Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-sensor Fused Localization Algorithm Based on Optimized Nearest Neighbor Search

Fast and accurate localization of mobile robot is one of the most critical problems in its field, by which the robot can obtain information about its environment and track its poses. An optimized adaptive Monte Carlo localization (BBF_AMCL) based on a 2D raster map is proposed to solve the robot localization problem. The improved localization algorithm is based on 2D laser information, IMU, odometer, and cliff sensors for localization. First, the efficient nearest neighbor search technique is used to construct the likelihood domain map, which reduces the cost time to build the likelihood domain map and speeds up particle weight updating. Second, an expanded Kalman filter is utilized to combine data from the IMU and the odometer in order to increase a priori positioning accuracy, as well as to reduce the robot's judgment time for abduction by using cliff sensors to assess if abduction has happened, thus increasing real-time localization performance. When searching the nearest neighbor obstacle distance, the optimized nearest neighbor search algorithm will sort by the distance from the lookup point to the node's hyperplane, then continue to search the node with the highest priority, and when the set search time is exceeded, directly return the current nearest neighbor distance as the approximate nearest neighbor. Overall, the algorithm has significant improvement in the pre-processing localization phase and good accuracy improvement and time reduction in the real-time localization phase.
Comment on “Particle simulation of ion heating in the ring current” by S. Qian, M. K. Hudson, and I. Roth

In a recent paper, Qian et al. (1990) investigated an ion cyclotron wave (ICW) instability and heating of thermal ions in a plasma with a loss cone distribution of hot ions. This study is an extension of a simulation study (Omura et al., 1985) of helium heating in an ICW instability driven by anisotropic hot protons with a bi-Maxwellian distribution. The dependence of the ion heating on the thermal H{sup +}/He{sup +} density ratio is studied by simulation runs with better spatial resolution. In the simulations of the ICW instability, a small percentage of the thermal helium ions are accelerated beyond the theoretical maximum velocity. This is because the excited waves are not exactly monochromatic as assumed in the theory, and a stochastic acceleration takes place in the presence of waves with different wave numbers and frequencies. To confirm that the parallel electric field (Qian et al., 1990, equation (10)) is not operative in the process of parallel heating, the author has performed a computer experiment where the parallel electric field is artificially eliminated from the equation of motion in the hybrid code. He assumed the same parameters of the simulation in the work by Omura et al. (1988). Themore » results shows that the dynamics of the thermal helium ions in the acceleration phase is the same and that the time history of the thermal energy of helium ions is almost identical with the results of Omura et al. (1988). The computer experiment evidently demonstrates that the parallel electric field is not responsible for the parallel heating. As a summary, the interaction of the forward and backward traveling waves plays an important and essential role in the process of parallel heating found in the ICW instability driven by a temperature anisotropy. « less
Dynamic Distributed-Layered Semi-Grant-Free NOMA Scheme for Uplink Transmissions

Grant-free (GF) transmission strategy and non-orthogonal multiple access (NOMA) scheme have been applied in massive machine-type communications (mMTC) to reduce the latency and improve the successful access probability. In this paper, a dynamic distributed-layered semi-GF (SGF) NOMA scheme is proposed by considering the trade-off between the successful access probability and the achievable spectrum efficiency. In the proposed scheme, an SGF model is adopted and each sub-band of a grant-based (GB) user has multiple power levels, which can support the access of the GF machine-type communication devices (MTCDs). To improve the spectrum efficiency of the GB users and the successful access probability of the GF MTCDs, the power allocation and power level choosing algorithm is proposed. Simulation results show that the proposed scheme can make a good trade-off between the successful access probability of MTCDs and the achievable spectrum efficiency of the GB users. Besides, the proposed scheme outperforms the traditional GF NOMA scheme.
Classification Of Brain Images For Identification Of Tumors

Early detection of brain tumors is very crucial as they grow extremely fast. To extend patients' life expectancy, correct treatment planning and precise diagnoses are critical. Manual diagnosis can be prone to errors and is a time-consuming and complex task for radiologists because of how minute variations in the tumor could lead to a completely different diagnosis. The proposed method is focused on creating an automated way of classifying brain MRI images by using SOTA models like VGG-16 and InceptionV3 and building on them. The brain MRI images are classified into four classes by extracting significant features and experimented with and without pre-processing. The experimental results have shown that the VGG-16 model used, although without any image augmentation, has given a high validation accuracy of 74%. The inceptionV3 model without image augmentation techniques reported a worse validation accuracy of 69%, defining VGG-16 to be the better classifier.
Design of a T-shaped UWB Textile Antenna for WiMax/WLAN Application

