Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics and Drivers of Vegetation Phenology in Three-River Headwaters Region Based on the Google Earth Engine

Phenology shifts over time are known as the canary in the mine when studying the response of terrestrial ecosystems to climate change. Plant phenology is a key factor controlling the productivity of terrestrial vegetation under climate change. Over the past several decades, the vegetation in the three-river headwaters region (TRHR) has been reported to have changed greatly owing to the warming climate and human activities. However, uncertainties related to the potential mechanism and influence of climatic and soil factors on the plant phenology of the TRHR are poorly understood. In this study, we used harmonic analysis of time series and the relative and absolute change rate on Google Earth Engine to calculate the start (SOS), end (EOS), and length (LOS) of the growing season based on MOD09A1 datasets; the results were verified by the observational data from phenological stations. Then, the spatiotemporal patterns of plant phenology for different types of terrain and basins were explored. Finally, the potential mechanism involved in the influence of climatic and soil factors on the phenology of plants in the TRHR were explored based on the structural equation model and Pearson’s correlation coefficients. The results show the remotely sensed monitoring data of SOS (R2 = 0.84, p < 0.01), EOS (R2 = 0.72, p < 0.01), and LOS (R2 = 0.86, p < 0.01) were very similar to the observational data from phenological stations. The SOS and LOS of plants possessed significant trends toward becoming advanced (Slope < 0) and extended (Slope > 0), respectively, from 2001 to 2018. The SOS was the earliest and the LOS was the longest in the Lancang River Basin, while the EOS was the latest in the Yangtze River Basin owing to the impact of climate change and soil factors. Meanwhile, the spatial patterns of SOS, EOS, and LOS have strong spatial heterogeneity at different elevations, slopes, and aspects. In addition, the results show that the drivers of plant phenology have basin-wide and stage differences. Specifically, the influence of soil factors on plant phenology in the Yangtze River Basin was greater than that of climatic factors, but climatic factors were key functional indicators of LOS in the Yellow and Lancang river basins, which directly or indirectly affect plant LOS through soil factors. This study will be helpful for understanding the relationship between the plant phenology of the alpine wetland ecosystem and climate change and improving the level of environmental management.
An Approach for DVL-Aided SINS In-Motion Alignment Based on Observability Analysis

Currently, in-motion alignment for Strapdown inertial navigation system (SINS) is still a challenge, especially in underwater condition. This paper proposes an approach for DVL-aided SINS in-motion alignment from the perspective of observability. The core of the approach is an improved observability analysis method which overcomes the problems of existing methods. Based on numerical calculations, it can accurately and intuitively analyze the observability of the multi-dimensional complex navigation systems. In response to the problem that numerical calculations may introduce calculation errors, a solution based on prior knowledge of the system is presented. By utilizing the proposed observability analysis method to analyze the DVL-aided SINS in-motion alignment system, this paper has a further research on improving the alignment accuracy through observability incentives. Specifically, the factors which restrict the alignment accuracy are clarified, the reason for how the alignment accuracy is improved by trajectory design is explained, the question of whether the alignment accuracy can be further improved by more complex trajectories is studied, and the guidance for more efficient observability incentives is provided. Consequently, theoretical supports are presented to improve the alignment accuracy efficiently and purposefully. The effectiveness of the proposed approach is verified by simulations and semi-physical experiments.
Methods of Increasing the Energy Storage Density of Superconducting Flywheel

This paper presents methods of increasing the energy storage density of flywheel with superconducting magnetic bearing. The working principle of the flywheel energy storage system based on the superconducting magnetic bearing is studied. The circumferential and radial stresses of composite flywheel rotor at high velocity are analyzed. The optimization methods of the thickness distribution of the flywheel rim and the material selection of the flywheel in the multi-layer interference assembly with a certain size are given and used to maximize the energy storage density. Finally, the improved flywheel model is established, and the maximum energy storage density of the flywheel is increased by combining the above methods.
Resilient Observer-Based H∞ Control of Uncertain Nonlinear Networked Control Systems

This paper considers the resilient <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> dynamic control of nonlinear uncertain systems over a network assuming random communication packet dropouts, a subject which is seldom considered in the current literature for such uncertain systems. The uncertainties in the system and the controller are real, time-varying and norm bounded. Bernoulli distribution with white sequence is used to model the random packet losses with assumed conditions on the probability distribution. The resilient controller designed is an observer-based dynamic. The resulted closed-loop system is exponentially mean square stable and the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance is less than a prescribed level <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> for all admissible uncertainties. New sufficient conditions for the existence of such a controller are presented and proved based on the linear matrix inequalities (LMIs) approach. A numerical example is presented to demonstrate and show the effectiveness of the developed theory.
Sharpening Blurred Concrete Crack Images Captured by a Camera on a Moving Object by means of Deconvolution using convoluted PSF

