Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto-tuning methodology for configuration and application parameters of hybrid CPU + GPU parallel systems based on expert knowledge

Auto-tuning of configuration and application parameters allows to achieve significant performance gains in many contemporary compute-intensive applications. Feasible search spaces of parameters tend to become too big to allow for exhaustive search in the auto-tuning process. Expert knowledge about the utilized computing systems becomes useful to prune the search space and new methodologies are needed in the face of emerging heterogeneous computing architectures. In this paper we propose an auto-tuning methodology for hybrid CPU/GPU applications that takes into account previous execution experiences, along with an automated tool for iterative testing of chosen combinations of configuration, as well as application-related parameters. Experimental results, based on a parallel similarity search application executed on three different CPU + GPU parallel systems, show that the proposed methodology allows to achieve execution times worse by only up to 8% compared to a search algorithm that performs a full search over combinations of application parameters, while taking only up to 26% time of the latter.
Weighted Ensemble of Deep Convolution Neural Networks for Single-Trial Character Detection in Devanagari-Script-Based P300 Speller

The existing Devanagari-script-input-based P300 speller (DS-P3S) performs better mostly with 3–15 trials. This leads to poor information transfer rate (ITR) and a major concern in its real-time adaptation. In DS-P3S, the display paradigm is a matrix of $8\times 8$ size which has 28 more characters than the $6\times 6$ English paradigm. The increased number of characters leads to user-related issues such as a crowding effect, double flashing, adjacency distraction, task difficulty, and fatigue which increases the false detection rate. To tackle this, we propose an efficient single-trial character detection approach for DS-P3S using weighted ensemble of deep convolution neural networks (WE-DCNNs). The weighted strategy is constructed based on measured ensemble diversity to counter the instability by the individual classifier. Additionally, to reduce the false detection rate arising from a single trial, a new channel dropout-based character detection approach is introduced first time in this article. The ITR of 55.45 b/min and an average P300 classification accuracy of 92.64% achieved are comparatively higher than existing methods of DS-P3S. The significant reduction in tradeoff between bias and variance for the different subjects affirms the ease of applicability of the proposed model with just a single trial.
Stability Analysis and Design of Switched Saturated Systems Based on Antiwindup Technique

The problem of stability analysis and anti-windup design is investigated for a class of switched linear systems subject to actuator saturation by using the common Lyapunov function method. Suppose that a group of dynamic output controllers have been designed and they can stabilize the closed-loop system in the absence of saturation. Then, the anti-windup compensators are designed, which aims for enlarging the domain of attraction of system with saturation. Finally, the anti-windup compensators are calculated by solving an optimization problem with linear matrix inequalities (LMI) constraints. A numerical example is given to show the effectiveness of proposed method.
Fuzzy-Logic-Based Adaptive Internal Model Control for Load Frequency Control Systems with Electric Vehicles

The imbalance of generation and load demand leads to frequency deviation, and this problem may become more serious when intermittent wind power employed in grids. In recent years, the number of electric vehicles connected to power grids has increased rapidly. Electric Vehicles (EVs) can be used as distributed energy storages and controllable loads based on V2G technical, thereby, they have the potential for frequency regulation services, thus they are considered in the system. The LFC system in this study includes controller, governor, turbine generator, EVs, intermittent wind power and load. For controller design convenience, an EV aggregator model is proposed to represent all EVs in the system for reducing the system order. For further improvement of the system performance, we propose a fuzzy-logic-based adaptive two degree of freedom (TDF) internal model control (IMC) design method for load frequency control (LFC) in this paper. The controller parameters are tuned through designed fuzzy rules to achieve a better closed-loop response. Time domain simulations demonstrate that the proposed controller can improve the dynamic response as compared with other TDF IMC controller.
A Deeply Supervised Convolutional Neural Network for Brain Tumor Segmentation

Automated segmentation of brain tumor is of great importance for the diagnosis and treatment. Although manual segmentation could achieve high accuracy, it is time and labor consuming, and not available batch processing. Recently, deep neural networks represented by U-Net have been successfully applied on medical image segmentation and achieved state-of-the-art performance. However, there is no standard segmentation method that can produce desirable results for all images, various segmentation methods are optimized to deal with specific areas and imaging modes, which have strong pertinence. In this paper, we propose a U-Net like by integrating residual blocks, convolutions with dense connection and auxiliary outputs for multi-class brain tumor segmentation. Firstly, residual blocks are used in the contracting and expanding path to extract deep semantic information of images. Secondly, densely connected convolutions are applied in the last layer of contracting path to strengthen feature propagation and feature reuse. Three auxiliary outputs are then employed as deep supervision before each upper sampling to decrease the probabilities of vanishing gradient and avoid overfitting. Finally, we use weighted cross entropy and generalized dice loss as a fused loss function to address the class imbalance problem in brain tumor data. The proposed model is evaluated on the BraTS 2018 training dataset and achieves state-of-the-art performance, with average dice scores of 0.884, 0.806, 0.702 for the whole tumor, tumor core and enhancing tumor core, respectively.
Improved Visual Odometry Based on SSD Algorithm in Dynamic Environment