In this article, conductive electro-textile and cotton substrate is used to fabricate a T-shaped ultra-wideband (UWB) antenna. Using ANSYS high frequency structural simulator (HFSS), $\mathrm{S}11\leq-10\ \text{dB}$ and Voltage Standing Wave Ratio $(\text{VSWR})\leq 2$ is obtained for the coplanar waveguide (CPW)–fed antenna over the range of frequency from 3.5 to 12 GHz. The antenna's 50 mm x 38 mm x 0.6 mm dimensions allowed it to radiate in a directed manner with a 5 dBi average peak gain over the whole impedance bandwidth. This paper's main contribution is the use of a wearable antenna with a 159.24% wide impedance bandwidth.
Tapped Inductor Works on Switched Mode Power Converter for Electric Vehicle Power Conversion System

Switched-capacitor technique has a good application for voltage source circuits. On the other hand, the switched-inductor approach in current source circuits can also produce different possibilities for the output circuit. When the tapped inductor is added to the circuit, it will affect the load side again, and the way to access the circuit is different; it is conversion has an extensive range. This paper introduces the method, in the case where the components remain unchanged and the circuit connection mode changed, to make the output current change during the use of the relevant power circuit. How to calculate and design the power supply circuit you need according to your needs during the use of the appropriate power supply circuit. This circuit can be applied to photovoltaic cell output conversion, current mode motor control, and some energy distribution. It can also be used in electric vehicle (EV) power conversion. The current mode control is especially used for current mode charging of EV charger.
A Novel Abnormal Vibration State Recognition Method for Rolling Mill

The vibration of rolling mill is nonlinear and non-stationary. To effectively extract rolling mill vibration signal features for fault diagnosis, a method of feature vector extraction based on empirical wavelet transform (EWT) and fuzzy entropy (FE) is proposed. The method extracts the midpoint between the adjacent maximum values in the frequency domain of vibration signals, adaptively divides the Fourier spectrum, and constructs a suitable wavelet filter group to calculate different intrinsic mode functions (IMFs). Subsequently, calculate the fuzzy entropy of IMFs to be used as the feature vector. In addition, probabilistic neural networks (PNN) classifier is used to identify the types of vibration faults. Experimental results indicate that the accuracy of the method is higher than those based on Empirical mode decomposition (EMD) and multi-scale fuzzy entropy (MFE), under the limited sample condition. Thus, a referential diagnostic method is provided for practical applications.
A computationally efficient constrained predictive control law

A predictive control algorithm is presented that uses a weighted sum of linear quadratic (LQ) optimal predictions and 'mean level' predictions. Each time step only a single weighting parameter is solved for using a linear program problem hence giving a low computational load. The controller achieves LQ-optimality when constraints are inactive, and remains feasible (under mild conditions) if constraints are active.
Model-predictive control of a combined sewer system

Abstract Most reported applications of model-predictive control (MFC) have a narrow scope, with 1-5 variables to be regulated and a comparable number of manipulated variables. Several authors have claimed that global-optimization versions of MPC (such as DMC and QDMC) should be more useful for problems in which an entire system can be operated to achieve an economic and/or technical objective. In this paper, we describe the application of MPC to a large-scale, constraint-dominated problem: the minimization of combined-sewer overflows (CSOs) in the Seattle metropolitan area. The key decision variables are flowrates at 23 locations throughout the sewer network. There are approximately 40 output variables that must be kept between lower and upper bounds. The main issues addressed in the application are: (1) definition of an appropriate objective function for on-line optimization; (2) creation and maintenance of complex system models; and (3) use of state estimation to minimize the impact of disturbances and .
Non-isolated Multiport DC/DC Converters: Applications, Challenges, and Solutions

This paper presents an overview of different aspects of non-isolated Multiport Converters (MPCs). A systematic converter circuit derivation tool based on power flow graphs is presented. It assists power electronic engineers and researchers in creating and identifying multiport converter topologies that are more reliable and less complex. Then, major challenges with MPCs and some solutions are presented. These challenges are power-sharing and cross-regulation issues, limited or range-reduced operation modes, heavy computational burden and sensor requirement, and multi-parametric optimization and compromises. Reliability of MPCs and how component stress translates into failure calculations are discussed, followed by the Fault Tolerance (FT) feature to increase the reliability of MPCs.
Aiot Based Residential Smart Energy Meter with Power Saving Methodology