In this research, we aim to propose an image sharpening method to specify concrete cracks from degraded images captured by a camera on a moving object. We have tried new method "convoluted PSF" that is a convolution of PSF (out-of-focus blur) and PSF (motion blur). We worked on blurred image sharpening by means of deconvolution using convoluted PSF instead of PSF (out-of-focus blur) and PSF (motion blur) step by step. And bridge inspection sites could make this method more efficient with some speckled texture on walls for estimation of PSF (motion blur). The effectiveness of this proposed method is confirmed by deconvolution of degraded images acquired by a digital camera used in actual bridge inspections. This study is expected to help realize social infrastructure maintenance using a wide range of robotic technologies, and to solve the future labor shortage and shortage of engineers.
State Feedback Controller Tuning for Liquid Slosh Suppression System Utilizing LQR-LMI Approach

This paper presents a tuning constraint optimization approach in state feedback controller for liquid slosh suppression system. A suboptimal LQR method is employed to obtain the optimal gain parameters in minimizing the selected cost function. Due to complexity of the nonlinear slosh system, a partial linearization method was first performed to obtain its linear state space representation. Due to the presence of the large steady-state error caused by the implementation of only the state feedback gains, an additional integral term has also been introduced to mitigate its effects. A comparative assessment on the system performance is investigated between regular LQR and LQR-LMI control algorithms. The presented results indicated that the LQR-LMI exhibited better transient response performance as compared to the regular LQR for the case of moving the cart to its intended final position while ensuring the slosh motion is suppressed to a minimum angle.
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets

High penetration of electric vehicles (EVs) triggers challenges and opportunities for distribution system operators. Inverter-based EV chargers with active/reactive power control can be used to coordinate the EV fleet’s charging process while providing local volt/var regulation. This paper proposes an adaptive robust programming model for the charging scheduling of EV fleets that exploits their capability to locally support the grid via reactive power control. The proposed model aims at maximizing the aggregator’s revenue while considering the worst-case scenario in terms of active power losses at the supporting grid. Operational constraints of unbalanced three-phase distribution networks under demand uncertainty are also enforced. The proposed robust model is a min-max problem that can be linearized and solved using a column-and-constraint generation (C&CG) method. Tests performed in a 25-node distribution system illustrate the EV fleet’s capacity to support the grid while minimizing the total energy not supplied.
MOS Prediction for Mobile Broadband Networks Using Bayesian Artificial Intelligence

Mobile broadband (MBB) networks are growing fast with supporting high-speed internet access. Fifth-generation networks promise an enhanced MBB that offers a high-speed data rate and video streaming with ultra-low latency. Thus, monitoring the level quality of these services supported by network providers becomes essential. Mobile network operators continuously optimize their network performance to provide a better quality of service and quality of experience. Moreover, artificial intelligence has been used considerably in optimizations to efficiently meet the requirements of future mobile networks. In this paper, we propose a Bayesian network model to predict the minimum opinion score (MOS), which contributes to evaluating the network performance of video streaming services. The proposed model depends on several input data, namely, bite rate, stalling load, and round-trip time. The predicted MOS depends on prior probability distributions to generate posterior probabilities. The predicted MOS depends on these input data. Results demonstrate that the proposed model achieves a high prediction accuracy of 86%, with a mean square error of 0.34. The proposed model also has a robust performance design through various testing methods.
Simplified Current Tracking Strategy for LCL-Based Rectifier under Unbalanced Grid Voltage Condition

Unbalanced grid is a normal fault in power system, and in order to ensure the rectifier current tracking precision under this condition, the design and implementation of rectifier control strategies become more complex. In view of this, a simplified grid current tracking strategy is proposed, which is suitable for LCL-based rectifier operating in both balanced and unbalanced conditions. The grid current sinusoidal is achieved by tracking converter-side current of LCL filter. The current reference is only calculated by grid voltage and its 1/4 period lagging signal at stationary coordinates. Compared with conventional strategies, sequence separation and Phase-Locked Loop (PLL) are avoided. Besides, an improved active damping method is applied, which can accurately extract harmonic voltage and enhance the resonance suppressing effect. Simulation and experimental results verify the effectiveness of the proposed strategy.
Robust Sensor Fusion with Pairwise Dynamic Covariance Scaling for Localization in Urban Areas

