Three-stage Structure Research Articles

A Delayed Fractional-Order Predator–Prey Model with Three-Stage Structure and Cannibalism for Prey

A Delayed Fractional-Order Predator–Prey Model with Three-Stage Structure and Cannibalism for Prey

SSANet-BS: Spectral–Spatial Cross-Dimensional Attention Network for Hyperspectral Band Selection

Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this end, we propose an unsupervised BS method based on a spectral–spatial cross-dimensional attention network, named SSANet-BS. This network is comprised of three stages: a band attention module (BAM) that employs an attention mechanism to adaptively identify and select highly significant bands; two parallel spectral–spatial attention modules (SSAMs), which fuse complex spectral–spatial structural information across dimensions in HSI; a multi-scale reconstruction network that learns spectral–spatial nonlinear dependencies in the SSAM-fusion image at various scales and guides the BAM weights to automatically converge to the target bands via backpropagation. The three-stage structure of SSANet-BS enables the BAM weights to fully represent the saliency of the bands, thereby valuable bands are obtained automatically. Experimental results on four real hyperspectral datasets demonstrate the effectiveness of SSANet-BS.

Method for improving accurate measurement of the magnetic field of wireless passive micro-magnetic stimulator micro-coil

In recent years, the wireless passive micro-magnetic stimulator (WP-μMS) has emerged as a focal for implantable neuromagnetism modulation research. However, accurately measuring the magnetic field strength of the micro-coil presents a significant challenge due to its small size and low mutual inductance (M). Firstly, this paper establishes a theoretical model of magnetic coupling with a three-stage structure of transmitting coil-micro-coil-detecting coil. The calculation method of detecting the coil to measure the magnetic field is determined by calculating the M between each pair of coils. Secondly, the magnetic coupling resonance method enhances the measured value M to the nH-scale. This approach also eliminates the influence of the M between the transmitting coil-detecting coil on the measurement of the transmitting coil-micro-coil M, thereby achieving accurate measurement of the mT-scale magnetic field strength of the WP-μMS micro-coil. Finally, the effectiveness of the measured mT-scale magnitude magnetic stimulation is validated biologically.

Influence of Western Modernist Trends on Kyiv Music end Theoretical School In 1915–1925 Years

The purpose of the article is to reveal the scientific ideas of the first in Ukraine center of musicologists, formed in Kyiv in 1915–1925 years. The methodology was formed on the application of historical, gnoseological, formal-logical, systematic approaches using special-historical (comparatives and historical typology) methods of research. The scientific novelty of the article is that for the first time in Ukrainian musical historiography, an attempt was made to analyze the scientific, organizational and musicaltheoretical achievements of Ukrainian musicologists on the platform of a certain philosophical and cultural movement with its transformations in education and science on the example of the Kiev Scientific and Music School of the early twentieth century. The Conclusions. The group of the first professional musicologists in Ukraine should enter the history of Ukrainian music science as the Kyiv Music and Theoretical School. The unifying factor for them were common tasks (building the structure of national music education, searching for the essence of music science and the directions of its research research) and fundamental musical and theoretical principles. Filled with special knowledge and enriched with certain experience, leading figures of musical culture in Kiev in a short time (apprehension from 1916 to 1924) for the first time in Ukraine developed a three-stage structure of musical education of youth (B. Yavorsky), painted ways of development of musical science (B. Yavorsky, A. Butskoy). In its organizational activity, new post-revolutionary opportunities were used, and in scientific research they were based on the world's most common moderator philosophical theories. In the teachings about musical thinking and the structure of musical language B. Yavorsky and A. Butskoy consciously or intuitively embodied the ideas of many leading modernist philosophers in the West (especially E. Husserl, A. Bergson, V. Warringer). Despite the rejection of their innovative views at that time, some of them remain effective and relevant today.

