Miracle-dz/UniFAR-FTUs huggingface dataset:
Quadruped Locomotion Control Based on Two Bipeds Jointly Carrying Model

A novel gait planning and control framework was developed for quadruped locomotion of a robot. It modeled the quadruped robot as two bipeds carrying the body from the front and rear ends. We first mapped the relationship between the joint torques of support legs and the torso forces of the bipedal sub-robots. Then the equations describing the relationship between the quadruped body forces and the bipedal torso forces under various operating modes of the robot were deduced and solved. Virtual forces were generated on the quadruped body to manipulate its velocity and orientation. Then these virtual forces were distributed to the front and hind sub-robots to generate support leg torques. The state machines and gait generators for the two bipedal sub-robots were designed individually, resulting in the decoupling of the gait parameters in the front legs and hind legs. The effectiveness of the controller was validated through dynamic simulations.
VarNet: Exploring Variations for Unsupervised Video Prediction

Unsupervised video prediction is a very challenging task due to the complexity and diversity in natural scenes. Prior works directly predicting pixels or optical flows either have the blurring problem or require additional assumptions. We highlight that the crux for video frame prediction lies in precisely capturing the inter-frame variations which encompass the movement of objects and the evolution of the surrounding environment. We then present an unsupervised video prediction framework — Variation Network (VarNet) to directly predict the variations between adjacent frames which are then fused with current frame to generate the future frame. In addition, we propose an adaptively re-weighting mechanism for loss function to offer each pixel a fair weight according to the amplitude of its variation. Extensive experiments for both short-term and long-term video prediction are implemented on two advanced datasets — KTH and KITTI with two evaluating metrics — PSNR and SSIM. For the KTH dataset, the VarNet outperforms the state-of-the-art works up to 11.9% on PSNR and 9.5% on SSIM. As for the KITTI dataset, the performance boosts are up to 55.1 % on PSNR and 15.9% on SSIM. Moreover, we verify that the generalization ability of our model excels other state-of-the-art methods by testing on the unseen CalTech Pedestrian dataset after being trained on the KITTI dataset. Source code and video are available at
Pulsed-DC Discharge for Plasma CVD of Carbon Thin Films

A pulsed-dc discharge method for plasma chemical vapor deposition (CVD) was developed using a constructed versatile pulsed-dc generator and a vacuum discharge chamber. Argon (Ar) gas discharge experiments were performed under a variety of discharge conditions, and plasma conditions were evaluated using optical emission spectroscopy. The developed system can perform glow discharge by employing nearly rectangular, unipolar, and high-voltage negative pulses with a wide variation of voltage amplitudes and a set discharge current, pulse repetition rate, and duty cycle. Plasma conditions could also be tuned by varying the pulse parameters. A preliminary test of the deposition of diamond-like carbon (DLC) films on silicon (Si) substrates and ultrathin pyrolytic carbon (PyC) films on nanothickness metal-catalyzed glass substrates was performed with acetylene (C2H2) by selecting some typical deposition conditions based on the gas discharge experiments. The deposited films were characterized via Raman spectroscopy. The characteristic properties of DLC and PyC films were achieved. The DLC films demonstrated conventional hard insulating properties while the ultrathin PyC films exhibited conductive and semitransparent properties. These results revealed that the developed pulsed-dc discharge system for plasma CVD is applicable for the synthesis of insulating hard as well as soft conducting carbon thin films.
A Picosecond Pulse generator Using SRD diodes: Design, Analysis, and Measurements

In this paper, generating a sub-nanosecond Gaussian pulse using a very simple circuit configuration is demonstrated. The design is based on SRD diodes fed with a sinusoidal input. The generated pulse is a Gaussian with a full width at half maximum (FWHM) of 286ps and 1.586V of amplitude. Circuit operation is studied through multiple simulations and measurements.
The Modeling of HVDC Circuit Breakers for Small Time-step Real-time Simulation

In the simulations of voltage source converter (VSC) based DC grids, fast protection schemes for overhead lines, such as traveling wave protection unavoidably require a small time step when simulating high voltage direct current (HVDC) circuit breakers. In order to present protection processes accurately and realize hardware-in-the-loop (HIL) simulation, this paper proposes a modeling method of HVDC circuit breakers for small time-step real-time simulation. After using the transmission line modeling method(TLM) to solve the arrester and constant impedance model of the switch, the admittance matrix of the HVDC breaker will keep constant, which reduces computing time greatly. A simplified HVDC breaker is used to interpret the modeling method. And a test circuit is implemented on a field programmable gate array (FPGA) board, on which efficient and accurate simulation results are obtained.
Digital Twins of Manufacturing Systems as a Base for Machine Learning