In recent years, Simultaneous Localization and Mapping (SLAM) have made great progress in the field of robots. However, the majority of SLAM algorithms are currently designed and processed in static environments. In the dynamic environment, due to the existence of moving objects, the SLAM system is easy to mismatching the pose estimation, which will affect its positioning accuracy and mapping accuracy. In this paper, the traditional Oriented FAST and Rotated Brief (ORB)-SLAM2 system is improved to deal with these problems. Firstly, the Single Shot MultiBox Detector (SSD)algorithm is used to detect the dynamic targets in the scene, and then the multi-layer optical flow is constructed to remove the dynamic feature points by optical flow tracking. Secondly, the static feature points in the scene are matched and the pose are calculated to solve the problems caused by a large number of matches, such as large system computation and low operating efficiency. Finally, testing on the TUM datasets shows that the absolute trajectory error and the relative pose error of the improved ORB-SLAM2 system are significantly reduced compared to the traditional ORB-SLAM2 system.
An Impedance Model-Based Multiparameter Identification Method of PMSM for Both Offline and Online Conditions

Existing online motor parameter identification methods mostly depend on the fundamental frequency voltage equations, which lead to the unsatisfactory identification effect at low current and low speed operation. This article proposes a parameter identification method based on the high frequency (HF) equivalent impedance model of permanent magnet synchronous motor with HF signal injection at both the dq-axes. This method identifies the resistance and the dq-axis inductances offline and online, along with the flux linkage online. In order to improve the identification accuracy, the parameter sensitivity analysis-based algorithm is proposed to detect the resistance and the flux linkage. Meanwhile, the inverter nonlinearities and the HF influence on parameter identification are compensated effectively. In order not to affect the normal operation of the motor drive, the selection of the amplitude, and the frequency of the injected signal is investigated. The proposed method is validated on a 2.2-kW motor and confirmed by finite element analysis. The experimental results show the good identification effect in different operation conditions.
Finite-time Attitude Tracking for Spacecraft With Unknown Boundary Disturbance and Angular Velocity

This paper investigates a velocity-free finite-time attitude control scheme for rigid spacecrafts with unknown boundary external disturbance. By using the quadratic Lyapunov function (QLF) method, a finite-time observer is designed to estimate unknown angular velocity and boundary of external disturbance. Combining adaptive control technique with adding a power integrator (AAPI), a finite-time controller is proposed. Rigorous Lyapunov-based analysis shows that both the observation errors and tracking errors can converge to the equilibrium point in finite time under the designed control law. Numerical simulation results illustrate the performance of the proposed control scheme.
A Dual Active Clamp DC–DC Converter With High Voltage Gain

In this article, a novel dual active clamp converter with high voltage gain is proposed for dc–dc applications. The active clamps’ switches turn on and off under zero-current switching (ZCS). In addition, the main switch turns on under ZCS condition, as well. Therefore, switching losses are significantly reduced. The converter is also capable of operating under quasi-resonance (QR) mode. In this mode, switching losses are reduced further by lowering the voltage and current, at which the main switch turns off. In comparison with the conventional interleaved converter, the voltage gain of the proposed converter is almost the same, but the number of semiconductors and switching loss are significantly reduced. The switching frequency of the active clamps’ switches is half of the main switching frequency. The operation principles of the proposed converter are studied in both pulsewidth modulation and QR modes, and the advantages are investigated. A 200-W prototype is implemented to validate the desired functionality of the converter, and experimental results are compared in both modes.
Interactive Question-Posing System for Robot-Assisted Reminiscence From Personal Photographs

Reminiscence is a lifelong activity that happens throughout our lifespan. While memories can serve as topics in people’s everyday conversations, recalling the past can also help us build self-esteem and increase our level of happiness. In this paper, we aim to develop a robot companion that helps people to recollect their memories from personal photographs. We focus on how a robot can associate concepts relevant to the content in the photographs and evoke people’s memories by asking questions that are both relatable and engaging. To understand the content of a picture, we applied deep learning techniques in order to recognize events, objects, and scenes in it. Then, these observations and any user utterances are considered in a Markov random field (MRF)-based algorithm that contains common sense knowledge of a number of events, with loopy belief propagation being used to infer possible associated concepts and topics. Afterward, the robot poses appropriate questions about the selected topics, guiding the user to reminisce through conversation. Our results show that the proposed system can pose related and appropriate questions to interact with the user, and has the potential guide the user to recall the past in an organized way.
Simultaneous Temperature Estimation of Multiple Gratings Using a Multi-Layer Neural Network