Reduced-Sensor-Based Multistage Model Reference Adaptive Control of PV-Fed PMSM Drive for Water Pump

Typically, photovoltaic (PV) array fed permanent magnet (PM) synchronous motor based water pumping system uses a current sensor to realize maximum power point tracking (MPPT) of PV array, and an encoder to obtain rotor speed. These sensors reduce reliability and increases cost of system. Besides, they are sensitive to both the temperature and electromagnetic noise. This necessitates the elimination of both the PV current sensor and encoder from the system. Thus, first, a multistage model reference adaptive control (MS-MRAC) for encoderless operation of system is presented in this article. The MS-MRAC is a three-stage structure wherein motor speed is estimated by first-stage, while resistance and PM flux linkages are estimated by the remaining two stages. Thus, the MS-MRAC is independent of motor parameters like resistance and PM flux linkages unlike conventional MRAC. Then, the motor speed as estimated by MS-MRAC is used to estimate PV array power, which along with PV voltage is used to realize MPPT, thereby eliminating PV current sensor. The effectiveness of presented control technique is shown through simulated results followed by their experimental validation under different operating scenarios.

Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques

As rice is one of the world's most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores.

Multi-timescale Affinely Adjustable Robust Reactive Power Dispatch of Distribution Networks Integrated with High Penetration of PV

Photovoltaic (PV) power generation has highly penetrated in distribution networks, providing clean and sustainable energy. However, its uncertain and intermittent power outputs significantly impair network operation, leading to unexpected power loss and voltage fluctuation. To address the uncertainties, this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch (MTAAR-RPD) method to reduce the network power losses as well as alleviate voltage deviations and fluctuations. The MTAAR-RPD aims to coordinate on-load tap changers (OLTCs), capacitor banks (CBs), and PV inverters through a three-stage structure which covers multiple timescales of “hour-minute-second”. The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min. The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time. The three stages are coordinately optimized by an affinely adjustable robust optimization method. A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively. The proposed method is verified through theoretical analysis and numerical simulations.

A Fully Integrated Low-Dropout Regulator with Improved Load Regulation and Transient Responses.

A fully integrated low-dropout (LDO) regulator with improved load regulation and transient responses in 40 nm technology is presented in this paper. Combining adjustable threshold push–pull stage (ATPS) and master–slave power transistors topology, the proposed LDO maintains a three-stage structure within the full load range. The proposed structure ensures the steady-state performance of LDO and achieves 0.017 mV/mA load regulation. The ATPS consumes little quiescent current at light load current condition, and the turn-on threshold of the ATPS can be adjusted by a current source. Once the value of current source is set, the turn-on threshold is also determined. A benefit of the proposed structure is that the LDO can be stable from 0 to 100 mA load current with a maximum 100 pF parasitic load capacitance and a 0.7 pF compensation capacitor. It also shows good figure of merit (FOM) without an extra transient enhanced circuit. For the maximum 100 mA load transient with 100 ns edge time, the undershoot and overshoot are less than 33 mV. The dropout voltage of the regulator is 200 mV with input voltage of 1.1 V. The total current consumption of the LDO was 24.6 μA at no load.

Evaluation of strategic energy alternatives determined for Northern Cyprus with SWOT based MCDM integrated approach

Today, energy management is of strategic importance, and evaluating options is an important step for decision-makers in energy management. The lack of strategy for the future of Northern Cyprus in the energy sector constitutes the main motivation of this article. The application of the proposed methodology to Northern Cyprus is novel and has not been applied in any of its scientific areas before. This study proposes an integrated approach to identifying strategic energy alternatives with Multi-Criteria Decision Making (MCDM) methods based on Strengths, Weaknesses, Opportunities, and Threats (SWOT). The proposed model has a three-stage structure; in the first stage, SWOT analysis was used for the strengths, weaknesses, opportunities, and threats of internal and external factors affecting the energy sector, along with TOWS analysis to determine alternative strategies. In the second stage, analytical network processing (ANP) was used to weight the SWOT factors, and in the third stage, fuzzy ideal solution similarity order performance (FTOPSIS) methods were used to rank alternative energy strategies. In the final results, alternative energy strategies proposed for the Northern Cyprus energy sector were ranked according to their priority values. While the most preferred energy strategy by the decision-makers was establishing an interconnected connection to the mainland, the least preferred strategy was determined as laying a natural gas pipeline to the mainland. Both options point to the strategic and geopolitical critical importance of Northern Cyprus’ location in the Eastern Mediterranean in energy transmission. On the other hand, it is gratifying that the strategy based on primary energy takes the bottom rank.

