Two-stage Structure Research Articles

Microgravity Decoupling in Torsion Pendulum for Enhanced Micro-Newton Thrust Measurement

To enhance the accuracy of micro-Newton thrust measurements via a torsion pendulum, addressing microgravity coupling effects caused by platform tilt and pendulum mass eccentricity is crucial. This study focuses on analyzing and minimizing these effects by alleviating reference surface tilt and calibrating the center of mass during thrust measurements. The study introduced analysis techniques and compensation measures. It first examined the impact of reference tilt and center of mass eccentricity on the stiffness and compliance of the torsion pendulum by reconstructing its dynamic model. Simscape Multibody was initially employed for numerical analysis to assess the dynamic coupling effects of the tilted pendulum. The results showed the influence of reference tilt on the stiffness and compliance of the torsion pendulum through simulation. An inverted pendulum was developed to amplify the platform’s tilt angle for microgravity drag-free control. Center of mass calibration can identify the gravity coupling caused by the center of mass position. Based on the displacement signal from the capacitive sensor located at the end of the inverted pendulum, which represents the platform’s tilt angle, the pendulum’s vibration at 0.1 mHz was reduced from 5.7 μm/Hz1/2 to 0.28 μm/Hz1/2 by adjusting the voltage of piezoelectric actuator. Finally, a new two-stage torsion pendulum structure was proposed to decouple the tilt coupling buried in both pitch and roll angle. The study utilized theoretical models, numerical analysis, and experimental testing to validate the analysis methods and compensation measures for microgravity coupling effects in torsion pendulums. This led to a reduction in low-frequency noise caused by ground vibrations and thermal strains, ultimately improving the micro-Newton thrust measurement accuracy of the torsion pendulum through the platform’s drag-free control.

A two-stage stochastic programming approach for dynamic OD estimation using LBSN data

Estimating origin–destination (OD) demand is essential for urban transport management and traffic control systems. With the ubiquity of smartphones, location based social networks (LBSN) data has emerged as a new rich data source with broad urban spatial and temporal coverage highly suitable for OD estimation. Its nature of confirmed trip purpose (activity) and activity chain host makes it more advantageous than other data (e.g., household travel surveys and traffic network detection). On the other hand, LBSN data is a more direct and accurate representation of demand patterns and can remove the significant burden of developing traffic models and estimating simulation-based objective functions. However, thus far, most LBSN-based estimation models only focus on static (day-level) OD estimation, making less use of those characteristics. To this end, this paper establishes a two-stage stochastic programming (TSSP) framework integrating the activity chains to model activity-level dynamic mobility flows using LBSN data. The first-stage model aims to minimize the errors introduced by the inter-zone OD flows alongside the expected errors of the check-in patterns. The second-stage model attempts to minimize the errors produced by the considered check-in pattern scenarios. Markov chain Monte Carlo (MCMC) sampling is used to generate plausible check-in scenarios. A generalized Benders decomposition (GBD) algorithm is presented to solve the two-stage stochastic programming model. We conduct the experiments on the case study of Tokyo, Japan, under the employment of the generalized least squares (GLS) estimator. The results show that algorithm convergence can be guaranteed within several iterations. The approach can provide satisfactory estimations of check-in patterns, zonal production and attraction, and OD flows. Furthermore, multiple objective function states are tested for evaluating the completeness of the proposed framework and exploring its potential for simplification and extension. Incorporating specific penalty terms into the objective function also provides a way to verify the reliability of the two-stage structure and validate the effectiveness of the model. Finally, we discuss the model enhancement from the perspectives of online OD estimation by integrating with LBSN simulations, network-wide OD extrapolation using appropriate scaling methods, and removing the user-side data requirement by leveraging activity chain modeling. The proposed framework provides a novel and effective approach to OD demand estimation using LBSN data.

Sound-Event Detection of Water-Usage Activities Using Transfer Learning.

In this paper, a sound event detection method is proposed for estimating three types of bathroom activities-showering, flushing, and faucet usage-based on the sounds of water usage in the bathroom. The proposed approach has a two-stage structure. First, the general sound classification network YAMNet is utilized to determine the existence of a general water sound; if the input data contains water sounds, W-YAMNet, a modified network of YAMNet, is then triggered to identify the specific activity. W-YAMNet is designed to accommodate the acoustic characteristics of each bathroom. In training W-YAMNet, the transfer learning method is applied to utilize the advantages of YAMNet and to address its limitations. Various parameters, including the length of the audio clip, were experimentally analyzed to identify trends and suitable values. The proposed method is implemented in a Raspberry-Pi-based edge computer to ensure privacy protection. Applying this methodology to 10-min segments of continuous audio data yielded promising results. However, the accuracy could still be further enhanced, and the potential for utilizing the data obtained through this approach in assessing the health and safety of elderly individuals living alone remains a topic for future investigation.