This paper introduces a method to do simultaneous temperature estimations of multiple gratings in an FBG array. The method involves training a multi-layer neural network using simulated training data. The network is then used to estimate the temperature changes of multiple gratings simultaneously using only the experimentally obtained spectrum of the FBG array. The versatility of the method is seen from the results of three different setups. That is the broadband lightsource with OSA, the broadband lightsource with spectrometer, and incoherent optical frequency domain reflectometry. The method can estimate the temperature changes with high accuracy and low root mean squared error (RMSE) for the setups under consideration. Finally, the method is shown to be capable of simultaneous estimation of temperature changes for 19 FBGs using the BLS setup with a maximum absolute error of <inline-formula> <tex-math notation="LaTeX">$6.64~\mathrm {\textbf {K}}$ </tex-math></inline-formula> and an RMSE of <inline-formula> <tex-math notation="LaTeX">$1.69~\mathrm {\textbf {K}}$ </tex-math></inline-formula> in only 1.74 ms/FBG.
Enhancement of Sampling Based DDoS Detecting System for SDN

Nowadays, Distributed Denial of Service (DDoS) attacks have gained increasing popularity and have been a major factor in a number of massive cyber-attacks. It could easily exhaust the computing and communicating resources of a victim within a short period of time. Therefore, we have to find the method to detect and prevent the DDoS attack. Recently, there have been some researches that provide the methods to resolve above problem, but it still gets some limitations such as low performance of detecting and preventing, scope of method, most of them just use on cloud server instead of network, and the reliability in the network. In this paper, we propose solutions for (1) handling multiple DDoS attacks from multiple IP address and (2) handling the suspicious attacks in the network. For the first solution, we assume that there are multiple attacks from many sources at a times, it should be handled to avoid the conflict when we setup the preventing rule to switches. In the other, there are many attacks traffic with the low volume and same destination address. Although the traffic at each node is not much, the traffic at the destination is much more. So it is hard to detect that suspicious traffic with the sampling based method at each node, our method reroute the traffic to another server and make the analysis to check it deeply.
Comparison of Physics Study Results at the Technical Universities in Different Countries

We consider the standardized Force Concept Inventory (FCI) test to be a very useful tool for determining the level of students' knowledge of Physics (Kinematics and Dynamics) and identifying the level of Newtonian mechanics understanding. The evaluation of conceptions before starting the students study at the university, that mean before beginning of semester, by using FCI pretest results can help lecturers to organize physics teaching processes better and it can help students to eliminate misconceptions. We deal with an evaluation of the freshmen’ conception in physics teaching and learning at the following technical universities in different countries: the Faculty of Electrical Engineering and Information Technology of University of Žilina in Slovakia and the Faculty of Electronics of Ryazan State Radio Engineering University named after V.F. Utkin in Russia.
Network-based synchronous control of memristive neural networks with denial of service attacks

This letter is concerned with the issue of global asymptotically synchronization of two neural networks with the denial of service attacks in the communication channels. The memristive neural networks which are with memory, adaptability and high parallel processing ability are considered. A master-slave framework with networks attacks are proposed. Compared with the prior literatures, the DoS attack, which can destroy the network communication, has been modeled by Bernoulli distributed variable. Some sufficient conditions are derived to ensure the synchronization of networks by using Lyapunov function approach. Finally, a numerical example is given to illustrate the effectiveness of obtained results.
Review of NAND Flash Information Erasure Based on Overwrite Technology

The lack or incorrect implementation of storage device privacy and secret-related information erasure methods will cause serious security risks for personal privacy and national construction security. Although information erasure based on overwrite for magnetic storage media is relatively mature, it is difficult for these mature technologies to be directly used in the currently widely used NAND flash memory due to its complex structure and other reasons, and its security is in doubt. The recycling industry of waste electronic equipment, mainly smart phones, is facing the problem of lack of information security clearance. In addition to the hidden dangers of information security, this situation also leads to the low recovery rate of waste electronic equipment, which leads to potential waste of resources and environmental pollution. In view of the above problems, this article reviews the information erasure for NAND flash based on the overwrite technology to support the in-depth research of the information erasure technology. At first, it introduces the definition and classification of information erasure. Then, the characteristics of NAND flash memory is analyzed. Thirdly, it summarizes the information erasure technology based on overwriting. And the research difficulties and application difficulties of information erasure technology for NAND flash memory is discussed and analyzed. Finally, conclusions and prospect the future research direction of overwrite-based information erasure technology for NAND flash are given out.
An Optimal Tracking Control Method with Q-learning for Discrete-time Linear Switched System