Research on a rectangular microwave filter with rectangular groove

In this paper, a rectangular microwave filter with rectangular groove is designed. The filter adopts symmetrical three-stage structure. The first section is the slot line waveguide section to realize the input / output of microwave signal, the second section is the transition section, which adopts the impedance gradient structure, and the third section is the sspps section composed of periodic symmetrical rectangular groove and 90 degree bending structure. By adjusting the geometric size of the rectangular groove and its air gap, the passband width and stopband characteristics of the filter can be adjusted, and its microwave sub wavelength binding effect can be further improved, making the anti space electromagnetic interference ability of sspps filter more excellent. The filter can be applied to L ∼ s Band civil microwave communication system.

Ролевая игра как способ развития презентационных умений на иностранном языке

The purpose of the article is to consider role-playing in the process of foreign language training at technical universities. The introduced form of the educational process contributes to the formation of foreign language presentation skills in a professional field of a future engineer. A foreign language competence confirms a high competency of a specialist. Role-playing business situations in an educational process are carried out on the basis of an integrated approach where knowledge from the professional field is transferred to the game with the help of a foreign language. Methods of creating typical situations at work in the classroom contribute to the formation of a favorable environment for the development of business qualities and foreign language skills in a professional sphere. In the course of the research a three-stage structure of role-playing, its content in relation to the stated conditions are described. Difficulties that arise during the game and possible methods to overcome them are listed. It is concluded that role-playing related to the professional activities of future engineers motivate them to learn a foreign language and bring the learning process closer to real professional communication. The practical experience gained in the classroom will be a valuable experience for making a successful career in the field of engineering.

Comparison of Efficiency-Based Optimal Load Distribution for Modular SSTs with Biologically Inspired Optimization Algorithms

The battle of currents between AC and DC reignited as a result of the development in the field of power electronics. The efficiency of DC distribution systems is highly dependent on the efficiency of distribution converter, which calls for optimized schemes for the efficiency enhancement of distribution converters. Modular solid-state transformers (SSTs) play a vital role in DC distribution networks and renewable energy systems (RES). This paper deals with efficiency-based load distribution for solid-state transformers (SSTs) in DC distribution networks. The aim is to achieve a set of minimum inputs that are consistent with the output while considering the constraints and efficiency. As the main feature of modularity is associated with a three-stage structure of SSTs, this modular structure is optimized using ant lion optimizer (ALO) and validated by applying it to the EIA (Energy Information Agency) DC distribution network which contains SSTs. In the DC distribution grid, modular SSTs provide the promising conversion of DC power from medium voltage to lower DC range (400 V). The proposed algorithm is simulated in MATLAB and also compared with two other metaheuristic algorithms. The obtained results prove that the proposed method can significantly reduce the input requirements for producing the same output while satisfying the specified constraints.

High Quality Object Detection for Multiresolution Remote Sensing Imagery Using Cascaded Multi-Stage Detectors

Deep-learning-based object detectors have substantially improved state-of-the-art object detection in remote sensing images in terms of precision and degree of automation. Nevertheless, the large variation of the object scales makes it difficult to achieve high-quality detection across multiresolution remote sensing images, where the quality is defined by the Intersection over Union (IoU) threshold used in training. In addition, the imbalance between the positive and negative samples across multiresolution images worsens the detection precision. Recently, it was found that a Cascade region-based convolutional neural network (R-CNN) can potentially achieve a higher quality of detection by introducing a cascaded three-stage structure using progressively improved IoU thresholds. However, the performance of Cascade R-CNN degraded when the fourth stage was added. We investigated the cause and found that the mismatch between the ROI features and the classifier could be responsible for the degradation of performance. Herein, we propose a Cascade R-CNN++ structure to address this issue and extend the three-stage architecture to multiple stages for general use. Specifically, for cascaded classification, we propose a new ensemble strategy for the classifier and region of interest (RoI) features to improve classification accuracy at inference. In localization, we modified the loss function of the bounding box regressor to obtain higher sensitivity around zero. Experiments on the DOTA dataset demonstrated that Cascade R-CNN++ outperforms Cascade R-CNN in terms of precision and detection quality. We conducted further analysis on multiresolution remote sensing images to verify model transferability across different object scales.