Electrical energy serves a vital role in modern world technology. Everyday power utilization is increasing because all the applications depend on electric power. The excessive power utilization of the residential electric load was a vital problem in unexpected energy purchases& purchases were to be aware the real-time power utilization is greater/lesser than the previous day's energy utilization. Here we are not discussing the GSM technology because it will create problems related to SIM cards and SIM devices consume extra power utilization for device operation. The quick message forward through the network& limit the message service stopped the user is not aware. Also, existing technologies share information about power consumption limits. The major problem of this system interrupts the power supply when it exceeds the power limit. Consumers expect an uninterrupted power supply. If we run an unexpected electrical load at the away of humans, the system accuses the notification to the purchaser for consciousness. The formation will attain, unlink the power supply by ML algorithm with the help of sensor and relay after a pre-defined time. This proposed work is AIOT based residential energy meter not only for power consumption information and billing analysis,: This is helpful for the user will aware of the electricity utilization and to reduce the power wastage cost of energy utilization. The convenience of this proposed work does not lose any data in power interrupt. AIOT protocol fed all the databases of electrical load and utilization details. The special focusing of this proposed work is an automated system for reducing the unexpected power usage schedules the usage load detail & it will be helpful for both consumers and distributors.
Position-Insensitive WPT System with an Integrated Coupler Based on ANN Modeling and Variable Frequency Control

This paper proposes a position-insensitive wireless power transfer (WPT) system which can realize zero phase angle (ZPA) and constant voltage (CV) output only by primary variable frequency control. An integrated electromagnetic coupler is introduced, as well as its 3D simulation model and equivalent circuit model. The self-compensating principle of the WPT system is presented, and the condition for achieving ZPA and CV output is given. Through Maxwell simulation, vast amounts of data about capacitances and inductances of the coupler varying with the transfer distance and misalignment distance is obtained. With the obtained data, the artificial neural network (ANN) model based on back propagation algorithm is established for parameter prediction, so as to solve the resonant frequency. Therefore, the switching frequency can automatically follow the change of the transfer distance and misalignment distance of the coupler. When the transfer distance, misalignment distance and load resistance change, the output voltage of the system can keep constant without any DC/DC control circuit or voltage regulation control. Finally, a simulation model is built, which verifies the feasibility and effectiveness of the proposed WPT system.
Numerical tests of modularity

We propose some numerical tests for identifying L-functions of automorphic representations of GL(r) over a number field. We then apply the tests to various conjectured automorphic L-functions, provid- ing evidence for their modularity and the associated Riemann hypotheses. Our chief examples are the Hasse-Weil L-functions attached to curves of genus 2 over Q and to elliptic curves over Q( √ −1). We discuss also three miscellaneous applications. The first two include the L-functions of high symmetric powers of Ramanujan'sand the modular form in S2(� 0(11)). The third application is an even 2-dimensional icosahedral Galois repre- sentation over Q, which conjecturally corresponds to a Maass form of eigenvalue 1 .
A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

In the process of classifying fresh-cut flowers, the classification accuracy of the algorithm plays a vital role in the control of quality stability, uniformity, and price of fresh-cut flowers, while the classification speed of an algorithm determines the possibility of industrial application. Currently, research on fresh-cut flower classification focuses on the breakthrough of classification accuracy, ignoring the real-time processing speed of the terminal, which seriously affects the use of fresh-cut flower online classification technology. In this study, RGB images and depth information data of 434 rose flowers were collected using a binocular stereo depth camera. Combined with the actual production line classification environment, a set of data argumentation solutions was developed under the condition of limited samples. The architecture was established and optimized based on the ShuffleNet V2 network backbone unit, transfer learning was performed, and an appropriate attention mechanism was invoked to classify flowers of five specifications. The experimental results showed that the proposed network structure had a competitive advantage in terms of parameter quantity, classification speed, and accuracy compared with traditional networks without an attention mechanism and other lightweight networks. The classification accuracy on the 3-channel (RGB channel) flower dataset and the 4-channel (RGB and depth channel) flower datasets were 98.891% and 99.915%, respectively, and the overall prediction classification speed can reach 0.020 seconds per flower. Compared to the fresh-cut flower classification machines currently on the market, the speed of the proposed method has a great advantage. These advantages are of great significance for the design and development of fresh-cut flower classification and grading systems, and the proposed method is instructive for the identification and application of multichannel data in the future.
Dynamic Elliptical Shaping Control for Swarm Robots

Solving the robotic swarm coverage problem for an elliptical area has various applications for exploring novel environments. Solutions for this problem should cover a specified ellipse and seamlessly adapt to changing numbers of robots. Previous solutions used techniques such as formation control, vector fields, and neural networks. While these techniques were successful, they all lacked one or more of the three key tenants of swarm elliptical attraction: complete coverage of an ellipse with commandable parameters, simplicity for scaling in the number of robots, and adaptive sizing. Additionally, no previous work presented guidelines for ensuring that the swarms could successfully and safely converge to the commanded ellipse without collisions. In contrast, this work presents a novel swarm elliptical attraction behavior with all three key tenants with guidelines for ellipse and swarm parameter selection. First, a new Lyapunov stable elliptical attraction behavior for Reactive Particle Swarms is presented. The behavior commands robots to cover the entire ellipse area for a specific semimajor axis, eccentricity, and orientation. Additionally, dynamic interagent spacing naturally ensures coverage for different numbers of robots. Second, the work presents a novel adaptive sizing algorithm that varies the ellipse’s semimajor axis based on the swarm state. The adaptive sizing algorithm specifies the eccentricity and orientation using time-varying functions. Third, guidelines for selecting the number of robots, commanded ellipse area, obstacle avoidance distance, and robot communication range that allow for successful aggregation to the commanded ellipse are presented. All three of the results are verified using simulation and hardware-in-the-loop trials.
The effect of Brownian motion on the bulk stress in a suspension of spherical particles

