Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure design of a novel self-adaptive climbing mechanism for the amphibious bionic robot

In this paper, an amphibious robot compounded with wheel and fin is proposed which integrated two propulsive motions with land walking and underwater swimming. The robot also has the ability of self-adaptive climbing in the complex environment, i.e. the robot moves forward on land with the paddles wheels on the ground and turns the rockers upward for climbing the obstacle. The structure of the robot is designed and the principle of the self-adaptive climbing mechanism is described. The performance of obstacle crossing is analyzed. The process of self-adaptive climbing of the amphibious robot is simulated by using the ADAMS. Finally ˈ the experimental prototype is developed and tested.
Consensus of Uncertain Heterogeneous Multi-Agent Systems Under a Directed Graph

In this paper, we focus on leaderless consensus problem of uncertain heterogeneous multi-agent systems with parametric uncertainties under a directed graph, in which there are two different types of agent dynamics including a first-order one and a second-order one. We propose a distributed consensus algorithm for each agent, where only the relative position information is interacted among the agents. The positions of the agents all converge to a stationary point, and the final equilibrium point is derived. Finally, we present some numerical simulations results to show the effectiveness of the proposed control algorithms.
Smart Kost: a Proposed New Normal Boarding House Controlling and Monitoring System in Industry 4.0 Era

Industry 4.0 is the term for automation and transparency of data using the latest technology. This concept can be applied in several aspects of life besides the manufacturing industry. The Boarding house is a type of industry that has been thrived in D.I. Yogyakarta. It became a place of residence for students and workers in urban areas. As one type of business, boarding houses need a good management system for the best service and run efficiently. At this time the boarding house is still managed traditionally. Along with the development of IoT (Internet of Things) technology, several problems and its challenges can be solved with this technology. 5 types of sensors are used to realize this research. The sensor sends data and then the information is processed in a software application. This research uses a mobile application and integrates the sensors to monitor and control boarding rooms. This research can be used to face the new normal challenges, using the latest technology. The challenge of contactless process, energy efficiency, open information, and data can be well prepared by boarding house owners. The proposed system is predicted to save electricity costs for each bedroom by approximately 38% and also affect the saving of electricity bill payments by managers.
Energy efficiency maximization strategy for Sink node in SWIPT-enabled sensor-cloud based on optimal stopping rules

Leveraging energy harvesting abilities in wireless network devices has emerged as an effective way to prolong the lifetime of energy constrained systems. The system gains are usually optimized by designing resource allocation algorithm appropriately. However, few works focus on the interaction that channel's time-vary characters make the energy transfer inefficiently. To address this, we propose a novel system operation sequence for sensor-cloud system where the Sinks provide SWIPT for sensor nodes opportunistically during downlink phase and collect the data transmitted from sensor nodes in uplink phase. Then, the energy-efficiency maximization problem of the Sinks is presented by considering the time costs and energy consumption of channel detection. It is proved that the formulated problem is an optimal stopping process with optimal stopping rules. An optimal energy-efficiency (OEE) algorithm is designed to obtain the optimal stopping rules for SWIPT. Finally, the simulations are performed based on the OEE algorithm compared with the other two strategies to verify the effectiveness and gains in improving the system efficiency.
Event-Triggered Based Sliding Mode Control of Second-Order Underactuated System with Unknown Dynamics

In this paper, the trajectory tracking control problem of second-order underactuated system with unknown dynamic and prescribed performance constraint was discussed. Event-triggered sliding mode control combined with adaptive method is investigated to solve the problem. By introducing the constraint transformation, which is used to convert the constrained system into unconstrained one, and prescribed performance function (PPF), the steady and transient performance of closed-loop system are guaranteed. After obtained the unconstrained system, an event-triggered adaptive sliding mode control was proposed. The proposed method is robust to uncertain disturbance and saves computing and communication resources. Then, through the Lyapunov theory, the update laws of control law and adaptive laws were obtained, and also the stability of closed-loop control system was proved. Finally, the simulation results demonstrate the effectiveness of proposed control approach.
Low-Cost FMCW Radar Human-Vehicle Classification Based on Transfer Learning

