Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-View Deep Learning Framework for EEG Seizure Detection

The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual inspection from long-term EEG readings, automatic EEG seizure detection has garnered increasing attention among researchers. In this paper, we present a unified multi-view deep learning framework to capture brain abnormalities associated with seizures based on multi-channel scalp EEG signals. The proposed approach is an end-to-end model that is able to jointly learn multi-view features from both unsupervised multi-channel EEG reconstruction and supervised seizure detection via spectrogram representation. We construct a new autoencoder-based multi-view learning model by incorporating both inter and intra correlations of EEG channels to unleash the power of multi-channel information. By adding a channel-wise competition mechanism in the training phase, we propose a channel-aware seizure detection module to guide our multi-view structure to focus on important and relevant EEG channels. To validate the effectiveness of the proposed framework, extensive experiments against nine baselines, including both traditional handcrafted feature extraction and conventional deep learning methods, are carried out on a benchmark scalp EEG dataset. Experimental results show that the proposed model is able to achieve higher average accuracy and f1-score at 94.37% and 85.34%, respectively, using 5-fold subject-independent cross validation, demonstrating a powerful and effective method in the task of EEG seizure detection.
Design of a Lightweight, Ergonomic Manipulator for Enabling Expressive Gesturing in Telepresence Robots

Recent research on telepresence robots demonstrates that while they enable new heights of remote communication, there still exists challenges for both local and remote users in creating a connectedness one only encounters in face-to-face interactions. A large part of communication is beyond hearing and vision. Tangible interactions, expressive gestures, and physical referencing represent three of the primary social behaviors missing in the current telepresence experience. There is an inherent, subconscious quality to these physical actions that has been shown to allow more expressive and engaging communication. In this project we present the design, fabrication, and initial performance validation of a lightweight, ergonomic manipulator with a heavy, anthropomorphic end effector that enables gesturing capabilities for telepresence interactions.
Elastic Structure Preserving Impedance (ESπ)Control for Compliantly Actuated Robots

We present a new approach for Cartesian impedance control of compliantly actuated robots with possibly nonlinear spring characteristics. It reveals a remarkable stiffness and damping range in the experimental evaluation. The most interesting contribution, is the way the desired closed-loop dynamics is designed. Our control concept allows to add a desired stiffness and damping directly on the end-effector, while leaving the system structure intact. The intrinsic inertial and elastic properties of the system are preserved. This is achieved by introducing new motor coordinates that reflect the desired spring and damper terms. Theoretically, by means of additional motor inertia shaping it is possible to make the end-effector interaction behavior with respect to external loads approach, arbitrarily close, the interaction behavior that is achievable by classical Cartesian impedance control on rigid robots. The physically motivated design approach allows for an intuitive understanding of the resulting closed-loop dynamics. We perform a passivity and stability analysis on the basis of al physically motivated storage and Lyapunov function.
Protection settings transmitted over SCTP protocol in intelligent substations

Usually, MMS packets are used to encapsulate the information of protection settings and TCP is the suite of protocols used to transmit the MMS packets in smart substations. Since TCP’s own mechanism, such as vulnerability to Head-of-Line (HoL) blocking attacks, the protection settings of TCP transmission often fail to meet the high reliability requirements of important power information. A new method of SCTP protection setting and transmission based on IEC61850 intelligent substation information structure is proposed in this paper. To realize seamless handover of double MMS network during failure time, the SCTP’s multi host function is optimized. To avoid HoL blocking attacks, the multi stream mechanism is used. This paper further studies the realization method and implementation process of SCTP transmission with protection setting in detail.
Online Path Planning and Compliance Control of Space Robot for Capturing Tumbling Large Object

This paper presents the path planning and coordinated control of a space robot with a manipulator for capturing a rotating large object. As the grasping point on a rotating large object is translationally moving fast, an appropriate strategy and coordinated motion control of the spacecraft base and robotic arm must be employed for approaching and tracking such a grasping point. In this paper, we propose a robust control scheme including the online path planning and compliance control for grasping such a target. The path planning is derived in a simple form that allows the desired end-effector trajectory to be easily modified in real-time using the newly updated states without complex numerical calculation. In addition, the compliance control allows the end-effector to track the planned trajectory or the moving grasping point, while using contact force feedback to reduce the end-effector position error from the grasping point when capturing the target. This end-effector motion is implemented by coordinated control on the spacecraft base and robotic arm, which can suitably alter their distribution of motion according to scenes using a weighted pseudoinverse matrix. Experiments are conducted to demonstrate the validity of the proposed path planning and compliance control.
Robust Visual-Inertial State Estimation with Multiple Odometries and Efficient Mapping on an MAV with Ultra-Wide FOV Stereo Vision

