Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-assembly fabrication of 3D flower-like ZnO hierarchical nanostructures and their gas sensing properties

In this paper, three-dimensional flower-like ZnO hierarchical nanostructures were fabricated from the thermal-decomposition of 3D zinc hydroxide carbonate precursor, which was synthesized by a urea hydrothermal method with block copolymer F127 (EO106-PO70-EO106) as the morphology director. XRD, IR, UV-vis, SEM, TEM, TG and N2 adsorption–desorption isotherms have been employed to characterize the products. The influences of synthesis parameters such as reaction time, the type of zinc sources, and species concentration on the morphologies of the products were systematically studied. It was found that the reaction time played a key role in determining the final morphology of porous ZnO. On the basis of experimental results, a possible formation mechanism of the 3D flower-like ZnO hierarchical nanostructures was discussed. More importantly, the gas sensing tests indicated that the sensor made from porous ZnO hierarchical nanostructures exhibited better gas sensing properties to n-butanol compared with the sensor based on the commercial ZnO nanoparticles. The enhancement in gas sensing properties was attributed to their unique 3D hierarchical nanostructures, high surface areas, and greater number of surface active sites.
A novel combined in-oil PD sensor with AE and UHF methods for PD detection in transformer, part 2: experimental verification

A combined in-oil partial discharges (PD) sensor with acoustical emission (AE) and ultra high frequency (UHF) methods for PD detection in transformer is designed and developed, according to the disadvantages and advantages of electromagnetic and acoustic PD measurements shown in IEC TS 62478 Edition 1.0 2016–08. It is formed by placing an AE sensor inside the end part of UHF probe to fit for the transformer oil valve, which works independently of each other with two output channels for AE and UHF PD signals measurements. Test results of a 110 kV transformer with built-in tip, suspension and surface insulation defects show that the combined UHF/AE in-oil PD sensor designed and developed can effectively detect the electromagnetic wave and ultrasonic signal with a specific time difference $\Delta\mathbf{t}$ generated by the weak discharges in the transformer oil, which provides a new mixed electromagnetic and acoustic method for PD measurements of transformer.
A robot control platform for motor impaired people

Brain-machine interfaces (BMI) open new opportunities to control robotic devices as they provide the feasibility to translate brain signals into commands. Severely motor impaired people who have lost muscle control could benefit from this technique to control assistive devices, which support them in daily life. However, non-invasive BMIs can distinguish only a few different commands with relatively high error rates, which makes the asynchronous control of a robot with multiple degrees of freedom challenging. Here, we introduce a novel robotic grasping system, which combines scene recognition techniques and autonomous path planning with user interaction instantiated by a hybrid control system based on the electroencephalogram and the electrooculogram. The results show that healthy subjects can reliably perform a grasp-and-place task, arranging four objects at defined positions within 133-331s (193.6 ±61.5s), while they require only a few corrections. Our robot control platform proved to work solely with electrophysiological control signals and thus, constitutes a basis to perform various robot actions initiated by motor-impaired people.
Automatic Smart UAV search of lost floating target in ocean environment based on high dense clustering

In this paper, we present a new rescuing mission based on non-stationary lost target searching methodology exploiting the high dense zone extraction. Our searching method is based on the determination of the critical searching zones (by density) relying on clustering techniques applied on particle trajectories history. Starting from the extracted critical high dense zones, we defined a circular searching location around each critical zone to be used by Multi Unmanned Aerial Vehicle (UAV) for a parallel searching mission. We conducted an experimental study to evaluate our searching methodology by using MeanShift and DBscan clustering techniques covering 68517km2 surface area. We concluded that Meanshift performs better than DBscan by obtaining 51 % (of 100 different searching mission) are less or equal to 10 KM as closest UAV distance to the lost target with an average of 70 minutes (characterizing the searching delay) which could be considered as a good result.
Immune Plasma Algorithm: A Novel Meta-Heuristic for Optimization Problems

The recent global health crisis also known as the COVID-19 or coronavirus pandemic has attracted the researchers’ attentions to a treatment approach called immune plasma or convalescent plasma once more again. The main idea lying behind the immune plasma treatment is transferring the antibody rich part of the blood taken from the patients who are recovered previously to the critical individuals and its efficiency has been proven by successfully using against great influenza of 1918, H1N1 flu, MERS, SARS and Ebola. In this study, we modeled the mentioned treatment approach and introduced a new meta-heuristic called Immune Plasma (IP) algorithm. The performance of the IP algorithm was investigated in detail and then compared with some of the classical and state-of-art meta-heuristics by solving a set of numerical benchmark problems. Moreover, the capabilities of the IP algorithm were also analyzed over complex engineering optimization problems related with the noise minimization of the electro-encephalography signal measurements. The results of the experimental studies showed that the IP algorithm is capable of obtaining better solutions for the vast majority of the test problems compared to other commonly used meta-heuristic algorithms.
A Data-driven Methodology for Transient Stability Assessment Based on Broad Learning System