Detection and classification of moving targets is an essential feature in many applications like road surveillance systems, autonomous cars, and smart gate systems. Multi-chirp sequence Frequency Modulated Continuous Wave (FMCW) radars with a 2D FFT processing can be used to produce a Range-Doppler images (R-D maps) containing the signature of the target. However, in low-cost FMCW radars, these images suffer from many problems like low-resolution and ambiguity. Such problems can make the image look unrealistic as well as hard to process and classify. In this paper, we propose a human-vehicle classification method based on Transfer Learning. The classification is done by processing the R-D maps generated by a low-cost short range 24 GHz FMCW radar with a convolutional Neural Network (CNN). The adopted CNN succeeded to reach a 96.5% accuracy in discriminating humans from vehicles.
Research on Impedance Control Method of Legged Robot with Gait and Load Adaptive Capability

Due to the characteristics of discontinuous support and flexible manoeuvring characteristics, the legged robot is expected to play an important role in military, rescue and transportation tasks. The high dynamic characteristics of movement and the high uncertainty of the environment and load make the robot’s compliant control particularly important, especially when facing complex environments and rough terrain. Impedance control is an effective method to solve this problem. The traditional impedance control structure has an upper limit on the response time of the system, and parameter setting generally depends on experience. This paper presents an improved impedance control method for legged robots. We have improved the structure by introducing force changes and decomposed the controller into force-position conversion factors and impedance subsystems, which can greatly shorten the response time of the system. We also improved the method of parameter setting. Simultaneously, we propose an optimization target and optimization method for impedance control parameters, aiming to improve the adaptability of the legged robot to the asynchronous state and load. We have realized the impedance control of the adaptive parameters on the platform of both single robot and the quadruped robot, and the experiment verifies the effectiveness of the method.
A Novel Multiple Motor Imagery Experimental Paradigm Design and Neural Decoding

Motor imagery (MI) is a typical active brain-computer interface (BCI), which can be decoded by the pattern of EEG when the subject conduct motor imagery (e.g. left hand, right hand, feet and tongue.). In this paper, we proposed a novel multiple MI experimental paradigm which includes left hand, right hand, both hands, and feet. Based on the proposed paradigm, ten subjects participated in the data collection experiment at Jiangmen Brain-like Computation and Hybrid Intelligence R&D Center. Furthermore, we proposed an algorithm for MI neural decoding. Firstly, combining the one-vs-rest (OVR) strategy with the filter bank common spatial pattern (FB-CSP), we extract the features of multiple-class MI. Secondly, F-score optimization is used for features selection and then features are feed to a spiking neural network (SNN) for classification. Evaluation experimental results show that the averaged accuracy for four MI classification was up to 61.30% (average kappa: 0.482). The feasibility and effectiveness of the experimental paradigm prove that it can be used as an alternative way and has practical significance.
Characteristic insights on industrial cyber security and popular defense mechanisms

Due to the deep integration of information technology and operational technology, networked control systems are experiencing an increasing risk of international cyber attacks. In practice, industrial cyber security is a significant topic because current networked control systems are supporting various critical infrastructures to offer vital utility services. By comparing with traditional IT systems, this paper first analyzes the uncontrollable cyber threats and classified attack characteristics, and elaborates the intrinsic vulnerabilities in current networked control systems and novel security challenges in future Industrial Internet. After that, in order to overcome partial vulnerabilities, this paper presents a few representative security mechanisms which have been successfully applied in today's industrial control systems, and these mechanisms originally improve traditional IT defense technologies from the perspective of industrial availability. Finally, several popular security viewpoints, adequately covering the needs of industrial network structures and service characteristics, are proposed to combine with burgeoning industrial information technologies. We target to provide some helpful security guidelines for both academia and industry, and hope that our insights can further promote in-depth development of industrial cyber security.
Visibility Enhancement in Low Light Images with Weighted Fusion of Robust Retinex Model and Dark Channel Prior

Images captured under poor illumination or at night time doesn’t have significant details as compared to images captured under proper lighting conditions. These images, when used for computer vision applications might be the reason for undesirable output. So, these kinds of images are not suitable for observation and analysis is case of any computer vision application. To solve this problem, visibility enhancement in low light images with weighted fusion of robust retinex model and dark channel prior based enhancement method have been proposed in the literature. The paper proposes visibility enhancement in low light images with weighted fusion of robust retinex model and dark channel prior based enhancement. The validation of proposed method is judged based on entropy. The performance based on the entropy as measure, is evaluated for proposed system and compared with the other existing popular low light image enhancement techniques. For rigorous validation, different weights combinations are explored in the proposed fusion based image enhancement method.
High Contrast Gratings (HCG) MEMS-Tunable VCSEL Compact Model

