Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Learning Control for Cascaded Impedance-Controlled Compliant Exoskeleton With Adaptive Reaction to Spasticity

The cascade impedance control (CIC) is a well-known architecture framework for rehabilitation exoskeleton with compliant joint (e.g., a series elastic actuator). The cascaded position–torque–velocity control loop of the CIC system can deal with specific issues, such as stiction or the accuracy of output impedance. In this article, the control loops of the CIC are extended by the traditional iterative learning control (ILC), and then we examine and compare three types of frameworks, namely, torque learning, impedance learning, and trajectory learning. Their advantages, such as reducing the lag of the output trajectory with a low-gain impedance controller (safety), are discovered. Furthermore, the exoskeleton system is upgraded with the ability of variable impedance. In this part, a fuzzy logic system is proposed. This system uses the electromyography (EMG) signals of the subject and the exoskeleton torque as input, and the decisions on the variable impedance as output. The experiment verifies that the proposed algorithm can effectively decrease the impedance of the exoskeleton when detecting a spasticity from the subject and can maintain the original dynamics of the system when the subject performs a normal movement. Afterward, the effectiveness of the fuzzy logic system together with the ILC-based CIC is experimentally verified in the case of subject–exoskeleton collaborative.
Data-Driven Estimation of Forces Along the Backbone of Concentric Tube Continuum Robots

Concentric tube continuum robots (CTCRs) belong to the family of continuum robots with applications in minimally invasive surgeries. Because of this application domain, measuring the external forces along the body of the robot is paramount. CTCRs are made up of thin elastic rods and are intended to be applied inside the human body, where conventional sensor-based measurements are not feasible. Consequently, research is resorting to estimate the forces through geometric, numeric, or optimization methods. However, these methods often suffer from slow convergence. In this paper, we introduce a novel data-driven approach for estimating contact forces along the body of a CTCR that offers an estimation precision comparable to the current state-of-the-art optimization-based approaches, but exhibits nearly two orders of magnitude faster convergence. The proposed method is scalable and exhibits a significant performance in response to a wide range of external forces. The approach was evaluated in simulations and on a real 2-tube CTCR.
A new secure and efficient elliptic curve cryptosystem

The design of new elliptic curve cryptosystem is still an interest to community of cryptographers. In this article, we propose a new secure and efficient elliptic curve cryptosystem. The scheme requires a single public and private keys and needs 59���� � �� and 30���� � �� for encryption and decryption algorithm respectively. The communication costs is given by 2||. The estimated computational speed for the encryption is 49.35% and for the decryption is 97.07% .
Simultaneous Motion and Stiffness Control for Soft Pneumatic Manipulators based on a Lagrangian-based Dynamic Model

A soft continuum manipulator with tunable stiffness can not only take advantage of high compliance for safe adaptation in unknown environments, but also circumvent the drawbacks of instability and low loading capability. The high nonlinearity of soft manipulators and the strong coupling between actuation and stiffness-tuning make their simultaneous control challenging. In this work, a novel approach to simultaneous control of actuation and stiffness-tuning is proposed for soft pneumatic manipulators. With a piecewise-constant curvature assumption, a Lagrangian-based dynamic model with realistic approximation is used for control design, where the dynamics of the stiffness-tunable mechanism is incorporated. An extended Kalman filter (EKF) is proposed to estimate unmeasurable states including the stiffness and the velocity. A nonlinear model predictive control (NMPC) framework is developed first in the configuration space, and then extended to the task space, for simultaneous motion and stiffness control under inflation and vacuum pressure constraints. Simulation results are presented to support the efficacy of the proposed approach.
High-gain Output Feedback Control Design for a Class of Uncertain Nonlinear Systems Using Gaussian Processes *

This paper considers the tracking control problem of a class of uncertain nonlinear systems with partial noisy measurements. To cope with the uncertainties including the unknown nonlinear dynamics and unmeasured state variables, simultaneous estimation of all the hidden states and unknown dynamics are required for the controller design. In this paper, a high-gain observer delivers a good property of disturbance rejection and provides state estimates which serves as the training data for learning the unknown dynamics. Because the measurements are corrupted by noise, this leads to estimation error in the state estimates. Since the Gaussian process (GP) has high flexibility to capture the complex unknown functions by using very few parameters and it inherently handles measurement noise, GP model is employed to learn the unknown dynamics from the state estimates. This provides critical information for the control design such that the unknown dynamics is compensated and a good tracking performance is delivered. In short, the high-gain observer provides state estimates for the GP model to learn the unknown dynamics from noisy measurements, which enables the development of the controller. Comparisons against ℒ1 adaptive control and high-gain output feedback controller without GP learning are carried out.
SDF-Based Graph Convolutional Q-Networks for Rearrangement of Multiple Objects

