Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Position Joint Particle Filtering Method for Vehicle Localization in Urban Area

Robust localization is a prerequisite for autonomous vehicles. Traditional visual localization methods like visual odometry suffer error accumulation on long range navigation. In this paper, a flexible road map based probabilistic filtering method is proposed to tackle this problem. To effectively match the ego-trajectory to various curving roads in map, a new representation based on anchor point (AP) which captures the main curving points on the trajectory is presented. Based on APs of the map and trajectory, a flexible Multi-Position Joint Particle Filtering (MPJPF) framework is proposed to correct the position error. The method features the capability of adaptively estimating a series of APs jointly and only updates the estimation at situations with low uncertainty. It explicitly avoids the drawbacks of obliging to determine the current position at large uncertain situations such as dense parallel road branches. The experiments carried out on KITTI benchmark demonstrate our success.
Study on Partial Stratified Resampling for Particle Filter Based Prognosis on Li-Ion Batteries

Accurate online prognosis of engineering systems plays a vital role in prognosis and health management (PHM) technologies to ensure safety, prevent damage and economic loss. The particle filter (PF) algorithm has proved to be an effective method for prognostics. However, the PF algorithm suffers from serious particle degeneracy and particle impoverishment problems. Most of the studies in the literature focus on solving the particle degeneracy problem but at a heavy computational cost. In this study, we aim to explore the time efficient Partial Stratified Resampling algorithm which can be used for online state estimation problems and compare it with conventional Stratified Resampling algorithms. The accuracy and precision of the algorithms are validated using the lithium-ion battery data sets from CALCE® research group.
SMA based wrist exoskeleton for rehabilitation therapy*

This paper presents a rehabilitation wearable exoskeleton for wrist joint with two degrees of freedom (DOF), flexion-extension and adduction-abduction (radial and ulnar deviation), actuated with Shape Memory Alloy (SMA) based actuators. Thanks to this type of actuators, the proposed device presents a very light weight and noiseless operation, in comparison with similar devices. The preliminary results obtained over real tests with the wrist exoskeleton are presented. This prototype demonstrates that SMA actuator technology is a viable alternative when investigating possible improvement of rehabilitation robotic devices in terms of weight, size and cost.
VARO-Fi: A Variable Orientable Gripper to Obtain In-Hand Manipulation

This paper proposes a novel gripper or end-effector named VARO-fi (VARiable Orientable fingers with translation), with the aim of obtaining human like prehensile manoeuvre such as, in-hand manipulation. The 4 fingered VARO-fi consists of 9 degrees of freedom and it can perform several in-hand manipulation tasks which have been described in this paper. Moreover, the gripper is a simplification of previously proposed gripper platform called Dexclar. The derivation of VARO-fi has been presented and its capabilities have been demonstrated by experiments. Although a generic convex payload is considered as a primitive in the design of VARO-fi however, it is capable to address manipulation for other regular shaped payloads, which has been proven by experiments. A comparison is also illustrated in order to underline the strength of the novel gripper with respect to the state of the art.
MAP - A Mobile Agile Printer Robot for on-site Construction

In this paper, we present a Mobile Agile Printer (MAP) construction robot; a highly agile, 4-legged, omnidirectional robot capable of 3D printing large structures. To overcome dynamic challenges when operating within an outdoors construction site, MAP incorporates a high-DoF 3D printing system connected to a mobile platform with novel features designed to enable disturbance rejection and live adaption to the robot's pose. In doing so, we demonstrate the benefits of designing construction robots with a focus on agility, a compact working volume and ability to operate within a potentially unlimited workspace. Performance tests were conducted showing smooth omni-directional motion as a key requirement for maintaining low 3D printing trajectory deviations over a large volume. In doing so, we show that MAP has the ability to construct in new ways more sensitive to its environment, context and concurrent on-site operations.
Evaluation of Spatial Resolution of Neutron Profile Monitor in LHD

The vertical neutron camera (VNC) has been developed to measure neutron emission profile in deuterium plasmas of the large helical device (LHD). The experiment on spatial resolution evaluation for VNC was carried out in November 2016 by using a 252Cf neutron source of 800 MBq. The neutron source was introduced into the LHD vacuum vessel through an aluminum pipe from an upper diagnostics port to the equatorial plane. The source was placed at the following two positions. The first position is just on the cylindrical collimator axis, and the second position is in the middle of two neighboring collimator axes. The stilbene scintillation detectors are aligned radially at the collimator end to detect unscattered fast-neutrons generated in a deuterium plasma. The 3-D neutron transport calculations by using a general-purpose Monte Carlo N-particle code 6 reproduced the experimentally obtained neutron counting rate, indicating that VNC has sufficient spatial resolution and the crosstalk between two neighboring collimators is a fairly small fraction. It is expected that the VNC on LHD will function as a tool for the study on the radial transport of energetic ions.
Virtual Engineering of a Fusion Reactor: Application to Divertor Design, Manufacture, and Testing

