Miracle-dz/UniFAR-FTUs huggingface dataset:
A Shared Telemanipulation Interface to Facilitate Bimanual Grasping and Transportation of Objects of Unknown Mass

The teleoperation of robots with human-like capabilities may pose significant challenges to the human operator due to the kinematic complexity and redundancy of these robots. Bimanual telemanipulation represents such a challenging task that requires precise coordination of the two arms to perform a stable bimanual grasp on an object and eventually transport the object while maintaining the grasp. In this work, we present a shared control telemanipulation interface to facilitate the bimanual grasping and transportation of objects of unknown mass. With the proposed method, the robot is able to transport the object maintaining autonomously a sufficient amount of grasping force while accepting commands from the operator to reach the desired location. As humans do, it is not necessary to know the weight of the object in advance; instead, the robot estimates it during the lifting phase. On the basis of the estimated weight, the required amount of grasping force is computed. During object transportation, the robot autonomously regulates the grasping forces in a shared control fashion, allowing the operator to seamlessly command only the trajectories of the object. The proposed method has been implemented and validated on the CENTAURO robot, a quadrupedal platform with a humanoid dual arm upper body, performing experiment where objects of different weights and dimensions must be picked up and transported.
Large Signal Stability of Grid-Tied Virtual Synchronous Generator Using Trajectory Reversing

This paper proposes the utilization trajectory reversing based algorithm to study the large signal stability of virtual synchronous generator (VSG) based Micro-grid. The algorithm is used to provide an estimation of enlarged region of asymptotic stability (ROA) of the VSG. To overcome the complexity of finding a proper expression to represent the enlarged ROA, the algorithm utilizes convex hull algorithm which expresses the enlarged ROA by a set of finite number of inequalities and equations. Moreover, this paper proposes a new technique to generate uniformly distributed initial points needed by the algorithm. The proposed technique provides an easy and direct method to balance the computation time with the number of required initial points. A test case is conducted of VSG connected to an infinite bus to validate the accuracy of the proposed technique and algorithm.
Mapless Navigation of Modular Mobile Robots using Deep Reinforcement Learning

The modular mobile robots can utilize their many degrees of freedom in variable configurations to adapt to unknown environments. The mapless navigation task implemented by modular mobile robots is more challenging than traditional mobile robots, since the motion planner requires more efforts on the coordination of modular units to guarantee integrity and flexibility. In this work, we address this challenge by proposing a novel mapless navigation framework which contains a two-level planner based on deep reinforcement learning (DRL). In our framework, the upper-level planner determines a high-level task mode and the lower-level outputs motion control commands, and the two planners update at different time scales. The results of simulated experiment conducted on Webots platform show that, the proposed framework allows modular mobile robots to use raw sensory information to efficiently explore and accomplish multitask in unknown environments.
Comparative Study of Double-Stator Vernier Machines with Different Interior Permanent-Magnet Arrangements for Unmanned Aerial Vehicles

Currently, multi-port machines have attracted increasing concern in unmanned aerial vehicle (UAV) applications. In this paper, a comparative study of a series of DS interior-PM vernier (DS-IPMV) machines for UAVs is proposed. Based on the traditional double-V-shape-PM structure, the consequent PM arrangement, namely the “single-V-shape-PM” structure, and also the “double/single-spoke-PM” arrangements with PM inclination change, are derived. Through performance comparison, magnetic characteristics of the four machines have been well clarified. Particularly, effects of the consequent structure on the increment of the PM utilization rate, reluctance torque and torque ripple are proven. Moreover, the influence of the PM inclination on the flux allocation of the double stators has been specifically discussed.
Approach for Rigorous Evaluation of a Battery Fuel Gauge

A battery management system (BMS) is crucial for the safe and reliable operation of a battery pack. During use, it is important to monitor the remaining charge in the battery, known as the state of charge (SOC), to preserve battery health and lifetime. However, the SOC of a battery cannot be directly measured and it is approximated by the battery fuel gauge (BFG) using several empirical approaches. The accuracy of the SOC calculated by the BFG is affected by (i) temperature (ii) charging/usage history (iii) hysteresis and relaxation effects. Evaluating the SOC values reported by a BFG remains a challenging problem due to the fact that it is not possible to know the true SOC value. Consequently, indirect measures were developed to evaluate the SOC estimates reported by a BFG. In this paper, three BFG evaluation metrics: the Coulomb counting (CC) metric, the open circuit voltage (OCV) metric and the time-to-voltage (TTV) metric are demonstrated. The present paper is focused on demonstrating the implementation details of the above three BFG evaluation metrics. The proposed metrics are modified versions of previously reported ones to make the BFG evaluation more robust. Voltage and current data generated from a battery simulator and a BFG based on the extended Kalman filter algorithms were employed to demonstrate the proposed evaluation scheme. The battery in the simulator is set to an Rint approximation of the equivalent circuit model (ECM) and the BFG is set to assume the knowledge of the ECM model parameters. Voltage and current measurements were simulated based on a noisy model with zero mean and known standard deviation. Under these assumptions, the BFG under evaluation produced less than 1% error in SOC and less than 15 minutes in TTV error. These values, produced under the known model assumption, can be taken as a benchmark for the same voltage and current measurement noise statistics.
Core Loss Distribution in a Switched Reluctance Motor – Linear and Nonlinear Analysis

Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Hence, SRM modeling is a most demanding task, in particular core losses. Non-sinusoidal flux density waveforms in different stator and rotor core sections, in addition to lamination non-uniform distribution are challenging phenomena to be addressed. This is still an ongoing research field. The purpose of this paper is to develop a comparative analysis between a linear and non-linear simulation model for core loss distribution in a three-phase 6/4 SRM. Five different steady-state operation modes will be addressed.
The missing bulge globular clusters in M31 - New optical candidates

A new method to attack the question of the 'missing' globular clusters in the bulge of M31 is used. Image-processing techniques were used on 13 videocamera fields to obtain an accurate photometric census of stellar objects in M31's bulge down to a limiting B magnitude of 21. This luminosity distribution is compared with the Bahcall-Soneira model of galactic foreground stars. A statistically significant excess of bright images in the luminosity range of globular clusters at M31's distance is found. If the optical candidates considered prove to be globular clusters, they would double the number of known globular clusters in the surveyed region. The colors of a subsample of the candidates are the same as those of the known globular clusters. It is concluded that the previously observed flattening away from a de Vaucouleurs law in the radial distribution of M31 may be an observational selection effect. As an offshoot of this analysis, no evidence is found for very luminous stars in the inner bulge of M31. The lack of such stars indicates that there has not been active star formation (with a normal IMF) in the recent past. Coupled with the existence of many planetary nebulae in the bulge, this may strengthen the case for a galactic wind in M31's bulge.
A Digital Twin Model for COP Prediction in Refrigeration System using Combined Machine Learning Method

Accurate prediction of coefficient of performance (COP) in refrigerant system is beneficial to the overall system performance evaluation and dynamic operation optimization. In this paper, a digital twin model of COP prediction in refrigeration system based on combined machine learning method is proposed. Firstly, several influencing factors are selected guided by the mechanism knowledge, and the correlation coefficients between impact factors and COP value are calculated to further verify their importance in COP prediction. Secondly, the convolutional neural network (CNN) and multilayer perceptron (MLP) with intrinsic weight optimization are used to capture the characteristics between the influencing factors and COP, while the long short term memory (LSTM) method is used to capture the characteristics of the time series data. Meanwhile, the weights of the CNN, LSTM and MLP methods are optimized in real time by the particle swarm optimization (PSO) algorithm to minimize the difference between the COP value of physical system and the predicted COP value of digital twin model to realize online self-update. Finally, simulation results show that the MAE, MAPE and RMSE obtained by the proposed method are respectively 7.01 %, 1.71% and 0.08% lower than those obtained by CNN method, 0.92%, 0.24% and 0.01% lower than those obtained by LSTM method, and 2.41%, 0.58% and 0.02% lower than those obtained by MLP method, which have proved that the prediction result of proposed digital twin model for COP prediction is more accurate.
Advanced vehicle tracking in persistent aerial surveillance video

This paper presents a relational graph based approach to track thousands of vehicles from persistent wide area airborne surveillance (WAAS) videos. Due to the low ground sampling distance and low frame rate, vehicles usually have small size and may travel a long distance between consecutive frames, WAAS videos pose great challenges to correct associate existing tracks with targets. In this paper, we explore road structure information to regulate both object based vertex matching and pair-wise edge matching schemes in a relational graph. The proposed relational graph approach then unifies these two matching schemes into a single cost minimization framework to produce a quadratic optimized association result. The experiments on hours of real WAAS videos demonstrate the relational graph matching framework effectively improves vehicle tracking performance in large scale dense traffic scenarios.
A Wideband and High-Gain Terahertz Wave Slot Antenna

Achieving unidirectional radiation with high gain at broadside direction is of particular interest for point-to-point communications, wireless power transfer, radar systems, and various other wireless systems [1] [2]. Conventional approaches frequently employ fabry-perot cavity [3] [4], horn [5], Yagi-Like multilayer antenna [6] to obtain high directivity. These designs are often designed at microwave band. Expect these antenna type, slot antenna is widely used in millimeter-wave band due to its advantages such as low profile, simple structure and can be easily used to conformal surface. However, traditional slot antenna suffer from narrow bandwidth and low antenna gain. There are some methods such as modify the shape of slot, adding parasitic element to enhance slot bandwidth. These methods can hardly be scaled to millimeter-wave band due to the fabrication limitation and high profile.
A Skeleton-based View-Invariant Framework for Human Fall Detection in an Elevator

