Miracle-dz/UniFAR-FTUs huggingface dataset:
Starfish Inspired Milli Soft Robot With Omnidirectional Adaptive Locomotion Ability

Bionic robot inspired by creatures in nature attracts attention from researchers due to its effective locomotion mode and high environment adaptability. Despite the recent achievements in bionic research and robotics, the bionic robots are almost simple morphology imitation. While the imitation of functional texture and microstructures are also important, which always makes a significant difference to the performance of robot. In this work, we proposed a starfish inspired milli soft robot with excellent locomotive performance and great environmental adaptability. Different from the simple imitation of locomotion mode in the previous literature, we introduce the micro tube feet structures of starfish into the robot design. The magnetization measurement and friction test result indicate that the microstructures of tube feet can strengthen the driving performance of robot, reduce the motion resistance from ground, and enhance the obstacle-climbing ability. Benefit from the radial symmetry shape of the starfish, the milli soft robot can achieve omnidirectional movement under the actuation of magnetic field. Moreover, the large deflection modeling of the variable cross section antenna is built by analyzing the infinitesimal element, and two motion states of milli soft robot are compared. The successful locomotion demonstrations, including moving with “S” trajectory, adapting to wet surface and gravel surface, overcoming obstacles, indicate the potential application of the starfish inspired milli soft robot in harsh environment.
A Load Adaptive DPS Control for DAB with Reduced Current Stress for Wide Load and Voltage Range

This paper presents a load adaptive DPS control for a dual active bridge (DAB) DC-DC converter. The control is effective for DAB used for charging of EVs (Electric Vehicles) with pre, CC (Constant Current) and CV (Constant Voltage) charging modes. The DPS (Dual-Phase-Shift) control provides an efficient selection of phase shift ratio, which reduces the switch current stress and improves the power transfer efficiency during both pre and CC charging mode. During CV charging mode, DPS control with the bidirectional inner phase shift control is used to improve control range of outer phase shift ratio at low loading condition. A method for the selection of both phase shifts, is presented in order to minimize peak inductor current at different voltage conversion ratio faced by DAB during whole charging range. A 10kW DAB DC-DC converter is designed and its control is validated through MATLAB/Simulink results. A set of results at different charging voltage and charging rates are presented with steady state and dynamic performances.
2D Laser and 3D Camera Data Integration and Filtering for Human Trajectory Tracking

This paper addresses a robust human trajectory tracking method through the data integration of 2D laser scanner and 3D camera. Mapping the deep learning-based 3D camera human detection onto the point cloud of the depth information to build up the 3D bounding box-represented human and using the state-of-the-art 2D laser-based leg detection are the main data streams of the human tracking system. The human-oriented global nearest neighbour (HOGNN) data association, inspired from the Hall’s proxemics, was developed to improve both the 3D camera-based and 2D laser-based human detection techniques. The dual Kalman filters are employed to track the human trajectory in parallel. The integration of the 3D camera-based and 2D laser-based human tracking is the key function of the system providing the real-time feedback for both the HOGNN to reduce false-positives of the camera-based and laser-based human detection and the Kalman filter to enhance the quality of the human trajectory tracking under uncertain environmental conditions. We implemented the sensor integration on ROS and validated it through real-world experiments.
A Transformerless 1-φ, 5-Level Half-Bridge PV Inverter Configuration Based on Switched-Capacitor Technique

Leakage current is a serious problem in the transformerless solar PV inverter configurations. The flow of leakage current is mainly due to the high-frequency oscillations in the voltage across PV parasitic capacitance (vCPV). Half-bridge (HB) and common ground type inverter topologies are highly suitable for eliminating these oscillations as these configurations allow direct electrical connection between the source and the load/grid ground. However, they suffer from either poor dc-bus utilization or asymmetric operation leading to dc-offset in the output voltage. In this article, a novel 1-φ, 5-level inverter is proposed, which employs a HB structure to eliminate voltage oscillations from vCPV without compromising with the dc-bus utilization or symmetric operation. This is achieved by integrating a switched capacitor network with the HB structure. All the necessary analysis and design procedures are included to support the various claims. The claims are further validated using MATLAB simulations and experiments on a laboratory prototype. The proposed topology is compared with the various existing topologies of similar nature (like 5-level output voltage, common ground or HB type configuration and 1-φ) to prove its superiority.
A Hybrid Semantic Segmentation Based on Level-Set Evolution Driven by Fully Convolutional Networks

