Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Signal Model for Peak Current Controlled Coupled SIDO Buck Converters

This paper proposes a complete small-signal model (SSM) for peak current-mode (PCM) controlled coupled inductor single-input dual-output (CI-SIDO) buck converter. The complete SSM is obtained by combining the current modulator SSM with the converter SSM. In PCM control, the current modulator is an essential part responsible for generating the duty cycle. The current modulator is formed with the combination of the comparator and SR flip-flop. The SSM of the current modulators is developed by deriving the expressions of duty cycles in terms of reference control voltages, sensed inductor currents and input voltage. The proposed SSM is relevant for designing the outer loop and predicting instability, dynamic characteristic of the complete system. MATLAB simulation and experimental results support the proposed model’s accuracy in predicting steady-state and dynamic characteristics. The accuracy of the proposed model in predicting the steady-state and dynamic characteristic is confirm using MATLAB simulation and experimental results.
Image Enhancement from Illumination Variation to Improve the Performance of Simultaneous Localization and Mapping Technique

The Simultaneous Localization and Mapping (SLAM) is a research problem that has been explored by scholars. The SLAM contribution to autonomous robot navigation has attracted the attention of many researchers and over the decades, many SLAM techniques with outstanding achievement has been presented to resolve the SLAM problem. However, there are factors such as environmental noises that can limit the performance of SLAM techniques. These environmental noises can create misunderstandings in image analysis with an erroneous effect on the robot navigation path. In this study, attention will only focus on shadow and light intensity environmental noise and in resolving this issue, filters were presented to modify the original Monte Carlo algorithm to improve its effectiveness. MATLAB was used for the simulation experiment and evaluation is based on qualitative and quantitative methods. The experimental results acquired were compared to the original Monte Carlo algorithm without filters and the results obtained were amazing. The accomplishment of the modified Monte Carlo algorithm has led to enhancement in robot path and in real-life implementation will facilitate self-directed navigation, exploration, and route planning while it decreases accident rates and injuries for human working in an unstable environment.
Remote Sensing Image Classification for Spatial Information Extraction of Panax notoginseng Fields

Chinese herbal medicine has played an important role in the treatment of the novel coronavirus patients. Machine learning-based remote-sensing techniques play a significant role in the quantitative resource inventory of materia medica resources, particularly to explore the monitoring abilities for sustainable utilization and biodiversity protection of the cultivated medicinal plant Panax notoginseng in the macrocosm. Until now, to the best knowledge, concrete planting patterns of Panax notoginseng are still poorly known. In this study, two popular supervised classifiers, i.e., support vector machine and artificial neural network, are employed to conduct mapping Panax notoginseng fields in Wenshan city, Yunnan province, China. It only targets a single class of interest, and there are 8,072 and 8,749 polygons extracted, whilst the planting areas of Panax notoginseng are estimated as 35.45 and 32.47 square kilometers achieved by SVM and ANN, respectively. Meanwhile, special concerns raised in terms of remote sensing mapping for modeling planting patterns and monitoring plant rotations of perennial Panax notoginseng cultivated under shade-net structures based on the experiments.
The Course Selection Platform in Colleges and Universities of Network Load Balance Based on Server Cluster

Under the background of the new credit system reform in higher education, colleges and universities face some tricky questions such as the high data connection is not successful, the high concurrent data access congestion business problem, the hardware resources are old and insufficient. These problems lead to students' Course selection is not smooth and difficult course selection monitoring management. The old hardware resources can be carried out server cluster architecture to increase the number of business processing web through VMWAREV SPHERE 6.5. To open double links to distribute service requests through combining HAPROXY-1.8. 19 software load balancing and F5_ BIG_ IP hardware load balancing. Then the database side stores business data synchronously through the server cluster. This architecture expands the number of concurrent connections on the web side, improves the stability of the connection through dual link channels, and ensures the data access security through database clusters. In the actual course selection process of more than one year, the course selection business is well handled, the number of concurrent connections is greatly increased, almost no congestion occurs, and students have a good experience in course selection. Therefore, it turns out that this architecture can provide a safe, stable and reliable solution, which has a certain promotion significance.
A D-Band Power Amplifier in 65-nm CMOS by Adopting Simultaneous Output Power-and Gain-Matched Gmax-Core

