Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Rain Characteristics of Composite Insulators

The rain flashover of insulators in rainy weather is an important factor which affects the external insulation of electric power system. In view of rain flashover problem of insulators, this paper carried out a lot of artificial rain test under different rainfall conditions for suspension composite insulators with different parameters. Selected the number of water column, the number of water droplets and the proportion of the water column to the large sheds spacing as the rain characteristic variables. And the rain characteristics of insulators were studied. Experimental results indicated that rainfall had a significant influence on the rain characteristic variables of insulator. When the rainfall increased, the number of water columns, the number of water droplets and the proportion of the water column to the large sheds spacing increased, and the possibility of bridging between the water column and the water droplets in the two large sheds increased, and the more easily the insulators were flashover. Shed configuration has effect on the rain characteristic variables of insulator. Under the same conditions of rainfall, the larger the rain area of the shed, the more water columns and water droplets between the shed units. The increase in the shed spacing can reduce the possibility of bridging and improve the utilization ratio of leakage distance.
Geometric Optimization of a Large Scale CDPR Operating on a Building Facade

This paper deals with the optimization of the geometry of a Cable-Driven Parallel Robot (CDPR) dedicated to large-scale construction applications. Since the maximum cable tension is a critical parameter in the design of the CDPR components, the geometry of the CDPR is optimized by minimizing the lowest maximum cable tension that ensures the validity of wrench-feasibility constraints. The geometric design procedure used in this paper consists of two phases, the CDPR cable connections is selected in the first phase followed by a second phase where the geometric parameters are optimized. The result of this procedure is an original fully-constrained CDPR geometry.
A Universal Gripper Using Optical Sensing to Acquire Tactile Information and Membrane Deformation

The universal gripper has attracted attention due to its simple structure and advanced grasping ability for irregularly shaped objects. In this research, we propose a novel design for a granular-jamming-based gripper which uses a transparent filling and a semi-transparent membrane to allow optical sensing to detect both deformation of the membrane and the object being grasped. By adjusting the refractive index of an oil mixture to the refractive index of the granular bodies, we produced a fully transparent filling that allows the use of a camera inside the universal gripper. In this paper, we present the materials and development of our prototype, and describe the experimental confirmation of the prototype's performance. We showed that our prototype was able to grasp cylindrical and rectangular objects between 10 to 70 mm length while also tracking the deformation of the gripper.
Reward Function Evaluation in a Reinforcement Learning Approach for Energy Management

In the past decade, the energy needs in WBANs have increased due to more information to be processed, more data to be transmitted and longer operational periods. On the other hand, battery technologies have not improved fast enough to cope with these needs. Thus, miniaturized energy harvesting technologies are increasingly used to complement the batteries in WBANs. However, this brings uncertainties in the system since the harvested energy varies a lot during the node operation. It has been shown that reinforcement learning algorithms can be used to manage the energy in the nodes since they are able to make decisions under uncertainty. But the efficiency of these algorithms depends on their reward function. In this paper we explore different reward functions and seek to identify the most suitable variables to use in such functions to obtain the expected behavior. Experimental results with four different reward functions illustrate how the choice thereof impacts the energy consumption of the nodes.
Research on the Calculation Method for the Parameters of the Simplified Schavemaker AC Arc Model

The arc fault occurs frequently in aircrafts due to the poor working environment of the cable which needs to be diagnosed and excluded promptly. Currently, the AC fault characteristics studies and the detection algorithms studies are dependent on the experimental data. However, the AC arc fault experimental data has randomness, so it is necessary to do some research on AC arc modeling. This paper researches AC arc modeling based on the simplified Schavemaker model. Since model parameters' values vary with different working conditions and parameter calculation methods are inaccurate, complex to operate and dependent on experimental data, this paper firstly proposes a calculation method based on experimental data which is easy to operate with high accuracy. Then, this paper proposes another calculation method based on neural network using the first calculation method to obtain training samples. Next, the neural network between the model parameters and the working conditions is constructed. Finally, the accuracy of the proposed calculating method for the model's parameters and the accuracy of the AC arc model are verified by comparing the arc characteristics' values of the experimental data and the simulation results.
Human-Robot-Cooperation Real Time Robot Path Planning for Dynamic HRC-Applications