In the engineering phase of modern manufacturing systems, simulation-based methods and tools have been established to face the increasing demands on time-efficiency and profitability. In the application of these simulation solutions, model-based digital twins are created, as multi-domain simulation models to describe the behavior of the manufacturing system. During the production process, a data-driven digital twin arises in the context of industry 4.0 based on an increasing networking and new cloud technologies. Recent developments in machine learning of fer new possibilities in conjunction with the digital twin. These range from data-based learning of models to learning control logic of complex systems. This paper proposes a combined model-based and data-driven concept of a digital twin. It shows how to use machine learning in connection with these models, in order to archive faster development times of manufacturing systems.
Evaluation of Photoelectric Characteristics of a Volume DBD Excited by Power Density Modulation

Photoelectric characteristics are an important reference for high-voltage discharges and are closely related to the excited mode of the high-voltage source. In this paper, we built a large-scale volume dielectric barrier discharge (DBD) system, which is composed of multilayer parallel-plate DBD electrodes and is excited by a high-voltage source in the power density modulation (PDM) mode. Subsequently, we analyzed the applied energy equation and evaluated the DBD systems photoelectric characteristics. The results show that the energy values of different applied voltage cycles during a PDM period exhibit significant differences. However, the average energy in a PDM period is approximately constant. Moreover, the equivalent capacitance of the DBD cell is a function of both the applied voltage and energy density. For the DBD cell, with the increase in both the applied voltage and energy loaded on the DBD cell, the total equivalent capacitance (<inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula>) is approximately constant, the dielectric capacitance (<inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ d}}$ </tex-math></inline-formula>) increases exponentially and decreases linearly, while the discharge gap capacitance (<inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ g}}$ </tex-math></inline-formula>) decreases and increases in the same cases. In addition, the relative intensities of the discharge emission increase and the energy efficiency ratios of the relative photoquantum yield of active species decline with the increase in both the applied voltage and energy density loaded on the DBD cell. The experimental results have been analyzed by evaluating the influence of the electrical parameters, and the underlying physical principles have been discussed. This paper clearly demonstrates that the photoelectric characteristics of the DBD reactor are significantly influenced by both the applied voltage and energy density, thus providing helpful insights into the energy evaluation and application of high-voltage discharges.
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

Traditional distance and density-based anomaly detection techniques are unable to detect periodic and seasonality related point anomalies which occur commonly in streaming data, leaving a big gap in time series anomaly detection in the current era of the IoT. To address this problem, we present a novel deep learning-based anomaly detection approach (DeepAnT) for time series data, which is equally applicable to the non-streaming cases. DeepAnT is capable of detecting a wide range of anomalies, i.e., point anomalies, contextual anomalies, and discords in time series data. In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series. DeepAnT consists of two modules: time series predictor and anomaly detector. The time series predictor module uses deep convolutional neural network (CNN) to predict the next time stamp on the defined horizon. This module takes a window of time series (used as a context) and attempts to predict the next time stamp. The predicted value is then passed to the anomaly detector module, which is responsible for tagging the corresponding time stamp as normal or abnormal. DeepAnT can be trained even without removing the anomalies from the given data set. Generally, in deep learning-based approaches, a lot of data are required to train a model. Whereas in DeepAnT, a model can be trained on relatively small data set while achieving good generalization capabilities due to the effective parameter sharing of the CNN. As the anomaly detection in DeepAnT is unsupervised, it does not rely on anomaly labels at the time of model generation. Therefore, this approach can be directly applied to real-life scenarios where it is practically impossible to label a big stream of data coming from heterogeneous sensors comprising of both normal as well as anomalous points. We have performed a detailed evaluation of 15 algorithms on 10 anomaly detection benchmarks, which contain a total of 433 real and synthetic time series. Experiments show that DeepAnT outperforms the state-of-the-art anomaly detection methods in most of the cases, while performing on par with others.
Study on the flashover characteristics of bird droppings along 110kV composite insulator

With the rapid increase of the number of birds, frequent bird droppings flashovers occur in high-voltage power transmission and transformation projects. In order to reduce bird damage and improve the reliability of power system, it is necessary to study the bird droppings flashover in laboratory. Due to the length and the shape of bird droppings is not easy to control, a cotton strip impregnated with the bird droppings simulating liquid was used to simulate bird droppings in this experiment. During the test, the relationship between the spatial position and the corona onset voltage and breakdown voltage was studied when the bird droppings fell from directly above and to the side of the grading ring. Studies have shown that when the distance between the bird droppings simulation strip and the grading ring is getting larger and larger, the breakdown voltage is getting higher and higher. The breakdown field strength gradually decreases as the gap distance increases, the value is close to the breakdown electric field strength with the rod-rod gap.
Conductive Knit-covered Pneumatic Artificial Muscle (k-PAM) Actuator