Single-Season Rice Area Mapping by Combining Multi-Temporal Polarization Decomposition Components and the Two-Stage Segmentation Method

Recently, Synthetic Aperture Radar (SAR) data, especially Sentinel-1 data, have been increasingly used in rice mapping research. However, current studies usually use long time series data as the data source to represent the differences between rice and other ground objects, especially other crops, which results in complex models and large computational complexity during classification. To address this problem, a novel method for single season rice mapping is proposed, based on the principle that the scattering mechanism of rice paddies in the early flooding period is strongly influenced by water bodies, causing the volume scattering to be lower than that of other crops. Thus, a feature combination that can effectively and stably extract rice planting areas was constructed by combining multi-temporal volume scattering in the early flooding period of rice using dual-polarization SAR data, so that a simple semantic segmentation model could realize high-precision rice mapping tasks. A two-stage segmentation structure was introduced to further improve the mapping result with the Omni-dimensional Dynamic Convolution Residual Segmentation model (ODCRS model) as the bone model. In the experiment, Suihua City, Heilongjiang Province was selected as the study site, and the VH/VV polarized data of Sentinel-1 satellite in 2022 was used as the data source. The mapping accuracy of the ODCRS model was 88.70%, and the user accuracy was 84.19% on the field survey data. Furthermore, experiments with different years and regions also proved the effectiveness and stability of the proposed method.

A 77 GHz Power Amplifier with 19.1 dBm Peak Output Power in 130 nm SiGe Process.

This article reports a two-stage differential structure power amplifier based on a 130 nm SiGe process operating at 77 GHz. By introducing a tunable capacitor for amplitude and phase balance at the center tap of the secondary coil of the traditional Marchand balun, the balun achieves amplitude imbalance less than 0.5 dB and phase imbalance less than 1 degree within the operating frequency range of 70-85 GHz, which enables the power amplifier to exhibit comparable output power over a wide operating frequency band. The power amplifier, based on a designed 3-bit digital analog convertor (DAC)-controlled base bias current source, exhibits small signal gain fluctuation of less than 5 dB and saturation output power fluctuation of less than 2 dB near the 80 GHz frequency point when the ambient temperature varies in the range of -40 °C to 125 °C. Benefiting from the aforementioned design, the tested single-path differential power amplifier exhibits a small signal gain exceeding 16 dB, a saturation output power exceeding 18 dBm, and a peak saturation output power of 19.1 dBm in the frequency band of 70-85 GHz.

The convergence mechanism and spatial spillover effects of urban industry-university-research collaborative innovation performance in China

ABSTRACT This study deconstructs the triple helix structure of urban industry-university-research collaborative innovation into a two-stage chain structure of knowledge innovation and technological innovation. Based on data of 285 China prefecture-level cities from 2004 to 2018, this study measures the urban industry-university-research collaborative innovation performance using the network data envelopment analysis model. Additionally, it clarifies its spatial convergence mechanism and spatial spillover effects using the spatial convergence test and spatial Durbin model. The results suggest that: (1) the performance of China's urban industry-university-research collaborative innovation at different stages shows a spatial pattern with obvious regional differences and diversified development modes. (2) The overall performance shows a trend of gradual σ convergence with a slightly fluctuating rate, and the gap is constantly decreasing. Additionally, it shows an absolute β convergence trend at different stages, with an obvious geospatial club convergence trend in the eastern, central, and western regions. (3) The spatial spillover effects of ‘research-to-production’ performance and overall performance are stronger than that of ‘university-to-research’ performance. Finally, the collaborative innovation performance of various cities is dominated by the polarisation effect, and the its role as the driving force of regional innovation needs to be strengthened.