Semantic segmentation network is one successful deep architecture for understanding key foregrounds. The fully convolutional networks (FCNs) can learn the regional masks by composing layered representations coarse-to-fine. Nevertheless, these increasing feature maps often fail to improve significantly the discrimination to segmentation details, due to the lack of accurate spatial representations and prior on salient foreground boundaries. This paper presents a hybrid semantic segmentation network based on a differentiable level-set evolving on multiple feature channels, which can be driven by the deep architectures of FCNs. To the level-set iterations, the re-initialization and Gaussian smoothing operations have been integrated to achieve an uniform training procedure. Also, this proposed wrapping level-set layer can be easily generalized to other existing deep segmentation architectures. Experimental results on various open datasets verify that, compared with related baselines, this hybrid framework significantly improves the spatial details and accuracy rates of the segmenting foreground boundaries.
Accurate and Efficient Thermal Analysis of Slow Wave Structures for Helix Traveling-Wave Tubes by Finite-Element Method

In slow wave structures (SWSs) for helix traveling-wave tubes, both thermal conductivities (TCs) of materials and thermal contact resistances (TCRs) are temperature-dependent, which have a great influence on the heat dissipation of helix SWSs. This article presents an accurate and efficient thermal analysis method of helix SWSs by finite-element method (FEM). In this method, a novel contact boundary condition (CBC) approach is proposed. This CBC approach can easily integrate various TCR models to simulate temperature-dependent TCRs accurately and conveniently. Specially for double nonlinearities arising from temperature-dependent TCs and TCRs within helix SWSs, a fast nonlinear solver named FMONS is also proposed to significantly improve the efficiency of the thermal simulation for helix SWSs. Based on this method, we further developed a 3-D thermal design tool for helix SWSs called TS2. Different from all the present FEM codes, for example, ANSYS, our TS2 can directly model temperature-dependent TCRs. By simulating various helix SWSs including a global model of the entire helix SWS, it is found that our TS2 has very high accuracy and efficiency in the thermal analysis of helix SWSs, which would be very useful for the design of high-power helix traveling-wave tubes.
A Comparative Study of Axial-Flux versus Radial-Flux Induction Machines

This paper presents the design process, detailed analysis, and a comparative study on the advantages of axial-flux induction machines (AFIM). A radial-flux induction machine (RFIM) is also studied as benchmark motor. Both motors are designed with six poles, 36 stator slots, and 32 rotor bars. Finite-element analysis is used to predict the performance of the two motors under both transient and steady state conditions. A complete comparative study along with the simulation results are reported. It is found that the studied AFIM has higher efficiency and power density.
Scale-Aware Graph-Based Machine Learning for Accurate Molecular Property Prediction

With great growth in the volume of chemical databases, machine learning receives significant attention from various scientific communities for efficient high-throughput screening of molecular properties and drug discovery on the millions of chemical compounds. In particular, graph neural networks (GNNs) have been widely studied in chemistry-related fields because a molecule is natively represented as a mathematical graph. In GNNs for the graph-level analysis, a global operation called readout is applied after node embedding to generate a graph-level embedding that represents characteristics of the whole graph. However, commonly used readouts frequently distort scale information of the graph and consequently degrade the prediction accuracy of GNNs. This problem becomes more serious in molecular machine learning because molecules have many important scale information (e.g., molecular weight and total energy). In this paper, we investigate this scale distortion problem in GNNs caused by the readouts for the first time and propose an efficient solution with a new attention-based readout. In the experiments, the proposed readout outperformed commonly used readouts on various GNN architectures.
An Enhanced Multilayer GI Based Control for Grid Integrated Solar PV System

This work presents an enhanced multilayer second-order generalized integrator (EMSOGI) based control approach for a double stage single phase topology based solar PV array interfaced with the grid. This PV system is integrated into the grid at the DC bus of the voltage source inverter (VSI). The PV system satisfies the nonlinear load demand at the PCC (Point of Common Coupling) and feeds the excess power to the grid. The control is able to transfer the power even when the system is subjected to adverse grid conditions. The control is able to solve the inherent problems of the most popular controller used, that is, SOGI and its advanced versions. The boost converter is operated using a P&O (Perturb and Observe) algorithm to maintain the peak power point tracing from the solar PV module. Furthermore, the obtained results of a developed model in MATLAB SIMULINK platform are observed to show that the IEEE-519 standard is duly followed under various adverse grid conditions.
Circulating Current Reduction in Parallel Grid Connected Two Level VSI using Hybrid Carrier Based Modulation

The power handling capacity of a grid connected converter system can be increased by connecting inverters in parallel. It offers advantages such as modularity, reliability and efficiency. The voltage difference between parallel connected inverters will cause a circulating current to flow between the inverters. This voltage difference occurs (i) when the carriers of both the inverters are phase shifted for interleaved PWM, (ii) when operating both the inverters with different power levels and (iii) due to hardware issues like device tolerances, dead time differences etc. Due to circulating current, the inverters conduct more current, which will effect the performance of parallel connected system and also result in additional conduction losses. In this paper, a hybrid carrier based modulation technique is proposed to reduce the circulating current when the parallel connected inverters (equal power sharing) are operated with interleaved PWM. The circulating current is directly related to the difference of common-mode voltages of the parallel connected inverters. The proposed technique reduces this difference by using a novel hybrid PWM method, where the carrier frequency is changed for some duration to reduce the overlap time of common mode voltage of two converters. Mathematical model and simulation results are presented to validate the proposed technique.
Towards a Wristed Percutaneous Robot With Variable Stiffness for Pericardiocentesis

