Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling and tolerant control of the time-delay networked control system based on pole configuration

For networkeded control systems with time delays, while considering the influence of external disturbances, a criterion is derived to ensure that the actuator is robust when a fault occurs, and a robust $H_{\infty}$ fault-tolerant controller design method is given. A fault diagnosis system is designed and fault-tolerant control is implemented using the closed-loop poles as fault information. The closed-loop system poles are used as input to an Extreme Learning Machine (ELM) fault diagnosis model to achieve fault-tolerant control of the different channels of the system actuator. Finally, a simulation example is used to verify the effectiveness of the described method.
Design and Characterization of AWARD: an Active Wearable Assistive and Resistive Device

Physical assistive robotic devices have demonstrated many desirable advantages in rehabilitation and augmentation, allowing for efficient and effective performance of activities of daily living (ADLs). However, active wearable devices that provide resistive capabilities to enable strength training have been difficult to develop. This is partially due to the required high force outputs during strength training. Developing soft, compact, compliant and lightweight robotic devices that exhibit both assistive and resistive capabilities is an even tougher challenge. This paper presents an Active Wearable Assistive and Resistive Device (AWARD) that is capable of providing assistance and resistance to the motion of fingers, the first of its kind. The device was actuated by six twisted string actuators (TSAs) with four TSAs using both stiff strings and conductive supercoiled polymer (SCP) strings and a further two TSAs using only stiff strings. Force sensitive resistors were used on the fingertips in assistive and resistive modes. The AWARD showed a maximum peak force of 13.55 N in finger extension and a maximum peak force of 8.66 N in finger flexion.
Wind Power Prediction Based on the Stacking Model of XGBoost and Random Forest

Due to the uncontrollability of wind energy, the conversion efficiency of wind is low. By improving the prediction accuracy of wind power, the conversion efficiency of wind energy can be greatly improved. For the purpose of solving the problems of overfitting of most wind power prediction models, the over-dependence of wind speed and the ignorance of other important features, this paper proposes a stacking method to integrate two models: XGBoost and Random Forest. Stacking of these two models can solve the above problems and greatly improve wind power prediction accuracy. The dataset from Kaggle verifies that the stacking model can extract the useful information contained in the data effeciently and has better accuracy than a lot of mainstream models. The evaluation metrics Mean Squared Error(MSE) and R2 of the stacking model are reduced by about 10% compared with the mainstream models.
A PID Controller based on ESO and Tuning Method

PID (proportional-integral-derivative) is a widely used form of industrial control because of its simplicity and effectiveness. Its tuning, however, is usually tedious and experience dependent. In this paper, a more general form of PID, denoted as $\text{PID}^{\mathrm{n}-1}$, is proposed as a variation of ADRC (active disturbance rejection control). In addition to dealing with high order dynamics, the $\text{PID}^{\mathrm{n}-1}$ has only three parameters $(b_{0},\omega_{\mathrm{o}}$ and $\omega_{\mathrm{c}})$ to tune, all of them have explicit physical meanings. This helps streamlining the controller tuning process and making $\text{PID}^{\mathrm{n}-1}$ easy to use in industry. The experiments verify the effectiveness of the proposed $\text{PID}^{\mathrm{n}-1}$ solution and the ease of tuning.
A Scheme for Uplink NOMA Communication with Intelligent Resource Allocation for mMTC Traffic over eMBB Traffic

In this work, a study is conducted on the coexistence of two services, enhanced mobile broadband (eMBB) and massive machine-based communication (mMTC), in a fifth generation (5G) wireless communication system. Coexistence of these two services can be achieved by network slicing and non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) decoding to improve spectral efficiency, but the block error rate (BLER) of mMTC devices needs to be considered for short packet communications (SPC). In this paper, we combine deep reinforcement learning (DRL) proximal policy optimization (PPO) algorithm to efficiently accommodate mMTC devices into available random access (RA) slots while guaranteeing the throughput of eMBB devices to achieve dynamic scheduling of resources to improve system throughput. The performance of the proposed method is compared with recent solutions in the literature, and simulation results show that the proposed method can guarantee a low outage probability for eMBB services and effectively improve the throughput of mMTC services.
Path Planning for Upper Limb Rehabilitation Based on Human Motion Feature Output

In the field of rehabilitation robot path planning, the trajectory of rehabilitation training is the premise and foundation of rehabilitation robot control. However, the existing movement trajectory generation methods still have many shortcomings, such as the accuracy of data collection and the effectiveness of optimization algorithms, and rarely consider the laws of rehabilitation medicine. In this paper, the human movement data during the upper limb rehabilitation training is collected through a three-dimensional movement capture system. According to the processed data, find the output of human upper limb movement feature and fit it as an output function to obtain the initial training path of the upper limb rehabilitation training. Adjust the weight of the index to obtain the optimal rehabilitation training path focusing on training effect or comfort by setting the rehabilitation training index. The golden section algorithm is used to calculate the upper limb output data constrained by the rehabilitation training index, and the final human body norm output function is obtained through Fourier fitting. The simulation and results show that the canonical output function improves the efficiency of rehabilitation training and ensures the effectiveness and comfort of upper limb rehabilitation.
Integral Sliding Mode Control for Partially Unknown T-S Fuzzy Systems Based on Reinforcement Learning Method

