Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Prediction Based on Multiple Futures for Dynamic Obstacle Avoidance of Mobile Robots

The ability to decide and adjust actions according to motion prediction of dynamic obstacles offers a flexible planning scheme and ampler reaction time to avoid potential impact. Prediction-based collision avoidance implies a two-stage decision-making process from motion prediction to action planning. One of the challenges in motion prediction is the movements of objects are usually non-deterministic and governed by multimodal models. Many studies have been made on motion prediction of dynamic obstacles and action planning for mobile robots separately. The objective of this work is to explore their coherence in terms of multiple future predictions by combining a data-driven motion prediction approach with a model-based control strategy. More specifically, we integrate motion prediction from deep learning models, Mixture Density Networks (MDNs) with a Non-linear Model Predictive Control (NMPC) framework. The deep learning models produce the multimodal probability distribution of future positions of dynamic obstacles, which is utilized by the MPC controller as a constraint. We show via simulation that the selected model provides valid predictions of motion in a dynamic environment. The prediction result endows the controller with the capability to avoid dynamic obstacles in advance.
Modeling and Experimental Evaluation of Z-Source Modular Multilevel Converter Using Reduced Inserted Cells Technique

The integration of a Z-source network with a modular multilevel converter (MMC) to provide voltage step-up function is proposed in this paper. The proposed Z-source modular multilevel converter (ZS-MMC) uses a Z-source network connected between the DC source and the DC-link terminals of the MMC. The operation principle of the ZS-MMC is presented utilising the reduced inserted cells (RICs) modulation technique. Compared to the quasi ZS-MMC previously developed by the authors, the ZS-MMC has small fundamental frequency component in the ZS inductor current which requires a smaller inductor size for the Z-source network and is also more efficient. The ZS-MMC is compared to other topologies that can accomplish buck and boost capabilities, such as the quasi Z-source MMC, the quasi Z-source cascaded multilevel converter and the full-bridge based MMC, to validate the viability of the proposed converter. The operation of the ZS-MMC employing the RICs technique is confirmed experimentally.
A Lyapunov Krasovskii Functional for Input-to-State Stability of Infinite-Delayed Systems

This paper mainly addresses input-to-state stability (ISS) of nonautonomous nonlinear systems with infinite delays. We provide a novel definition for ISS of infinite-delayed systems, which can be seen as an extension of the existing ISS definition. A Lyapunov Krasovskii functional is proposed for verifying ISS of infinite-delayed systems. Applying the ISS Lyapunov functional method, a link between ISS of an infinite-delayed system and the exponential stability of its corresponding zero-input system is established. It is shown that a nonlinear system with infinite delays is ISS if its corresponding zero-input system is globally exponentially stable, provided that some Lipschitz and/or Lipschitz-like conditions are satisfied. Two illustrative examples are provided to show the effectiveness of our results.
Selecting Part Feeding Policies with a Combined Optimization-Machine Learning Approach

In several industries, increasing attention is being devoted to the design and management of part feeding systems. This paper applies a combined optimization-machine learning (ML) approach for part feeding policies selection to the case of a truck assembly plant. According to this approach, feeding policies are selected through a ML model, trained using the output of an optimization model previously applied to a sample of parts. Results show that this approach leads to results close to the optimal ones, as the developed ML models are able to estimate the optimal policies for most of the parts.
Discovery of Mental Wellness via Social Analytics for Liveability in an Urban City

Smart cities, are often perceived as urban areas that use technologies to manage resources, improve economy and enhance community livelihood. In this paper, we share an approach which uses multiple sources of data for evidence-based analysis of the public's views, concerns and sentiments on the topic related to mental wellness. We hope to bring forth a better understanding of the existing concerns of the citizens and available social support. Our study leverages on social sensing via text mining and social network analysis to listen to the voices of the citizens through revealed content from web data sources, such as social media and public forums. By using hybrid data sources, we present the important considerations for mining inherent mental wellness concerns faced by the citizens. The outcome of the analysis includes, both the positive and negative sentiments towards mental wellness and draws relations to national level performance indicators relating to mental wellness. We hope our research could help authorities derive actionable plans for designing health services or public events that bring positive social mixing and happiness by addressing the mental wellness of the residents.
An Accelerated Distributed Online Gradient Push-Sum Algorithm in Time-varying Networks