The paper presents design, fabrication and characteristics of two kinds of conductive Knit-covered Pneumatic Artificial Muscle (it is called as k-PAM in the paper) actuators, in which two different knits are made by braiding silver-coated (conductive) yarn and spandex (non-conductive) yarn with different stitch methods. The k-PAM is able to measure the change in length of the actuator body according to the applied air pressures as well as the strain due to external force. A complete fabrication method is presented to make the actuator work for higher pressure (≥ 300[kPa]). Since the force generated by the actuator is decoupled from the external force, ultimately, it can be directly used to measure not only the length but also the force. Experimental validations are performed describing the characteristics of two different types of k-PAMs. It is expected that the k-PAM can be used directly for robotic applications in higher pressure condition, while the semi-permanent conductive knit provides the actuator with durability in high repetitive operation environment.
Adaptive Baseline Monocular Dense Mapping with Inter-Frame Depth Propagation

State-of-the-art monocular dense mapping methods usually divide the image sequence into several separate multi-view stereo problems thus have limited utilization of the information in multi-baseline observations and sequential depth estimations. In this paper, two core contributions are proposed to improve the mapping performance by exploiting the information. The first is an adaptive baseline matching cost computation that uses the sequential input images to provide each pixel with wide-baseline observations. The second is a frame-to-frame propagated depth filter which integrates the sequential depth estimation of the same physical point in a robust probabilistic manner. Two contributions are integrated into a monocular dense mapping system that generates the depth maps in real-time for both pinhole and fisheye cameras. Our system is fully parallelized and can run at more than 25 fps on a Nvidia Jetson TX2. We compare our work with state-of-the-art methods on the public dataset. Onboard UAV mapping and handhold experiments are also used to demonstrate the performance of our method. For the benefit of the community, we make the implementation open source11https://github.com/HKUST-Aerial-Robotics/Pinhole-Fisheye-Mapping .
Jet-HR1: Stepping Posture Optimization for Bipedal Robot Over Large Ditch Based on a Ducted-fan Propulsion System*

This paper reports the latest progress of an ongoing project utilizing a ducted-fan propulsion system to improve a humanoid robot's ability to step over a broad ditch with a height difference between the two sides. This work focuses on the methods of calculating the boundary and optimizing stepping posture to use less thrust and keep the robot balanced while stepping over the ditch. With the proposed methods and new two-dimensional gaits, the prototype robot, named Jet-HRl (Jet Humanoid Robot ver.l) was able to completely step over a broad ditch with 450mm in width (up to 97% of the robot's leg's length), and a height difference of 100mm between two sides.
The Co-Gripper: A Wireless Cooperative Gripper for Safe Human Robot Interaction

In this paper, we introduce a set of guidelines for the design of grippers suitable for a safe human robot/interaction in cooperative tasks. Modularity, adaptability, robustness, intuitive control, limited weight are some of the key elements that could allow to effectively spread these devices in industrial and service applications. Following such guidelines, we present the prototype of the Co-Gripper: a robotic device for cooperative manipulation tasks with humans. The gripper is composed of two pairs of fingers, actuated with two motors, that can be controlled in a coordinated way or independently. Each finger has a modular underactuated structure, composed of three phalanges connected by passive joints. The gripper is wireless, so it can be easily connected both to the robotic arms and on passive structures. We designed a wearable wireless control interface composed of a ring and a bracelet allowing a simple and intuitive activation of the gripper without limiting human operator's manipulation capabilities. We performed a set of tests to quantify gripper performance and to exploit its potentialities in human-robot cooperation tasks.
A Single-Channel SSVEP-Based BCI Speller Using Deep Learning

This paper aims to develop a speller system based on a bipolar single-channel electroencephalogram with sufficient accuracy. The proposed system consists of a custom-designed headset, a new virtual keyboard with 58 characters, special symbols, and digits, and a five-target steady-state visual-evoked potential (SSVEP)-based brain–computer interface (BCI) utilizing one-dimensional convolutional neural network (1-D CNN) for SSVEP frequency detection. The deep learning model is implemented and trained under the training mode before being applied in the operation mode of the system. To validate the proposed model, we acquire the training dataset with numerous testing conditions, including different frequency resolutions of the feature and different time-window lengths of analysis. Two types of features based on the frequency domain are investigated to compare their performances in terms of classification accuracy of the model. The experimental results from eight subjects shows that on average, the proposed model can classify five-class SSVEP data with a high accuracy of 99.2%. The proposed BCI is then employed in an online experiment of spelling the word “SPELLER” using 2-s time window. Consequently, the system achieves an average accuracy of 97.4% and an information transfer rate of 49 ± 7.7 bpm, showing the practicality and feasibility of implementing a reliable single-channel SSVEP-based speller utilizing 1-D CNN.
Transferring Visuomotor Learning from Simulation to the Real World for Robotics Manipulation Tasks