We propose a robust sensor fusion method that is for vehicle localization in urban areas. Our main contribution is to merge the absolute position measurements from the vision-based localization module and the consumer level GPS via suggesting a robust back-end, named Pairwise Dynamic Covariance Scaling (PDCS). PDCS is applicable even when the measurement uncertainty is unknown, which is possible because we let the system considers different sensor measurements as a pair. Specifically, we penalize measurements with large relative errors compared to their paired counterparts, instead of penalizing measurements with large absolute errors. As seen from the experiment in the real urban area, the proposed method is less sensitive to user parameters as verified by its steady localization performance over the varying parameters.
Live Migration of Virtual Machine and Container Based Mobile Core Network Components: A Comprehensive Study

With the increasing demand for openness, flexibility, and monetization, the Network Function Virtualization (NFV) of mobile network functions has become the embracing factor for most mobile network operators. Early reported field deployments of virtualized Evolved Packet Core (EPC) — the core network (CN) component of 4G LTE and 5G non-standalone mobile networks — reflect this growing trend. To best meet the requirements of power management, load balancing, and fault tolerance in the cloud environment, the need for live migration of these virtualized components cannot be shunned. Virtualization platforms of interest include both Virtual Machines (VMs) and Containers, with the latter option offering more lightweight characteristics. This paper’s first contribution is the proposal of a framework that enables migration of containerised virtual EPC components using an open-source migration solution which does not fully support the mobile network protocol stack yet. The second contribution is an experimental-based comprehensive analysis of live migration in two virtualization technologies — VM and Container — with the additional scrutinization on the container migration approach. The presented experimental comparison accounts for several system parameters and configurations: flavor (image) size, network characteristics, processor hardware architecture model, and the CPU load of the backhaul network components. The comparison reveals that the live migration completion time and also the end-user service interruption time of the virtualized EPC components is reduced approximately by 70% in the container platform when using the proposed framework.
Three-Phase Matrix-Based Isolated AC-DC Converter for Battery Energy Storage System

Three-phase matrix-based isolated AC-DC conversion for integration of battery energy storage is an emerging single-stage bidirectional AC-DC conversion application. This paper presents a dual-active-bridge (DAB) type three-phase matrix-based AC-DC converter along with its modulation, modes of operation and loss modelling for state-of-the-art SiC-MOSFET based converter design. Pertinent simulation results are provided to demonstrate the working principle of the converter at 2 kW over a few line frequency cycles as well as a few switching frequency cycles. The analysed converter’s efficiency estimates are compared to similar AC-DC conversion strategies to divulge its superiority. Subsequently, the design guidelines for converter fabrication along with a SiC-MOSFET based 2 kW converter prototype is presented for a battery integration application.
Fiber Optic Current Transformers for Transformer Differential Protection during Inrush Current: a Field Study

Saturation of electromagnetic current transformers (EMCTs) due to inrush current during re-energizing of a power transformer may lead to misoperation of differential protection. Fiber optic current transformers (FOCTs) can offer a solution to this problem by providing an accurate replica of the primary current during inrush conditions as they are free from saturation. This paper focuses on comparing EMCTs and FOCTs measurements of inrush current based on data obtained during a field test at a 500 kV substation. Waveform and harmonic analysis of the signals captured by a digital fault recorder from EMCTs and FOCTs reveal a significant difference in their RMS and harmonic content, thus demonstrating the influence of the saturation of the EMCTs on the current measurements. We also show that compared to EMCT, FOCTs measurements allow for better identification of the inrush current by means of DC component analysis.
Pulsating High Frequency Voltage Injection Strategy for Sensorless Permanent Magnet Synchronous Motor Drives

In order to improve the dynamic performance of the sensorless permanent magnet synchronous motor drive system, the modified pulsating high frequency voltage injection method is proposed. Firstly, the second order generalized integrator can be used to extract the high frequency voltage signal instead of band-pass filters. The specific frequency harmonic elimination unit is applied to obtain the rotor position error signal to avoid using the low-pass filter. Therefore, the system dynamic performance can be improved. In addition, the system parameter is easier to adjust than traditional methods with band-pass filters and low-pass filters. Finally, the simulation model and experimental platform are built to verify the effectiveness of the proposed method. The simulation and experimental results present that the dynamic performance of motor drive system can be improved.
Identifying Critical Uncertainties Impacting System Adequacy in Integrated Gas and Electricity Networks