In this paper, we propose a signed distance field (SDF)-based deep Q-learning framework for multi-object re-arrangement. Our method learns to rearrange objects with non-prehensile manipulation, e.g., pushing, in unstructured environments. To reliably estimate Q-values in various scenes, we train the Q-network using an SDF-based scene graph as the state-goal representation. To this end, we introduce SDFGCN, a scalable Q-network structure which can estimate Q-values from a set of SDF images satisfying permutation invariance by using graph convolutional networks. In contrast to grasping-based rearrangement methods that rely on the performance of grasp predictive models for perception and movement, our approach enables rearrangements on unseen objects, including hard-to-grasp objects. Moreover, our method does not require any expert demonstrations. We observe that SDFGCN is capable of unseen objects in challenging configurations, both in the simulation and the real world.
A Review on Optimal Power Scheduling Techniques

The electricity supply system is currently operated with a major contribution of grid power and a small percentage of Renewable Energy Sources(RES) which is used according to availability and demand. But the significance of RES in the power market is increasing and it can impact energy sharing greatly in the future. Electric Vehicles(EVs) are becoming more popular and can be considered as an essential load. Proper scheduling of load and RES plays a vital role in minimizing the cost of electricity and peak demand. This paper aims to review different techniques to optimally schedule power, compare various optimization algorithms based on their objectives, and discuss future trends. To gain a general understanding of the global implications of research on this topic, a bibliometric study is also carried out.
A Gradient Boosting Decision Tree Algorithm for An Adjustable Capacity Market Clearing Model

With the proposal of carbon peak and carbon neutrality, it is of great importance to take effective methods to deal with the high penetration of renewable energy, which may pose a threat to the reliability of the power grid. One promising way is to establish the power capacity market where the generation units are compensated for their capacity characteristics. In this paper, an adjustable capacity market model is developed, where renewable energy is consumed with priority. Meanwhile, the thermal power units and hydropower units obtain capacity fees for flexibility upgrading. The paper also considers capacity demand, clearing method, and transaction method. In addition, considering the significance of predicting capacity demand with high precision, the paper applied Gradient Boosting Decision Tree (GBDT) algorithm to forecast the demand. A real case is studied and analyzed numerically, whose results show the validity of the proposed model. hope that this paper can provide some useful thinking for the construction of the power capacity market.
Machine Learning Approach for Mixed type Wafer Defect Pattern Recognition by ResNet Architecture

Semiconductor manufacturing process involves various steps: fabrication of Wafers, testing of wafers, assembly of each single die and package level final test. Out of which, testing of wafer for defect detection and classification is the most important step where defective dies are eliminated. This improves the efficiency of the overall manufacturing process. Semiconductor wafer defects can be basic defects or mixed defects containing two or more basic defects. Since these defects varied from wafer to wafer and are complex, the accuracy obtained from traditional deep learning model is poor. To address this problem, a semiconductor mixed defect pattern recognition based on Residual Network (ResNet) Architecture is attempted. Among the ResNet architectures, ResNet50 is selected for wafer defect pattern recognition which has a 50 layer structure. The model is evaluated based on data set with 15 mixed defect patterns which were generated from WM-811K wafer map data set. Training, testing and validation of the data set is done. The performance parameters are generated and compared the performance with four different architectures including Convolutional Neural Network(CNN). The training results indicate that the ResNet50 shows a better performance in terms of accuracy, precision, recall and F1 score.
Joint Segmentation and Grasp Pose Detection with Multi-Modal Feature Fusion Network

Efficient grasp pose detection is essential for robotic manipulation in cluttered scenes. However, most methods only utilize point clouds or images for prediction, ignoring the advantages of different features. In this paper, we present a multi-modal fusion network for joint segmentation and grasp pose detection. We design a point cloud and image co-guided feature fusion module that can be used to fuse features and adaptively estimate the importance of the point-pixel feature pairs. Moreover, we develop a seed point sampling algorithm that simultaneously considers the distance, semantics and attention scores. For selected seed points, we adopt a local feature aggregation module to fully utilize the local spatial features in the grasp region. Experimental results on the GraspNet-lBillion Dataset show that our network outperforms several state-of-the-art methods. We also conduct real robot grasping experiments to demonstrate the effectiveness of our approach.
An Online Learning Based Extended Kalman Filtering Approach for Intelligent Vehicles Localization During Short-term GNSS Outages