Hand-eye coordination is a requirement for many manipulation tasks including grasping and reaching. However, accurate hand-eye coordination has shown to be especially difficult to achieve in complex robots like the iCub humanoid. In this work, we solve the hand-eye coordination task using a visuomotor deep neural network predictor that estimates the arm's joint configuration given a stereo image pair of the arm and the underlying head configuration. As there are various unavoidable sources of sensing error on the physical robot, we train the predictor on images obtained from simulation. The images from simulation were modified to look realistic using an image-to-image translation approach. In various experiments, we first show that the visuomotor predictor provides accurate joint estimates of the iCub's hand in simulation. We then show that the predictor can be used to obtain the systematic error of the robot's joint measurements on the physical iCub robot. We demonstrate that a calibrator can be designed to automatically compensate this error. Finally, we validate that this enables accurate reaching of objects while circumventing manual fine-calibration of the robot.
DNN-based Speech Recognition System dealing with Motor State as Auxiliary Information of DNN for Head Shaking Robot

In this paper, a deep neural network (DNN) based integrated background noise suppression and acoustic modeling for speech recognition proposed in which on/off state of the motor for the head shaking robot is employed as the relevant auxiliary information of the DNN input. Since the motor sound being generated when the robot is moving or shaking its head severely degrades the performance of the speech recognition accuracy, we propose to use the motor on/off state as additional information when designing the DNN-based recognition system. Our speech recognition algorithm consists of two parts including the feature mapping model for feature enhancement and the acoustic model for phoneme recognition. As for the feature mapping, the stacked DNN is designed for the precise feature enhancement such that the lower DNN and upper DNN are trained separately and combined after which the motor state is plugged into both the lower DNN and upper DNN in addition to the input noisy speech. Then, the acoustic model is trained upon the feature enhancement model in which the motor state is again used as the augmented feature. The proposed technique to suppress the acoustic and motor noises was evaluated in term of the phoneme error rate (PER) and showed a significant improvement over the conventional system.
Optimization of Drive System Based on Arc-Type Stator Asynchronous Motor

It is well-known fact that one of the weak points of electromechanical systems are reducers, the application of which results in weight and size indicators increase, system implementation cost increase as well as reliability and number of hours between failures decrease. For systems requiring high torque from electric drive at low speeds, most of the problems can be solved by electric drive system generation based on arc-type stator asynchronous motor, the design of which consists of a rotor with short-circuited windings and a stator which is made as open-circuited in a form of arc. The mathematical description of the arc-type stator motors, which are presently developed, is not quite appropriate for the development of control algorithms for these motors. The aim of the work is a mathematical model development with a frequency-controlled arc-type stator of asynchronous motor driven are based on by self-contained voltage inverters, in particular, for radar station antenna positioning systems use. The mathematical model of arc-type stator drive is developed, suitable for the Matlab software package.
Advisory Temporal Logic Inference and Controller Design for Semiautonomous Robots

In this paper, we present a method to learn (infer) and refine a set of advices from the trajectories generated in the successful and failed attempts in a task or game, in the form of advisory signal temporal logic (STL) formulas. Each advice consists of an advisory motion STL formula that characterizes the spatial–temporal pattern of the motion as a feature of success and an advisory selection STL formula as a criterion for the environment to select the advice. For the inference of advisory STL formulas, we provide a theoretical framework of perfect classification with a labeled set of trajectories with different time lengths. We design an advisory controller that can drive the robots to satisfy an advisory motion STL formula based on the advice selected according to the advisory selection STL formula. The advisory controller can advise or guide the human operators or the robots for better performance with the shared autonomy between the human operator and the controller. We provide two case studies to test the effectiveness of the advisory controller, one with a Baxter-On-Wheels simulator and the other with two quadrotors in an experimental testbed in iteratively improving the success rates of completing the tasks with the help of the designed advisory controller. Note to Practitioners—The method described in this paper can be used to obtain knowledge or patterns of the environment based on the trajectories generated by the human operators/demonstrators and utilize the obtained knowledge or patterns for improving the performance of the future operators. The obtained information is the logical statements about the waypoints or subgoals to be reached, and obstacles or dangerous regions to be avoided during certain time intervals, when the environment satisfies certain conditions also expressed in the form of logical statements. The methodology of inferring knowledge from data and designing advisory controllers for guiding or helping future practices is potentially useful in many applications where the dynamic mathematical model of the external environment is unknown.