Human-Robot shared workspace is a dynamic and unstructured environment. In such environment, pre-programmed robot paths might cause collisions or production disruptions. In order to solve this problem, motion planning framework has been proposed that adapts the robot's movement (path and speed)according to the human movement or any other dynamic obstacles in real time. Firstly, it defines the safety distance between robot and human or other obstacles. During run-time the 3D-Smart-Sensors capture the current position of human and other dynamic/static obstacles in human-robot shared workspace. The proposed framework plans and optimizes collision free robot trajectories with consideration for safety distance, path length and executing time. Once a new optimal trajectory is found, the framework controls the robot to adjust its movement paths. Moreover, the proposed framework can adjust the robot velocity based on the 3D-Zone Model of human-robot shared workspace. Therefore, the robot can reach its goal quickly and safely in a dynamic and unstructured environment.
State Recovery for Coarse-Grain TMR Designs in FPGAs Using Partial Reconfiguration

The operation of field-programmable gate arrays (FPGAs) in harsh environments like space entails the need for suitable fault-tolerance techniques of which Triple-Modular Redundancy (TMR) is most commonly deployed. While TMR is undoubtedly effective in masking faults, state recovery remains a problematic issue: Fine-grain TMR allows safe recovery, but incurs prohibitive area and performance penalties. In contrast, coarse-grain TMR has little overhead, but cannot safely provide recovery without roll-back or reset. We use the dynamic reconfiguration feature of modern FPGAs to augment an initially coarse-grain TMR with the ability of temporarily loading a fine-grain TMR design for forward-state-recovery. Therefore, we can seamlessly resume correct (fully redundant) operation in case of data-as well as configuration faults that occurred in the FPGA. As a proof of concept, the paper presents a showcase design and discusses distinctive properties of this new approach.
Total Variation and Signature-Based Regularizations on Coupled Nonnegative Matrix Factorization for Data Fusion

As an effective approach to gain the high-spatial-resolution hyperspectral images, data fusion is usually adopted to enhance the spatial resolution of hyperspectral images by the spatial information of multispectral images. In this paper, in order to remove the ill-posedness of well-known coupled non-negative matrix factorization, we formulate a well-posed fusion problem by incorporating total variation and signature-based regularizations for image smoothing and high-fidelity signature reconstruction. Then, the problem can be decoupled into two convex subproblems, which yield closed-form solutions separately by the alternating direction method of multipliers algorithms. Due to the large sizes of the problems, a few of constructed matrices and tensor operations are employed to simplify the expressions for reducing the computational complexities. Simulation and experimental results not only demonstrate that the performance of the proposed fusion algorithm is much better than that of state-of-the-art methods but also show that the total variation and signature-based regularizers are of paramount importance in yielding the high-spatial-resolution hyperspectral images.
Comprehensive Power Quality Control of DSTATCOM based on Electricity Price Penalty Factor

Due to the diversity of the user on-site power quality and the rated power limitation of the distribution static synchronous compensator (D-STATCOM), it might be impossible for the power quality index of users to fully meet related requirements. Therefore, in order to customize power quality, this paper proposes a comprehensive evaluation model of power quality based on the electricity price penalty factor. Limited by the capacity, an optimal compensation algorithm with the goal of minimizing the penalty for the user’s power quality is introduced, and solved by Lagrange multiplier method. The optimal algorithm presents different weighting coefficients in the cost index. The simulation results verify the effectiveness of the proposed optimal compensation strategy.
Cost of Transport Estimation for Legged Robot Based on Terrain Features Inference from Aerial Scan

The effectiveness of the robot locomotion can be measured using the cost of transport (CoT) which represents the amount of energy that is needed for traversing from one place to another. Terrains excerpt different mechanical properties when crawled by a multi-legged robot, and thus different values of the CoT. It is therefore desirable to estimate the CoT in advance and plan the robot motion accordingly. However, the CoT might not be known prior the robot deployment, e.g., in extraterrestrial missions; hence, a robot has to learn different terrains as it crawls through the environment incrementally. In this work, we focus on estimating the CoT from visual and geometrical data of the crawled terrain. A thorough analysis of different terrain descriptors within the context of incremental learning is presented to select the best performing approach. We report on the achieved results and experimental verification of the selected approaches with a real hexapod robot crawling over six different terrains.
Depth Estimation of Optically Transparent Microrobots Using Convolutional and Recurrent Neural Networks

Estimating the three-dimensional (3D) position of microrobots is necessary in order to develop closed-loop control techniques and to improve the user's 3D perception in the micro-scale. This paper describes a depth estimation method based on supervised learning for optically transparent microrobots of known geometry. The proposed methodology uses Convolutional Neural Networks (CNNs) combined with a Recurrent Network, in particular a Long Short-Term Memory (LSTM) cell for depth regression. The proposed depth regression model is independent of the 3D orientation of the microrobot and is robust to varying illumination levels while it uses learned data-specific features. The model is trained and validated using microscope images and ground truth data generated from 3D-printed microrobots imaged in an Optical Tweezers (OT) setup. The validation results demonstrate that the proposed trained model can reconstruct the depth of the microrobot independently of its 3D orientation with submicron accuracy for the test set.
DREGON: Dataset and Methods for UAV-Embedded Sound Source Localization