This paper investigates an online convex optimization problem on time-varying directed networks, where each agent holds its own convex cost function and the goal is to cooperatively minimize the sum of the global cost function. To tackle such optimization problems, an accelerated distributed online gradient push-sum algorithm is firstly proposed, which combines the momentum acceleration technique and push-sum strategy. Then, we specifically analyze the regret for the proposed algorithm. The theoretical result shows that the individual regret of the proposed algorithm achieves an improved rate with order of $\mathcal{O}\left( {\sqrt {1 + \log T} } \right)$, where T is the time horizon. Moreover, we implement the proposed algorithm in sensor networks for solving the distributed online estimation problem, and the results demonstrate the effectiveness of the proposed algorithm.
Patterns and Portfolios based on Stock Multiplex Networks

Stock market is an essential part of financial system and also a typical complex system. For complex systems with different attributes, multiplex networks can help to grasp their characteristics from different dimensions. In this paper, Chinese A-share stock market is investigated based on multiplex network using price data and industry classification information. According to the pattern analysis, industry classification information is fused into the community discovery process of multiplex network. Moreover, Two portfolio strategies naming diversified strategy and stabilized strategy are proposed, both of which are derived from multiplex network community structures. The diversified strategy is concentrated on risk diversification among stock market network, while the stabilized strategy is originated from evolution process of community structures. The back test indicates the return of both diversified strategy and stabilized strategy are much higher than that of market index and degree centrality strategy.
Fast Planar Surface Detection in Point Clouds Using Voxel-grid-center-constrained Iterative Adaptive Plane Extraction

Plane detection is a key pre-processing step in point clouds for many applications, such as buildings registration and automous cars. Many methods have been proposed for plane detection in point clouds, but most of them cannot meet the requirement of speedability. Therefore, in this paper, we propose a voxel-grid-center-constrained iterative adaptive plane extraction method that can meet the speedability without losing the accuracy of the detected planes. First, the point cloud is subdivided using an octree, and then a voxel grid is randomly selected for coplanar testing to find the plane in the point clouds. Second, in the plane detection phase, a voxel grid center constraint strategy is used to improve the efficiency of plane detection. Finally, an iterative adaptive plane extraction technique is used to improve the robustness. Experimental results demonstrate that the proposed method can achieve high performance on the synthesis datasets as well as on the real datasets.
Data Calibration Algorithm for Artificial Lateral Line Sensor of Robotic Fish on Improved LSTM

All the time, the measurement of swimming speed has long been a major research challenge in the field of robotic fish. The artificial lateral line system can accurately measure the speed without modifying the structure of the robot fish too much. In this paper, we developed a cantilever artificial lateral line(ALL) sensor based on piezoresistive effect by imitating the "SNs" cell structure of real fish, and established an accurate physical model for it considering the complex underwater boundary layer effect, which is conducive to the selection of hardware parameters such as sensor size and stiffness. In addition, a flowmeter-based speed calibration experimental platform was developed, based on which we proposed an "end to end" speed calibration algorithm based LSTM without filtering. Then a potential mapping relationship between the electrical signal of the sensor and the speed value of the flowmeter was obtained, which laid a strong foundation for the subsequent measurement of free-swimming speed of robotic fish by artificial lateral line system composed of this sensor.
Bandwidth-Enhanced High-Gain Full-Metal Filtering Slot Antenna Array Using TE101 and TE301 Cavity Modes

In this letter, a bandwidth-enhanced high-gain full-metal cavity-backed filtering slot antenna array is proposed. By replacing one radiating slot with 2 × 2 radiating slots, the enhanced gain is obtained without enlarging the antenna size. Besides, two cavity modes, i.e., TE101 and TE301, are combined to obtain an enhanced bandwidth. Metal ridges and metal posts are employed to make the two modes close to each other. Due to a similar radiating field of the two specific cavity modes under the proper perturbation and placement of the 2 × 2 radiating slots, a high gain over the enhanced bandwidth is obtained. In addition, the high-quality factor of the two cavity modes can inherently produce a filtering performance in terms of the realized gain and efficiency. The final measurement shows that the proposed slot antenna array can achieve 6.2% bandwidth, 97% total efficiency, and 10.4–11 dBi stable realized gain with a small aperture size of 0. 84λ2. Good agreement between the simulation and measurement has well validated the proposed design concept.
Enhancing convergence efficiency of self-propelled agents by diffusion concept