The energy industry's response to competitive intelligence in green marketing (case study of Qatar)

Competitive intelligence can be used as a catalyst to guide green marketing strategies as well as to create and maintain competitive advantage. The purpose of this article is to identify and rank the effective factors of competitive intelligence on green marketing and understand the effects of competitive intelligence components on green marketing components. This is a descriptive and applied research that was conducted on a case-by-case basis among the employees of 4 companies that are active in the field of energy industry in Qatar. The research tool is a questionnaire that confirmed the convergent and divergent validity of the two-stage structure. Analysis by PLS software showed that competitive intelligence has a positive effect on green marketing. Also, the ranking of the components was determined completely. The results of this research state that competitive intelligence as a strategic marketing management tool can modify their approaches to differentiate themselves from others and position themselves as industry leaders in sustainability. According to the study background, this research has an important contribution to the existing knowledge in terms of subject and geographical area and provides a suitable insight to the decision makers of this field.

Surface defect engineered-Mg-based implants enable the dual functions of superhydrophobic and synergetic photothermal/chemodynamic therapy.

Promoting metallic magnesium (Mg)-based implants to treat bone diseases in clinics, such as osteosarcoma and bacterial infection, remains a challenging topic. Herein, an iron hydroxide-based composite coating with a two-stage nanosheet-like structure was fabricated on Mg alloy, and this was followed by a thermal reduction treatment to break some of the surface Fe-OH bonds. The coating demonstrated three positive changes in properties due to the defects. First, the removal of -OH made the coating superhydrophobic, and it had self-cleaning and antifouling properties. This is beneficial for keeping the implants clean and for anti-corrosion before implantation into the human body. Furthermore, the superhydrophobicity could be removed by immersing the implant in a 75% ethanol solution, to further facilitate biological action during service. Second, the color of the coating changed from yellow to brown-black, leading to an increase in the light absorption, which resulted in an excellent photothermal effect. Third, the defects increased the Fe2+ content in the coating and highly improved peroxidase activity. Thus, the defect coating exhibited synergistic photothermal/chemodynamic therapeutic effects for bacteria and tumors. Moreover, the coating substantially enhanced the anti-corrosion and biocompatibility of the Mg alloys. Therefore, this study offers a novel multi-functional Mg-based implant for osteosarcoma therapy.

Directional distance based cross-efficiency evaluation and decomposition for parallel two-stage systems: An application to equity funds

As an effective approach in ranking decision making units (DMUs), cross-efficiency evaluation is only extended to two basic network systems: series and parallel, which is not applicable to the common complex network system in reality. By using the directional distance function (DDF), this paper extends the cross-efficiency evaluation to a complex system called the parallel two-stage system, which consists of several independent parallel processes and each of them is a sub-system with a general two-stage series structure. The proposed method is able to decompose the cross-efficiency measure of the system into the weighted average of those of processes, stages and overall stages, respectively, and as a result, successfully achieves the layer-by-layer decomposition of cross-efficiency measures under complex nested networks. An empirical study on 40 open-ended equity funds in China from 2018 to 2020 verifies that the newly proposed method not only has a stronger performance discrimination power than self-evaluation, but also can identify the relationships among efficiencies of the system, processes and stages, and meanwhile it can reflect the effects of processes and stages on the system. More importantly, our method has a better practical value for actual fund investment selection.

A Hybrid Quasi-Single-Stage AC-DC Converter with Low Twice-Line-Frequency Output Voltage Ripple

Power factor correction (PFC) converters have been frequently employed in various switching power supply devices to reduce input current harmonics. However, the PFC converter suffers from an obvious twice-line-frequency output voltage ripple due to the instantaneous power imbalance between constant output power and variable input power. Suppression of twice-line-frequency ripple usually can be realized by the post-stage DC-DC converter of the two-stage cascade PFC converter; however, the two-stage cascade PFC structure is challenging to realize high efficiency since the energy is transferred twice. To achieve high power factor, high efficiency, and low twice-line-frequency ripple, a hybrid quasi-single-stage (QSS) AC-DC converter is presented in this paper, which consists of a dual output hybrid Boost/Flyback PFC converter and a Buck ripple compensation circuit (RCC). The fundamental principles of the proposed converter and the critical conditions of operation mode transition are discussed in the paper. To confirm that the twice-line-frequency ripple is effectively suppressed, the small signal model of Buck RCC is built and analyzed. Moreover, the main characteristics, including operation mode transition angle, input current, power factor, and switching frequency of the proposed hybrid QSS AC-DC converter, are analyzed. By building a 120 W experimental prototype to validate the feasibility of the proposed hybrid QSS AC-DC converter, the experimental results show that the proposed converter can realize PFC function with high efficiency and extremely low twice-line-frequency output voltage ripple.