In this paper, a novel approach combining Q-learning method and two-stage optimization is proposed to solve linear quadratic tracking (LQT) problem for unknown discrete-time switched system. An augmented system consisting of switched subsystem and reference trajectory is built and Q-learning method is introduced to identify each subsystem without requiring any knowledge of augmented system state dynamics. Then, based on the optimal control laws for each subsystem obtained by Q-learning in advance, the optimal hybrid control law including switching mode and control input is obtained by the two-stage optimization framework. Furthermore, the optimality of the algorithm is proved. Finally, a simulation case is used to testify the effectiveness of the proposed algorithm.
HYDROLOGICAL CHARACTERISTIC OF THE DESERT PLANT PRUNUS MONGOLICA ON THE MONGOLIAN PLATEAU OF CHINA

Aims Prunus mongolica is a xeric shrub, a landscape plant, a water and soil conservation plant, and an ancient species in the desert and desert steppe of the Mongolian Plateau, China. It tolerates extreme aridity and poor soil, growing strongly in desert where annual rainfall is 200 mm and annual evaporation is 3 400-4 000 mm. Study of P. mongolica is useful for understanding plant succession on the Mongolian Plateau and for sustaining and restoring the local ecosystem. Methods The Abbe refractometer method, the natural dehydration method and the PV technique were used to analyze the hydrological characteristic of P. mongolica. Important findings The relative water content, saturation water content, critical saturation deficit and water potential of P. mongolica seedling leaves were 69%, 117%, 48% and -0.85 MPa, respectively. The relative water content, critical water content and leaf water potential of P. mongolica seedlings exposed to drought stress decreased to 48%, 39% and -1.97 MPa, respectively, but the saturation water content and the ratio of bound water/free water increased to 187% and 11.94, respectively. The natural dehydration experiment indicated that P. mongolica seedlings reached equilibrium after 102 h in normal condition, but the dehydration rate decreased and the dehydration equilibrium time was delayed after 3 d of drought stress. This proved that the water-holding ability of P. mongolica increased after drought stress. In comparing the dehydration rates of the xerophytic P. mongolica and the mesophytic P. pedunculata, we discovered that the dehydration tolerance capacity of P. mongolica was higher than that of P. pedunculata. Analysis of PV curve showed that osmotic potential (Ψπ100) of water saturation and osmotic potential (Ψπ0) at turgor loss point of P. mongolica seedling were -2.49 MPa and -3.11 MPa, respectively. The fact that the difference of Ψπ100 and Ψπ0 was very large and the values very low showed that P. mongolica had strong ability to regulate turgor pressure. The ratio of bound water/free water was relatively high (7.76), and the modulus elasticity of cell wall was 4.18 MPa. The low leaf water potential and low osmotic potential of P. mongolica were beneficial for absorption of water from soil depths. Our results indicated that P. mongolica was a strong xerophyte in terms of hydrology. Its high ratio of bound water and free water, low osmotic potential at saturation water content, osmotic potential at turgor loss point and low modulus elasticity of cell wall were the physiological fundamentals of its dehydration tolerance.
Study on Particle Filter Based Terrain Aided Navigation under the Ultra-low Resolution Map of the Arctic

The ultra-low resolution underwater terrain map of the Arctic region reduces the positioning and navigation ability of the underwater vehicle relying on terrain aided navigation. To study the navigation ability of AUVs under the ultra-low resolution terrain map, a TAN(Terrain-Aided Navigation) system based on the classical PF(particle filter) is constructed. Based on the TAN system, the effects of various parameters on the filter navigation accuracy are studied. By comparative analysis, the range of reasonable parameter set is obtained. The simulation results show that the PF-TAN system has strong robustness, and by setting parameters of PF reasonably, the AUV will perform great ability on path tracking under an ultra-low resolution map.
Rotorcraft Aerial Manipulator Control Based on Improved ADRC

A rotorcraft aerial manipulator (RAM) system is composed of a rotorcraft unmanned aerial vehicle (RUAV) and a multi degree-of-freedom (DOF) manipulator. There exists strong coupling between the RUAV and the manipulator. Disturbances from the manipulator and external environment have great influence on the robustness of the UAV controller. To deal with this problem, an improved active disturbance rejection control (ADRC) strategy based on dynamic center of mass (COM) compensation is proposed. Finally, flying and grasping experiments are conducted, and the experimental results validate the effectiveness of the proposed control strategy and the reliability of our aerial manipulator system.