Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fractional-Order Resonant Wireless Power Transfer System With Inherently Constant Current Output

Wireless power transfer (WPT) using magnetic-coupling resonant has been proved to be a promising transmission method. At present, most of the existing resonant WPT systems adopt integer-order inductors and capacitors. In fact, in addition to integer-order components, there also exists fractional-order inductors and capacitors, which possess richer characteristics than integral-order inductors and capacitors. However, there are few studies on the application of fractional-order components in resonant WPT system. Therefore, this paper proposes a fractional-order resonant WPT system, which is based on a fractional-order resonant circuit only incorporating a fractional-order capacitor. The coupled-mode theory model of the proposed WPT system is established, and the impact of different loads and orders on transfer characteristics is explored. The theoretical analysis demonstrates that the proposed system can achieve constant current output that is independent of load, just by choosing appropriate parameters of the fractional-order capacitor. Finally, an experimental prototype of the fractional-order resonant WPT system is built, and the experiment results verify the correctness of the theoretical analysis.
Gate-Voltage-Stress-Induced Instability in C8-BTBT Thin-Film Transistors with Aluminium Oxide as Gate Dielectric

Degradation behaviors of C8-BTBT thin-film transistors (TFTs) with aluminum oxide film as gate dielectric under various gate voltage stress are studied. A higher positive gate voltage stress brings a corelated higher positive shift in transfer characteristics in C8-BTBT TFTs while more negative gate voltage stress brings more negative shift. The trapped charges and the fixed charge generation may be responsible for the positive shift and the negative shift respectively.
A Q-Learning Based Cross-Layer Transmission Protocol for MANETs

The frequent change of mobile ad hoc networks (MANETs) have made it challenging to establish and maintain efficient communications in MANETs. Different from the traditional transmission protocols which focus on single layer improvement, we propose a novel cross-layer transmission protocol. At the MAC layer, this paper proposed a Carrier Sense Multiple Access with Multi-channel Automatic Synchronization MAC protocol(CSMA/MAS). By CSMA/MAS, all nodes in network can be synchronized and then served in a round-robin fashion without contention collisions. At the network layer, this paper modified the existing Q-learning based Routing protocol such as QGeo and QGrid. Finally, MAC delay will be provided to the network layer. This information can be utilized by the network layer to choose a better routing path for each packet. Simulation results show that the proposed transmission protocol can provide higher packet arrival ratio and lower end-to-end delay than the existing transmission protocol.
Experimental study on model- vs. learning-based slip detection

Vision and proprioception are traditional sources of information for robotic grasping, but they are insufficient to achieve a stable grasp without slippage or without applying an excessive force on the object. Tactile sensors can aid in this problem by providing spatial and temporal data on the contact between fingertips and object. In this work, tactile fingertip sensors are used to detect slippage through two separate methods: the first, using principles inspired by human tactile sensing, and the second, by using a convolutional neural network trained with suitably labeled test samples. To perform a fair comparison of the methods, two evaluations are performed using a test bench and a pick-and-place robotic application. Results show promising use of the model-based method to avoid translational slippage, as it was able to consistently keep objects from slipping without overloading the grasp. Limitations of both model- and learning-based approaches are identified and discussed.
Numerical modeling and experimental realization of wide bandgap ZnTe-based solar cells for semi-transparent PV application

We report on the modeling of semi-transparent ZnTe-based photovoltaic devices. We propose innovative strategies to markedly improve the performances, so far mostly hindered by a poor band alignment at the p-n interface. Alternative buffer materials are considered, and we investigate on the surface selenization of the ZnTE films as well as the feasibility of a purely ZnTe homojunction. Using a realistic set of state-of-the-art parameters, a semi-transparent solar cell with a potential efficiency exceeding 7.5% is demonstrated possible.
Detection and localization of pallets on shelves using a wide-angle camera

This paper proposes a method to detect and localize pallets on shelves using a wide-angle camera mounted on a vehicle several meters away along the shelves. Although wide-angle observation is a significant advantage in navigation, objects appear small and largely distorted in the observed image, thus creating a severe disadvantage for the detection and localization of objects. To solve this problem, we propose reprojecting wide-angle views onto a three-dimensional (3D) plane to obtain a good projection image for object detection, in which pallets appear in the same shape and size regardless of the location in the image. Since the image projected on the plane coplanar to the pallet's front surface satisfy the above condition, we approximate the projection plane using the front surface of the shelves with pallets on it. Owing to the constant appearance of the pallet on the projected image, it can be robustly detected using the matching with a fixed template. When a pallet is detected, it is three-dimensionally localized from the equation of the 3D plane of the projection plane and its 2D location in the projected image. Experiments using empirically captured scenes demonstrated the detection and localization ability of the proposed method. Despite small area in the original wide-angle view, pallets of interest were discriminatively detected from others and localized with acceptable accuracy.
Fuzzy Logic Control of an Uncertain Manipulator With Full-State Constraints and Disturbance Observer