Virtual engineering is being widely adopted in diverse technical fields and has the potential to accelerate the realization of tokamak fusion energy. In this paper, we demonstrate, for the first time, the use of virtual engineering techniques to enhance the development cycle of a water-cooled divertor plasma facing component. The divertor concept under development is the thermal break, which aims to increase heat flux limits and DEMO-relevance by alleviating the high thermal mismatch stress between a tungsten monoblock and a copper alloy cooling pipe. Response surface-based design search and optimization is used to explore the potential of the concept and to inform the design of a high-heat-flux (HHF) mock-up. In developing a brazing manufacturing procedure, computed tomography scanning and subsequent image-based finite-element modeling revealed a defective joining process which would result in unacceptable divertor performance. The same techniques were used to develop an improved brazing procedure that was used to manufacture the mock-ups. A production defect was discovered by ultrasonic qualification testing, but a “virtual twin” model confirmed the acceptable performance of the mock-up under HHF. Indeed, all mock-ups were then subjected to at least 100 cycles of 20 MW/ $\text{m}^{2}$ HHF testing, with no signs of damage. The development of these virtual engineering and modeling techniques will facilitate criteria for design and component rejection, and in future enable predictive maintenance of components in service. Practical trials and experiments will remain essential to complement and validate simulations.
Robot Programming Through Augmented Trajectories in Augmented Reality

This paper presents a future-focused approach for robot programming based on augmented trajectories. Using a mixed reality head-mounted display (Microsoft Hololens) and a 7-DOF robot arm, we designed an augmented reality (AR) robotic interface with four interactive functions to ease the robot programming task: 1) Trajectory specification. 2) Virtual previews of robot motion. 3) Visualization of robot parameters. 4) Online reprogramming during simulation and execution. We validate our AR-robot teaching interface by comparing it with a kinesthetic teaching interface in two different scenarios as part of a pilot study: creation of contact surface path and free space path. Furthermore, we present an industrial case study that illustrates our AR manufacturing paradigm by interacting with a 7-DOF robot arm to reduce wrinkles during the pleating step of the carbon-fiber-reinforcement-polymer vacuum bagging process in a simulated scenario.
Attitude Estimation from Polarimetric Cameras

In the robotic field, navigation and path planning applications benefit from a wide range of visual systems (e.g, perspective cameras, depth cameras, catadioptric cameras, etc.). In outdoor conditions, these systems capture information in which sky regions cover a major segment of the images acquired. However, sky regions are discarded and are not considered as visual cue in vision applications. In this paper, we propose to estimate attitude of Unmanned Aerial Vehicle (UAV) from sky information using a polarimetric camera. Theoretically, we provide a framework estimating the attitude from the skylight polarized patterns. We showcase this formulation on both simulated and real-word data sets which proved the benefit of using polarimetric sensors along with other visual sensors in robotic applications.
Modelling and Control of Multi-terminal MVDC Distribution Network

The revolution in the power industry has motivated a paradigm shift in the multi-terminal voltage source converter (VSC) medium voltage DC (MVDC) distribution network application from ship to power grid with numerous market-based applications. However, research in MVDC networks in the power grid is in its embryonic stages, hence exploring the existing primary control schemes in the well-developed VSC-HVDC systems as the benchmark for adoption remains an interesting research subject. Therefore, this paper proposes a 10kV radial multi-terminal MVDC distribution network in PSCAD/EMTDC with solar PV integrated and a preliminary study involving modelling and comparison of the master-slave, voltage margin and droop control schemes undertaken. The results show that the droop control is the most responsive and flexible achieving the best network DC voltage control and active power balance in the MVDC distribution network. The growing research interests in MVDC control provides an impetus into future study on adaptive droop control schemes.
Design and Evaluation of Torque Based Bipedal Walking Control System That Prevent Fall Over by Impulsive Disturbance

In this paper, we develop a bipedal robot control system that has an ability to perform instantaneous high power and flexibility to absorb an impulsive disturbance. We utilize a sensor-less whole body torque control method executed in a high responsive realtime distributed system. This system also includes a robust online walking controller that can avoid fall over caused by a strong collision with the robot's legs. We evaluated the proposed control system by hitting a rubber ball or adding a leg sweep disturbance and verified the functionality of the absorbing motion and the balance restoring motion.
A New Deep Transfer Learning Network for Fault Diagnosis of Rotating Machine Under Variable Working Conditions

