Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-sensor Fusion Framework for Obstacle Avoidance via Deep Reinforcement Learning

Deep reinforcement learning enables the agent to decide autonomously for obstacle avoidance, and the policy is learned by interacting with the environment without labelled data. However, the difference between simulation and reality affects the transferring of the policy. The data of lidar shows consistence between sim and real, but possesses limited filed of vision. And the data of camera have wide vertical view and rich features, but differs greatly between simulated and actual worlds. To solve the problem of avoiding irregular and dynamic obstacles, we propose a multi-sensor fusion framework for obstacle avoidance via deep reinforcement learning. After preprocessing the data from different sensors, we segment the color image from RGB camera and process the depth image according to the segmented image. We remove the traversable area from the depth image, assign the depth region labelled as person a coefficient of inflation and down sample the depth image to pseudo-lidar. Then we calibrate the data from lidar with pseudolidar and get calibrated lidar for subsequent policy network. To overcome the problem of avoiding moving obstacles, we integrate the Long Short Term Memory network into the Proximal Policy optimization network, and design corresponding training process. We train the network in simulation, and test it in both simulation and reality. The results demonstrate the effectiveness of our method.
Tools for Animation and Shape Manipulation: A Scoping Review

Objectives: This paper presents a review of the latest, state-of-the-art research on animation tools, shape de-formation and manipulation published over the last 20 years (from 2001 to 2021). By presenting a systematic classification of available tools and expressing the methods and limitations of each of these tools, our goal was to answer some of the questions faced by researchers who are seeking to work in this field. We posed three questions and answered them by reviewing the literature. Materials and Methods: Four databases (IEEE Xplore, ACM Digital Library, Springer, and Google Scholar) were searched for studies that introduce animation, deformation, or manipulation tools. The initial search yielded 296 articles. After evaluating them against the input criteria, 32 studies remain. Results: Out of the 32 studies we reviewed, four of these studies were related to shape manipulation and deformation, and the rest were related to animation tools. The results show that 24 of the tools developed are suitable for 2D manipulation or animation. Also, 28 cases were suitable for novice users. Conclusion: We presented a review of animation, deformation or manipulation tools, including methods and limitations. We also By referring to this work, the new researchers can identify their target community and determine what features previous papers have focused on and what limitations they have, and finally what choices they should be made to get started.
Clothing gripping and sorting based on a dual-arm collaborative robot

The aging speed is accelerated, and the folding of clothes will cause trouble to the elderly who are physically inconvenient. This paper proposes a dual-arm collaborative robot to help the elderly complete the folding of clothes. In this paper, the joint change matrix is established by the D-H method for the forward and reverse kinematics settlement of the robotic arm of the collaborative robot; The RRT* algorithm is used to plan the path of the collaborative robot to achieve better planning results. On this basis, the clothing into the grasping point acquisition and contour matching, improve the efficiency of folding in different types of clothing, and finally through the experiment for the corresponding demonstration.
Flexible Control Approach for DC Microgrid Oriented Electric Vehicle Charging Station

Photovoltaic (PV) and wind-based intermittent dis-tributed energy resources have a negative impact on the quality of the power supply of the DC microgrid-oriented electric vehicle charging station, resulting in numerous control issues. The DC link voltage of the DC microgrid can be automatically balanced and monitored by properly coordinating the operation of each energy source and storage device. In this paper, the converter controller parameters of the individual subsystems of the DC microgrid (i.e., wind, PV system, battery, fuel cell, and electrolyzer) are designed using the state-space transfer function tool to solve system complexity and handle the intermittent nature of renewable energy. Firefly algorithm combined with particle swarm optimization (FA-PSO) is used to design the DC microgrid controller to reduce/mitigate DC voltage fluctuations. The ability of the proposed control strategy to withstand changes in solar in-solation, wind speed, and load perturbation is evaluated. For the DC microgrid controller design, TS-fuzzy, gray wolf optimization (GWO), and an adaptive neuro-fuzzy inference system assisted by particle swarm optimization (ANFIS-PSO) controllers are all compared and validated through hardware implementation. The results show that the proposed FA - PSO controller outperforms the other control strategies in terms of performance.
Inverter-Induced Bearing Currents: A Thorough Study of the Cause-and-Effect Chains