In this paper, a fuzzy logic control strategy is proposed for solving trajectory tracking control issues of an uncertain manipulator. Fuzzy logic is utilized to compensate for nonlinear uncertainties in manipulator dynamics and full-state constraints are involved in full-state feedback controller design for ensuring motion constraints during movement processes. Disturbance observer (DO) is designed to counteract the effects of unknown nonlinear disturbances caused by friction force or other various forms of disturbance. Combining with Lyapunov theory and back-stepping method, the proposed method can guarantee error signals in closed-loop system semi-globally uniformly bounded (SGUB). In view of safe operation, tangent barrier Lyapunov functions (tBLFs) are chosen to maintain joint angle and velocity in a predefined constrained region in tracking processes. Finally, simulation results are carried out to show the effectiveness of our proposed control strategy.
Investigation of the Implement the Shoe Height and Slot Opening Width on Decreasing the Peak of Cogging Torque in PMSG

Cogging torque on an electric engine rises in a lot of research being undertaken to cut down this torque. This torque requires to be trimmed so as not to obstruct rotor rotation, lessen the noise and vibration it generates. The purpose of this research is to diminish the remaining cogging torque on permanent magnet synchronous generator (PMSG). The method employed is a multi-objective technique, particularly fractional slot number technique associated with adjusting the slot opening width (SOW) and stator shoe height (SH). There are 12 combinations that have been set consisting of 3 SOW steps with a width of 1.50 mm, 1.55 mm, 1.60 mm, while SH on the 6 step set are 2.3 mm, 2.2 mm and 2.1 mm, 2.0 mm, 1.9 mm and 1.8 mm. Data processing involves been carried out with the support of FEMM 4.2 software. Simulation results have verified that multi-objective methods can reduce cogging torque sharply. The finest design is a sequence of 1.55 SOW and 2.3 SH with maximum positive cogging torque values of 0,00062 Nm and -0,00064 Nm for negative peaks. The slight divergence in the value of the cogging torque illustrates an unbalanced magnetic pull (UMP) that takes place in symmetry.
An Enhancement in Energy Efficient Hybrid Routing Protocol Using WSN with Clustering Implementation

On account of wide combination of continuous applications Wireless Sensor Network (WSN) is the most sweltering examination field in the domain of PC sort out. Remote Sensor Network includes little, self-overseeing sensor centers passed on in a remote region to distinguish, assemble and system data and transmit it to the customer. In such framework centers can move and synchronize with the neighbors. As a result of conveyability of center points, mastermind changes dynamically and centers get included and emptied. In this paper, we are going to consider unmistakable cream coordinating shows in WSN. Different mutt coordinating shows perform well in different circumstance and their introduction is seen reliant on changed estimations. Moreover, we have proposed Energy Efficient Hybrid Routing Protocol (EEHRP) in WSN. There are different sensor center points in a Wireless Sensor Network (WSN) that have constrained imperativeness, amassing and the limit with regards to getting ready. A limited asset in a WSN is the power. The presentation of these structures can be improved by using clustering. The fundamental statute on which all the clustering estimations are based is that the framework essentialness is used in picking and gathering head for the most amazing imperativeness capability and the data is epitomized in the pack dependent on the center points that submit to the gathering head. The picked CH sends data from a concentrated center point. The prescribed work proposed LEACH/Low Energy Adaptive Clustering Hierarchy and Received Signal Strength Indicator (RSSI) which are essentialness capable controlling shows in remote sensor frameworks. The past is an auto dealing with, flexible method wherein the sensor center points mastermind themselves into sets known as gatherings. These sets select one cluster head so the essentialness usage is improved. Data gathering is also united with the objective that the amount of messages that are traded to the Base Station (BS) is lessened. This redesigns the framework future. While, in RSSI, the division information is used for evaluating the point of confinement of the battery. The partition between the center points is assessed for finding the circumstance of the motional articles in constrained regions. It has been exhibited that better execution is cultivated by the proposed frameworks.
A Novel Prediction Method Based on Improved Binary Glowworm Swarm Optimization and Multi-Fractal Dimension for P2P Lending Investment Risk

