Miracle-dz/UniFAR-FTUs huggingface dataset:
Landing Trajectory Generation using Gauss Pseudo-spectral Method

The soft landing of the Mars lander is one of the key problems of Mars exploration. The lander’s dynamic equation is nonlinear and is restricted by various constraints. Analytical solution is hard to be obtained. This paper focuses on planning the Mars landing trajectory by the Gauss pseudo-spectral method. The motion equations during the landing stage is firstly established. The trajectory optimization problem is then established with consideration to dynamic constraints, terminal state constraints, and landing site constraints. The control variables and state variables are discretized using Gauss pseudo-spectral method. Thereby a large-scale sparse nonlinear programming problem is formulated. The problem is then solved by SNOPT solver based on sequential quadratic programming algorithm. Simulation results show the efficiency of the proposed method.
Effect of local excitation-inhibition ratio on word recognition in hierarchical spiking neural network

The auditory neural coding is flexible to acoustic variability and can recognize sounds from competing sound sources. However, it remains unclear how neural circuits encode complex time-varying patterns of sound sources. Here we predict the ascending auditory pathway based on optimized hierarchical spiking neural network (HSNN). Primarily, HSNN model is built and it is found that the local state of HSNN plays an essential role in information processing, which depends on excitation/inhibition (E/I) ratio between the layers. Besides, the effect of E/I ratio on spiking activity propagation and word recognition of recurrent neural network (RNN) is investigated based on HSNN model. It is shown that the performance of sound signal propagation differs dramatically with different local network states. Particularly, an optimal E/I ratio enables HSNN to effectively propagate the spiking activity and filter out information irrelevant to word recognition. Collectively, this work provides a feasible tool to study the neural mechanism of ascending auditory pathway and digital recognition, contributing the field of machine vision and brain-like computing.
Observer Design for Space-Varying Delayed Distributed Parameter Systems

This paper considers the observer design problem of the space-varying delayed distributed parameter systems (DPSs) described by parabolic partial differential equations (PDEs). By using a Lyapunov-Krasovskii functional, an observer design is developed by spatial linear matrix inequalities (SLMIs) to ensure the exponential stability for the estimation error delayed DPS. In addition, to solve the SLMIs, the observer design for space-varying delayed DPS is formulated as an LMI feasibility problem. Finally, we verify the effectiveness of the proposed method via an example.
Non-technical loss detection using data mining algorithms

The non-technical losses are an important problem for the electric networks in the Region. However, its detection is possible using data mining. This work presents the implementation of clustering algorithms to detect non-technical losses using demand daily curves obtained from Advanced Metering Instruments (AMI). Three different clustering algorithms are compared, and their ability to identify outliers profiles is discussed. The study used synthetic data created with the Gaussian Hidden Markov Model adjusted with a common residential demand pattern from Guayaquil residential users. Results evidence the detection of 68% of the non-technical losses.
Design and Analysis of Concentrated Field TFPM Generator for Direct-Drive Wind Turbines

Transverse flux machines take advantage of high torque and power density, and feasible of increasing the number of poles in constant volume which make them suitable in low speed structures including wind turbine systems. The aim of this paper is to design a transverse flux permanent magnet generator for direct-drive wind turbines. A new concentrated flux structure with low cogging torque and large torque density is designed and designated by Fem results. The prototype is constructed and tested to verify the Fem results.
Accelerated Signal Processing of Burst-Mode Streamline Data for Low-Power Embedded Multi-Channel LiDAR Systems

Autonomous driving vehicle such as car or auto-mated guided vehicle (AGV), requires sensors to detect obstacles in its surrounding environment and feed the information back to the vehicle control system. Light detection and ranging (LiDAR) sensors, which have attracted attention as effective sensors for autonomous driving, provide distance values measured within field of view using a laser to generate three-dimensional (3D) coordinates. 3D coordinates have the advantage of providing more precise measurements of an object’s shape than other sensors and can also indicate the distance to the object. However, as the amount of data generated for a 3D point cloud and the number of sensors increases, the total amount of data to be processed increases. Therefore, the processing of this raw data in the main processor of an autonomous vehicle becomes a huge burden. In order to reduce the burden on the main processor of the autonomous vehicle, this paper intends to develop a real-time embedded system that primarily processes data from LiDAR sensors.
Trajectory Tracking Control and Vibration Control for a Rigid-Flexible Wing System