Pericardiocentesis is the drainage of excessive fluid in the pericardium surrounding the heart under ultrasound guidance. The percutaneous procedure comes with the risk of cardiac perforation since the needle is inserted towards the beating heart. This letter presents a novel percutaneous robot that features a flexible wrist made of shape memory alloy (SMA) spring which can vary its stiffness through temperature variation. The SMA spring can be heated and thus stiffened to allow insertion. At lower temperature, it behaves like a spring backbone that allows the needle to be bent away from the heart and thus minimizing the chance of cardiac perforation. A heating tube made of nichrome wire and PTFE tube was developed to be the heat source for the SMA spring. The robot has an outer diameter of 4 mm, similar to the dilator used in pericardiocentesis, and a lumen to pass a guidewire through. A stiffness model and kinematics model were developed and verified through experiments. The surface temperature of the SMA-based wrist was evaluated to be less than the tissue necrosis temperature. Lastly, we demonstrated that the robot could be inserted into gelatin phantoms of different stiffnesses and produce a wristed motion under ultrasound guidance.
High Stability Adaptive LDO using Dynamic Load Sensing for Low Power Management of Wireless Sensor Networks

Presented in this paper is the design of a low-dropout voltage regulator implemented in 65nm CMOS technology for low power management of wireless sensor networks for IoT technology. High stability is achieved using a dynamic load sensing technique where the sensed load current adjusts the bias of the error amplifier. The dynamic mechanism provides low/high bias current to the error amplifier as the load current decreases/increases, respectively. The LDO adapts and transforms from a 2-stage configuration at light-loads to a 3-stage configuration at high-load conditions for a more current-efficient system. Two power transistors are activated depending on the load conditions, with the main power transistor activated only if the load current is greater than the threshold current Ionof 919. 11µA, allowing for wide range of load application from 0 to 100 mA. Simulation results confirm that adaptive biasing technique through dynamic load sensing and subthreshold operation of the system enables the LDO to achieve ultra-low quiescent current of 930.21nA with power consumption of 744.17nW at no-load. A regulated output voltage of 600mV from 800mV to 1.2V supply voltage with a dropout voltage of 135mV.
Inverter Topology for Zero-Ventilated High Frequency Induction Heating Systems

Availability of wide range litz wire conductors have widened the application potential of induction heating systems, complete air-cooled versions have now emerged for certain application areas such as pre and post weld heat treatment of welding pipes, cap sealing etc. Similarly, the induction cap sealing process has also seen wider prospect of its use. Prominent of these applications are for sealing nutraceuticals, food, diary and quite a few pharma products, etc. Dust prone atmosphere prevailing in the process, often caused by the basic ingredient material to be sealed, needs dust proof enclosure. On the other hand, installations in dairy industry need exhaustive cleaning of enclosure regularly by water jet, making mandatory requirement of water tight enclosure. Such enclosure does not have any ventilation or air movement inside, even the free or natural convection is restricted. Deep study of thermal behavior of each component is prerequisite for controller's reliable performance. This article focuses on the role of inverter topology for design of reliable, zero-ventilated 1.5 kW, 50 kHz complete induction heating system that includes induction sealing head. The article would further detail the practical validation of chosen topology for design of power controller.
A Transformer Coupled Modular Multi-Active Half Bridge Charge Balancing Architecture in a Lithium-ion Battery Pack for Electric Vehicles

This paper proposes a modular transformer coupled multi-active half bridge (TxMAHB) charge balancing architecture for series connected Lithium ion battery (LIB) applications for Electric Vehicles. Unlike the traditional charge balancing circuits, the proposed modular TxMAHB architecture deals with the ac parameters across the transformer, and has the inherent soft switching ability and helps to reduce the switching losses and improves the efficiency. With the proposed TxMAHB architecture, the balancing time is linear with the initial imbalance in the state of charge (SOC). It is validated through extensive simulations and the results are compared with theoretical calculations for a 48 V, 40 AH Battery Pack designed for a Hybrid Electric Vehicle.
Non-Fixed Contact Manipulation Control Framework for Deformable Objects With Active Contact Adjustment