Machine learning based dynamic super twisting sliding mode controller for increase speed and accuracy of MPPT using real-time data under PSCs

Photovoltaic (PV) systems are created according to the series, parallel or series-parallel connection type of PV panels. Solar panels that form the PV system can have different irradiance values at different times of the day. This condition, which is called partial shading, causes the current and voltage values produced by the panels to fluctuate. Maximum power point tracking (MPPT) algorithms are used to ensure that PV systems operate at maximum power under all environmental conditions. MPPT algorithms are used to optimize the duty cycle of DC-DC converters in order to transfer maximum power to the load. In this study, a two-stage structure is used for MPPT. In the first stage, a reference voltage value is generated using the gaussian process regression (GPR) machine learning algorithm. In the second stage, the duty cycle of the PWM signal required for MPPT is optimized using the super twisting sliding mode controller (STSMC). The performance of the proposed method for three different scenarios is compared with the cuckoo search algorithm (CSA) from metaheuristic optimization algorithms and the perturb and observe (P&O) algorithm from classical optimization algorithms using real-time data. The software used to analyze and compare the researched methods is MATLAB/Simulink R2021b. The proposed method has performed better compared to both CSA and P&O for four conducted scenarios. Additionally, it was observed that the proposed method leads to less oscillation at the MPP point compared to P&O and CSA.

Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation

Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. However, hierarchical prediction uncertainty within the student model (intra-uncertainty) and image prediction uncertainty (inter-uncertainty) have not been fully utilized by existing methods. To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture. We also propose a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet) as the segmentation model. The two-stage structure complements with the uncertainty regularization strategy to avoid introducing extra modules in solving uncertainties and the aggregation mechanisms enable multi-scale and multi-stage feature integration. Comprehensive experimental results over the MoNuSeg and CRAG datasets show that our PG-FANet outperforms other state-of-the-art methods and our semi-supervised learning framework yields competitive performance with a limited amount of labeled data.

X-ray multi-scale microfabrication system and x-ray imaging evaluation system all in one beamline

We have completed a system that can achieve both deep x-ray lithography and submicron x-ray lithography with a single beamline by introducing the combination of x-ray plane and cylindrical mirrors. This x-ray lithography system can provide a large-scale microfabrication processing with 210 × 300 mm2 (A4 size). To exploit multiscale lithography, the beamline has a beam transport vacuum duct with a two-stage stacked structure and a 5-axis stage. This two-stage stacked structure allows us to fabricate both micron scale structures with high aspect ratios and submicron scale structures using the same beamline. In addition, x-ray imaging and computer tomography (CT) system are connected to the x-ray lithography system for nondestructive inspection and evaluation of the fabricated microstructures. The x-ray imaging system constructed this study has a relatively low energy range of x-ray energy in the beamline, which is in the range of 2–15 keV or less. Therefore, relatively good absorption contrast can be obtained for plastic materials, biomaterials, and the like. Since nondestructive imaging of the processed shape by x-ray lithography is possible, it is a very useful system in processing and evaluation can be performed simultaneously. This system also enables us to obtain the live images with keeping the creature alive in liquid using an indirect x-ray imaging system which converts x-ray images to visible light images through the fluorescent plate.

Research on Two-Stage Semi-Active ISD Suspension Based on Improved Fuzzy Neural Network PID Control.

To better improve the ride comfort and handling stability of vehicles, a new two-stage ISD semi-active suspension structure is designed, which consists of the three elements, including an adjustable damper, spring, and inerter. Meanwhile, a new semi-active ISD suspension control strategy is proposed based on this structure. Firstly, the fuzzy neural network's initial parameters are optimized using the grey wolf optimization algorithm. Then, the fuzzy neural network with the optimal parameters is adjusted to the PID parameters. Finally, a 1/4 2-degree-of-freedom ISD semi-active suspension model is constructed in Matlab/Simulink, and the dynamics simulation is carried out for the three schemes using PID control, fuzzy neural network PID control, and improved fuzzy neural network PID control, respectively. The results show that compared with adopting PID control and fuzzy neural network PID control strategy, the vehicle body acceleration and tire dynamic loads are significantly reduced after using the grey wolf optimized fuzzy neural network PID control strategy, which shows that the control strategy proposed in this paper can significantly improve the vehicle smoothness and the stability of the handling.

Fault-Tolerant SINS/Doppler Radar/Odometer Integrated Navigation Method Based on Two-Stage Fault Detection Structure.