This paper introduces DREGON, a novel publicly-available dataset that aims at pushing research in sound source localization using a microphone array embedded in an unmanned aerial vehicle (UAV). The dataset contains both clean and noisy in-flight audio recordings continuously annotated with the 3D position of the target sound source using an accurate motion capture system. In addition, various signals of interests are available such as the rotational speed of individual rotors and inertial measurements at all time. Besides introducing the dataset, this paper sheds light on the specific properties, challenges and opportunities brought by the emerging task of UAV-embedded sound source localization. Several baseline methods are evaluated and compared on the dataset, with real-time applicability in mind. Very promising results are obtained for the localization of a broad-band source in loud noise conditions, while speech localization remains a challenge under extreme noise levels.
Assessing the Impact of Analog-to-digital Converter Resolution on the Phase Difference Measurement Error in a Digital Receiver

The article studies the impact of reducing the resolution of an analog-to-digital converter of a digital receiver on the phase difference measurement error by the method of statistical modeling. Dependencies of the phase difference measurement error on the ratio of signal-carrier frequency and sampling frequency are shown. Dependencies of frequency-band maximum and frequency-band average phase difference measurement error on signal-to-noise ratio are provided. The results are given on modeling for analog-to-digital converter resolution of 8, 4, 2, 1 bits at the change of signal-carrier frequency within the range of 0.125 to 0.375 of sampling frequency, and the change of signal-to-noise ratio from −5 to 50 dB. The main peculiarities of the phase difference measurement error in case of small resolution of an analog-to-digital converter of a digital receiver are outlined, and recommendations on reducing it are worded.
A Data Representation Based on Block Wavelet Transform in Modified Form

The main idea of wavelet transform is to analyze a function at different scales. The transform results depend on how much it matches the function being analyzed. Wavelets at different resolutions produce different results. It's most important for the transform in block form, when execution is based on a source data fragments. Execution variants of block wavelet transform are considered. There are forms depending on the processing direction and invariant to it. A mathematical description is proposed in matrix form. The division of space-frequency plane for various processing options is presented for a one-dimensional signal. All main variants are considered. The transform results for the corresponding forms are shown.
A Multi-time Scale Coordinated Real Time Dispatch Model of CCHP-based Microgrid

Conventional real time dispatch models are usually based on the day ahead scheduling. If the day ahead prediction is far from accuracy, the real time dispatch may fail. To improve it, this paper proposes a multi-time scale coordinated real time dispatch model of CCHP-based microgrids. The proposed model is conducted based on the boundary information offered by a rolling schedule with a larger time resolution. To mitigate the influence of the cumulative prediction error, a feedback model is also integrated into the model. The main advantage of this model is that the state of charge (SOC) of the energy storage systems (ESSs) can be tightly controlled while the total cost can also be reduced. The real time dispatch model is converted into a mixed integer second order core programming (MISOCP) problem, which could be solved by mature algorithm. The methodology is tested on a modified IEEE 33-bus distribution system. Simulation results indicate that the proposed models are capable to mitigate the influence of prediction error. In addition, the SOC of ESS is controlled as expected, which is useful especially for energy reserve for the next scheduling period.
Characteristics of Negative Corona Discharge in Air at Various Gaps

The length of the air gap in a negative corona discharge, which affects many corresponding parameters in the discharge process, is of vital importance for the physical investigations. A hydrodynamic drift–diffusion approximation model considering photoionization utilizing the finite-element method has been used in needle-to-plane air gaps. Trichel pulses, pulse frequency, electron, and electric field distribution, and surface current density distribution on the anode plate were presented to study the effect of the gap length. An approximate exponential relationship was found to exist between the corona current and the gap length, and also a linear function was found between the pulse frequency and the gap length. The analysis shows that the characteristics of the Trichel pulse are strongly dominated by the length of the air gap.
Near-Field Gain Measurements Using the Distance Averaging Method: Linear Scanning Versus Matrix Scanning