In the last three decades, the study of inverter-induced bearing currents has become a very important line of research, and hundreds of works related to this theme can be found in the literature. Thus, several contributions concerning these currents were made, such as in the areas of modeling and simulation techniques, selection of appropriate mitigation method, developments of new mitigation devices, and damage monitoring within the bearings, among others. In this context, much information related to bearing currents cause-and-effect chains is available. However, many contributions that have been developed in recent years for describing the mechanisms behind the bearing currents occurrence can only be found in a fragmented way. Thus, great efforts must be employed to provide a clear comprehension of each bearing current type and all the corresponding issues. To overcome this problem, this article provides a broad overview of the physical principles related to the inverter-induced bearing current, by means of a detailed and didactic approach. A huge volume of information was compiled, describing the expected currents maximum amplitudes, their probability of occurrence, and the motor size which they are more expected to occur, among other relevant issues.
A Semi-Quadratic Buck PFC Converter with Less Input Current Distortion

In universal-line, AC-DC buck power factor correction (PFC) converters require high PF and less THD in input current. Due to pulsating input current in the conventional buck and quadratic buck converters, their PFC versions give poor PFC performance and demands increased input filter requirement. This paper introduces two switch-semi quadratic buck PFC converter in intermediate DC-DC conversion state. The salient features of this PFC topology are high step-down gain, smother input current waveform with less ripple content. The average current controlled method is employed, and converter is operated in CCM mode. To verify the effectiveness of this converter for PFC applications, simulations are performed for 110V, 50 Hz supply and for load of 24V,65W. The input current THD and PF are compared with the conventional buck PFC converter for identical input and output conditions.
Analysis of Different Charging methods of Batteries for EV Applications with Charge Equalization

The lithium ion batteries are used in many mod-ern day applications because of their unparalleled advantages over the other battery technologies. In the literature, different charging techniques have been discussed and it is very important to investigate the charging techniques for lithium ion batteries. Moreover for the Electric Vehicle (EV) application, the balancing of a series connected battery string becomes a topic of great importance. The problem of charge imbalance can highly degrade the performance and life expectancy of series connected batteries. With the help of a battery equalization charger, each battery will go through a balanced charging process and receive the same amount of charge regardless of the unique characteristics of each individual battery unit. In this paper Matlab simulink software has been used to investigate the charging characteristics of different algorithms such as constant voltage charging, constant current charging, hybrid constant current constant voltage charging and pulse charging. A bidirectional DC-DC converter is employed to attain a bidirectional power flow. In addition to that a positive negative pulse charging method is employed in order to equalize the state of charge of three individual battery units which were initially at different state of charge levels. A fast charge equalization is achieved with employing a separate controller for switching devices with energy redistribution process.
Real Time Mask Detection using Convolutional Neural Network

With the advent of Convolutional Neural Networks, the field of image classification has seen tremendous growth, with various previously impossible applications now being pursued. One such application is face mask detection, which is an important problem to solve, considering recent pandemic. The novelty of this work is the training of YOLO (You Only Look Once) framework for custom object detection, which in this case is face mask, based on some empirical rules for fine-tuning the performance. Also, image classification is proposed to be combined with tracker, in order to implement real world access grant system based on compliance shown by mask wearer.
Enhanced Wideband Photoresponse and Detectivity in In2S3/CIGS Heterojunction Based Photodetector

A detailed multiphysics (optical, electrical) simulation of a In2S3 - CIGS heterojunction-based photodetector has been presented. We employed the Ansys LUMERICAL Multiphysics tool for numerical simulation, which provides an automated process for executing device-level simulations and computes important outcomes including dark and photocurrents, responsivity, and bandwidth. CIGS is a potential light-harvesting material for wide-area broadband photodetection, despite the fact that it has not received much investigation. Our proposed work shows the broadband photosensitivity of the photodiode with responsivity over 0.8 AW-1, external quantum efficiency above 100% and detectivity over 1012Jones within 400nm to 1000 nm when fed to a bias voltage of 1V. It was observed that CIGS based photodetector demonstrated ability to detect even weak light.
Constant Voltage Charging Curve Based Features and State of Health Estimation of Li-ion Batteries: A Comprehensive Study