The assumption of fixed contact between robots and deformable objects (DOs) is widely used by previous DO manipulation (DOM) studies. However, the fixed contact setting is inapplicable to many real-life applications due to various factors, such as the end-effector's type and the DO's intrinsic material properties. In such cases, the non-fixed contact (NFC) configuration, which has not been well-investigated, usually demonstrates better applicability in terms of contact flexibility and payload capacity. In this letter, we investigate the problem of DOM with NFC, particularly the contact condition characterization and active contact adjustment strategy. To this end, we propose a versatile index and an optimization-based method for the contact description and adjustment. Then a systematic control framework combining both deformation control and active contact adjustment control is proposed for task-level DOM control. The feasibility and effectiveness of the proposed method were verified via the presented experiments.
An Integrated Four-leg Three-phase Transformerless PV Inverter with Voltage Boosting Capability

The leakage current minimization and voltage-boosting are the two essential features for transformerless PV inverter configurations. In this paper, a conventional four-leg three-phase inverter is retrofitted with an inverting buck-boost converter to achieve the voltage boosting capability. The proposed power circuit configuration facilitates power transfer either directly from the PV source or through the buck-boost converter. Further, a new group of PWM schemes is introduced for the proposed power circuit configuration. These modulation strategies are capable of eliminating the switching frequency transitions from the common-mode voltage while maintaining the dc-link utilization for the inverter. The proposed modulation strategy results in a higher effective frequency for the fourth leg to optimize the design of the boosting inductor. The working principle of the proposed power converter is validated with simulation results.
A Single Phase Integrated Battery Charger with Active Power Decoupling for Electric Vehicles

Electric vehicles play an important role in reducing the effects of climate change. The main components of an electric vehicle are an electric motor and its associated driving circuit, which is powered by a rechargeable battery. This paper briefly discusses the classification of various charging architectures of electric vehicles such as on-board chargers and off-board chargers. Vehicles with dedicated on-board chargers are equipped with separate circuits for driving and charging, which increases the size, weight, cost and charging time of battery of the vehicle, compared to the conventional electric vehicles with off-board charging architecture. In order to overcome these challenges, this manuscript describes a novel single phase integrated battery charging topology for electric vehicles, which is capable of accomplishing the charging operation by a direct connection to the single phase grid at home or workplace, without including any extra components into the existing motor driving circuit. But single phase grid-connected energy conversion systems suffer from power oscillations, with frequency twice as that of the grid frequency. This inherent phenomenon effects the performance of the loads, which in the case of electric vehicles, is a battery. With the help of proposed charging topology, the inherent second harmonic ripple on the DC bus voltage can be eliminated using an active decoupling circuit and various challenges associated with the electric vehicle technology such as higher cost, limited range and long recharging times for batteries can be addressed very effectively. The simulation results are provided for the proposed charging topology.
SELIS BDA: Big Data Analytics for the Logistics Domain

In this paper we present the SELIS Big Data Analytics and Machine Learning System (BDA), an open-source cloud-enabled elastic system that has been designed and implemented in order to address data related issues from the logistics domain. By taking into consideration real-life data analytics needs from more than 40 EU logistics providers we present the detailed SELIS BDA architecture along with the generic data and execution model devised to accommodate their diverse needs. We describe the main technologies we have utilized to realize the respective offering and justify our choices from the wider open-source Big Data systems community. We experimentally test our offering under various workloads where we prove that it can scale to serve a large number of concurrent requests while its abstraction/orchestration poses a very small overhead compared to the stand-alone Big Data systems. We believe that the SELIS BDA can be an easy-to-use entry point for the big data analytics world for any logistics company especially from the SME domain.
Towards Smart Data Selection From Time Series Using Statistical Methods

Transmitting and storing large volumes of dynamic / time series data collected by modern sensors can represent a significant technological challenge. A possibility to mitigate this challenge is to effectively select a subset of significant data points in order to reduce data volumes without sacrificing the quality of the results of the subsequent analysis. This paper proposes a method for adaptively identifying optimal data point selection algorithms for sensor time series on a window-by-window basis. Thus, this contribution focuses on quantifying the effect of the application of data selection algorithms to time series windows. The proposed approach is first used on multiple synthetically generated time series obtained by concatenating multiple sources one after the other, and then validated in the entire UCR time series public data archive.
Single Active Switch High Voltage Gain DC-DC Converter with Reduced Devices Stress

This paper proposes a non-isolated high voltage gain dc-dc converter with reduced voltage stress across semiconductor devices. Coupled inductors and switched capacitor network are the two boosting techniques employed for attaining high voltage gain. The stored energy in leakage inductance is recycled and utilized to enhance the output voltage with the help of a passive clamp circuit. Equivalent circuits for different modes are used to explain the operating principle of the converter and finally, the design expressions are presented. A laboratory prototype of 400 W with an input voltage of 25–50 V and regulated output voltage of 400 V is fabricated and obtained experimental results are provided to justify the workability of the converter.