The frequent bankruptcy incidents of peer-to-peer (P2P) lending industry have damaged the benefits of investors in China, and how to accurately and efficiently predict the investment risks of P2P lending becomes an important problem. For this very reason, a novel prediction method based on improved binary glowworm swarm optimization and multi-fractal dimension (IBGSOMFD) for P2P lending investment risk is proposed. Firstly, we propose an improved binary glowworm swarm optimization, abbreviated IBGSO, by uniformly designing an initial population using the good-point set theory, improving the moving way of glowworms, and introducing the mechanism of population diffusion and variation. Secondly, IBGSO combined with multi-fractal dimension (MFD) is applied to feature selection, and the optimal subset extracted from the original dataset can be efficiently achieved utilizing IBGSO, which can reduce its redundant attributes, and retain its pivotal attributes of P2P lending investment risk. Finally, an investment risk prediction model of P2P lending based on support vector machine (SVM) is established, which can accurately and efficiently predict the investment risk of P2P lending. Experimental results on 6 University of California Irvine (UCI) benchmark datasets show that IBGSOMFD outperforms other state-of-the-art approaches in predictive ability and calculative efficiency, and its effectiveness and significance. After the performance verification of IBGSOMFD, this work looks at how it can be applied in the risk prediction of P2P lending investment in China to maintain a stable market order.
A Time-Interleaved 14-bit 500MS/s Charge-Domain ADC with Mix-signal Mismatch Calibration

A two channel time-interleaved 14-bit 500MS/s charge domain pipelined ADC with mix-signal for-ground mismatch calibration method based on binary search is proposed. The offset and gain error mismatch between the two channels are calibrated in the mix-signal for-ground calibration. The 14-bit 500MS/s charge domain pipelined ADC is designed and realized in a 1P6M 0.18$\mu$m CMOS process. Test results show the 14-bit 500MS/s ADC achieves the signal-to-noise ratio of 69.8 dBFS and the spurious free dynamic range of 82.3dB, with 72.1MHz input at 500MS/s, while consumes the power consumption of 365mW.
A Digitally Controlled CMOS Analog Baseband Circuit with Low Noise for 7681GHz Automotive Radar

This paper presents a digitally controlled CMOS analog baseband (ABB) circuit with low noise for 76-81GHz automotive radar, implemented in CMOS 65nm GP technology. The ABB consists of two high pass filters, two programmable gain amplifiers, one low pass filter, and one buffer. As a part of the receiver, ABB provides 42dB dynamic gain range and reconfigurable bandwidth from 100 kHz/ 200 kHz/ 1 MHz/ 5 MHz to 2 MHz/ 5 MHz/ 10 MHz/ 20 MHz. The entire configuration is digitally controlled by SPI. The typical noise Figure of the receiver is 10.2dB without ABB, 15.3dB with ABB at minimum gain, and 12.2dB with ABB at maximum gain. The ABB occupies a chip area of 1.620*0.445 (mm2) without pad and consumes 24mA current. The peak-to-peak differential output voltage of ABB is up to 2V.
An Algorithm for Dynamically Computing Grade Ranges in Academic Environment

Evaluating students in a semester examination and assigning them grades or marks is a debatable topic. In this paper, we discuss relative merits and demerits of grading and absolute marking systems. In grading, the ranges can be either predefined or variable. We propose an algorithm for dynamically computing the grade ranges. We evaluate different types of grading systems on the basis of inflation or deflation of marks. We suggest to use a scheme that is hybrid of the absolute marking system and the grading system for enhancement of the fairness in evaluating the performance of students.
LSTM Based Network Flow Forecasting in High-Speed Railway Data Network

Network flow forecasting is the basis of network performance analysis and bandwidth allocation. It is of great significance for routing path selection in network service establishment process. The accurate flow forecasting can effectively reduce the service blocking probability. Different forecasting models based on recurrent neural network (RNN) have got extensive attention recently. However, there is relatively less research focusing on network flow forecasting, which is an essential part of future network management. In this paper, we utilize long short term memory (LSTM) model to forecast network flow tendency. To avoid overfitting issue, dropout method is introduced into LSTM. The flow information is collected from High-Speed Railway data network and preprocessed to obtain time-flow series. Two time-flow series are used to verify the network flow forecasting accuracy. Experiments show that the LSTM model can provide accurate forecasting results. The median MAPE of two series forecasting are 0.014 and 0.043, respectively. A shuffled data is used to train the LSTM model too, and corresponding forecasting results indicate that the model has a good generalization ability and it can be applied in different networks.
Robust Geman-McClure Based Nonlinear Spline Adaptive Filter Against Impulsive Noise

This paper first proposes nonlinear spline adaptive filter based on the robust Geman-McClure estimator (SAF-RGM). The proposed algorithm is obtained by minimizing the cost function relied on the Geman-McClure estimator. Since the Geman-McClure estimator can remove outliers with large amplitude from dataset, the proposed algorithm can obtain the excellent performance in the impulsive noise. Moreover, the mean and mean square behaviors of the SAF-RGM algorithm are analyzed. Simulations are conducted to confirm that the proposed SAF-RGM algorithm achieves better performance than the existing spline nonlinear adaptive filtering algorithms. Besides, simulation results validate the theoretical conclusions.
Use of a Quasi-Optimal Correlation Algorithm for Modelling of a Microrelief Texture in Optical Electronic Measurement Systems