The State of Health (SOH) of Li-ion batteries is usually estimated from the charging or discharging curves. While the discharging curves of EV batteries are highly dynamic in nature, the Constant Voltage Constant Current (CCCV) protocol is the most common charging method. This makes the charging curve based SOH estimation of EV batteries a preferred choice over the discharging curves. CCCV protocol can have single or multiple CC sections and can start from different State of Charge level of batteries. On the other hand, CV charging section is reported to be stable and is complete enough for estimating SOH, especially, in case of Level-I charging. However, the CV charging based SOH estimation is underexplored. More importantly, there are multiple health indicating features reported in the literature and many more can be derived, but details discussion on their impact on the SOH estimation are underexplored. In this paper, we focus on providing the correlations between the various health indicating features and their potential in estimating SOH in EV batteries. We have used a total fifteen features and the most popular datasets of NASA and MIT for the study. The study, based on Pearson coefficient of correlation, shows that the time to reach a particular current (in this case 0.5Amp) is the most dominant health indicating feature in both the dataset. Further assessment using machine learning model shows that SOH estimation using this feature, ‘time to reach 0.5 Amp’ provides highest accuracy or minimum Root Mean Square Error compared to using other features. This paper sets a clear guideline for future researchers in selecting the appropriate CV features for SOH estimation of Li-ion batteries in EVs.
AI-Driven Pheno-Parenting: A Deep Learning Based Plant Phenotyping Trait Analysis Model on a Novel Soilless Farming Dataset

Agriculture 4.0 will be data-driven and utilise modern technology in order to monitor plant life cycles and traits, resulting in better yield. Pheno-parenting, a new concept, derives certain methodologies from plant phenotyping, which monitors plants at various stages of their life cycle and supports their growth by deploying modern tools and technologies. In this work, a small-scale hydroponic system was set up for plant life cycle image data collection and analysis, which consists of thirty plants of three species (i.e., petunia, pansy, and calendula), with ten plants of each species grown. A Deep Neural Network (DNN)-based approach has been adopted to analyse key tasks such as plant species recognition, growth analysis, health analysis, and yield stage identification. Calendula plants have been correctly recognised with above 95% detection accuracy in all test cases. The result thus obtained indicates that side-view images are more effective at identifying species and tracking growth. On the other hand, top-view photographs do a better job of capturing the texture and colour characteristics of leaves and budding flowers. The Growth Development Index (GDI) metric, which first rises with an increase in nutrient input up to 31 ml and then saturates, has been proposed to provide a better understanding of plant growth, health, and productivity.
Three-Dimensional Resultant Vibration Based on Hydraulic Excitation

For studying a three-dimensional vibrating screen based on the fluid-filled pipe, a vibration testing system based on hydraulic excitation was established. The pressure wave generated from the vibration exciter will stimulate the pipe network to vibrate. The elastic plate served as the vibrating screen whose dynamic characteristics can be revealed by experiments. According to the measured signals, the twodimensional and three-dimensional resultant vibration trajectories were obtained. By comparison, the three-dimensional vibration trajectory is more complicated. Furthermore, served as the three-dimensional vibration screen whose screening efficiency is more higher, indicating that the three-dimensional vibrating screen based on the hydraulic excitation has been verified effectively. So we can conclude that a new vibration mechanism is found and this work is expected to serve for the optimum design of the hydraulic excitation system and play a guiding role to experimental research in future. Keywords-hydraulic excitation; vibration exciter; acceleration transducer; resultant vibration
Linear and non-linear monotone methods for valuing financial options under two-factor, jump-diffusion models

The evolution of the price of two financial assets may be modeled by correlated geometric Brownian motion with additional, independent, finite activity jumps. Similarly, the evolution of the price of one financial asset may be modeled by a stochastic volatility process and finite activity jumps. The value of a contingent claim, written on assets where the underlying evolves by either of these two-factor processes, is given by the solution of a linear, two-dimensional, parabolic, partial integro-differential equation (PIDE). The focus of this thesis is the development of new, efficient numerical solution approaches for these PIDE’s for both linear and non-linear cases. A localization scheme approximates the initial-value problem on an infinite spatial domain by an initial-boundary value problem on a finite spatial domain. Convergence of the localization method is proved using a Green’s function approach. An implicit, finite difference method discretizes the PIDE. The theoretical conditions for the stability of the discrete approximation are examined under both maximum and von Neumann analysis. Three linearly convergent, monotone variants of the approach are reviewed for the constant coefficient, two-asset case and reformulated for the non-constant coefficient, stochastic volatility case. Each monotone scheme satisfies the conditions which imply convergence to the viscosity solution of the localized PIDE. A fixed point iteration solves the discrete, algebraic equations at each time step. This iteration avoids solving a dense linear system through the use of a lagged integral evaluation. Dense matrix-vector multiplication is avoided by using an FFT method. By using Green’s function analysis, von Neumann analysis and maximum analysis, the fixed point iteration is shown to be rapidly convergent under typical market parameters. Combined with a penalty iteration, the value of options with an American early exercise feature may be computed. The rapid convergence of the iteration is verified in numerical tests using European and American options with vanilla payoffs, and digital, one-touch option payoffs. These tests indicate that the localization method for the PIDE’s is effective. Adaptations are developed for degenerate or extreme parameter sets. The three monotone approaches are compared by computational cost and resulting error. For the stochastic volatility case, grid rotation is found to be the preferred approach. Finally, a new algorithm is developed for the solution of option values in the non-linear case of a two-factor option where the jump parameters are known only to within a deterministic range. This case results in a Hamilton-Jacobi-Bellman style PIDE. A monotone discretization is used and a new fixed point, policy iteration developed for time step solution. Analysis proves that the new iteration is globally convergent under a mild time step restriction. Numerical tests demonstrate the overall convergence of the method and investigate the financial implications of uncertain parameters on the option value.
Security Authentication Protocol for Massive Machine Type Communication in 5G Networks