This article focus on the integral sliding mode control for a class of partially unknown T-S fuzzy systems on the basis of reinforcement learning technique. In the equivalent control, the control term is described as a form of feedback control based on the percompensation technique, such that the optimal control performance can be satisfied. Then the control gain is design by solving the algebraic Riccati equation according to the integral reinforcement learning (RL) method, and the requirements of ex-act information of system matrices is relaxed during the solving process. Base on the Lyapunov stability theory, the convergence and stability of the designed sliding mode control optimization scheme are analyzed. Finally, a numerical example is given to verified the effectiveness of the proposed theorems.
Indoor Single Station 3D Localization Based on L-shaped Sparse Array

In recent years, the research on indoor 3D localization has attracted a lot of attention in the applications of smart home, smart factory and other fields. However, as it is difficult for the hardware to meet the Nyquist sampling rate of half wavelength in the 5G/6G cases, using conventional subspace methods will lead serious pseudo peaks in parameter estimation, resulting in a sharp decline in estimation accuracy. In order to solve this problem, we propose a sparse parameter estimation and 3D localization method based on orthogonal matching pursuit algorithm (OMP). Firstly, we design an L-shaped sparse antenna to construct a sparse array manifold. Based on 2D angle of arrival (AoA) and time of flight (ToF), we construct a 3D parameter estimation model and a 3D localization method based on direct path. Then we convert the 3D parameter coupling estimation into two 2D parameter coupling estimation. Finally, we verify the feasibility of the proposed method through Wireless Insite simulation platform.
Disturbance Estimation and Compensation Based on Frequency Response in an Islanded Microgrid

Due to the large deployment of inverter-based power sources; the overall inertia of islanded microgrids is low. This results in an increase in the initial rate of change of frequency (RoCoF) and a decrease in the minimum value of frequency (frequency nadir) in case of any disturbance on the load side or generation side. This paper presents a dynamic frequency model for estimating the power imbalance in islanded microgrids and supplying the required power using the primary frequency control technique. This model of a microgrid is developed in Typhoon real-time hardware-in-the-loop simulator using renewable energy sources and parallel operated synchronous generators for real-time validation.
Secondary Reheat Steam Temperature Prediction Based on Hybrid Deep Learning

An accurate prediction model of the secondary reheat steam temperature in the ultra-supercritical double-reheat coal-fired power plant is difficult to build due to the characteristics of strong nonlinearity, multi-variable coupling, large inertia, and various disturbances. This paper proposed a joint data-driven prediction method based on GRU-Pooling-Attention to improve the prediction accuracy and training speed. Firstly, CatBoost algorithm is taken to calculate the importance of features, and the input feature set is selected combined with expert knowledge. Then the GRU is adopted to learn the time series relationship between data, the pooling method is embedded to reduce the number of parameters and to highlight key values, and the attention mechanism is introduced to assign different weights to the output of each time step of GRU to reflect the large delay and large inertia of the system. The hybrid deep learning method is applied to the prediction of the secondary reheat steam temperature based on historical data, and the results show that it can obtain accurate and fast multi-step prediction. The trained model can be used to explore the dynamic characteristics of the processes.
P2SL: Private-Shared Subspaces Learning for Affective Video Content Analysis

Most existing methods on affective video content analysis are dedicated to first obtain visual and audio representations, and then fuse them into a compact representation. However, these fusion techniques are often challenged by the modality gaps between heterogeneous modalities. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework named Private-Shared Subspaces Learning(P2SL), which projects each modality into two distinct subspaces. The first subspace is shared by all modalities, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is private to each modality and captures their characteristic features. Specifically, we introduce inter-modal similarity loss and intra-modal loss to obtain the robust features. In order to capture cues in different grains, global and local features are extracted from both visual and audio signals. Experiments show that our proposed approach outperforms the state-of-the-art methods and demonstrate the effectiveness of our approach.
A Matching Based Spatial Crowdsourcing Framework for Egalitarian Task Assignment