This paper proposes a simultaneous output power- and gain-matching technique in a sub-THz power amplifier (PA) design based on a maximum achievable gain (<inline-formula> <tex-math notation="LaTeX">$G_{max}$ </tex-math></inline-formula>) core. The optimum combination of three-passive-elements-based embedding networks for implementing the <inline-formula> <tex-math notation="LaTeX">$G_{max}$ </tex-math></inline-formula>-core is chosen considering the small- and large-signal performances at the same time. By adopting the proposed technique, the simultaneous output power- and gain-matching can be achieved, maximizing the small-signal power gain and large-signal output power simultaneously. A 150 GHz single-ended two-stage PA without power combining circuit is implemented in a 65-nm CMOS process based on the proposed technique. The amplifier achieves a peak power gain of 17.5 dB, peak power added efficiency (PAE) of 13.3 and 16.1 %, saturated output power (<inline-formula> <tex-math notation="LaTeX">$P_{sat}$ </tex-math></inline-formula>) of 10.3 and 9.4 dBm, and DC power consumption of 86.3 and 52.4 mW, respectively, under the bias voltage of 1.2 and 1 V, which corresponds to the highest PAE, gain per stage and <inline-formula> <tex-math notation="LaTeX">$P_{out}$ </tex-math></inline-formula> per single transistor among other reported CMOS D-band PAs.
Modulation, Detection, and Performance for Noncoherent Massive SIMO Systems for Hypersonic Vehicle Downlink Integrated Channel

For hypersonic flight, high-dynamic plasma sheaths surrounding vehicles deteriorate communication quality. In this work, hypersonic vehicle downlink integrated channels that cascade the plasma sheath channel and Rice fading channel are first established, and the probability density functions (PDFs) of the integrated channel amplitude and phase are derived theoretically. Then, a downlink SIMO system over the integrated channel is considered, including a single antenna transmitter and a single ground receiver equipped with a large number of antennas. For this system, a noncoherent telemetry scheme is proposed, a transmitter that modulates information only in the power of the symbols, and an adaptive maximum likelihood (ML) receiver which demodulates symbols using the PDF of the average received power (ARP) across the antennas. Motivated by the efficient power utilization, an asymptotically optimal constellation design in terms of symbol error rate (SER) with an increasing number of antennas is proposed, under an assumption that CSI statistics are available. The PDF of ARP is well estimated by a reduced reversible jump markov chain monte carlo (RRJ-MCMC) algorithm and used for symbol demodulation and achievable rate derivation. The numerical results demonstrate that the optimal constellation scheme outperforms the amplitude shift keying (ASK) constellation scheme and is robust against the plasma sheath attenuation with moderate antennas or a large line-of-sight (LOS) component.
Numerical Simulation of the Discharge Dynamics of C₄F₇N-N₂ and the Influence of Buffer Gas