This paper mainly proposes control laws to address trajectory tracking and vibration suppression problems for a two-link rigid-flexible wing system. Based on Hamilton’s principle, partial differential equations (PDEs) and ordinary differential equations (ODEs) are proposed to describe the dynamical model of the wing. The boundary controllers are designed to restrain the deformations both in bending and twisting, and to achieve the trajectory tracking. We can accurately regulate the motion of the wing to track a desired spatiotemporally varying trajectory with the implement of trajectory tracking control and vibration control. In addition, the stability of this wing system is proven by utilizing Lyapunov’s direct method. Finally, we can verify that the designed controllers are effective according to the numerical simulations.
Robust Finite-Time Passivity and Synchronization of Coupled Complex-Valued Memristive Neural Networks with Parameter Uncertainty

This paper deals with the robust finite-time passivity (RFTP) and robust finite-time synchronization (RFTS) problems for coupled complex-valued memristive neural networks (CCVMNNs) with parameter uncertainty. By virtue of constructing a suitable controller combined with employing Lyapunov functional method, a sufficient RFTP condition is provided for the considered CCVMNNs. Furthermore, a RFTS criterion under the condition that the CCVMNNs achieves RFTP is also derived. At last, a numerical example with simulation substantiates the correctness and validity of the acquired results.
Design of Wideband High-Efficiency Circularly Polarized Folded Reflectarray Antenna

This communication presents a wideband high-efficiency circularly polarized (CP) folded reflectarray antenna (FRA) based on the CP selective surface (CPSS). The proposed CPFRA is comprised of a simple CPSS on the top layer, a wideband reflectarray plane on the bottom layer, and a wideband planar CP antenna as the primary source. First, the simple CPSS based on the traditional Pierrot structure is designed. The CPSS can reflect one CP wave, while it can transmit its orthogonal CP wave. Then, a subwavelength element is used to design the wideband reflectarray, and a wideband planar CP microstrip patch array is selected as the feed. According to the ray tracing principle, the distance between the upper CPSS and the bottom planar reflectarray is reduced to half of the distance between the planar reflectarray and the imaginary feed approximately. At last, we design a wideband high-efficiency CPFRA. Measured results of the fabricated prototype reveal that its maximum gain at 10 GHz is 27.8 dBic, its 3 dB gain bandwidth is 15%, its 3 dB axial-ratio (AR) bandwidth is 23%, and its maximum aperture efficiency is about 45.4%. Good broadband and low-profile characteristics make the proposed CPFRA have potential value in some wireless communication system.
Visual Object Detection: A Review

Object detection is a vitally important fundamental task in computer vision, and is applied in a number of scenarios including autonomous driving, robots, and industrial detection. Since 2014, object detection has become a research focus with the introduction of deep learning, which attracted a lot of attention. Even though object detection has developed rapidly in recent years with the power of deep learning, the ideas and motivations of many new models are still inspired by traditional algorithms. Therefore, it is necessary to understand the technical evolution of object detection to grasp its future direction. This paper provides a short survey on object detection, focusing on a comprehensive review of the technical development history in the past two decades. Secondly, the characteristics of each development period are summarized and some research hotspots in recent three years are given out. Then, the typical applications of object detection are illustrated. Finally, the paper discusses the possible challenges and development directions of object detection in the future.
State-of-Charge and State-of-Energy Estimation for Lithium-ion Batteries Using Sliding-Mode Observers

Based on the equivalent circuit model of a lithium-ion battery, this paper proposes a sliding mode observer for its State-of-Charge (SoC) estimation. In order to further estimate the State-of-Energy (SoE) of lithium-ion batteries, a real-time SoE estimation algorithm is established using the super-twisting technique. The Lyapunov stability theory is utilized to prove the proposed SoC and SoE estimation algorithms. Finally, the experimental data of Dynamic Stress Test (DST) and Federal Urban Driving Schedule (FUDS) test verify the accuracy and robustness of the proposed SoC and SoE estimation algorithms.
Bionic Body Wave Control for an Eel-Like Robot Based on Segmented Soft Actuator Array

Using body wave to swing forward is the most common propulsion method for underwater creatures, which can achieve flexible swing and multi-directional steering. Related robotics researches in recent years employed rigid structure and hinge connection to build the entire swing body, which were driven by conventional motors. However, soft robotics provides an alternative to make the robot behave like a bionic fish in terms of swing flexibility, actuation and efficiency. In this article, the authors develop the entire eel-like fish with a fully soft flexible body, which is composed of four fiber-reinforced, bidirectionally bending, fluidic elastomer actuators (FEAs) as its tail. This robotic fish tail has multiple segments connected in series with different waveforms to make the overall fish tail exhibit fluctuating forward performances. The FEA uses the typical dual-chamber configuration, and PWM sine wave signals are utilized to effectively control such a hydraulic mechanism. With experiments, the swing of each tail section is tested, and various factors with respect to the swing frequency and amplitude are studied. The results illustrate the feasibility and superiority of the proposed body wave control method.
Adaptive Multi-Task Human-Robot Interaction Based on Human Behavioral Intention