Antenna gain can be measured in a multipath site by moving the antenna under test away from the probe antenna at different distances, and by assessing a normalized transfer function as an average figure over the entire data set. In an earlier work, we provided a statistical explanation to the reduction of the multipath effects. Another possible explanation is based on the synthetic aperture principle, by assimilating the set of positions of the probe antenna to an antenna array. In this paper, we compare linear scanning to matrix scanning in order to draw optimal choice criteria for the grid of measuring positions. Measurements were performed on a Vivaldi antenna, in the near-field zone.
Adaptive Fault Diagnosis Observer-Based Fault Tolerant Control Design for a Jumbo Jet Transport Aircraft

An adaptive fault diagnosis observer-based fault tolerant control method is proposed for the actuator faults of a jumbo jet transport aircraft. This method not only can simplify design procedures, but also can guarantee good performance when part of the actuators fails. Considering the linear motion characteristics of flight mode for the jumbo jet transport aircraft, a linear model is firstly established in state space using the small perturbation method. Then, a novel augmented fault diagnosis observer design is introduced to relax the strictly positive real (SPR) requirement of the system. Meanwhile, considering their performances simultaneously, a dynamical output feedback fault tolerant control design, which can be designed separately from the fault diagnosis observer, is proposed. Finally, the effectiveness of the proposed method is demonstrated through its application to the simulated longitudinal motion of the Boeing 747-100/200.
On the Kinematics of Wheeled Motion Control of a Hybrid Wheeled-Legged CENTAURO robot

Legged-wheeled robots combine the advantages of efficient wheeled mobility with the adaptability to real-world terrains through the legged locomotion. Due to this hybrid mobility skill, they can excel in many application scenarios where other mobile platforms are not suitable for. However, their versatile mobility increases the number of constraints in their motion control where both the properties of legged and wheeled systems need to be considered. Relevant schemes for legged-wheeled platforms so far have been developed exploiting separate motion control of the wheeled and legged functionalities. This paper discusses the legged-wheeled motion kinematics without constraining the camber angles of the wheels, and it proposes a first-order inverse kinematics scheme that stabilizes the legged-wheeled system in the wheeled motion. Furthermore, the work adopts a floating base model that allows to easily incorporate the legged motion to the scheme. The developed controller is tested in simulation and experiments on a legged-wheeled centaur-like robot - CENTAURO.
Integrating a Security Operations Centre with an Organization’s Existing Procedures, Policies and Information Technology Systems

A Cybersecurity Operation Centre (SOC) is a centralized hub for network event monitoring and incident response. SOCs are critical when determining an organization’s cybersecurity posture because they can be used to detect, analyze and report on various malicious activities. For most organizations, a SOC is not part of the initial design and implementation of the Information Technology (IT) environment but rather an afterthought. As a result, it is not natively a plug and play component therefore there are integration challenges when a SOC is introduced into an organization. A SOC is an independent hub that needs to be integrated with existing procedures, policies and IT systems of an organization such as the service desk, ticket logging system, reporting, etc. This paper discussed the challenges of integrating a newly developed SOC to an organization’s existing IT environment. Firstly, the paper begins by looking at what data sources should be incorporated into the Security Information and Event Management (SIEM) such as which host machines, servers, network end points, software, applications, webservers, etc. for security posture monitoring. That is, which systems need to be monitored first and the order by which the rest of the systems follow. Secondly the paper also describes how to integrate the organization’s ticket logging system with the SOC SIEM. That is how the cybersecurity related incidents should be logged by both analysts and nontechnical employees of an organization. Also, the priority matrix for incident types and notifications of incidents. Thirdly the paper looks at how to communicate awareness campaigns from the SOC and also how to report on incidents that are found inside the SOC. Lastly the paper looks at how to show value for the large investments that are poured into designing, building and running an SOC.
Towards Event-Driven Object Detection with Off-the-Shelf Deep Learning

Event cameras are an emerging technology in computer vision, offering extremely low latency and bandwidth, as well as a high temporal resolution and dynamic range. Inherent data compression is achieved as pixel data is only produced by contrast changes at the edges of moving objects. However, current trends in state-of-the-art visual algorithms rely on deep-learning with networks designed to process colour and intensity information contained in dense arrays, but are notoriously computationally heavy. While the combination of these visual technologies could lead to fast, efficient, and accurate detection and recognition algorithms, it is uncertain whether the compressed event-camera data actually contain the required information for these techniques to discriminate between objects and a cluttered background. This paper presents a pilot study in which off-the-shelf deep-learning is applied to visual events for object detection on the iCub robotic platform, and analyses the impact of temporal integration of the event data. We also present a novel pipeline that bootstraps event-based dataset annotation from mature frame-based algorithms, in order to more quickly generate the required datasets.