In addition to becoming increasingly interconnected and interdependent, energy networks are facing more uncertainties associated with the rapid penetration of intermittent renewable generators as well as the electrification of heat and transport. A suitable probabilistic framework is necessary to account for these uncertainties and the associated risks to energy supply security. This work identifies the most critical input uncertainties affecting the electricity system adequacy in integrated gas and electricity networks using various sensitivity analysis techniques (one-at-a-time, correlation-constrained Morris and Spearman correlation). A case-study is carried out using three test cases with growing renewable penetrations in an integrated 9-bus electricity and 8-node gas network, where the input uncertainties include wind power, photovoltaic power, electric loads and gas loads. Results show that the most critical uncertainties vary between electric loads and wind power depending on the renewable penetrations. Compared to the Spearman correlation method, the correlation-constrained Morris method provides identical results but with considerably reduced computational time.
pH ISFET Stabilization Time Measurement Analysis and Its Readout and Control Circuit

Ion sensitive field effect transistors (ISFET) is used to measure ion concentration in liquid solutions. An ISFET that is used to measure ion concentration in liquid solutions for the first time, requires certain amount of time before actual measurements can be realized. This amount of time is called stabilization time. Typically, this stabilization time varies with external conditions such as type and conditions of the solution under test and performance of the ISFET sensors. The aim of the study is to determine stability criterion to automatically determine the stabilization time of ISFET sensors by utilizing a readout and control circuit and microcontroller. Our results show that the stabilization time of sensors used is about 50 to 60 seconds. The authors have tested commercial Ta2O5 pH ISFET sensors. In the future the authors wish to analyze other ions as well.
Probabilistic Aggregated Load Forecasting with Fine-grained Smart Meter Data

Probabilistic load forecasting (PLF) has been extensively studied to characterize the uncertainties of future loads. Traditional PLF is implemented based on the historical load data itself and other relevant factors. However, the prevalence of smart meters provides more fine-grained consumption information. This paper proposes a novel probabilistic aggregated load forecasting algorithm that makes full use of fine-grained smart meter data. It first applies clustering-based methods for point aggregated load forecasting. By varying clustering algorithms, multiple point forecasts can be obtained. On this basis, different quantile regression models are implemented to combine these point forecasts in order to form the final probabilistic forecasts. Case studies on a real-world dataset demonstrate the superiority of our proposed method.
Single-Layer Phase Screen With Pointing Errors for Free Space Optical Communication

Single-layer phase screen is a classical, simple and accurate approach for simulating the optical field under turbulence, and it is generally used in free space optical communication (FSOC). However, conventional single-layer phase screen studies have rarely considered the pointing errors from FSOC, which prevents the theoretical results for mobile FSOC from being verified using simulated optical fields. This work establishes a simulation method for the single-layer phase screen with pointing errors. Statistics on simulated optical field with pointing errors indicated that the previous theoretical results for the long-term average irradiance and irradiance fluctuation probability density function (PDF) for mobile FSOC can be modified to better fit the simulation results. In addition, the received power fluctuation PDF, average bit-error-rate (BER), and the influence of non-zero mean pointing errors are investigated through the simulated optical field with pointing errors. The proposed simulation method provides a powerful approach for understanding and evaluating the comprehensive influences of atmospheric turbulence and pointing errors on the FSOC.
State-of-the-art Multivariate Regression with a General Nk Hidden Multi-Layer Feed Forward Neural Network Model

We have introduced a novel multivariate regression model, called General Nk Hidden Multi-Layer Feed Forward Neural Network Model. The model prediction power comes essentially from a combination of Universal Approximation theorem, the Stochastic Gradient Descent convergence, and finally the use of ADAM optimizer which is also (at least) locally convergent. This Nk-architecture is proposed to fit any multivariate regression task, as well as any classification task when a softmax activation gate function is applied to the output layer nodes. The model can easily be augmented to thousands of possible layers without loss of predictive power, and has the potential to overcome our difficulties simultaneously in building a model that has a good fit on the test data, and don’t overfit. Its hyper-parameters, the learning rate, the batch size, the number of training times (epochs), the size of each layer, the number of hidden layers, all can be chosen experimentally with cross-validation methods. We have run some experiments with the Mulan Project Datasets [29] to illustrate the performance of the model against Random Forest with a number of estimators from 5 to 10, and a maximum depth from 10 to 30. Not only has the model surpasses the Random Forest model in all tested configurations, but, we have also found this General Nk Hidden Multi-layer Feed Forward Neural Network to be so effective as it reaches state-of-the-art performance for multivariate regression in terms of Mean Squared Error.