As one of the three major applications of 5G, massive machine type communication (mMTC) is mainly oriented to network access scenarios for massive devices. mMTC focuses on solving the problem that traditional mobile communication cannot well support the Internet of Things and vertical industry applications. According to the current 3GPP standard, these massive devices still use the traditional authentication process to realize mutual authentication with 5G core network, which brings a lot of communication and computing overhead. In addition, privacy protection will also be threatened in the authentication process. In order to alleviate the signaling congestion during authentication and solve the insecurity in authentication, this paper proposes a group authentication scheme for mMTC. Due to the characteristics of low power consumption and massive connection, the scheme mainly adopts lightweight encryption operation to avoid the computational burden of equipment and server. We verify the security of our scheme by using BAN logic to formally analyze the scheme. Then, through informal analysis, our proposed scheme can not only avoid signaling blocking and provide mutual authentication but also resist various possible attacks. Through performance evaluation, it is proved that our scheme has better efficiency.
Review of the Nonlinear Filters in the Land Data Assimilation

The land data assimilation research has become the emerging domain in the geoscience,the data assimilation algorithm obtained rapid development and widespread application taking the nonlinear filter as representative's.The extended Kalman filter,unscented Kalman filter,the ensemble Kalman filter and the SIR particle filter are discussed in the Bayesian theory framework from the viewpoint of the recursive Bayesian estimation.Towards the problems in the application of the ensemble Kalman filter and the SIR particle filter,some techniques that can improve the filter performances are also reviewed,such as the covariance localization,the covariance inflation,the double ensemble Kalman filter,the perturbations in the ensembles,the model forcing and parameters,the ensemble square root Kalman filter and the improved variants of the particle filters.The advantages and disadvantages of each filter as well as the applied perspective and the future research directions are discussed.
Quantifying the acoustics packing density of fish schools with a multi-beam sonar

Multi-beam (swath) sonar systems provide the capability to ensonify an entire aggregation of fish in a single pass. However, estimation of abundance and discrimination between species via the use of target strength are considerably more complex than using traditional echosounders, because they ensonify targets at a much wider range of incidence angles. The beam pattern and along beam resolution of multi-beam swaths can produce individual sample volumes that are of similar magnitude to an individual fish (particularly for large fish, say >1m in length). If individual fish can be resolved, (either as a single fish within a sample, or as multiple contiguous samples that delineate a single fish), and if one assumes that this situation applies to the whole school, acoustic packing density can be determined by dividing the volume of the school by the number of detected acoustic targets. This estimate is proportional to the actual packing density of the fish, defined as the number of fish per unit volume of water. Acoustic backscatter of fish from a number of schools comprising different species were collected off Perth, in 2005 and 2007, using a Reson Seabat 8125 and 7125 respectively. Nearest neighbour distances of between 1 and 3 body lengths were observed and packing density of acoustic targets showed distinct variation between some species. However, schools of the same species also displayed different acoustic packing densities at different stages of their growth and development. Such differences were more difficult to observe in schools of fewer fish because the variations in packing density had less impact on the overall volume of the smaller schools associated with fewer fish. Therefore discrimination between species was only deemed possible when surveying two species of different sized fish at the same time. Video ground truth data is recommended to confirm species composition whatever the type of school observed.