Due to the greenhouse effect of SF<sub>6</sub>, the search for new environmentally friendly substitute gases has become an urgent task for the electrical industry. In this article, we have employed a 2-D fluid model to study the characteristic of streamer discharges in the C<sub>4</sub>F<sub>7</sub>N-N<sub>2</sub> mixture. The discharge process and insulation performance of the C<sub>4</sub>F<sub>7</sub>N-N<sub>2</sub> mixture with different C<sub>4</sub>F<sub>7</sub>N content are compared under the same conditions. In a5.07% C<sub>4</sub>F<sub>7</sub>N-N<sub>2</sub> mixture, the streamer expands continuously and reaches the top boundary quickly. In 7% and 13.1% C<sub>4</sub>F<sub>7</sub>N-N<sub>2</sub> mixture, the streamer is gradually stagnated in the simulation domain. The streamer radius shows a decreasing trend, and a low electron density area appears behind the streamer head as the streamer moves forward. The streamer discharge simulation in 50% SF<sub>6</sub>–50% N<sub>2</sub> is also performed to compare its insulation performance to the C<sub>4</sub>F<sub>7</sub>N-N<sub>2</sub> mixture. The results show that the discharge is suppressed more heavily in 13.1% C<sub>4</sub>F<sub>7</sub>N-N<sub>2</sub> than in 50% SF<sub>6</sub>–50% N<sub>2</sub>. The influence of buffer gas is studied by comparing the discharge process in C<sub>4</sub>F<sub>7</sub>N-N<sub>2</sub> and C<sub>4</sub>F<sub>7</sub>N-CO<sub>2</sub> with 13.1% C<sub>4</sub>F<sub>7</sub>N content. It is found that the discharge is more easily to a breakdown in C<sub>4</sub>F<sub>7</sub>N-CO<sub>2</sub> than that in C<sub>4</sub>F<sub>7</sub>N-N<sub>2</sub> in our simulation configuration. The simulation provides a way to optimizing the buffer gas ratio of new environmentally friendly gases from a microscopic perspective.
Modeling a Symmetrically-Notched Continuum Neurosurgical Robot With Non-Constant Curvature and Superelastic Property

A neurosurgical robot with 3-dimensional (3D) distal manipulation can increase instrument workspace for reaching peripheral regions of intracranial lesions to achieve complete lesion removal and minimal brain manipulation in a keyhole procedure. In this work, we designed a Nitinol-based symmetrically notched continuum robot based on the neurosurgical clinical constraints. A mechanics model is developed for the robot using a second order differential equation that exploits the shearing force formulation based upon the Euler $-$ Bernoulli moment-curvature equation. It affords non-constant curvature analysis, and integrates the piecewise linear material model with three stages that closely approximates the nonlinear superelastic property of Nitinol. A solution was introduced to this complex mechanics model based on the fourth order Runge-Kutta method with variable transformation, and an iterative approach. Comparison with the experimental data using our proposed model shows its high modeling accuracy in terms of the bending angle and tip position for a single symmetric notch bending as well as 2D bending motion of the robot. The significantly improved accuracy over previous models justifies the added complexity of our modeling approach and could better guide the determination of the notch parameters during robot design.
A Pattern-Based SpGEMM Library for Multi-Core and Many-Core Architectures

General sparse matrix-matrix multiplication (SpGEMM) is one of the most important mathematical library routines in a number of applications. In recent years, several efficient SpGEMM algorithms have been proposed, however, most of them are based on the compressed sparse row (CSR) format, and the possible performance gain from exploiting other formats has not been well studied. And some specific algorithms are restricted to parameter tuning that has a significant impact on performance. So the particular format, algorithm, and parameter that yield the best performance for SpGEMM remain undetermined. In this article, we conduct a prospective study on format-specific parallel SpGEMM algorithms and analyze their pros and cons. We then propose a pattern-based SpGEMM library, that provides a unified programming interface in the CSR format, analyses the pattern of two input matrices, and automatically determines the best format, algorithm, and parameter for arbitrary matrix pairs. For this purpose, we build an algorithm set that integrates three new designed algorithms with existing popular libraries, and design a hybrid deep learning model called MatNet to quickly identify patterns of input matrices and accurately predict the best solution by using sparse features and density representations. The evaluation shows that this library consistently outperforms the state-of-the-art library. We also demonstrate its adaptability in an AMG solver and a BFS algorithm with 30 percent performance improvement.
Human Trajectory Prediction Model and Its Coupling With a Walking Pattern Generator of a Humanoid Robot