This paper considers the emergency behavior detection problem inside an elevator. As elevators come in different shapes and emergency behavior data are scarce, we propose a skeleton-based view-invariant framework to tackle the camera view angle variation issue and the data collection issue. The proposed emergency fall detection model only needs to be trained for a target camera, which is deployed in an elevator at a manufacture’s lab, from which a large amount of training data can be collected. The deployment of a source camera, which is in a customer-side elevator, hence can be customized and almost no training effort is needed. Our framework works in four stages. First, a 2D RGB input image is taken from the source camera and a 2D human skeleton is obtained by 2D pose estimation (AlphaPose). Second, the 2D skeleton is converted to a 3D human skeleton by 3D pose estimation (3D pose baseline). Third, a pre-trained rotation-translation (RT) transform (Procrustes analysis (PA)) aligns the 3D pose representations to the target camera view. Finally, a dual 3D pose baseline deep neural networks (D3PBDNN) model for human fall detection is proposed to perform the recognition task. We gather a human fall detection dataset inside different elevators from various view angles and validate our proposal. Experimental results successfully attain almost equivalent accuracy to that of a source camera-trained model.
Research and Design of a Humanoid Cushioning Foot for Robot Jumping

A novel structure of humanoid foot is proposed in this paper to reduce the impact force and absorb the oscillation of the jumping robot landing. Referring to the structure of the human foot, a humanoid foot with bionic bones and joints was designed. The dynamic model of the structure of the humanoid foot was established for quantitative simulation and optimization of the parameters. The entity of the new foot was processed. Then, landing impact experiments were carried out to compare the impact force absorbing ability of different parts of the foot and other feet under the same conditions. Finally, the humanoid foot was installed on the robot ATHLETE to test the foot's performance indicators in the case of actual robot landing. This proved that the new foot can reduce the impact force and absorb oscillation better than the rubber pad foot.
Notch UWB Filter Based on Four-mode Resonator

In order to build a bandpass filter for ultra-wideband applications, a new configuration of a four-mode resonator was given in this paper. A stepped impedance resonator loaded with three open stubs serves as the basis for the proposed four-mode resonator's design. The four-mode resonator's resonant properties are investigated using a conventional analytical technique based on odd-/even-mode analysis. The filter has a relative bandwidth of 116% and a passband bandwidth of $3.1 \sim 11.7$ GHz. Asymmetric interdigital coupled lines feed the four-mode resonator to create a controlled notched band. The central frequency of the notch is 6.8 GHz. The filter is 17 mm $\times 5$ mm in size.
A Framework for Human Motion Estimation using IMUs in Human-Robot Interaction

The field of human-robot interaction (HRI) covers a wide range of interactions between humans and robots in various settings. In most cases, both the human and the robot should know the status of each other and interchange data accordingly. Particularly, informing the robot about the human poses and gestures is critical for safe and reliable interaction. We present an open-source inertial measurement unit (IMU)-based human motion estimation framework designed for HRI applications which can be used with multiple robotic systems. The presented framework is developed as a Robot Operating System (ROS) package and takes on a bridge role between the human and the robot by utilizing the estimated human motions to produce a desired robot goal pose. Although there are different human motion tracking systems that are used in gaming, film making, industrial and medical applications today, they are either quite costly, tedious to set up or system dependent. Also, those systems have some limitations to be used in industrial environments such as suffering from occlusion and obstacles between the tracker system and the human, sensitive to light changes etc., and require long calibration steps before each use. The availability of a versatile human motion estimation framework that can be easily used in different HRI scenarios is handy for industrial applications. Some examples of usages of the proposed framework both in simulation and real-world applications are demonstrated in this paper. We aim our package to be a useful boosting tool to develop IMU-based human motion estimated HRI applications and auxiliary templates to develop more complex HRI scenarios for research and development purposes.
Effect of Construction Materials on Indoor Positioning System using Bluetooth Low Energy

Indoor positioning system (IPS) has become increasingly important due to its wide applications in indoor navigation, location-based services (LBS), and disaster relief / recovery. Among the technology candidates, Bluetooth Low Energy (BLE) provides low cost and low power consumption. This paper studies the effect of construction materials on the localisation accuracy of an IPS based on trilateration using received signal strength indicators (RSSI) from BLE beacon nodes. Through experiments, we found that plasterboard walls and glass walls provide better localisation accuracy as compared to wooden walls. Furthermore, knowledge about the room-level location of BLE beacon nodes can help improve the localisation accuracy.
Reinforcement Learning-based Controller for Thermal Management System of Electric Vehicles

