGE types, while the inclined static air-gap eccentricity (ISAGE) has been rarely investigated. This paper proposes an improved air-gap eccentricity model and presents a study on the electromagnetic-mechanical properties in synchronous generators under typical ISAGE running conditions. Such electromagnetic-mechanical properties include not only the magnetic flux density (MFD) variations, but also the mechanical responses of the electromagnetic torque (EMT) and rotor unbalanced magnetic pulls (UMP). The typical ISAGE factors include: 1) a varied rotor inclined angle θ, and 2) different values of axial inclined distance z (indicates the offset combination case of the two rotor ends). This work is based on the MFD analysis calculation. Theoretical analysis, finite element analysis, and experimental study are carried out, respectively, by taking a 5 kVA two-pole prototype generator as the study object. It is shown that the electromagnetic-mechanical properties of the generator will change when the inclined eccentricity occurs. The EMT, and the rotor UMP as well as the vibrations will increase as the rotor inclined θ and z increase. Specifically, the 2nd harmonic has the most significant variation.

Electricity generation via metal oxide-air moist interaction

Harvesting electricity from ambient moisture and water droplets through the innovative technology of hydrovoltaics has lately gained attraction as it only requires water molecules' intrinsic energy and is applicable in different weather conditions. We present a device prototype for electricity generation from air humidity. The study investigates the complex interplay of water-solid interactions, focusing on band gap differences of n-type and p-type couple metal oxides and their performance in electricity generation. The research develops Moist-Electric Generators (MEGs) using ZnO as an n-type metal oxide with various p-type metal oxides (MnOx, CuO, and CoOx) on carbon paper substrates. Among these, the ZnO/CoOx MEG exhibits superior performance, producing an open circuit voltage of nearly 0.5 V, a current of around 9.5 µA for more than 7 days, with an areal and maximum power density of 1.48 µWcm2- and 4.5 µWcm2- at 95 % RH. Detailed characterization techniques, including XRD, XPS, and SEM, confirm the successful synthesis of ZnO and CoOx electrodes. The study demonstrates the sensitivity of the ZnO/CoOx MEG to changes in relative humidity and investigates its long-term stability. The results highlight the significance of water dissociation and minor redox processes for efficient energy generation, paving the way for advanced applications in renewable energy technologies.

Multi-fold geometric phase metasurface with versatile operations for transmission and reflection

We propose a high efficiency wideband three-fold geometric phase metasurface for versatile operation of transmission and reflection. The transmission coefficient as high as 87 % is achieved in the frequency range of f1 (15.4–15.8 GHz), while equal transmission and reflection are achieved in two frequency bands represented by f2 (14.6–15.2 GHz & 16–17 GHz) with maximum coefficient reaches 49 %. With geometric rotation, the phase shifts of the cross-polarized transmission and co-polarized reflection are six times the rotation angle within the frequency range of 14.6–17 GHz. Furthermore, by elaborately breaking the mirror symmetry while preserving rotational symmetry, interesting features of resonance frequency shift and mode splitting are observed, offering a more fruitful approach for versatile operations. To substantiate the proposed design, a metasurface prototype for vortex beam generation is fabricated and verified by microwave measurement.

Small-Scale thermoelectric incinerator prototype for sustainable waste management and power generation

Small-Scale thermoelectric incinerator prototype for sustainable waste management and power generation

Prediction and decision of free-piston linear generator on starting process for multi-fuel adaptability

Free-piston linear generators, due to their variable compression ratio, are considered to possess excellent multi-fuel adaptability. Further, fuel-flexible is a weighty research topic for addressing energy source limitations, meeting powertrain generalization requirements, and achieving energy savings and emission reductions. The initial concern to solve is the rapid starting of various fuels within the same free-piston linear generator. This study aims to introduce a mechanical resonance starting performance prediction model based on an artificial neural network and provides a control decision method based on single-objective optimization with soft constraints to achieve flexible starting of a variety of fuels. This paper also assessed the starting performance using the free-piston linear generator prototype and conducted a comprehensive parameterized analysis to investigate the influence of parameters and control variables on starting performance. Using the starting decision method proposed, the control variable values for ethanol starting were derived on the prototype, resulting in driving current at 3.5A, intake air mass flow at 95 standard liters per minute, and intake air temperature at 335 K. This decision value effectively met the predefined requirements, successfully igniting and starting the prototype with ethanol. This outcome serves as a robust validation of the fuel-flexible capabilities within free-piston linear generator systems.