Learning from demonstrations with Probabilistic Movement Primitives (ProMPs) has been widely used in robot skill learning, especially in human-robot collaboration. Although ProMP has been extended to multi-task situations inspired by the Gaussian mixture model, it still treats each task independently. ProMP ignores the common scenario that robots conduct adaptive switching of the collaborative tasks in order to align with the instantaneous change of human intention. To solve this problem, we proposed an alternate learning-based parameter estimation method and an empirical minimum variation-based decomposition strategy with projection points, combining with linear interpolation strategy for weights, based on a Gaussian mixture model framework. Alternate learning of weights and parameters in multi-task ProMP (MTProMP) allows the robot to obtain a smooth composite trajectory planning which crosses expected via points. Decomposition strategy reflects how the desired via point state is projected onto the individual ProMP component, rendering the minimum total sum of deviations between each projection point with the respective prior. Linear interpolation is used to adjust the weights among sequential via points automatically. The proposed method and strategy are successfully extended to multi-task interaction ProMPs (MTiProMP). With MTProMP and MTiProMP, the robot can be applied to multiple tasks in industrial factories and collaborate with the worker to switch from one task to another according to changing intentions of the human. Classical via points trajectory planning experiments and human-robot collaboration experiments are performed on the Sawyer robot. The results of experiments show that MTProMP and MTiProMP with the proposed method and strategy perform better.
ℋ∞ Consensus for Discrete-time Multi-agent Systems Subject to Distributed Delay and Switching Topologies

This paper is concerned with the issue of weighted ${\mathcal{H}_\infty }$ consensus for discrete-time multi-agent systems with time-varying delay and switching topologies. The objective is designing a dynamic output feedback protocol in the presence of external perturbations such that the system achieves consensus in mean square and has a prescribed ${\mathcal{H}_\infty }$ performance level. Firstly, the ${\mathcal{H}_\infty }$ consensus design is converted to a standard ${\mathcal{H}_\infty }$ control problem by reconstructing the closed-loop system as a reduced-order system. Then, by means of the multiple Lyapunov-Krasovskii functional method, the delay-dependent bounded real lemma is derived to guarantee the stability of closed-loop system. Based on these, sufficient conditions for the existence of the required dynamic output feedback protocol is developed in the form of a series of solvable linear matrix inequalities. Finally, numerical example and simulation results are given to illustrate the effectiveness of the obtained results.
A Reweighted ℓ1-Minimization Algorithm for Joint Topology and Line Parameter Identification in Electric Grids

Topology and line parameter information is essential to the operation and control of electric grids. However, the gird topology may frequently change with the wide adoption of distributed energy resources, and the line parameters may be missing or inaccurate. The widespread deployment of advanced metering infrastructure enables the line parameters estimation and topology identification from a data-driven perspective. In this paper, we propose a reweighted ℓ1-minimization algorithm to deal with the joint topology and line parameter estimation problem by leveraging the sparsity of topology structure. A modified alternating descent sub-optimal method is proposed to estimate the admittance matrix of the power system using nodal voltage and current phasor measurements. Extensive simulations performed on IEEE 14 and 57-bus benchmark systems are presented to substantiate the effectiveness of the proposed algorithm.
Tampering Detection of LiDAR Data for Autonomous Vehicles

The realization of autonomous driving technology relies on high-precision sensors such as LiDAR to perceive the environment. Recent studies have shown several possible attack scenarios to tamper with LiDAR data. The existing 3D LiDAR data forensics approaches have disadvantages of being unsuitable for automatic driving scenes or causing performance degradation of deep learning algorithm employed by vehicles. In this paper, we propose a security verification technology for 3D LiDAR data, which utilizes watermarking technology to detect whether the data has been tampered in real time, while avoiding too much the performance degradation of the target detection algorithm caused by the distortion of the watermark. Specifically, point cloud is divided into two parts according to whether it overlaps with the previous frame of data. The watermarking algorithm based on spherical ring segmentation is employed to embed hidden information into the non-overlapping part, while the another part uses the redundant information of the previous frame for similarity detection. The performance of proposed framework is evaluated on the KITTI dataset, and the effectiveness of the method is demonstrated.
Orbit Determination and Orbit Uncertainty Estimation using Neural Network Based Modified State Observer