The here presented flying system uses two pairs of wide-angle stereo cameras and maps a large area of interest in a short amount of time. We present a multicopter system equipped with two pairs of wide-angle stereo cameras and an inertial measurement unit (IMU) for robust visual-inertial navigation and time-efficient omni-directional 3D mapping. The four cameras cover a 240 degree stereo field of view (FOV) vertically, which makes the system also suitable for cramped and confined environments like caves. In our approach, we synthesize eight virtual pinhole cameras from four wide-angle cameras. Each of the resulting four synthesized pinhole stereo systems provides input to an independent visual odometry (VO). Subsequently, the four individual motion estimates are fused with data from an IMU, based on their consistency with the state estimation. We describe the configuration and image processing of the vision system as well as the sensor fusion and mapping pipeline on board the MAV. We demonstrate the robustness of our multi-VO approach for visual-inertial navigation and present results of a 3D-mapping experiment.
Post-fault Transient Stability Assessment Based on k-Nearest Neighbor Algorithm with Mahalanobis Distance

To enhance the security of power system, fast and accurate transient stability assessment methods are highly in demand. Many works have been done to explore the stability classification by searching and matching based on the post-fault PMU measurement data. Generally, the Euclidean distance is adopted to evaluate the similarity of different time series data. However, the correlation of the sample attributes is ignored in the classifier based on Euclidean distance. Besides, due to the imbalance feature of the transient stability assessment, costs of false classifications are unequal. For example, classifying the stable samples into unstable ones introduces less operational risks than classifying unstable samples into the stable. To handle above technique challenges, this paper proposes a k -Nearest Neighbor algorithm using Mahalanobis distance for transient stability assessments, in which the correlation of critical time series data is carefully considered. To strengthen the cost-sensitive feature of the proposed classifier, training processes are optimized regarding an area under the curve index. Finally, a case study is presented where the IEEE 10-machine 39-bus system is used for tests. Results of classifiers with different distance are compared. Test results validate the efficacy of the proposed k-Nearest Neighbor algorithm using Mahalanobis distance. Besides, it is also demonstrated that using Mahalanobis distance, the stability assessment can be accurate and robust even only based on a few measured states in form of time series data.
Cooperative Control for Knee Joint Flexion-Extension Movement Restoration

This paper describes a cooperative control approach that combines the use of a powered knee joint orthosis along with Functional Electrical Stimulation (FES) for knee joint flexion-extension movement restoration. A closed-loop adaptive control and an open-loop FES of the quadriceps muscle group are combined together to track a desired knee joint angle trajectory of flexion/extension movements. A nonlinear disturbance observer is used to estimate the torque provided by the subject's muscles through the FES. Simulations and experiments with a healthy subject show the feasibility of the proposed approach. Experiments show the repeatability of motion and the complementarity between the torque provided by the quadriceps muscle through FES and the one delivered by the orthosis actuator to ensure satisfactory tracking of the desired trajectory.
Edge-Based Robust RGB-D Visual Odometry Using 2-D Edge Divergence Minimization

This paper proposes an edge-based robust RGB-D visual odometry (VO)using 2-D edge divergence minimization. Our approach focuses on enabling the VO to operate in more general environments subject to low texture and changing brightness, by employing image edge regions and their image gradient vectors within the iterative closest points (ICP)framework. For more robust and stable ICP-based optimization, we propose a robust edge matching criterion with image gradient vectors. In addition, to reduce a bad effect of outlier residuals, we propose an improved edge registration problem of 2-D edge divergence minimization in the manner of an iterative reweight least squares (IRLS)motion estimation. To accelerate the proposed approach, a pixel sub-sampling method is employed. We evaluate estimation performance of our method in changing brightness conditions and low-textured scenes. Our approach shows more robust motion estimation than state-of-the-art methods while maintaining comparable accuracy in challenging image sequences at real-time (25 Hz)operation.
Towards Autonomous Auto Calibration of Unregistered RGB-D Setups: The Benefit of Plane Priors