Initial experimental testing of a hybrid solar-dish Brayton cycle for combined heat and power (ST-CHP)

The Solar Turbo Combined Heat and Power (ST-CHP) project developed a novel solar-gas hybrid prototype for combined heat and power generation in Pretoria, South Africa. A vacuum-membrane faceted parabolic dish with a large-pipe open-cavity receiver was coupled to a counterflow recuperator, a combustion chamber, a micro gas turbine with air bearings and a phase change thermal energy storage unit containing solar salts. This study aimed to validate the electrical output of the full-scale dish-Brayton prototype, addressing the literature gap on micro-scale dish-Brayton plants' in-field power generation. Solar hybridization of micro gas turbine technology can significantly reduce combustion fuel consumption. A performance analysis under relevant conditions revealed a late afternoon micro gas turbine output intermittently peaking at 0.4kWe and subsequently stabilizing to a steady state of 0.145kWe at 130krpm. A SimuPact numerical model and an analytical model supplemented the telemetry data to reduce interference in the experimental setup and fully characterize the ST-CHP prototype performance, estimating a steady-state turbine isentropic efficiency of 57%, a compressor efficiency of 71% and a collector efficiency of 17% due to the late afternoon steady-state point. Analytical case studies revealed that fuel savings of between 12% and 33% at the combustion chamber were achievable from the solarized preheating. A subsequent test of the micro gas turbine without solar hybridization or a recuperator resulted in 1.05kWe being generated. The study confirms the dish-Brayton prototype's viability for combined heat and power generation, producing an initial full-scale performance characterisation during in-field testing, and highlighting the impact of solar hybridization on turbine electrical output. Optical efficiency, insulation effectiveness, pressure losses, and turbine operating conditions were identified as critical areas requiring optimization to improve electrical output. The lessons learned, and the calibrated numerical model may be used to optimize the performance of the dish-Brayton plant in future work. The successful in-field full-scale power generation of the ST-CHP prototype adds to the available literature on dish-Brayton technology and brings the technology closer to a commercial product.

Bimodal semantic fusion prototypical network for few-shot classification

Few-shot classification learns from a small number of image samples to recognize unseen images. Recent few-shot learning exploits auxiliary text information, such as class labels and names, to obtain more discriminative class prototypes. However, most existing approaches rarely consider using text information as a clue to highlight important feature regions and do not consider feature alignment between prototypes and targets, leading to prototype ambiguity owing to information gaps. To address this issue, a prototype generator module was developed to perform interactions between the text knowledge of the class name and visual feature maps in the spatial and channel dimensions. This module learns how to assign mixture weights to essential regions of each sample feature to obtain informative prototypes. In addition, a feature refinement module was proposed to embed text information into query images without knowing their labels. It generates attention from concatenated features between query and text features through pairwise distance loss. To improve the alignment between the prototype and relevant targets, a prototype calibration module was designed to preserve the important features of the prototype by considering the interrelationships between the prototype and query features. Extensive experiments were conducted on five few-shot classification benchmarks, and the results demonstrated the superiority of the proposed method over state-of-the-art methods in 1-shot and 5-shot settings.

Multidisciplinary Design Methodology for Micro-Gas-Turbines—Part I: Reduced Order Component Design and Modeling

Abstract Ultramicrogas turbines (UMGTs) for electric power generation up to 1 kW are a viable replacement technology for lithium batteries in drones due to their high energy density. Previous research has shown that small-scale effects disqualify conceptual design practices applied to larger gas turbines owing to highly coupled, nonlinear component interactions. To fill this gap, we propose an interdisciplinary conceptual design and analysis framework based on reduced order models. To this end, the current work is divided into two parts covering component design and system integration, analysis, and optimization. In Part I, automated conceptual design of all engine subcomponents is elaborated facilitating interdependent reduced order models for compressor, turbine, combustor and high-speed generator while also considering additive manufacturing constraints. In a second step, the reduced order performance models are compared to computational fluid dynamics (CFD) Reynolds-averaged-Navier–Stokes (RANS) simulations of various turbomachinery geometries as well as experimental data of combustor and high-speed generator prototypes, showing good agreement and thus validating the component modules. In conclusion, the first part of this work elaborates an automated and efficient method to conceptual design of all components required for a functional UMGT. Since the strategy is applicable independent of component arrangement and engine layout, the proposed methods offer a universal framework for small gas turbine generators.