In recent years, the well-known Vicsek model (VM) has attracted the interest of many researchers. In this paper, we investigate a revised Vicsek model based on diffusion concept, named as diffusion Vicsek model. The proposed diffusion Vicsek model can use weights to make the agents in the cluster more dispersed without introducing additional repulsive forces. In diffusion Vicsek model, the λ is introduced to adjust the degree of dispersion. When the value of λ is larger, it is more conducive to increase the dispersion of the cluster. Especially when λ = 0, the diffusion model will become the original Vicsek model. In the simulation experiments, the original Vicsek model and the diffusion Vicsek model are compared. The results show that there is an optimal λ under different number of agents N, communication radius R and absolute velocity v0, which effectively improves the convergence speed of the cluster.
Back-Stepping Integral Sliding Mode Control with Iterative Learning Control Algorithm for Quadrotor UAV Transporting Cable-Suspended Payload

In order to cope with external disturbances such as cable-load and wind power during a flight motion, a robust controller based on combination of a nonlinear strategy and iterative algorithm is considered in this paper for full nonlinear dynamic of Quadrotor UAV. The main contribution of this article is combination of back-stepping and sliding mode control with a repetitive algorithm to compensate the error terms. The stability of the mentioned strategy is analyzed by Lyapunov function. The simulations show that despite the cable-suspended payload the mentioned methodology can reject the external disturbance and force the error terms toward zero during a flight motion.
A Novel Variable Relational Network Construction Method for Plant-wide Monitoring in Chemical Production

With the development of the modern industries, the plant-wide monitoring of the chemical production process is growing significantly. The accurate and effective production variable relational network construction is the basis of the plant-wide monitoring. Compared with the conventional methods depending on the prior production knowledge, the paper proposes a data-driven method integrated the maximal information coefficient and Granger causality test for analyzing the time-vary data to construct the variable relational network. Based on the constructed network and parameters obtained by Bayesian network, not only can the normal or abnormal production states be monitored by statistics, but the fault roots are diagnosed by Bayesian reasoning. The effectiveness and practicality of the proposed monitoring scheme is validated by TE process. The obtained monitoring results can help operators understand the abnormal production state better and guide decision-makers to take the suitable actions for improving the production in chemical process.
A New Approach Based on PCA for Identifying Power Flow Control Infeasibilities

This paper describes a new methodology to identify conflicts between power flow control devices that can cause New-ton's method convergence problems. The proposed technique is based on applying the principal component analysis (PCA) to the control sensitivity matrix. This matrix directly relates the control mismatch functions and control variables. The results presented for small-scale and large-scale power systems were compared to the analysis of the eigenvalues of the power flow Jacobian matrix, indicating the proposed methodology effectiveness.
Fast terminal sliding mode control for cooperative braking of the vehicle platoon

The cooperative braking has the potential of significantly enhancing vehicle platoon economy, safety and traffic capacity. The main target of the cooperative braking is to ensure interconnected vehicles keep within the safe spacing range and stop at the desired target stopping positions smoothly, accurately. In order to achieve above-mentioned goals, this paper proposes a novel fast terminal sliding mode controller for cooperative braking of vehicle platoon with consideration on unknown dynamics. Firstly, the longitudinal vehicle dynamics model is presented. Then, for the purpose of achieving finite-time consensus of the cooperative braking of vehicle platoon, a prescribed position braking policy with safety gap constraints is adopted to avoid serial rear-end collisions and a novel distributed fast terminal sliding mode control strategy is adopted to speed up the convergence of the cooperative braking controller and eliminate the influences of unknown dynamics . Finally, the simulation results are demonstrated to validate our controller and cooperative braking policy.
2-D Beam-Steerable Generalized Mikaelian Lens With Unique Flat-Shape Characteristic

A novel two-dimensional (2-D) beam-steerable generalized Mikaelian lens is presented in this letter. The main difference of proposed lens with respect to conventional Luneburg, Maxwell fish-eye and Eaton lens is its unique flat-shape characteristic, which provides great advantages in designing the low-profile flat-shape lens antenna, avoiding using extremely complex conformal mapping methods. The all-dielectric low-cost lens prototype presenting desired graded permittivity profile is fabricated by 3-D printing. The measurement results at 10 GHz show 2-D beam-steering capabilities from −20° to 20° in both the E-and H-plane with around 18.9 dBi of realized gain with less than ∼1.2 dB variation and low-side lobe can be achieved, revealing great potentials for developing the low-profile and cost-effective lens antenna for microwave applications.
Improved Attention Mechanism and Residual Network for Remote Sensing Image Scene Classification

Aiming at the problem that the accuracy of traditional remote sensing image classification model is not ideal, a classification method based on improved attention mechanism and residual network is proposed. In order to prevent overfitting, we choose the network structure of ResNet18 as the framework. Meanwhile, ResNet18 has a small number of parameters and a fast calculation speed. Then, a parallel attention mechanism, ProCBAM, is proposed and added to the BasicBlock of the residual network. By optimizing the representation of the feature map from the spatial dimension and channel dimension of the feature map, more detailed image information is learned and image recognition errors are reduced. The experimental results on the open source dataset show that the accuracy is 93.86%, and a more accurate image classification model is successfully trained.
Disturbance Compensation Based Backstepping Control for Hypersonic Vehicle