The effect of Brownian motion of particles in a statistically homogeneous suspension is to tend to make uniform the joint probability density functions for the relative positions of particles, in opposition to the tendency of a deforming motion of the suspension to make some particle configurations more common. This smoothing process of Brownian motion can be represented by the action of coupled or interactive steady ‘thermodynamic’ forces on the particles, which have two effects relevant to the bulk stress in the suspension. Firstly, the system of thermodynamic forces on particles makes a direct contribution to the bulk stress; and, secondly, thermodynamic forces change the statistical properties of the relative positions of particles and so affect the bulk stress indirectly. These two effects are analysed for a suspension of rigid spherical particles. In the case of a dilute suspension both the direct and indirect contributions to the bulk stress due to Brownian motion are of order ø2, where ø([Lt ] 1) is the volume fraction of the particles, and an explicit expression for this leading approximation is constructed in terms of hydrodynamic interactions between pairs of particles. The differential equation representing the effects of the bulk deforming motion and the Brownian motion on the probability density of the separation vector of particle pairs in a dilute suspension is also investigated, and is solved numerically for the case of relatively strong Brownian motion. The suspension has approximately isotropic structure in this case, regardless of the nature of the bulk flow, and the effective viscosity representing the stress system to order ϕ2 is found to be \[ \mu^{*} = \mu(1+2.5\phi + 6.2\phi^2). \] The value of the coefficient of ø2 for steady pure straining motion in the case of weak Brownian motion is known to be 7[sdot ]6, which indicates a small degree of ‘strain thickening’ in the ø2-term.
Event Message Clustering Algorithm for Selection of Majority Message in VANETs

The trustworthiness of nodes in Vehicular Ad-Hoc Networks (VANETs) is essential for disseminating truthful event messages. False messages may cause vehicles to behave in unintended ways, creating an unreliable transportation system. The efficiency and reliability of the transportation system can be obtained through trustworthy vehicular nodes providing correct event messages. In a VANET, the consensus issue can be resolved by employing blockchain. Even if we employ blockchain in a VANET, the trustworthiness of each message recorded needs to be verified separately since the blockchain itself does not guarantee the trust level of each event message. For instance, when there are multiple conflicting messages associated with a single accident on the road, a vote based on majority opinion can be considered one option for making a decision regarding the accident. In this work, we design the VANET event message clustering algorithm (VEMCA) to resolve the conflicting message problem. Furthermore, we develop a simulator for the VANET environment that demonstrates how the clustering algorithm can be used for event message validation. Experimental results show that our algorithm outperforms state-of-the-art clustering algorithms in terms of accuracy, precision, recall, f1-score, and computational time.
Rapid Estimation of Climate–Air Quality Interactions in Integrated Assessment Using a Response Surface Model

Air quality and climate change are substantial and linked sustainability challenges, and there is a need for improved tools to assess the implications of addressing these challenges together. Due to the high computational cost of accurately assessing these challenges, integrated assessment models (IAMs) used in policy development often use global- or regional-scale marginal response factors to calculate air quality impacts of climate scenarios. We bridge the gap between IAMs and high-fidelity simulation by developing a computationally efficient approach to quantify how combined climate and air quality interventions affect air quality outcomes, including capturing spatial heterogeneity and complex atmospheric chemistry. We fit individual response surfaces to high-fidelity model simulation output for 1525 locations worldwide under a variety of perturbation scenarios. Our approach captures known differences in atmospheric chemical regimes and can be straightforwardly implemented in IAMs, enabling researchers to rapidly estimate how air quality in different locations and related equity-based metrics will respond to large-scale changes in emission policy. We find that the sensitivity of air quality to climate change and air pollutant emission reductions differs in sign and magnitude by region, suggesting that calculations of “co-benefits” of climate policy that do not account for the existence of simultaneous air quality interventions can lead to inaccurate conclusions. Although reductions in global mean temperature are effective in improving air quality in many locations and sometimes yield compounding benefits, we show that the air quality impact of climate policy depends on air quality precursor emission stringency. Our approach can be extended to include results from higher-resolution modeling and also to incorporate other interventions toward sustainable development that interact with climate action and have spatially distributed equity dimensions.