Machine learning is promising in vibration signalbased fault diagnosis because of its full use of big data and nonlinearity extracting capability. However, in real-world application, the network trained by a vibration signal dataset will be applied to target signal datasets with different distributions, which can be triggered easily by rotating speed oscillation and load variation. Hence, based on transfer learning, some vibration signal-based methods which are robust to working conditions have been proposed to address this problem. Nevertheless, most of them need target datasets in network training, and the network should be trained whenever it meets a new vibration signal dataset. So we construct a three-stage deep fault diagnosis network utilizing adaptive batch normalization (AdaBN), which is highly efficient for free of target datasets in training and does not need repeated training in its application. In the first stage, the vibration signal samples are processed into more regular and discriminative frequency spectra. In the second stage, a fourlayer AdaBN based deep neural network (DNN) is pre-trained by stacked autoencoders (SAE) and then finely tuned only using the source dataset. In the final step, the trained network is modified to diagnose samples from the target dataset. Extensive experiments on a gearbox and a bearing dataset, and comparisons with some other fault diagnosis methods verify its effectiveness.
Detecting decompositions of sulfur hexafluoride using Ge modified SWCNT: a theoretical evaluation

We performed the detection of SF<inf>6</inf> decompositions using germanium modified single wall carbon nanotube (SWCNT) using first-principles with density functional theory. Among three typical SF<inf>6</inf> decompositions, Ge-SWCNT exhibits the largest chemical interactions with SO<inf>2</inf> but as to SOF<inf>2</inf> and SO<inf>2</inf>F<inf>2</inf>, there are only weak physical interactions. SO<inf>2</inf> acts as an electron acceptor with 0.17 e transferring from Ge-SWCNT to gas molecule. The response and recovery properties of Ge-SWCNT based sensor are also evaluated based on calculation results. The Ge-SWCNT has obvious selectivity to SO<inf>2</inf> among these three kinds of SF<inf>6</inf> decompositions and could have application value in SF<inf>6</inf> based electric equipment.
Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning

Navigation in unknown indoor environments with fast collision avoidance capabilities is an ongoing research topic. Traditional motion planning algorithms rely on precise maps of the environment, where re-adapting a generated path can be highly demanding in terms of computational cost. In this paper, we present a fast reactive navigation algorithm using Deep Reinforcement Learning applied to multi rotor aerial robots. Taking as input the 2D-laser range measurements and the relative position of the aerial robot with respect to the desired goal, the proposed algorithm is successfully trained in a Gazebo-based simulation scenario by adopting an artificial potential field formulation. A thorough evaluation of the trained agent has been carried out both in simulated and real indoor scenarios, showing the appropriate reactive navigation behavior of the agent in the presence of static and dynamic obstacles.
TEORI BELAJAR ALIRAN BEHAVIORISTIK SERTA IMPLIKASINYA DALAM PEMBELAJARAN IMPROVISASI JAZZ

Tulisan ini bermaksud menjelaskan implikasi teori belajar behavioristik terhadap pem-belajaran improvisasi jazz. Menurut teori behavioristik, belajar adalah perubahan tingkah laku sebagai akibat dari adanya interaksi antara stimulus dan respon. Dengan kata lain, belajar merupakan bentuk perubahan yang dialami mahasiswa dalam hal kemampuannya untuk bertingkah laku dengan cara yang baru sebagai hasil interaksi antara stimulus dan respon. Jazz adalah musik yang mencakup kualitas seperti berayun, improvisasi, interaksi kelompok. Pada musik jazz dalam melakukan improvisasi dikenal dengan istilah “pancingan”. Salah satu pemain melakukan semacam stimulus terhadap pemain yang lain untuk melakukan perubahan style maupun perubahan modus. Dengan stimulus yang berulang-ulang, maka pemain yang lain dengan spontan melakukan respon.
$\Phi$ Clust: Pheromone-Based Aggregation for Robotic Swarms

In this paper, we proposed a pheromone-based aggregation method based on the state-of-the-art BEECLUST algorithm. We investigated the impact of pheromone-based communication on the efficiency of robotic swarms to locate and aggregate at areas with a given cue. In particular, we evaluated the impact of the pheromone evaporation and diffusion on the time required for the swarm to aggregate. In a series of simulated and real-world evaluation trials, we demonstrated that augmenting the BEECLUST method with artificial pheromone resulted in faster aggregation times.
An Equivalent Modeling Method for Multi-port Area Load Based on the Extended Generalized ZIP Load Model

To model the load area and simplify the electrical network, a multi-port area load equivalent modelling method (ALEEM) based on the extended generalized ZIP load model (EGZIP) is proposed. Different from the traditional ZIP load, the EGZIP load model incorporates the voltage magnitudes and voltage phase angles of all boundary buses, which can equivalently model the area load with multiple boundary buses more accurately. The load flow calculation considering the EGZIP is derived and analyzed. Also, based on the hierarchical and partitioning characteristics of the power grid, a multi-port equivalent strategy is proposed to reduce the number of parameters to be identified in the equivalent model. For parameter identification, the currents measured at varying operation conditions on the boundary bus are used to construct the least square estimation (LSE) problem. The interior point method is used to identify the model parameters. The simulation test conducted on the 87 bus system proves the equivalent model derived from the proposed ALEEM based on EGZIP has higher accuracy and the multi-port equivalence strategy can reduce both the number of parameters to be identified and the time consuming on equivalent process.
Predictive Model of Energy Market Indicators Based on Continued Approximation for Series Sample Prior to the Moment of Prediction