Data Envelopment Analysis in Three-Stage Network Structure with Slack Based Measure (SBM)

Data envelopment analysis (DEA) is a non-parametric technique for measuring and analyzing the relative efficiency of the Decision-Making Unit (DMU) having multiple homogeneous types of inputs and outputs. Traditional DEA treats DMU as black boxes and calculates their efficiencies by considering their initial inputs and final outputs. But the overall efficiency of DMU is directly depended upon the performances of intermediate processes of operations. In many situations, DMUs have a three-stage network structure. Against the aforesaid backdrop, this paper introduces a non-oriented and non-radial the slacks-based measure (SBM) of efficiency framework for the three-stage network production process. In the proposed model we have considered both series and parallel relationship between the inputs and outputs of different independent and dependent stages. Additionally, this research is aimed at measuring the Indian banking efficiency by using the three-stage network DEA model.

Deep Ensemble Machine Learning Framework for the Estimation of Concentrations.

Background:Accurate estimation of historical (particle matter with an aerodynamic diameter of less than ) is critical and essential for environmental health risk assessment.Objectives:The aim of this study was to develop a multiple-level stacked ensemble machine learning framework for improving the estimation of the daily ground-level concentrations.Methods:An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily concentrations. The framework has a three-stage structure: At the first stage, four base models [gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)] were used to generate a new data set of concentrations for training the next-stage learners. At the second stage, three meta-models [RF, XGBoost, and Generalized Linear Model (GLM)] were used to estimate concentrations using a combination of the original data set and the predictions from the first-stage models. At the third stage, a nonnegative least squares (NNLS) algorithm was employed to obtain the optimal weights for estimation. We took the data from 133 monitoring stations in Italy as an example to implement the DEML to predict daily at each grid cell from 2015 to 2019 across Italy. We evaluated the model performance by performing 10-fold cross-validation (CV) and compared it with five benchmark algorithms [GBM, SVM, RF, XGBoost, and Super Learner (SL)].Results:The results revealed that the prediction performance of DEML [coefficients of determination and root mean square error ] was superior to any benchmark models (with of 0.51, 0.76, 0.83, 0.70, and 0.83 for GBM, SVM, RF, XGBoost, and SL approach, respectively). DEML displayed reliable performance in capturing the spatiotemporal variations of in Italy.Discussion: The proposed DEML framework achieved an outstanding performance in estimation, which could be used as a tool for more accurate environmental exposure assessment. https://doi.org/10.1289/EHP9752

A Novel Fuzzy PI Control Method for Variable Frequency Brushless Synchronous Generators

With the application of more electric aircraft (MEA) technology, variable frequencies and high power ratings become import features of aero-generators. The brushless synchronous generator, which has a three-stage structure, is the most commonly used type of aero-generator. Due to the variation of operating conditions, the implementation of generator controllers becomes more and more difficult. In this paper, a state space model of a generator is derived and the influence of different operating conditions on the frequency response characteristics of the generator is revealed. Based on a fuzzy PI controller, an additional fuzzy logic controller is applied to modify the PI parameters of the voltage loop by introducing the generator speed to cope with the speed variation. Finally, the results of the simulations and experiments demonstrate that the dual fuzzy PI controller can improve both the steady-state and dynamic performance of the brushless synchronous generator, verifying the previous theoretical study.

Determinants of Quality of Health Insurance Services in Brazil

This research proposes the application of a hierarchical and multidimensional model for quality measurement in health insurance services. The modelling assumes that perceived health insurance quality is composed of a three-stage hierarchical structure, which includes primary quality dimensions, interaction, environment conditions and outcome and each primary dimension is supported from the following sub-dimensions: Interaction quality (attitude), behaviour and expertise; environment conditions quality (environment conditions), design and social factors; and outcome quality (waiting time, tangibles and valence). For model validation, structural equation system is used. The application through structural equation modelling showed that only the outcome quality is significant to the overall perceived quality with the health insurance, and significant sub-dimensions for the study were social conditions, waiting time and tangible. The application of the modelling provided the estimation of the determinants of quality in the service. The determinants of perceived quality can help managers in the direction of improvement actions in order to increase the quality perceived by the users of health insurance services.