To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system.

Efficiency Improvement and Ripple Suppression Method Based on Partial Power Processing for Wireless Power Transfer System

For a conventional wireless power transfer (WPT), two-stage structure is one of the most common topologies, of which the efficiency will be dramatically decreased by a cascaded dc-dc converter. In this work, a partial power processing (PPP) structu-re for WPT system is proposed to improve the system efficiency and reduce output ripple. The receiver is divided into the main receiver and the auxiliary receiver. Most of the power is trans-ferred to the load through the main receiver, while the auxiliary receiver is used to regulate the output voltage. As a result, the switching loss and device stress of the dc-dc cascaded at receiver can be significantly reduced. Besides, the optimal switching phase can be obtained by analyzing the output waveforms of two receiv-ers to minimize the output ripple. A 500-W prototype is built to validate the feasibility of the proposed system. With the same output capacitor, controlling the switching phase can reduce the output ripple by 36% compared with that without the control. The processing power of the proposed system is from 4% to 29% when the input voltage changes from 190 V to 250 V. The efficiency is improved by 2.4% compared with the two-stage structure under the same input and output conditions.

An S-band MMIC LNA in GaAs 0.15-μm pHEMT

The study adopts a monolithic microwave integrated circuit (MMIC) low-noise amplifier (LNA) applied in S-band was designed using ADS simulation software, GaAs 0.15-um pseudomorphic high electron mobility transistor (pHEMT) process. The LNA small signal gain is greater than 30.8 dB, gain flatness is ±0.45 dB, noise coefficient is less than 1.5 dB, input and output return loss is greater than 10 dB, and the chip area is 1.2*2.4 mm2. The low noise amplifier is composed of a two-stage cascade structure. Gain flatness, gain control, and bandwidth broadening are improved by RLC negative feedback network. The minimum noise coefficient circle and maximum gain circle are narrowed by adding negative feedback to achieve better noise coefficient and gain.

A 14-bit 1-GS/s Pipelined-SAR ADC in 28-nm CMOS with Foreground and Background Calibration

A 14-bit 1-GS/s Pipelined-Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) in 28-nm CMOS is presented in this paper. In the initial phase, a pair of SAR ADCs employing the Time-Interleaved (TI) technique are employed to attain the necessary sampling rate. A multi-comparators structure is used to increase the speed and improve the mismatch. We have implemented a foreground calibration technique to address capacitor mismatch, optimizing its processing logic to enhance the chip’s speed. The interstage operational amplifier adopts a two-stage structure. Some structural changes are presented to optimize its bandwidth and phase margin. A background calibration method for gain error is implemented. In the subsequent phase, we employ four SAR ADCs utilizing the TI method. The outcomes demonstrate that the ADC delivers a Signal-to-Noise and Distortion Ratio (SNDR) of 65.58 dB and a Spurious-Free Dynamic Range (SFDR) of 72.12 dB at a 1-GS/s sampling rate without calibration, and 69.02 dB SNDR and 81.26 dB SFDR at the same 1-GS/s sampling rate with calibration. The core layout occupies an area of 0.6 mm2 and consumes 225.02 mW of power.

Simulation and Experimental Study on Harmonic Optimization of Magnetically Controlled Reactor Based on Multistage Saturated Magnetic Valve Structure

The magnetically controlled reactor adjusts its output capacity based on the principle of magnetic saturation. Due to the iron core working in the saturated region, higher harmonics will be generated in the working current. A large number of harmonics will affect the power quality and safety of the power system. This study derives the expression for the harmonic content of a magnetically controlled reactor with a multistage saturated magnetic valve core structure. The magnetization curves of different iron core structures can be obtained through expressions. This study used different magnetization curves for simulations of magnetically controlled reactors. The suppression effect of the multilevel magnetic valve structure on harmonics was analyzed through simulation results. A prototype of a magnetically controlled reactor with a two-stage magnetic valve structure was manufactured, and dynamic response tests were conducted. The experimental results show that the harmonic content in the working current of the magnetically controlled reactor with a two-stage magnetic valve structure is less than 2%, which is much smaller than the harmonic content of ordinary magnetically controlled reactors in the past. This article verifies the suppression effect of the improved magnetic valve structure on harmonics through experiments.

Low noise optimization of two-stage gearbox housing structure based on acoustic contribution analysis and topology optimization

Low noise optimization of two-stage gearbox housing structure based on acoustic contribution analysis and topology optimization