Bioinspired 1T-MoS2-based aerogel beads for efficient freshwater harvesting in harsh environments

Freshwater scarcity is one of the most critical issues worldwide, particularly in arid regions, stemming from population growth and climate change. Inspired by the hydrophilic bump structures of desert beetles, 1T-MoS2-based aerogel beads with porous structures and CaCl2-crystal loading (termed as MoAB-m@CaCl2-n) were prepared for freshwater harvesting. Metallic-phase MoS2 nanospheres exhibit excellent photothermal conversion abilities, facilitating solar-driven water desorption and evaporation. Owing to the synergistic effect of its localized surface features, hydrophilic groups, and dispersive CaCl2 particles, MoAB-2@CaCl2-2 efficiently harvests water from atmosphere with a superior moisture adsorption capacity (0.18–0.82 g g−1) at a wide range of relative humidity (10 %-70 %). Under one-sun illumination, MoAB-2@CaCl2-2 demonstrates an outstanding solar-driven water evaporation rate of 2.25 kg m-2h−1. The water evaporation rate from soil (water content = 20 %) is 1.19 kg m-2h−1, which is sufficient for sustainable freshwater generation from the soil in arid regions. More importantly, the multifunctional MoAB-2@CaCl2-2-based homemade freshwater generation prototype delivers a certain amount of water harvesting (0.99 g g−1 day−1) on a rainy day and provides an impressive daily freshwater yield (53.7 kg m−2) under natural sunlight. The integrated device exhibits excellent efficiency and practicality and offers a feasible method for freshwater harvesting in harsh environments.

Performance Prediction and Optimization of Single-Piston Free Piston Expander-Linear Generator Based on Machine Learning and Genetic Algorithm

This paper proposed a single-piston free piston expander-linear generator (SFPE-LG) prototype applied to organic Rankine cycle systems. Two valve timing control strategies, namely, time control strategy (TCS) and position control strategy (PCS), were developed. Based on the experimental data, a back propagation neural network (BPNN) prediction model was established. The effects of structural parameters such as neural network layers, transfer function, training function, hidden layer nodes, and learning rate on the prediction accuracy of this BPNN model were discussed. The training and prediction accuracy of the BPNN model was verified using 5-fold cross-validation and Wilcoxon signed-rank test. Moreover, the BPNN model was integrated with a genetic algorithm to predict and optimize the maximum output power of the SFPE-LG. The results showed that the BPNN model used to predict the motion characteristics and output performance of the SFPE-LG exhibits strong learning ability and high prediction accuracy. Notably, the prediction accuracy of the BPNN model is significantly higher under the PCS compared to TCS. The effect of hidden layer nodes on mean square error (MSE) and correlation coefficient (R) is greater than that of the learning rate. When the number of hidden layer nodes exceeds 30, the BPNN model consistently achieves low MSE and high R. The optimization results showed that the SFPE-LG can obtain a maximum output power of 141.69 W under the TCS, when the working parameters are inlet pressure of 0.7 MPa, intake duration of 35 ms, load resistance of 67 Ω, and expansion duration of 104 ms, respectively.

A small sample bearing fault diagnosis method based on ConvGRU relation network

Considering that in the fault diagnosis of bearing based on relation network, using the sample mean value as the class prototype for each class is susceptible to outliers, resulting in inaccurate class prototypes, this paper proposes a convolutional gate recurrent unit (ConvGRU) relation network fault diagnosis model; firstly, the model utilizes a embedding module to extract bearing fault features, and then uses the ConvGRU as a learnable class prototype generator to generate class prototypes for each class. Secondly, a relation module is utilized to measure the similarity between class prototypes and the sample features of the query set, obtaining relation scores, and ultimately achieving fault diagnosis. In order to test the validity and advantages of the model, experimental verification and analysis were conducted on the case western storage rolling bearing dataset. The results of the experiment show that the class prototypes generated by the ConvGRU class prototype generation module have better discrimination and accuracy compared to the class prototypes generated by the relation network. In the 10-way 5-shot experiment, the accuracy of the model proposed in this paper reaches 99.60%, which increases by 6.63%, 5.10%, 4.80%, and 1.75% compared to k-nearest neighbor, convolutional neural network, prototypical network, and relation network. The method proposed in this paper helps to generate more accurate class prototypes and has a certain effect on improving the accuracy of model fault diagnosis.