In order to improve the accuracy of the dynamical model used in the satellite orbit determination, a novel technique called modified state observer, is presented for the estimation of uncertainties in nonlinear dynamics and, in addition, providing the estimate of the system states. The observer structure contains an online neural network whose outputs are the uncertainties in the system. To demonstrate the utility of the presented algorithm for this class of problem, the observer is applied in two cases: estimating the uncertainty caused by just the J2 perturbation and estimating all dynamical perturbations and satellite state of a GPS satellite. Simulations are presented, and results show that the observer can accurately estimate the perturbation and the satellite states.
Probabilistic Model-Based Global Localization in an Airport Environment

This paper presents a method for 3D robot localization in an airport environment using an enhanced adaptive Monte Carlo localization algorithm with sensor input from a multi-beam lidar. The occupancy grid is computed based on the known geometry of various airplane types. By inserting adaptive particle noise, an estimated global pose can be obtained reliably in stationary conditions with a low number of initial particles. As the probabilistic particle filter converges, the particle noise and the number of particles are reduced. Robot odometry is used to propagate the candidate particles when moving. The algorithm has been implemented within the Robot Operating System (ROS) framework and can run in real-time on a low-power computing device on the robot. Comparison of the numerous enhancements are shown in simulation. The results have been validated in practice on multiple airplanes at two airports showing good performance.
A New Fault Detection Method From Data-Based Dissipativity Theory

In this paper, a novel fault detection scheme for linear process systems using data-based dissipativity theory is developed. The data-based dissipativity is learned from the available input/output trajectories. Dissipativity of a process is an input/output property, which may not be valid when a fault occurs. In light of this fact, in the proposed approach, the dissipativity of a process is tailored such that it is fault-sensitive, i.e., no longer valid when faults occur. By adopting the storage functions and supply rates in the quadratic difference form, the dissipativity conditions are represented as quadratic functions of the input/output trajectories of the process, which captures much more detailed dynamical features compared to traditional dissipativity. These dissipativity properties are determined offline by solving an optimization problem with linear matrix inequality constraints. The online diagnosis algorithm involves checking of inequalities on input/output trajectories only, which makes the design much simpler. The proposed approach is illustrated using a case study of fault detection of a heat exchanger.
Joint Detection and Parameter Estimation Under Unknown Prior Probability

This paper addresses the joint detection and parameter estimation problem when the prior probability of different hypothesis is unknown. We build an unconstrained optimization model that minimizes the weighted combination of the average estimation cost and detection error probability. The optimal decision rule is given by solving this optimization model. Since the decision threshold is related to the unknown prior probability, we define an average risk as the mean square error between the joint cost computed by the true prior probability of the hypothesis and the guessed one, respectively. After that, by minimizing the defined average risk, we obtain a best guess and replace the true prior probability with it to make final decisions. Simulation results demonstrates the validity of our proposed method compared with other selection criterions.
A Survey of the Role of Viewability Within the Online Advertising Ecosystem

Within the online advertising ecosystem, viewability is defined as the metric that measures if an ad impression had the chance of being viewable by a potential consumer. Although this metric has been presented as a potential game-changer within the ad industry, it has not been fully adopted by the stakeholders, mainly due to disagreement between the different parties on the standards to implement and measure it, and its potential benefits and drawbacks. In this study, we present a survey of the role that viewability can have on the main challenges of the online advertising ecosystem depicting the main applications, benefits and issues. With this objective, we provide an overall picture of how viewability can fit within the ecosystem, which can help the different stakeholders to work on its adoption, integration and establishing a research agenda.
Leveraging Data Mining Strategies for Predictive Modeling of COVID-19 Outbreak and its Socio-economic Impacts: A Simulation for Public Health in Iran

We present a thorough analysis of socio-economic impacts of COVID-19 on public health through data mining strategies including correlation index matrix, auto-regressive integrated moving average, decision trees, heatmaps and statistical performance evaluation. We acquired and filtered data for mortality and outbreak prediction through key features such as total cases, daily new cases, active cases, total deaths, daily new deaths, newly recovered, death rate and recovery rate for 54 days. The socio-economic impacts of the pandemic through quantitative analysis of stock market index, currency inflation, gasoline prices, interest rate, consumer price index and crude oil prices were also investigated. With correlation index matrix and heatmaps, we discovered the nature and intensity of interdependency of these features and developed the regressive estimation model to forecast the values of inter-related features for 10 days. We observed a highest correlation of +0.95 between recovery rate and total infected cases. We also observed an inverse correlation of -0.81 between daily new cases and recovery rate due to unexpected rise in outbreak. Also, the mild but positive index for economic impacts, such as currency inflation, depict the virus’ adverse impact on the fiscal situation. The statistical representation of the developed prediction models through bar charts show outstanding performance when evaluated on the benchmarking merits of mean absolute error, root mean square error, relative error and percentage accuracy.