It is known that Hypersonic Vehicle (HV) is kind of system with strong coupled and highly nonlinear characteristic. In this paper, the dynamic model of HV is divided into velocity subsystem and altitude subsystem by analyzing the longitudinal model of HV. The altitude subsystem can be further decomposed into three loops: flight-path loop, pitch loop and pitch rate one. Backstepping control method is used in the design of virtual control variableˈ and tracking differentiator (TD) is selected to compute the first derivative of virtual variable, which solves the "explosive differentiation terms" in backstepping control. The extended state observers (ESO) are used to estimate and compensate the nonlinear component caused by inner and outer disturbance during the flight of the HV. The simulation results show that the control system developed has good performance and robustness under uncertain disturbance conditions.
Detection of cotter pins missing of connection fittings on transmission lines of power system

Connection fittings are widely used in the transmission lines. However, the missing of cotter pins usually happens to the bolts of connection fittings due to the complex environment and aging. Cotter pins missing detection is one of the most time-consuming and labor-intensive parts of manual judgment. It belongs to the small target detection because the cotter pins with less features in the images are much smaller than other components on transmission lines. This paper designs a three-stage cotter pin missing detecting method based on pictures taken by UAVs. The first stage utilizes YOLOv4 to detect insulators and locate the connection fittings by extending the predicted bounding box of the insulator in the original pictures. The second stage is a small object detection model based on an object network called Faster R-CNN with ResNet-101 backbone. The network of this stage detects the bolts on the connection fittings and divides these bolts into two categories. The third stage utilizes the DenseNet121 classification network to identify the integrity or missing of the cotter pins. The experimental dataset consists of a public dataset and pictures captured by the UAVs from state grid. The result shows the accuracy of the bolts detection exceeds 90%, and the accuracy of front pins missing detector exceeds 85%. However, the lower accuracy of lateral pins detector due to the samples of dataset.
A Rapid Damage Assessment Method Based on Association Rules of Feature Factors

The rapid assessment of structural damage under finite conditions is of great significance for ensuring structural safety. A rapid damage assessment method based on association rules of feature factors is studied. The entire monitoring area into several small monitoring unit, using active Lamb wave monitoring method, in each monitoring unit for damage characteristic factor. The characteristic factors of the same monitoring unit and adjacent monitoring units are correlated by the association rules of the characteristic factors. So as to realize the rapid assessment and detection of damage occurrence and expansion. The results are verified by experiments on typical composite structure specimens. The experimental results show that the method can realize the rapid monitoring and expansion estimation of structural damage, and intuitively display the location, scope and development trend of damage, and has a certain application prospect.
Soot Blowing Optimization for Coal-fired Boilers with Ash Accelerated Deposition Model based on Gamma Process

In view of the unreasonable timing and quantitative soot blowing operation of coal-fired boiler heating surface, this paper takes the data of cleaning factor as the monitoring index, puts forward the minimum cost rate as the optimization objective, and establishes the soot blowing optimization strategy with ash accelerated deposition model based on gamma process. Firstly, gamma distribution is used to describe the ash characteristics of coal-fired boiler heating surface, and an accelerated ash deposition model is established with a continuous non negative random variable which obeys exponential distribution; then, the soot blowing optimization model of boiler heating surface is established with the minimum average cost rate function. Finally, taking the off-line data of the heating surface cleaning factor of a 300MW Coal-fired Unit as an example, the PSO multi-objective optimization algorithm is used to optimize, and the minimum average cost rate is obtained and verified.
Predicting length of stay with administrative data from acute and emergency care: an embedding approach

Hospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly.
A Link Prediction Similarity Index Based on Enhanced Local Path Method

The index of link prediction based on local path considers the contribution of third order neighbors on the basis of the common neighbor index, which has strong randomness and ignores the influence of the particularity of network topology on particle transition probability. In order to resolve this problem, this paper proposes a enhanced local path (ELP) link prediction similarity index. The algorithm considers the contribution of the third order path should have the relationship with the degree of the nodes at both ends of the path. By calculating the similarity of node vectors, it redefines a similarity index based on enhanced local path. We apply it to four different types of networks. Finally, the index proposed in this paper is compared with five classical similarity indexes. The results show that the prediction algorithm based on enhanced local path index has higher accuracy than other indexes.