This paper proposes a data-driven methodology for transient stability assessment (TSA) based on constructing a transient stability boundary (TSB). Without stacking the network layers, the TSB construction algorithm makes a broad expansion in the neural nodes thereby forming a clear structure that can be theoretically analysed. While preserving a high accuracy and generalization ability, the TSB expression is clear, differentiable and therefore applicable to dynamic security constrained problems. Furthermore, a transfer learning strategy (TLS) is employed to build TSBs from a limited number of samples in a time-saving way. The possibilities of the developed method are tested via case study that uses the IEEE 39-bus test system. The case study confirmed that the introduced algorithm is highly precise and insensitive to the number of available samples/parameters. This indicates that the proposed method is effective, robust and that as such it may serve as a valuable tool of online TSA.
Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders

We present a model of the self-calibration of active binocular vision comprising the simultaneous learning of visual representations, vergence, and pursuit eye movements. The model follows the principle of Active Efficient Coding (AEC), a recent extension of the classic Efficient Coding Hypothesis to active perception. In contrast to previous AEC models, the present model uses deep autoencoders to learn sensory representations. We also propose a new formulation of the intrinsic motivation signal that guides the learning of behavior. We demonstrate the performance of the model in simulations.
Learning Bipartite Graph Matching for Robust Visual Localization

2D-3D matching is an essential step for visual localization, where the accuracy of the camera pose is mainly determined by the quality of 2D-3D correspondences. The matching is typically achieved by the nearest neighbor search of local features. Many existing works have shown impressive results on both the efficiency and accuracy. Recently emerged learning-based features further improve the robustness compared to the traditional hand-crafted ones. However, it is still hard to establish enough correct matches in challenging scenes with illumination changes or repetitive patterns due to the intrinsic local properties of local features. In this work, we propose a novel method to deal with 2D-3D matching in a very robust way. We first establish as many potential correct matches as possible using the local similarity. Then we construct a bipartite graph and use a deep neural network, referred to as Bipartite Graph Network (BGNet), to extract the global geometric information. The network predicts the likelihood of being an inlier for each edge and outputs the globally optimal one-to-one correspondences with a Hungarian pooling layer. The experiments show that the proposed method can find more correct matches and improves localization on both the robustness and accuracy. The results on multiple visual localization datasets are obviously better than the existing state-of-the-arts, which demonstrates the effectiveness of the proposed method.
Forward and Inverse Approaches to Model Calibration for Uncertain Data *

This article proposes a framework for calibrating parametric models according to data subject to uncertainty. Data uncertainty might be caused by a poor metrology system, measurement noise, model-form uncertainty or by the inability to directly measure the inputs and/or outputs of interest. The formulations developed, called Forward Maximum Likelihood (FML) and Inverse Maximum Likelihood (IML), are applicable to datasets with and without uncertainty. The FML approach performs the calibration in the space of the model’s output thereby requiring repeated model simulations. Conversely, the IML approach leverages an ensemble of solutions to an inverse problem in order to perform the calibration in the space of the model’s parameters. The potential loss of performance incurred by the IML approach is often justified by a sizable reduction in computational cost. In addition, we use chance-constrained optimization to eliminate the effects of outliers on the calibrated model. This practice yields a model that increases the likelihood of most of the data in exchange for a reduction in the likelihood of a few of the worst-performing data points. Metrics for evaluating the benefits and risks of outlier elimination are also presented.
Fabrication and Optical Properties of Plasmonic Nanocomposite Structures

In this work, we demonstrate a single-step laser-assisted fabrication of thin plasmonic nanocomposite thin films. The common processes taking place in Ag:Au:TiO $_{\mathbf{2}}$ thin nanocomposite films under UV laser irradiation are discussed. We describe the main nanostructure formation mechanisms, as well as their effect on the optical properties of the films. The obtained results pave the way for the implementation of such materials in modern photonics and optoelectronics devices, photocatalytic systems, sensing, and SERS due to their unique and well-controlled optical properties.
A Static Gait Generation for Quadruped Robots with Optimized Walking Speed*