In the last few years novel color and depth (RGB-D) sensors have greatly pushed robot perception. To enable a precise pixel-wise fusion of color and depth information good calibration is needed. The calibration determines the intrinsic parameters, the extrinsic parameters, and corrects for depth errors. While classic calibration approaches involve a dedicated calibration target and a trained expert, the autonomous calibration of such camera systems for robots operating in unknown environments is still an open problem. It demands for robust methods that do not need an expert to set up or tune the algorithm. Hence, we present a robust calibration algorithm that utilizes structure from motion (SfM) reconstructions as a calibration target and incorporates plane priors in the optimization to improve the convergence behavior and improve the calibration robustness. We evaluate our method against the state of the art performing over 300 experiments on ten different datasets, and show a significant improvement of the calibration accuracy.
State Estimate Recovery for Autonomous Quadcopters

A method for recovery from the complete loss of the state estimate is presented for autonomous quadcopters. Given an aerodynamic force model, the only measurements used to reinitialize the state estimate by means of a bank of extended Kalman filters are the angular rate and linear acceleration measurements of an IMU. The method is integrated within a complete recovery logic on a quadcopter platform and experimentally evaluated.
Structural Degradation Modeling Framework for Sparse Data Sets With an Application on Alzheimer’s Disease

The rapid development of information technologies provided unprecedented big data environments for condition monitoring and degradation analyses. However, the available big data sets are often sparse with a limited number of observations per recorded unit. For example, in many healthcare systems, data are collected from a large number of patients, but the available observations from each patient are quite limited. Unfortunately, most of the existing approaches for data-driven degradation modeling may not work well in this scenario as they either pool the information from the population or require rich historical observations in each unit. To address the challenges in “sparse data environments,” this paper proposes a structural degradation modeling framework (SDM). The SDM is inspired by the recommender system, which provides recommendations about specific items for the user. In addition, it is also tailored to the needs of degradation modeling. In particular, the framework takes into consideration: 1) the available data from the unit of interest; 2) the population characteristics; 3) the relationship between the available units; and 4) the precision of the available units. Simulation studies and a case study that involves the Alzheimer’s disease (AD) neuroimaging initiative data set are conducted, which shows satisfactory performance of the proposed method. Note to Practitioners—This paper proposes a framework for modeling and predicting the degradation level and/or condition of units with time. Our framework is particularly useful where many units have missing and/or limited degradation observations. Essentially, our proposed method integrates two important ideas: 1) leveraging the available data from the unit of interest to improve the modeling fitting of the individual unit over the observed time domain and 2) considering the relationship between the available units to extract proper and accurate population characteristics to address the challenge of limited observations. The proposed approach is validated via simulation studies as well as a healthcare case study based on AD. In the future research, we will further explore the extension of the proposed method such as considering more generic degradation models and optimal parameter tunings.
A SiC IGBT-Based DC/DC Converter for Medium-Voltage DC Distribution

A high ratio DC/DC converter for medium voltage DC distribution is put forward first. The DC/DC converter is built with state-of-the-art Silicon Carbide (SiC) Insulated Gate Bipolar Transistor (IGBT). The DC/DC converter is made up of a two-stage structure, in which the high-voltage stage is a three-level neutral-point clamped (NPC) circuit to reduce the breakdown voltage of the switches. Low-voltage stage uses two-level VSC circuit. These two stages are connected by a high-frequency transformer to improve utilization of the core material, while it can also help reduce the size of DC/DC converter; The control schemes for different stages of the DC/DC converter are designed in detail, the effectiveness of which is proved by theoretical analysis. Then simulation based on PSCAD is carried out according to different load condition and power flow direction to verify the dynamic performance of whole system. The simulation results show that the DC/DC converter proposed in this paper can not only realize bidirectional power flow and high conversion ratio (10kV / 400V) of energy, but also have good dynamic performance when the load changes on the low-voltage side, which can well satisfy the needs of DC power distribution.
Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis

Intelligent compound fault diagnosis of rotating machinery plays a crucial role for the security, high-efficiency, and reliability of modern manufacture machines, but identifying and decoupling the compound fault are still a great challenge. The traditional compound fault diagnosis methods focus on either bearing or gear fault diagnosis, where the compound fault is always regarded as an independent fault pattern in the process of fault diagnosis, and the relationship between the single fault and compound fault is not considered completely. To solve such a problem, a novel method called deep decoupling convolutional neural network is proposed for intelligent compound fault diagnosis. First, one-dimensional deep convolutional neural network is employed as the feature learning model, which can effectively learn the discriminative features from raw vibration signals. Second, multi-stack capsules are designed as the decoupling classifier to accurately identify and decouple the compound fault. Finally, the routing by agreement algorithm and the margin loss cost function are utilized to train and optimize the proposed model. The proposed method is validated by gearbox fault tests, and the experimental results demonstrate that the proposed method can effectively identify and decouple the compound fault.
Detecting and Picking of Folded Objects with a Multiple Sensor Integrated Robot Hand