Color Image Restoration Exploiting Inter-Channel Correlation With a 3-Stage CNN

Image restoration is a critical component of image processing pipelines and for low-level computer vision tasks. Conventional image restoration approaches are mostly based on hand-crafted image priors. The inter-channel correlation of color images is not fully exploited. Motivated by the special characteristics of the inter-channel correlation (higher correlation for red/green and green/blue channels than for red/blue) in color images and general characteristics (green channel always shows the best image quality among the three color components) of distorted color images, in this paper, a three-stage convolutional neural network (CNN) structure is proposed for color image restoration tasks. Since the green channel is found to have the best quality among all three channels, in the first stage, the network is designed to reconstruct the green component. Then, with the guidance of the reconstructed green channel from the first stage, the red and blue channels are reconstructed in the second stage with two parallel networks. Finally, the intermediate reconstructions from the previous stages are concatenated and further refined jointly. We demonstrate the capabilities of the proposed three-stage structure with three typical color image restoration tasks: color image demosaicking, color compression artifacts reduction, and real-world color image denoising. In addition, we integrate pixel-shuffle convolution into our scheme to improve the efficiency, and also introduce a quality-blind training strategy to simplify the training process for the compression artifacts reduction task. Extensive experimental results and analyses show that the proposed structure successfully exploits the spatial and inter-channel correlation of color images and outperforms the state-of-the-art image reconstruction approaches.

Experimental investigation on flow characteristics of ionic wind induced by wire electrode

ABSTRACT The idea of applying ionic wind cooling technology without moving parts to portable electronic devices is becoming a reality. In this study, the wire-wire type and wire-mesh type ionic wind generators reported in a few references are investigated while copper wire is used as the emitter electrode. The flow characteristics of ionic wind are qualitatively described under a variety of structural parameters, and quantitatively analyzed by calculating the correlations defining relationships between flow field and electric field. The results of u-V relation indicate that the square of the ionic wind velocity is proportional to the voltage. Moreover, the optimal parameters of single-stage generators are presented, such as electrode gap, electrode angle, electrode polarity, wind velocity and efficiency. Finally, the results of multi-stage structures with wire-mesh electrode show that superimposing multi-stage electrode pairs can enhance the wind velocity and the three-stage structure is optimal with a velocity increase of 26.8% over the single-stage structure, but the practicality will be limited due to low efficiency.

A conceptual framework and practical structure for implementing ecosystem condition accounts

Ecosystem condition is a fundamental component in the ecosystem accounting framework as part of the System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA). Here, we develop a conceptual framework and present a practical structure for implementing ecosystem condition accounts to contribute to the revision process of the SEEA EEA, focussing on six core elements: (1) developing a common definition of ecosystem condition, (2) establishing a conceptual framing for ecosystem condition, (3) portraying the role of condition within the SEEA EEA accounting system, (4) deriving an inclusive multi-purpose approach, (5) describing the components of condition accounts and (6) developing a three-stage structure for reporting accounts. We develop a conceptual framework for an inclusive condition account, building on an ecological understanding of ecosystems upon which definitions, concepts, classifications and reporting structures were based. The framework encompasses the dual perspectives of first, the interdependencies of ecosystem composition, structure and function in maintaining ecosystem integrity and second, the capacity of ecosystems to supply services as benefits for humans. The following components of ecosystem condition accounts are recommended to provide comprehensive, consistent, repeatable and transparent accounts: (1) intrinsic and instrumental values, together with ecocentric and anthropocentric worldviews; (2) a formal typology or classification of characteristics, variables and indicators, based on selection criteria; (3) a reference condition used both to compare past, current and future levels of indicators of condition and as a basis for aggregation of indicators; and (4) a three-stage approach to compiling accounts with increasing levels of information and complexity that are appropriate for different purposes and applications. The recommended broad and inclusive scope of ecosystem condition and the demonstrated practical methods for implementation of accounts will enhance the ecosystem accounting framework and thus support a wider range of current and potential applications and users.