High contrast grating (HCG) microelectromechanical structure(MEMS)-tunable VCSELs have advantages that make them promising for applications like optical coherence tomography (OCT), light detection and ranging (LIDAR), and dense wavelength division multiplexing (DWDM). Circuit simulator-compatible laser compact models can aid circuit designers in improving the performance of systems that use them. While a compact model for VCSEL already exists, it does not sufficiently describe the behavior of tunable VCSELs. This paper discusses the creation of a compact model for HCG MEMS-tunable VCSELs implemented in Verilog-A using semiconductor laser rate equations. The simulated output power, laser diode voltage, and emitted wavelength at varying applied MEMS tuning voltage of the compact model agrees with the measured values with mean percentage errors of 7.0%, 5.3%, and 0.32%, respectively.
Resilient Consensus Control for Networked Lagrangian Systems With Constant Time Delay and External Disturbance

In this paper, we consider the resilient consensus control problem of networked Lagrangian systems subject to constant time delay and external disturbance. A resilient local controller only using the neighbouring information is proposed. By adding a sliding-mode-like term in the distributed controller, the effect of the external disturbances can be restrained. In addition, the proposed model-independent consensus controller also has constant delay tolerance. Comparing with existing approaches, the proposed control method is model-independent and resilient to constant time delay and external disturbances. Finally, the effectiveness of the proposed resilient controller is verified by using simulation results.
Effect of Multi-Layered Winding on Eddy Current Loss in Magnets of PMSM

An analytical model is used to predict the eddy current loss in magnets of Permanent Magnet Synchronous Motor with a fractional number of slots per pole, in which stator is wound for different winding layers. The analysis accounts for space harmonics and time harmonics that are caused by winding distribution and current distortion respectively. The results obtained from model are validated by finite element analysis and are shown that winding configuration with higher values of subharmonics has more influence on the eddy current loss.
Targetless Extrinsic Calibration for Multi-beam LIDARs with Narrow Overlapping Field of View

Limited by the requirements of mounting height and occlusion avoidance in adjacent areas, the configuration of multiunit light detection and ranging (LiDAR) sensors rather than a single omni-directional LiDAR has gradually become the first choice for low cost and mass-produced unmanned ground vehicles. However, accurate calibration of multiunit LiDARs is challenging since the conjugate features in scan points cannot always provide enough constraints, which is exacerbated by narrow overlapping field of view. To address this problem, we propose a novel automatic extrinsic calibration method for multi-beam LiDARs without any special targets. Specifically, we decompose the extrinsic parameter estimation into two parts. One is to extract the ground plane equations for each LiDAR and establish a loss function based on the corresponding ground normal to estimate the z, roll, and pitch offsets relative to the ground. The second is to convert the 3D horizontal-adjusted points to 2D occupancy grid map (OGM) and the distance transform (DT) of it. By projecting the overlapped points to the DT field, an energy loss function is constructed to get x, y and yaw offsets between the multi-LiDARs. Both simulated and realistic experiments show that our method does not require manual intervention and specific objects to complete the automatic extrinsic calibration. Compared with the existing methods, ours has obvious advantages in accuracy, noise resistance and stability to initial parameters. Our code is open sourced on github.
Switched LPV Modeling and H2 Control for Hybrid Suspension with Mass Variation

This paper focuses on the issue of modeling and multi-objective controller design for road adaptive hybrid suspension by means of a switched linear parameter-varying (LPV) framework. For a vehicle equipped with hybrid suspension which consists of a multi-stage adjustable damper and an actuator, the damping coefficients need to be adjusted according to the road conditions and the mass of vehicle may change with the load. The hybrid suspension is modeled as a switched LPV system, which takes into account the mass variation. A mode-dependent persistent dwell-time switching logic is introduced to govern the slow and fast switching, which probably coexists among the damping coefficient variations under complex road conditions. Finally, a novel switched LPV state-feedback controller is designed, which guarantees the stability and H2 norm performance of the underlying system. The numerical simulation is given to verify the superiorities of the proposed hybrid controller under the whole range of road conditions.
Model Predictive Control Based on Hybrid ADMM with Node Error

A lot of controlling problems can be transformed into optimization problems under a network through modeling. Distributed algorithms provide many advantages over centralized solutions. In this paper, Hybrid Consensus Alternating Direction Method of Multipliers (H-CADMM) is proposed to solve these problems with additional random node errors and applied to distributed model predictive control (DMPC). By observing the simulation results, the algorithm requires fewer iterative steps than D-CADMM under the same solution accuracy conditions.
An Effective Method for Satellite Mission Scheduling Based on Reinforcement Learning