Traversing at a high speed while maintaining stability is important for the application of quadruped robots. Prior works mainly concentrated on optimizing the stability margin of quadruped robots when walking through a variety of terrains. However, the problem of improving quadruped robots’ walking velocity with static gait is less concerned in their works. In this paper, the static gait planning problem is considered under the assumption that a set of irregular footholds on the rough terrain is given, and two approaches are proposed to improve the walking speed. The first one is a distance optimization algorithm, which can minimize the moving distance of the center of gravity (COG) in the stance phases based on the stability and the kinematic constraint. The other is a velocity optimization algorithm, which enables the body and the feet to move at the highest velocity with the joint angular velocity limit. The joint application of these two optimization algorithms significantly improves the walking speed of the quadruped robot. Simulation results in V-REP are presented to demonstrate the effectiveness of the proposed approaches in improving the walking speed. Compared with the traditional gait planning techniques, one that moves the robot with the optimal stability margin, and the other that moves the robot without optimizing the velocity, our algorithms increase the average walking velocity by 81.6% and 32.8%, respectively.
A Multi-Criteria Contrast Enhancement Evaluation Measure using Wavelet Decomposition

An effective contrast enhancement method should not only improve the perceptual quality of an image but should also avoid adding any artifacts or affecting naturalness of images. This makes Contrast Enhancement Evaluation (CEE) a challenging task in the sense that both the improvement in image quality and unwanted side-effects need to be checked for. Currently, there is no single CEE metric that works well for all kinds of enhancement criteria. In this paper, we propose a new Multi-Criteria CEE (MCCEE) measure which combines different metrics effectively to give a single quality score. In order to fully exploit the potential of these metrics, we have further proposed to apply them on the decomposed image using wavelet transform. This new metric has been tested on two natural image contrast enhancement databases as well as on medical Computed Tomography (CT) images. The results show a substantial improvement as compared to the existing evaluation metrics. The code for the metric is available at: https://github.com/zakopz/MCCEE-Contrast-Enhancement-Metric
Several developments in learning control of quantum systems

This paper summarizes several recent achievements in the area of learning control of quantum systems and draw several new directions for future research. Three learning algorithms including gradient method, differential evolution and reinforcement learning are introduced for quantum control. Quantum state control in closed and open quantum systems is analyzed, where gradient method and differential evolution are employed, respectively. The approach of deep reinforcement learning for quantum gate control is introduced, and a sampling-based learning control is illustrated for robust control of quantum gates.
Main Insulation Optimization of DC Wall Bushing Based On Field Grading Material

Electrical bushings, an important power system component, are critical to the reliability of power systems. This study analyzes the main insulation design of a new type of adaptive DC wall bushing. The main insulation design principles, the ratio of the uniform-voltage layer to the leakage-current-limiting layer, and the principles of nonlinear material parameter control are discussed. Simulations and analyses of finite element models are conducted to optimize the main insulation. The results can be used as guidelines for the design and manufacture of nonlinear material DC bushings.
The Influence of Down Conductor Torsion on the Mechanical Properties of the Main Transformer End of Substation

Four-bundle down conductor is prone to additional torsion in the installation process of it, which may affect the safety and stability of the end of bushing. In this paper, the down conductor of the main transformer bushing is taken as the research object, and ABAQUS as the platform. A fined finite element model is established to analyze the mechanical influence of the twisting method and twisting angle of the down conductor on the end of main transformer bushing. The results suggest that the stress of the bushing terminal and the terminal board increases non-linearly with the increase of the down conductor around its centerline or overall torsion; When the torsion angle is greater than 5°, the stress of the parts increases greatly, and the torsion angle should be controlled. The results of this paper lay the foundation for further optimizing the installation method of the main transformer bushing and improving the installation accuracy during the construction installation process.
Hybrid C18-Bonded Silica Phases: Application in Acetaminophen Adsorption

Hybrid silicas bearing octadecylsilane ligands and Lewis acid centres were synthesized by grafting and sol-gel methods. In grafting, Cp2ZrCl2 was employed, while ZrCl4 was used in the sol-gel method. A very low grafted metal content (ca. 0.1 wt% Zr/SiO2) was achieved, with the carbon percentage being dependent on the grafting time. However, a much higher Zr content (up to 20.5 wt% Zr/SiO2) and carbon content (up to 49.3%) was obtained by the sol-gel method. The surface concentration of alkyl groups varied between 0.2 and 2.8 μmol/m2. The solids were characterized by infrared spectroscopy (IR), ultraviolet diffuse reflectance spectroscopy (DRS), atomic force microscopy (AFM), scanning electron microscopy (SEM) and nitrogen adsorption. The behaviour of the phases towards the adsorption of acetaminophen was evaluated. Two phases showed a higher retention capacity than that exhibited by the commercial octadecyl phase. Recovery results remained between 70% and 100%.
An Energy-aware Greedy Heuristic for Multi-objective Optimization in Fog-Cloud Computing System