Real-time and accurate localization is a prerequisite for intelligent vehicles control. GNSS is an important information source for localization. However, GNSS signal may be short-term blocked by large buildings and tunnels inevitably. Therefore, it is a practical issue to retain localization accuracy during short-term GNSS outages. By improving the modeling accuracy of the vehicle motion and sensor measurements, localization is expected to maintain a satisfactory performance during GNSS short-term outages. In this paper, dual neural extended kalman filtering approach (DN-EKF) is introduced to compensate for the unmodeled errors of vehicle motion and statistical modeling error of sensor measurement noise, and consequently improves estimator accuracy. Experiments on our test platform have demonstrated the effectiveness of proposed method during GNSS short-term outages. It is worth noting that the proposed method in this paper is open-ended. Therefore, it can be easily integrated with other solutions to further improve the performance of localization.
Constrained Control of UAV Swarms Using Event-triggered Distributed Explicit Reference Governor

In this paper, we propose a distributed strategy to control and drive multiple UAVs to the target locations under the confined environment with obstacle and agent avoidance. The explicit reference governor (ERG) is a simple and systematic approach that provides constraint handling capabilities to prestabilized systems. When designing the ERG, one of the main challenges is the determination of dynamic safety margin that ensures constraints satisfaction. To do this, the robustness to uncertainty is taken into the margin design and provides the invariance property; the dynamic event-triggering mechanism is applied to the optimization of the threshold in the designed margin subject to concave collision constraints. Numerical simulations are performed to investigate the performance of our proposed methodology.
ANGELS - Smart Steering Wheel for Driver Safety

The automotive industry increasingly recognizes the importance of human-machine interaction in enhancing the driving experience and improving driver safety. Human factors, such as drowsiness and attention deficits, play a primary role in safe driving. There are several research and commercial solutions to address these issues. However, they analyze vehicle behavior and are unable to assess the driver’s state in a timely manner. A novel approach to this problem is to monitor the driver’s physiological signals. In this context, Photoplethysmography (PPG) is a noninvasive technique that monitors cardiac activity and can provide information regarding the driver’s state. This work introduces ANGELS, an embedded system that exploits PPG signals to monitor drivers in a non-invasive way. ANGELS is a low-cost and low-power system that can be integrated into the steering wheel of a car. It acquires and processes the driver’s PPG signals in real-time and enables heart rate monitoring without requiring accelerometer data to remove motion artifacts. We perform an experimental assessment using the Maserati driving simulator. ANGELS features a mean absolute error on heart rate detection of 1.19 BPM with a latency of 10 s and power consumption of only 130 mW. These results demonstrate that it is a reliable and promising solution for improving driver safety.
Event triggering based adaptive tracking of uncertain nonlinear systems with unknown control coefficients

In the framework of event-triggered control, the question of practical tracking of a sort of nonlinear systems with serious uncertainty is addressed in this work by virtue of adaptive output feedback control scheme. Especially, system nonlinearities not only depend on the unmeasurable states, but also allows the existence of output polynomial function. By combining extended Lyapunov matrix inequalities, non-identification adaptive technology and dynamic scaling transformation technology, this article proposes an adaptive output feedback control scheme within the event triggering control framework to realize global practical tracking control of closed-loop system. Simultaneously, it must be mentioned that the proposed control method includes dual gain: static gain and dynamic gain, where the latter can effectively compensate for severe uncertainty, output polynomial functions, time-varying control coefficients, and errors caused by event triggering mechanism; in addition, so as to reduce the transmission frequency of control signals, the event triggering mechanism on account of fixed threshold strategy is adopted. By means of Barbălat’s lemma and Lyapunov’s stability theorem, one can prove that the designed control strategy insures that the closed-loop system states are bounded under any initial conditions, while the tracking error is driven to a prescribed interval after a limited time. Also, the Zeno behavior is successfully ruled out. Eventually, one illustrative example is put forward to verify the correctness of the presented approach.
Increasing Admittance of Industrial Robots By Velocity Feedback Inner-Loop Shaping

Admittance and impedance controllers are often purely feedforward, using measured external force or motion, respectively, to generate a reference for an inner-loop controller. In this case, the range of dynamics which can be rendered is limited by the inner-loop, which causes, e.g. contact stability issues for low admittance industrial robots in stiff contact. When both position and force are measured, feedback control can be added to more flexibly reshape the rendered dynamics. This paper uses velocity feedback to increase the admittance of motion-controlled industrial robots in force control applications. This allows an industrial robot with a lower intrinsic admittance, which may be needed for payload, speed, or accuracy, to realize a higher admittance by control, allowing lighter manual guidance and safer contact. This is achieved by a modified disturbance observer, where an inverse dynamic model estimates external forces and amplifies them with positive feedback. This approach is compared with using positive velocity feedback with a shaping filter. Here, velocity reference calculated by the virtual admittance model is modified by the DOB (Dist-Add) or the positive velocity feedback (Vel-Add). When combined with an outer-loop admittance controller, these methods can render a higher admittance while maintaining contact stability compared to standard feedforward admittance control.
Empirical weather dependency of heat pump load: Disentangling the effects of heat demand and efficiency