In order to smoothly perform interactions between a humanoid robot and a human, knowledge about the human locomotion can be efficiently used. Indeed, in a human-robot collaboration, a prediction model of the human behaviour allows the robot to act proactively. In this letter, an optimal control based model predicting the human Center of Mass (CoM) trajectory during gait is presented. A Walking Pattern Generator (WPG) based on non-linear model predictive control is, then, introduced in order to generate the robot CoM and footsteps along the predicted trajectory. The combination of the human trajectory prediction model and this new WPG aims to allow the robot to proactively walk along with a human instead of passively follow him. These models have been tested in simulation on Gazebo on a TALOS humanoid robot model using measured human trajectories. To perform the CoM and foot trajectories computed by the WPG, a real-time whole-body controller is used. This controller is a Quadratic Program which solves the inverse dynamics of the robot at torque level.
Graph-Based Clustering of Dolphin Whistles

An effective method for detecting the presence of dolphins is by using passive acoustic monitoring (PAM), where pod size indications can be estimated by counting individual whistles. The detection of dolphin whistles is commonly applied on a time-frequency representation, followed by denoising and whistle tracking to evaluate the number of whistles. However, due to harmonics, multipath and time-varying signal-to-noise ratio, a single dolphin whistle may be associated with multiple whistle-traces. Thus, as a first step towards evaluating dolphins’ abundance, our goal is to cluster individual whistle traces into unique whistles. Our scheme measures the similarity between each pair of whistle traces, and estimates the likelihood of whistle traces sharing the same cluster. Clustering is formalized as an optimization problem, aims to maximize the stability of clusters. Formalizing the problem as a minimal-cut optimization on a graph provides an effective solution based on spectral decomposition of the graph-Laplacian. Our model of the likelihood sharing cluster provides a physically-meaningful method to calculate the graph's connectivity parameters, thereby leading to a robust blind clustering. Based on numerical simulations and real recordings of dolphin whistles at sea, we demonstrate the applicability of our solution and its advance beyond alternative approaches.
A Type-3 Modified SOGI-PLL With Grid Disturbance Rejection Capability for Single-Phase Grid-Tied Converters

This article presents a type-3 second-order generalized integrator phase-locked loop (SOGI-PLL) algorithm for single-phase system. Disturbances, such as dc offset and harmonics, are present in the practical grid voltages, due to occurrence of faults, sensors, and analog-to-digital converters. The type-3 SOGI-PLL exhibits fast dynamic performance with gain and phase-lead compensation. However, the general type-3 SOGI-PLL introduces fundamental frequency oscillations in the estimated quantities at the output, when grid voltage contains disturbances. The incorporation of filters for suppression of frequency oscillations, due to dc offset, reduces the bandwidth of PLL, which affects the dynamic response of PLL by introducing phase delay. Thus, a modified notch-filter SOGI (MNF-SOGI) based type-3 SOGI-PLL is proposed with frequency adaptive control to eliminate the phase error, without affecting the dynamic performance. A detailed analysis is presented on the effect of dc offset and scaling error. To enhance dynamic response, filtering, and stability designed guideline based on settling time is also discussed. The proposed type-3 MNF-SOGI-PLL has better grid parameter estimation and fast response with inherent dc offset rejection capability. The effectiveness of the proposed type-3 MNF-SOGI-PLL is evaluated under various grid disturbance conditions and experimentally verified.
MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters

Determining thresholds of the primary control loops (System 1) of an additive manufacturing (AM) process is challenging when realizing System 1 with its fast and intuitive capability for adapting to different metal powers, machine configurations, and process parameters. Based on the convolution neural network and long short-term memory models, this letter presents a secondary tuning loop (System 2) to classify the types of melt-pool images (MPIs) from a coaxial camera online, suggest polishing parameters, and determine the control thresholds of System 1 offline. Case studies indicate that the thresholds and parameters of System 1 including smoke discharging, powder coating, and laser polishing of control loops of a laser powder bed fusion (LPBF) machine can be more deliberatively and logically decided by the proposed MPI-based System 2.
Design and Analysis of FCSTAR, a Hybrid Flying and Climbing Sprawl Tuned Robot