The ubiquity of mobile internet has led to the success of Spatial Crowdsourcing platforms like real-time taxi-hailing services, online food ordering services, etc. A critical component of such services is the task assignment algorithm employed for assigning the tasks to the workers of the platform. Our study of the literature in this domain shows that most of the task assignment algorithms developed for spatial crowdsourcing platforms address the problem from a utilitarian perspective, i.e., they optimise for only kind of entity. In contrast, we address the task assignment problem in spatial crowdsourcing platforms from an egalitarian perspective. An egalitarian approach aims to optimise the expectation of all entities involved. Specifically, we aim to minimise the waiting time for the customers and workers, while maximising the profit earned by the platform. To the best of our knowledge, ours is the only study that achieves this objective in a fully-online setting, with deadlines for both customers and workers. We propose two heuristic algorithms to solve the problem, and evaluate our algorithms on a real taxi-trips records dataset. Our algorithms exhibit a superior performance than the state-of-the-art algorithm for the fully-online bottleneck matching problem with deadlines, in terms of solution quality, running time and response time.
Research on satellite beam hopping technology based on Digital Video Broadcast-Satellite Second Generation/Return Channel via Satellite

Satellite communication technology is rapidly developing towards the direction of high throughput and high flexibility as well as terminal miniaturization. DVB-S2/RCS standard as the second generation of satellite digital video broadcasting standard has been widely used and studied because of its outstanding performance advantages in system capacity and transmission capacity. The available spectrum resources in satellite communication system are increasingly short and the terminal traffic suddenly surge as well as satellite resource allocation is inflexible. Beam hopping technology as the latest communication technology in recent years has the advantages of dynamic resource allocation, improving service throughput and resisting strong directional interference. Combining DVB-S2/RCS and beam hopping technology and applying it to satellite communication system will greatly improve system throughput and provide richer interactive services. This paper mainly discusses DVB-S2/RCS standard, frame structure and frame structure design of beam hopping system. The combination of DVB-S2/RCS and beam hopping technology can improve the overall performance of communication system through simulation verification.
A 59-fs-rms 35-GHz PLL with FoM of −241-dB in $0.18-\mu \mathrm{m}$ BiCMOS/SiGe Technology

A wideband and ultra low-noise Phase-Locked Loop (PLL) circuit is designed and implemented in a $0.18\ \mu \mathrm{m}$ BiCMOS/SiGe technology to operate at a nominal frequency of 35.68 GHz. Incorporating a multi-phase phase frequency detector along with a high frequency reference signal, the bandwidth of the PLL was maximized to reduce the phase noise and jitter contribution of the forward path loop components within the frequency band of interest. A multi-phase phase comparator also relaxes the constraints imposed by the sampling nature of the PLL, allowing for a more convenient performance optimization with reduced jitter peaking and more optimal loop characteristics. The PLL was measured to have a Phase Noise of −113.3 dBc/Hz at an offset frequency of 1 MHz, and a total integrated jitter of 59 fs-rms integrated from 1 kHz to 100 MHz, consuming 194.6 mW with a jitter-power FoM −241.6 dB. The power dissipation of the proposed PLL is lower than implementations in similar technology nodes, while obviously higher than designs made in advanced CMOS/FinFET technologies. The PLL has been designed in BiCMOS/SiGe $0.18 \mu \mathrm{m}$, aiming to be integrated together with power amplifiers in a 3D structure targeted for the next generation 5G systems.
Performance analysis of adaptive K for weighted K-nearest neighbor based indoor positioning

With the large deployment of WiFi networks, in-door localization using WiFi fingerprinting with received signal strength has been widely studied. One of the common localization methods is weighted K-nearest neighbor method (WKNN), which localizes the user to the weighted center of the K best matching points. The performance of this method is affected by the choice of parameter K. Once tuned, this parameter is usually applied to all test samples. In this paper, we study how far localization performance can be improved if this parameter is adapted for different test points. We show with two public access datasets that adapting parameter K for different test points can potentially improve localization performance by over 45% compared to the baseline of only choosing the closest neighbor. Additionally, we analyze the dataset to obtain some stochastic thresholds for dataset filtering and K selection.
The Near-Field Capacity Analysis for Large Antenna Array

Large antenna array is a promising technology to achieve extremely high data rate for the next-generation communication. However, current design and performance analysis methods are usually based on the far-field and planar-wave assumption, which may cause mismatch in realistic propagation environment as the near-field region of large antenna array could be much wider than ordinary array. To solve this problem, a method based on network analysis is proposed and the scattering parameter is used to analysis the capacity of large antenna array in its near-field region. Comparison between our method and classical method is proposed to demonstrate the effectiveness of our methods.
Complex-valued Reinforcement Learning Based Dynamic Beamforming Design for IRS Aided Time-Varying Downlink Channel