Decentralized electric heat pumps are an important component in many future energy scenarios. However, their dispatch and efficiency are highly weather-dependent, which is often inadequately considered in the scenarios. This contribution aims to address this shortcoming by analyzing a large-scale empirical dataset of monitored heat pumps from the UK. More specifically, we propose to model heat pump behavior as a function of temperature equivalents, which do not only capture ambient temperature but also wind-driven infiltration and solar heat gains as determinants of heat demand. Based on these temperature equivalents, we derive regression models for the (normalized) heat supply and for the coefficient of performance (COP) of the heat pumps. These models can be used to better disentangle and represent the variability of heat pumps heat pumps in future energy system analyses.
Visual Pitch and Roll Estimation For Inland Water Vessels

Motion estimation is an essential element for autonomous vessels. It is used e.g. for lidar motion compensation as well as mapping and detection tasks in a maritime environment. Because the use of gyroscopes is not reliable and a high performance inertial measurement unit is quite expensive, we present an approach for visual pitch and roll estimation that utilizes a convolutional neural network for water segmentation, a stereo system for reconstruction and simple geometry to estimate pitch and roll. The algorithm is validated on a novel, publicly available dataset22https://git.ios. htwg-konstanz.de/dgriesse/constance_orientation_dataset/archive/main/constance_orientation_dataset-main.zip recorded at Lake Constance. Our experiments show that the pitch and roll estimator provides accurate results in comparison to an Xsens IMU sensor. We can further improve the pitch and roll estimation by sensor fusion with a gyroscope. The algorithm is available in its implementation as a ROS node33https://github.com/dionysos4/water_surface_detector.
Understanding information diffusion about open-source projects on Twitter, HackerNews, and Reddit

The diffusion of information about open-source projects is a key factor influencing the adoption of projects and the allocation of developer efforts. Developers learn about new projects, and evaluate their quality and importance by accessing the related information. Social media is an important channel for information diffusion about open-source projects, with previous research suggesting the existence of a social media ecosystem that consists of multiple platforms and collectively supports information diffusion in open source.With different features supporting information diffusion, the same piece of information likely reaches different developer communities on different platforms, which attracts the attention and contribution of different developers and thus influences the success of open-source projects. Despite its importance, few works looked at the identity of the developer community that project-related information reaches on social media platforms and its associated impact on the discussed project.In this work, we track social media discussions on open-source projects on three different platforms: Twitter, HackerNews, and Reddit. We first describe the dynamics of project-related information diffusion across platforms, and we analyze the association between the number of posts on each platform, and the number of developers attracted to the discussed project from different communities. We find that posts about open-source projects first appear on Twitter and HackerNews, then move more towards Reddit. The number of project-related posts on Twitter mostly associate with the attracted developers from communities that are close to the project’s main contributor, while posts on other platforms associate more with the attention from remote communities.
Exploring Robot-Assisted Optical Coherence Elastography for Surgical Palpation

Optical Coherence Elastography (OCE) is a method that discerns local tissue stiffness using optical information. This method has recently been explored for laryngeal cancer tumor margin detection but has not been widely deployed clinically. Part of the challenge hindering such clinical deployment is the need for controlled high-precision mechanical probing of the tissue. This paper explores the concept of robot-assisted optical coherence elastography(OCE) and presents a preliminary system integration used to demonstrate the approach for stiffness mapping and discerning tumor margins. The approach is demonstrated on a custom Cartesian stage robot, and a custom-built OCE system comprised of an 830 nm broad-band laser with a vector-analysis method for phase gradient estimation and strain imaging. The paper illustrates one of the advantages of robot-controlled probing in terms of increasing the accuracy of the OCE system in a large range of displacement and strain. By leveraging motion information from the robot, online re-calibration of the OCE strain map may be achieved, thereby reducing OCE errors. After calibration, it is shown that the error in estimating the local Young's modulus is 0.485% in the silicon phantom and 0.531% in the agar phantom. These results suggest that future integration of optical coherence tomography(OCT) in clinically deployable robots may offer advantages in enabling local stiffness map estimation using OCE.