With the increasing number of satellites in orbit and the growing observation missions, how to make an allocation scheme with the maximization of total profit effectively has become increasingly important. In this paper, an effective method based on Reinforcement Learning is proposed to solve satellite mission scheduling problem, in which the arrival missions are arranged immediately without waiting all missions collected. Firstly, a mathematical model based on Markov Decision Process is established, whose goal is to find an optimal policy to maximize the accumulated reward. Then, Asynchronous Advantage Actor-Critic algorithm with neural network is used to assign missions to different satellites. The simulation experiments with comparison to first come first service algorithm and genetic algorithm are conducted, which demonstrates that the proposed method performs well with respect to real-time speed and solution quality.
An X-ray photoelectron spectroscopy study of hydrous ruthenium oxide powders with various water contents for supercapacitors

Hydrous ruthenium oxide powders annealed at different temperatures were studied by X-ray photoelectron spectroscopy (XPS) and X-ray diffraction. The transition from the amorphous to the crystalline state was followed by recording the Ru 3d 5/2 XP-spectra. The peak shape of the O 1s XP-spectra provides information about composition and morphology. Comparison of the water amount obtained from deconvolution of the O Is signal with the one calculated from gravimetric analysis demonstrates that the physically adsorbed water in the amorphous material evaporates under ultrahigh vacuum conditions. The observed decrease of chemically bound water with increasing annealing temperature is attributed to an increase of particle size.
Swarm control of UAV based on optimal rigid graph and new community partition

In terms of unmanned aerial vehicle (UAV)formation control, the traditional master-slave centralized formation control method requires a lot of information interaction, and the requirements for communication between aircraft are relatively high. In order to maintain the connectivity of large-scale UAVs and solve the problem of stable formation in group swarm control, a new multi-agent containment algorithm based on the optimal rigid graph com-munity division method is proposed. First, the optimal rigid graph is used to fully cover the multi-agents randomly distributed in three-dimensional space. And we judge the importance of each node. On this basis, the community is divided, and then the UAV group is modeled. Then we analyze the UAVs swarming characteristics under the containment strategy, so that the drone can achieve position collection and speed matching under traction. Finally, the algorithm is simulated and compared to verify that the new algorithm is reasonable in the application of UAV swarm control. The simulation test results show that the algorithm can make the UAV group reduce information interaction, minimize communication redundancy, and quickly converge into a stable formation.
Research on Intelligent Decision Technology for Multi-UAVs Prevention and Control

In recent years, unmanned aerial vehicle technology has been continuously developed and matured, and the level of intelligence has been continuously improved. "Black Flying" brings instability to social security, while UAVs bring convenience to routine production and daily life. In addition, with the continuous development of UAV cluster technology, the future trend of air combat will gradually turn to cluster cooperation. UAV cluster combat will also be changed from concept to reality, from theory to practice. The outstanding performance of artificial intelligence technology in game tasks clearly shows that strategy based on human experience will be difficult to compete with intelligent algorithms in future confrontations. Considering the future demand for multi-UAVs prevention and control, this paper relies on advanced intelligent technologies such as genetic fuzzy trees, multiobjective particle swarm optimization algorithms, reinforcement learning and deep neural networks, aiming at the complex, dynamic, and strong interference, focusing on researching core issues such as the construction of multi-UAV s prevention and control environment models, the generation of compound interception strategies, and the selflearning of autonomous UAV countermeasures. This paper is devoting to exploring new strategy generation methods and proposing solutions to the problem of multi-UAVs prevention and control.
Identification of grey-box nonlinear models based on parallel computing-based grid search

This paper proposes a new method to estimate unknown parameters of nonlinear grey-box models based on parallel computing-based grid search. The main idea of the proposed method is to calculate loss functions corresponding to search grids via multiple CPU cores, and obtain the parameter uncertainty range from output fitness values of all calculated models. The advantage of the proposed method is to ensure from results for all search grids that the estimated model is indeed the global optimal solution subject to the search accuracy resolution, while the existing genetic algorithm only converges in a probabilistic sense to the global optimal solution so that it sometimes diverges to local optimal solutions. The disadvantage of the proposed method is a long time cost, which however can be alleviated to an acceptable level by exploiting multiple CPU cores. Numerical examples based on the three-tank benchmark system are provided to illustrate the proposed method.