Semi‐Solid Thermo‐Electrochemical Cell Based Wearable Power Generator for Body Heat Harvesting

AbstractA high‐performance TiO2/GO‐Fe3+/2+‐based N‐type thermo‐electrochemical cell (TEC) is demonstrated and prepared P‐N combined power generator prototypes combining BST/FeCN3‐/4−‐based P‐type TEC that is reported in the previous work. The functional groups on the TiO2/GO in the electrolyte can adjust the solvation structure of Fe3+/2+ to improve the Se value. In addition, TiO2 acts as a relay reaction site to improve charge transport efficiency. The synergistic effect results in Se of 2.53 mV K−1 and a maximum output power density of 0.38 W m−2. Besides, both flexible and rigid power generator prototypes are prepared. It is worth noting that the flexible prototype can generate an open‐circuit voltage of 1.27 V and power an electronic watch by using the temperature difference between the human body and the environment. This work demonstrates the potential of TEC for low‐grade heat energy recovery and powering wearable devices.

Development of Patented Autonomous Quantum Gravitational Electric Energy Generator Prototypes

After quantum physics modelling of Gravitational interaction from hypotheses, we performed experimental confirmation experiments of predicted increased Faraday’s induction. Brief and low energy electric discharges were made, at room temperature, into partially superconducting Graphite based devices, patented and named “emitters”, inserted in series with the primary low inductance of a transformer. The high voltage secondary inductance of that transformer was connected to a capacitor, so the secondary current oscillated during several milliseconds.
 
 We Measured the global energy efficiency of Faraday’s induction, during each electric discharge into the emitter of the primary circuit. We also measured evolution of the peak primary discharge current, and of its derivative, versus the initial charge voltage. We observed systematically a much larger than 100% energy efficiency. That energy efficiency increases with the primary discharge energy. The peak discharge current is observed to be much larger than predicted by Ohm’s Law, and the discharge current derivative is also observed to be much larger than classically predicted.
 
 Same experiments, performed with “normal conductive devices (control)”, gave energy efficiencies much lower than 100 %, independent of the stored energy. And their peak discharge currents and derivatives followed Ohm’s Law.
 
 From these confirmations of predicted results, we proposed and tested successfully concepts of Autonomous electric Generators prototypes, extracting their energy from the cosmological Gravitational quantum field. Their industrial use into electric vehicles should preserve Earth fossil energy resources, as well as reduce the detrimental climate effects of greenhouse gases emissions. Hypotheses are suggested to explain the observed experimental facts.

Instability Mechanism in Thermoelectric Mg2(Si,Sn) and the Role of Mg Diffusion at Room Temperature

Mg2(Si,Sn) shows great promise as thermoelectric material as it is made from non‐toxic, abundant, and cost‐effective elements offering high performance. This has been emphasized by several thermoelectric generator prototypes, demonstrating technological maturity. However, material stability is paramount for large‐scale applications whereas we reveal here that the thermal stability of n‐type Mg2(Si,Sn) may be limited even at room temperature (RT). Integral thermoelectric properties measurements, locally resolved Seebeck coefficient analysis, scanning electron microscopy/energy‐dispersive X‐ray spectroscopy, and atomic force microscopy are employed to assess changes of n‐type samples stored in ambient atmosphere for years, revealing the evolution of the carrier concentration and transport properties in the material as well as surface degradation. This is caused by the diffusion of loosely bound Mg from the bulk towards the surface and subsequent oxidation, leading to a change of Mg‐based intrinsic defect concentrations, thereby degrading the thermoelectric performance. This microscopic mechanism is backed up by first‐principles calculations, revealing that Mg diffusivity in Mg2(Si,Sn) is high at RT and that diffusion occurs mainly via Mg vacancies. The observed much faster degradation of Sn‐rich Mg2(Si,Sn) can be correlated with the higher density of Mg vacancies in Mg2Sn compared to Mg2Si, as predicted from defect formation energies.