The intelligent reflecting surface (IRS) is an artificial metasurface making the communication environment smart and controllable. The IRS on an aerial platform (AIRS) expands the wireless network to the three-dimensional space, thus improving the degree of freedom (DoF) for the signal adjustment. Since the AIRS-enabled wireless channel is generally time-variant in practice, herein, this paper considers the time-varying characteristic of the downlink channels, and proposes a complex-valued ResNet-based deep Q-learning (DQN) algorithm to maximize the sum-rate at user equipment (UE) side, by jointly designing the transmit beamforming at base station (BS) side and the reconfigurable phase shifts at AIRS side. Our results reveal that the proposed complex-valued deep reinforcement learning (DRL) approach shows stronger generalization ability in comparison with the real-valued DRL algorithms, and is validated to be able to mitigate the problem of gradient vanishing and improve the performance over the time-varying downlink channels.
Attention Based Adversarial Attacks with Low Perturbations

Deep neural networks are vulnerable to adversarial examples generated by black-box attacks with tiny perturbations. However, black-box based transferability attacks usually add perturbation to the whole image. It is easy for the defender to detect the adversarial examples. Inspired by attention modules, we propose a method named Gradient-mask and Attention-whey (GM&AW) to reduce redundant noise and maintain attack effect of them in this work. During Gradient-mask iterations, we only choose the regions with greater gradient and update them along the direction of gradient. Then, we utilize Attention-whey optimization combining attention mechanism and query to further decrease noise. Extensive experiments on Imagenet demonstrate that our approach can achieve enormous decrease on noise in $\ell_{2}$ norm and keep attack success rate for black models.
A Novel ADRC Design with Cascade of Observers for Rigid-body Motion Control Systems

This paper considers the disturbance rejection problem for general rigid-body motion control systems. A novel active disturbance rejection control (ADRC) design with cascade of one high gain observer and one reduced-order extended state observer is proposed. It is shown that compared with the regular ADRC, the proposed ADRC method only needs the control direction, avoiding the difficulty of choosing the nominal control input gain. Moreover, such novel design has the same order as that of normal extended state observer. Moreover, it can be proved that the resulting closed-loop system is semi-globally uniformly bounded (SGUB). Finally, simulation results of a 1 degree-of-freedom (DOF) manipulator control system demonstrate the effectiveness of our method.
A 38GHz Deep Back-Off Efficiency Enhancement PA with Three-Way Doherty Network Synthesis Achieving 11.3dBm Average Output Power and 14.7% Average Efficiency for 5G NR OFDM

We propose a mmWave three-way Doherty output network synthesis methodology that can realize close-to-ideal dual-peaking Doherty active load modulation while absorbing the device parasitic capacitance. Following this methodology, we present a 38GHz PA prototype, achieving 13.7% / 11.0% PAE at 9.5dB / 11.5dB back-off, which are the highest among reported silicon PAs operating at 30GHz and above. Tested under 1-CC and 2-CC 5G NR FR2 64-QAM OFDM signals in the Band n260, the PA demonstrates state-of-the-art average output power (11.3dBm) and average efficiency (14.7%).
Sensorless Contact Force Observation with Friction Distinguishment in Heavy Legged Robot Via PMSM Load Torque and IMU

This paper aims at sensorless observation of contact force and friction for the heavy-legged robot (HLR) driven by electric cylinders. It is known that the acquisition of contact force is challenging but significant for HLR due to the high self-weight. Additionally, as the main driving device, the electric cylinder contains considerable friction and disturbances, which greatly influence the accuracy of observed contact force. As a significant novelty, a method for contact force and friction observation through permanent magnet synchronous motor (PMSM) load torque observer is proposed in this paper. The proposed approximate PMSM model containing dynamics of PMSM, the electric cylinder, and the HLR is established. The motor speed is utilized to observe the friction disturbance and the external torque based on the sliding mode observer (SMO) theory. Centroidal dynamics and the information of inertial measurement unit (IMU) are introduced to distinguish the contact force included in the observation result of the proposed model. The entire algorithm is validated in the MATLAB-SIMSCAPE environment, and the observation results show that this algorithm is reliable and accurate in the utilization of HLR.
A Novel Compact Underactuated Tendon-Driven Mechanism with Shape Memory Alloys

An underactuated mechanism, which has less input than the active degree of freedom, is often used for grippers such as robot hands. The use of this mechanism can contribute to the reduction of actuators, that is, the reduction in size and weight. However, many of the conventional underactuated mechanisms are equipped with ordinary springs at the joints and are driven by a motor and they tend to be bigger and bulkier. Therefore, we have developed a compact underactuated tendon-driven mechanism using a lightweight shape memory alloy (SMA) actuator and a superelastic SMA sheet with an elastic range that cannot be achieved by conventional metal leaf springs. It has a range of motion of over 140 deg and can lift a weight of 60 g by energizing the SMA actuator. Using the shape memory effect and the superelastic effect of SMA, it is possible to exert a large force while keeping the mechanism compact and lightweight with a length of 13 mm and a weight of 0.14 g.