The paper discusses the use of a quasi-optimal correlation algorithm for constructing a two-dimensional autocorrelation function for a binary image of the studied microrelief texture of precision machined surfaces. The binary image was formed using an optical-electronic system and computer processing of halftone microrelief images. It has been established that the most informative characteristic of the microrelief of the studied surfaces that are significantly different from one another with a given probability is the average amplitude of the variable component of the autocorrelation function
Predicting protein functions through non-negative matrix factorization regularized by protein-protein interaction network and gene functional information

Protein function prediction is necessary for understanding life and is valuable for application on drug design and health care. It is still a big challenge to predict protein function correctly by integrating multi-biological information. In this work, we propose a novel non-Negative Matrix Factorization (NMF) based method regularized by PPI network and GO similarity network, namely PONMF, for protein function prediction, which decomposes the known GO-protein association matrix into two low-rank matrixes for proteins and GO terms. In the course of factorization, PONMF incorporates the label matrix factorization term, an additional network regularization term and a GO similarity regularization term into the objective function. Finally, the potential protein functions are predicted by referring to the product of the two low-rank matrixes. PONMF not only successfully integrates diverse biological information to predict protein functions, but also naturally partitions proteins into different modules and infers functions from the proteins in the same modules. Our methods as well as the other two state-of-the-art methods (UBiRW and NMFGO) are applied to predict functions for protein of S. cerevisiae and H. sapiens. The prediction results show that PONMF outperforms the other two existing methods.
Robust Rigid Registration Algorithm Based on Correntropy and Bi-Directional Distance

Point set registration is a key method in computer vision and pattern recognition. In this paper, the correntropy and bi-directional distance are introduced into registration framework and a new robust registration model for RGB-D data is proposed. Firstly, as registering point sets with smooth structure, such as surface or plane, is easy failed, the color and position information is fused to establish more precise correspondence between two RGB-D data sets. Secondly, to reduce the influence of noises and eliminate outliers, the registration model based on the maximum correntropy criterion is established. Thirdly, the bi-directional distance measurement is introduced into the registration framework to avoid the model being trapped into local extremum. In addition, to solve this new registration problem, a new iterative closest point (ICP) algorithm is proposed, which converges to the local optimal solution by iterations. In the experiments, the proposed algorithm achieves more robustness and precise registration results than other algorithms.
Simulation and Multi-Objective Evaluation of Reuse Potential of Waste Recycling System for Oil And Gas Industry

The paper presents the results of simulation and evaluation of the reuse potential of the system for the oil-contaminated wastes recycling. Reuse potential of OCW is considered as a comprehensive quantitative indicator of the possibility of the effective waste recycling that can be measured by a combination of heterogeneous criteria. The approach based on the basic CCR model and Super-efficiency model of the Data Envelopment Analysis method is suggested to evaluate reuse potential. One of the particular quantitative indicators for estimating the waste reuse potential is a resource value of the OCW. The resource value of wastes is a quantitative assessment of physicochemical composition and properties of wastes, which determines the degree of suitability of waste for use as material resources in recycling technologies associated with their recycling, recovery and regeneration. Obtained relative estimations allow one to select the best technologies and storage facilities for the OCW recycling with respect to resource value and reuse potential.
Fractals Study and Its Application

The overall of this paper is a review of fractal in many areas of application. The review exposes fractal definition, analysis, and its application. Most applications discussed are based on analysis from geometric and image processing studies. Patterns of some fractals will be discussed. Some simulation results are supplied to illustrate the discussion. Simulation resulted are from various software and tools. Some principles of fractals with informative patterns have been simulated. Whereas the simulations could support some recommendations for prospective purposes and applications. The prospective application may help in predictive pattern of many fields. The predictive pattern will lead to pattern control and pattern disruptions.
Quadrupedal Locomotion on Uneven Terrain With Sensorized Feet

Sensing of the terrain shape is crucial for legged robots deployed in the real world since the knowledge of the local terrain inclination at the contact points allows for an optimized force distribution that minimizes the risk of slipping. In this letter, we present a reactive locomotion strategy for torque controllable quadruped robots based on sensorized feet. Since the present approach works without exteroceptive sensing, it is robust against degraded vision. Inertial and force/torque sensors implemented in specially designed feet with articulated passive ankle joints measure the local terrain inclination and interaction forces. The proposed controller exploits the contact null-space in order to minimize the tangential forces to prevent slippage even in case of extreme contact conditions. We experimentally tested the proposed method in laboratory experiments and validated the approach with the quadrupedal robot ANYmal.