This letter presents a hybrid flying and climbing robot which we named FCSTAR. This new robot, an upgraded version of FSTAR, is designed for climbing over slopes and is fitted with a sprawling mechanism and propellers that allow it to both run and fly using the same motors. By using thrust reversal and its 4-wheel drive, the robot can drive over steep slopes including walls and reach high places where it cannot fly, such as pipes, sewage channels, ventilation ducts, etc. The combined capabilities of flying, running and climbing allows the FCSTAR to fly over, crawl underneath or climb over obstacles. We first describe the design of the robot and the configuration of the propulsion and flying systems, including the design considerations for its improved climbing capabilities. We then present a model of climbing and analytically compare the different modes of operation to define the optimal design configuration. Finally, we present multiple experiments that we performed using our newly developed design of the FCSTAR robot. The results show that this experimental robot can drive on inclined surfaces and even climb on vertical walls. It can reach ground speeds of up to 3.2 m/s, and fly like a regular quadcopter (see attached video).
Representation Learning With Dual Autoencoder for Multi-Label Classification

Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label data. The crucial problem of multi-label classification is the more robust and higher-level feature representation learning, which can reduce non-helpful feature attributes from the input space prior to training. In recent years, deep learning methods based on autoencoders have achieved excellent performance in multi-label classification for the advantages of powerful representations learning ability and fast convergence speed. However, most existing autoencoder-based methods only rely on the single autoencoder model, which pose challenges for multi-label feature representations learning and fail to measure similarities between data spaces. To address this problem, in this paper, we propose a novel representation learning method with dual autoencoder for multi-label classification. Compared to the existing autoencoder-based methods, our proposed method can capture different characteristics and more abstract features from data by the serially connection of two different types of autoencoders. More specifically, firstly, the algorithm of Reconstruction Independent Component Analysis (RICA) in sparse autoencoder is trained on patches on all training and test dataset for robust global feature representations learning. Secondly, with the output of RICA, stacked autoencoder with manifold regularization (SAMR) is introduced to ameliorate the quality of multi-label features learning. Comprehensive experiments on several real-world data sets demonstrate the effectiveness of our proposed approach compared with several competing state-of-the-art methods.
Analysis and Optimization of Torque Ripple Reduction Strategy of Surface-Mounted Permanent-Magnet Motors in Flux-Weakening Region Based on Genetic Algorithm

One of the parameters required for a high-performance drive of a surface-mounted permanent magnet synchronous motor (SPMSM) operating in the flux-weakening region is the torque ripple reduction. In general, the torque ripple results from many reasons, such as the machine structure, oscillations of measured speed, etc. Hence, this article proposes an optimized strategy that works in conjunction with a variable switching frequency pulsewidth modulation algorithm to reduce the torque ripple peak. For the modulating algorithm, the updated frequency changes linearly with the desired torque ripple and its predicted maximum peak. For the proposed strategy, the reflection of proportional-integral (PI) parameters is observed at the torque ripple caused by the current ripples. Therefore, a robust method for adjusting PI parameters is proposed that relies on the improved fitness function that can minimize the regulators' error and maximize the drive stability bandwidth in the flux-weakening region. This objective function is optimized offline using a genetic algorithm optimization technique. Meanwhile, a third-order generalized integral flux observer is applied in this article, providing evidence of torque ripple reduction. For driving in the flux-weakening region, the reference magnitude of the duty cycles can produce the required d-axis reference current to prevent the saturation of current controllers. Finally, comprehensive simulations and experiments are presented to validate the proposed strategy effectively. In comparison with the conventional method, the proposed strategy can reduce the copper and switching losses simultaneously and control the current and speed in the flux-weakening region efficiently.