Robotic picking of folded objects such as books is required for picking various objects. As a folded object is easily unfolded, it is difficult to carry it stably and place it in a desired pose due to its dangling part. For overcoming this difficulty, we propose a trial-and-error picking system using our Suction Pinching Hand, which can push the dangling part up with pinch grasp until the object lifted with suction grasp is folded. That system utilizes proximity sensors on the hand to predict whether folding will succeed with a current hand pose and decide whether to retry with another pose. Also, proximity sensors, flex sensors and an air pressure sensor are used to deal with uncertainty of the image recognition, the hand hardware and suction grasp. We evaluate our proposed system with experiments of picking and placing folded objects. It is confirmed that our proposed system realizes picking with the ability of our Suction Pinching Hand to carry folded objects stably and place them in desired poses. It is also proved that our proposed system is robust against the uncertainty.
Computation of Safe Path Velocity for Collaborative Robots

This paper presents a method for numerically computing the highest path velocity that a collaborative robot can attain, while complying with safety requirements. The safety requirements are obtained from ISO/TS 15066 that describes a collaborative method called power and force limiting, which specifies safe collisions between humans and robots. In particular, we assume that a path is given and compute the point-wise maximal path velocity that ensures a safe impact, i.e., the paper provides no considerations on the post impact safety.
Comparison of Speed and Accuracy of Chosen Magnetometer Calibration Algorithms in the Presence of External Interference

The paper deals with the robustness of the calibration algorithms based on the neural networks against external interference. The chosen neural networks distinguish only in the learning rule. For a comparison the backpropagation, backpropagation with momentum and resilient propagation learning algorithms were chosen. The algorithms were examined from the calibration speed and accuracy point of view defined by the specified criterions. Tests were performed in the form of simulations with the applied measured periodical interference and the periodical interference with the random occurring amplitude modulation. The goal of the article is to prove the fundamental robustness of the algorithm against the interference and the response of the chosen algorithms to the various external interference types.
Temporally Smooth Privacy-Protected Airborne Videos

Recreational videography from small drones can capture bystanders who may be uncomfortable about appearing in those videos. Existing privacy filters, such as scrambling and hopping blur, address this issue through de-identification but generate temporal distortions that manifest themselves as flicker. To address this problem, we present a robust spatiotemporal hopping blur filter that protects privacy through de-identification of face regions. The proposed filter is meant for on-board installation and produces temporally smooth and pleasant videos. We apply hopping blur to protect each frame against identification attacks, and minimise artefacts and flicker introduced by the hopping blur. We evaluate the proposed filter against different identification attacks and by assessing the quality of the resulting videos using a subjective test and objective measures.
A 3D Template Model for Healthy and Impaired Walking

Several modeling studies, which address neuromuscular control in impaired unperturbed gaits, were performed to predict human strategies to cope with lateral asymmetries in the body. Experimental studies show different step length and stance time relations between limbs in walking of stroke patients. By extension of a bipedal SLIP (spring-loaded inverted pendulum) based model and the corresponding controllers to 3D space, we focus on different features of the pathologic gaits. The introduced model is based on an extension of the FMCH (force modulated compliant hip) and VBLA (velocity based leg adjustment) model to 3D space. With the proposed model, asymmetric leg and control parameters can result in similar gait patterns as observed in experiments. These parameters comprise hip stiffness and rest angles in FMCH models and the tuning parameter of VBLA for foot placement. It is shown that asymmetries in muscle properties (e.g. stiffness) and leg adjustment can play an important role in generating pathologic gaits.
Multimodal Environment Dynamics for Interactive Robots: Towards Fault Detection and Task Monitoring

Interactive robots offer improved performance in tasks with environmental uncertainty, but accommodating environment input weakens predictions of contact force or position trajectories, making the identification of subtask completion or faults difficult. This paper develops a task monitoring approach for complex assembly tasks that involve transitions between discrete environment dynamic modes. In semi-structured environments, these dynamic modes and their transitions are approximately known a priori, allowing task monitoring through estimation of the current mode and fault detection as a deviation from expected, desired dynamic mode transitions. This allows a more natural description of many interactive tasks, improving robustness to variations in force or position trajectories that impedance control seeks to address. The ability of impedance and admittance controlled robots to identify their environment is investigated, making consideration of joint and end-effector physical compliance. Prior information on environment dynamics and mode transitions allow recursive estimates of dynamic mode suitable for online use, under both full state knowledge and only force/position measurements. Experiments with an admittance controlled robot in a gear assembly task validate the approach.