The thermal management system in electric vehicles (EV) becomes significant because the performance of the system is highly correlated with the driving range, reliability, and safety of the electric vehicles. Therefore, a controller of the thermal system should be designed to minimize the tracking error and power consumption while satisfying constraints. In this study, a reinforcement learning (RL)-based controller is proposed. This paper presents the selection of states and design of the reward function of RL for the thermal management system of EV. The controller is trained by the sequential learning method that is based on Deep Q-network (DQN) and adjusted for fast convergence of multi-input problems. The results show better performance compared with the rule-based controller.
MUSIC Algorithm for Near Field Point-like Scatterers’ Localization

The problem of localizing point-like scatterers in near-field configuration is addressed. In particular, MUSIC algorithm is applied in this particular scenario. The scatterers are assumed to be illuminated by an unknown incident field and the scattering coefficients turn to be correlated. Accordingly, the correlation matrix is rank deficient. In order to cope with this drawback, we propose and numerically check a rank restoring procedure tailored for near-field configurations.
Electric Vehicle Charging Behavior Prediction using Machine Learning Models

The global spread of the COVID-19 pandemic has significantly impacted the electric vehicle (EV) industry. The lockdown restriction has resulted in a significant shift in the use of public charging infrastructures. This paper investigates the effects of COVID-19 on electric vehicle users' charging behavior before, after, and during COVID-19 lockdown restrictions, using the data from a public charging facility from the City of California. In this study, we performed data visualization using K-means and hierarchical clustering analysis. This work uses the vehicle's connection and disconnection time to identify common charging pattern identification and charging behavior where K-means clustering outperforms the hierarchical clustering for all three different scenarios modelled. In addition, prediction of collective charging session duration is achieved using Machine Learning Models, Random Forest and XgBoost. We achieved a mean absolute percentage error (MAPE) of 0.146 and 0.151 percent for XgBoost and Random Forest respectively.
Collision avoidance of USV by model predictive control-aided deep reinforcement learning

This paper proposes a model predictive control (MPC)-aided deep reinforcement learning (DRL) algorithm to address the collision avoidance problem of unmanned surface vessel (USV). By combining with MPC, the complexity of the learning model will decrease, and the obstacle avoidance path will be safer. In this control and learning synthesized algorithm, the DRL network learns and generates velocity, and the MPC controller adjusts USV to navigate by tracking this velocity. The simulation indicated that the proposed algorithm could successfully avoid obstacles with steady and safe status.
Sizing and Energy Management Strategy of a Hybrid Energy Storage System for EVs

Nowadays, hybrid and full-electric vehicles are promising solutions to reduce carbon emissions related to mobility. In this scenario, hybrid energy storage systems are under analysis, and continuous efforts are being made to cope with sizing and managing different sources to improve efficiency and increase sources lifetime. In the context of the IEEE VTS Challenge 2022, this paper proposes the storage system sizing and the energy management strategy for a vehicle powered by batteries and supercapacitors.
Laser Velocimeter Measurements in the Pump of an Automotive Torque Converter Part II – Effect of Pump Speed and Oil Viscosity

The velocity field inside a torque converter pump was studied for two separate effects: variable pump rotational speed and variable oil viscosity. Three-dimensional velocity measurements were taken using a laser velocimeter for both the pump mid- and exit planes. The effect ofvariable pump rotational speed was studied by running the pump at two different speeds and holding speed ratio (pump rotational speed]turbine rotational speed) constant. Similarly, the effect of viscosity on the pump flow field was studied by varying the temperature and]or using two different viscosity oils as the working fluid in the pump. Threedimensional velocity vector plots, through-flow contour plots, and secondary flow profiles were obtained for both pump planes and all test conditions. Results showed that torque converter mass flows increased approximately linearly with increasing pump rotational speed (and fixed speed ratio) but that the flow was not directly proportional to pump rotational speed. However, mass flows were seen to decrease as the oil viscosity was decreased with a resulting increased Reynolds number; for these conditions the high velocity regions were seen to decrease in size and low velocity regions were seen to increase in size. In the pump mid-plane strong counter-clockwise secondary flows and in the exit plane strong clockwise secondary flows were observed. The vorticities and slip factors were calculated from the experimental results and are presented. The torque core-to-shell and blade-to-blade torque distributions were calculated for both planes. Finally, the flow fields were seen to demonstrate similitude when Reynolds numbers were matched.
An Improved Weighted K Nearest Neighbors Algorithm for WLAN Localization

For indoor location estimation based on Wi-Fi fingerprinting, how to reduce localization effort while maintaining high location estimate accuracy is of critical concern. The paper introduces a new approach Reward and Penalty Localization (RP-Loc) algorithm, which extends the classic WKNN Localization algorithm. The parameter of observable Access Point (AP) occurrence rate is added into the fingerprint database in the offline training phrase to increase the increase the anti-interference ability. The experimental results prove that the RP-Loc algorithm exhibits superior performance in terms of location accuracy and robustness.