As an complement of cloud computing, fog computing provides computing services with closer geographic distance and focuses on distributed computing. In this paper, we consider the bi-objective task scheduling problem with heterogeneous resources in a fog-cloud computing system. There are two minimization objectives: energy consumption and delay. We formulate a workload allocation problem model involving fog devices (FDs) and the cloud servers (CSs). The computing resources are heterogeneous on the energy consumption, processing capability and delay. An energy-aware greedy heuristic algorithm (EG) is developed to search for Pareto Front solutions. For the problem under discussed, Experimental results indicate that the proposal algorithm is effective and robust compared with the comparison algorithm.
Learning Co-Occurrence of Laughter and Topics in Conversational Interactions

This paper describes experiments to learn laughter co-occurrences with dialogue contributions. The dialogue data belongs to the special type of First Encounter Dialogues where the interlocutors meet each other for the first time and where laughter mainly functions as a sign of politeness or relief of embarrassment. The earlier studies have shown that there is a correlation between the speaker’s utterance content (topic) and non-verbal communication (laughter and body movement) while in this paper we seek to learn the correlations via a neural model. The results show that there seems to be a weak correlation in our data.
Extremal Region Analysis based Deep Learning Framework for Defects Detection

A maximal stable extremely region (MSER) analysis based convolutional neural networks (CNN) for unified detects detection framework is proposed in this paper. Our proposed framework utilizes the generality and stability of MSER to generate the desired defects candidates. Then a specific trained binary CNN classifier is adopted over the defects candidates and produces the final determined defects. Experiments on various defects dataset over different categories have demonstrated the effectiveness of our proposed framework. Beside, we can adjust the parameter settings in MSER to satisfy different requests over various industries (high precision, high recall, etc).
CNN-based Superquadric Parameter Prediction via RGBD Image

—Superquadrics represent various types of primitive shapes in a single equation with several parameters. Su-perquadric parameters of an object is estimated from 3D point cloud. However, the task of superquadric parameter estimation is computationally expensive and it is unacceptable for robot applications. Therefore, the concept of our research is to predict superquadric parameters directly from an image. We apply regression Convolutional Neural Network (CNN) that predicts parameters from images to superquadric parameter estimation. Moreover, we use not only depth but also RGB image to predict superquadric parameters for improve the accuracy of prediction. Experiments show that proposed CNN with the input of RGB-D image predicts more accurate parameters than baseline methods.
Corporate bankruptcy forecast using RealAdaBoost

Critical elements in the process of predicting corporate bankruptcies are selection of financial indicators and construction of a forecast model. Many traditional methods implement these two tasks separately, and do not guarantee the optimality on the whole process. This study uses the RealAdaBoost algorithm so that the selection of indicators and the construction of a model can be realized within a single coherent framework in the process of forecasting bankruptcies. RealAdaBoost iteratively chooses a financial indicator and the weight of learning samples that are mistakenly classified by the indicator increases in the next iteration step, and because of this, a combination of indicators that complement each other is naturally extracted. Setting financial ratios generated from the balance sheets and profit-and-loss statements of 150 failed firms and 150 continuing firms as candidates of indicators, our proposed method selects the following four ratios in most cases: 1) Net income before taxes and other adjustments / Current liabilities, 2) Total investments and other assets / Capital stock, 3) Total liabilities / Fixed assets, and 4) Owned capital / Capital stock. Discrimination of RealAdaBoost with four financial ratios shows an identification rate of 0.893 under the leave-one-out cross validation.
A Robust Harmonic Elimination Method for Three Phase Grid-Connected Inverters Using a Digital Lock-in Amplifier and FLL

The output quality of the grid connected inverters has become a serious concern in this era due to the increase in use of distributed generation and renewable energy sources. Though the THD of the inverter is limited less than 5% by the standards such as IEEE-519, it is very difficult to achieve it over the wide load range. The poor quality of the inverter output is mainly attributed to the incomplete harmonic compensation caused by the inaccurate harmonic detection. In this paper a robust harmonic elimination method for three phase grid-connected inverters using a Digital Lock-in Amplifier (DLA) and DLA based Frequency Locked Loop (FLL) is proposed. Due to the outstanding characteristics of the DLA the harmonics can be detected with high accuracy and compensated completely. In addition the grid frequency variation is detected by the DLA based FLL and used to detect the harmonic accurately under the grid frequency variation. The validity of the proposed method is proved by both PSIM simulations and experiments with a 10KW grid connected inverter. THD of the inverter output is 4.62% at 10% load.