Autoregression models use a linear combination of previous time series as a prediction. This article suggests a prediction algorithm based on creating approximation of prior series sample as a linear combination of an existing set of samples. Here, prediction is an obtained linear combination of the continuations of approximating samples. The problem of finding the coefficients of the linear combinations is written as an A. N. Tikhonov's extremum problem of least-squares with a regularizer, which allows creating a system of equations for evaluating the unknown parameters. Solving the system of equations is carried out using the singular spectrum analysis (SSA). Regularity helps to increase the solving quality in noisy conditions. After isolating the singular components of the system of equations matrix, the solution can be obtained in analytical form. The parameters governing the quality of the solution are the regularization coefficients and the set of components of the singular decomposition. The prediction efficiency index is estimated based on the discrepancies between the prediction and actual values of the series for the given depth of the prediction. The prediction model search is done by choosing the best model on the set of values of the regularization parameter with different sets of eigenvectors that determine the solution space. The proposed method was tested on different time series. The examples of prediction of the energy market indicators prove its effectiveness.
Classification of Underlying Causes of Power Quality Disturbances Using Data Fusion

This paper develops a data fusion technology based modeling framework for classifying the underlying cause of power quality (PQ) disturbances. First, the moving-window technique is used to cluster disturbance period with the consideration of the temporal propagation of disturbance energy. Secondly, the PQ disturbance measurements, equipment switching action data and alarm events are integrated by utilizing entity matching method. Then, the distributed mining of association rules is designed to obtain strong association rules within integrated data for describing the relationship between PQ features and event causes. The analysis results have good generalization performance. Finally, the real grid data were taken as an example to verify the effectiveness and practicability of the proposed method. The test results show that the proposed method can analyze the relationship between the typical PQ disturbance features and event causes effectively. This relationship is meaningful for power quality improvement.
Dual Resonant Voltage Droop Compensation for Bipolar Solid-State Marx Generator Topologies

This paper discusses a novel methodology to increase the voltage droop compensation range in generalized solid-state bipolar high-voltage Marx modulators with one resonant stage. To maintain the modularity characteristic of bipolar Marx modulators, the imbedding of additional resonant stages is not straightforward. Nevertheless, this paper proposes to add a second resonant stage to the already existing resonant-type voltage droop compensation stage. This second stage increases the voltage droop compensation percentage of a generalized solid-state bipolar Marx generator. The compensation of the bipolar pulse voltage droop is now achieved by adding two auxiliary synchronous compensation resonant voltages, to the output capacitive decaying voltage. Simulation and experimental results are presented for six Marx plus two resonant stages, 16% voltage droop, with 3-kV pulse amplitude, 100- $\mu \text{s}$ pulsewidth, 40-ms relaxation time, and 10-Hz pulse repetition rate.
Cognition-enabled Framework for Mixed Human-Robot Rescue Teams

With the advancements in robotic technology and the progress in human-robot interaction research, the interest in deploying mixed human-robot teams in rescue missions is increasing. Due to their complementary capabilities in terms of locomotion, visibility and reachability of areas, human-robot teams are considerably deployed in real-world settings, albeit the robotic agents in such scenarios are normally fully teleoperated. A major barrier to successful and efficient mission execution in those teams is the lack of cognitive skills in robotic systems. In this paper, we present a cognition-enabled framework and an implemented system where robotic agents are equipped with cognitive capabilities to naturally communicate with humans and autonomously perform tasks. The framework allows for natural tasking of robots, reasoning about robot behavior, capabilities and actions, and a common belief state representation for shared mission awareness of robots and human operators.
Towards Real-Time Capable Simulations with a Containerized Simulation Environment

The handling of non-rigid objects, such as cables, with industrial robots is characterized by nonlinear, timedependent and location-dependent equations for the object's behavior. To manipulate them in a desired way, real-time capable simulations for control that are able to run in cycle-time are required as well as highly accurate and detailed simulations for path planning. We introduce a common simulation environment that is wrapped in a container with the advantages of including all dependencies and being easy to set up and orchestrate. Automated code generation is used to set up a standardized communication and virtualization in a Docker container such that only the simulation itself has to be provided by a user. The advantage of containerization, compared to the simulation running natively, is finally demonstrated with a sample robot simulation under heavy CPU load.