Miracle-dz/UniFAR-FTUs huggingface dataset:
Underground Sedimentary Rock Moisture Permeation Damage Assessment Based on AE Mutual Information

Moisture permeation would lead to damage accidents. It is of great significance to measure and monitor the moisture states of underground sedimentary rock engineering in real time. In this research, a reliability assessment method for underground rock based on acoustic emission (AE) mutual information was proposed. First, the mutual relationship between multiple AE parameters and moisture permeation damage was analyzed. Then the mechanical parameters were fit by the probabilistic method, so that moisture permeation damage could be assessed with a data-driven approach. The experiment showed that this method could identify samples with different moisture permeation damage levels. The moisture permeation damage could be predicted in the early stage. Moreover, this method could reduce sensor error through probabilistic analysis.
Adaptive Admittance Control for Cooperative Manipulation using Dual Quaternion Representation and Logarithmic Mapping

This paper addresses the problem of admittance control for bimanual and cooperative manipulators. First, a novel admittance controller is devised to be consistent with the geometry of the cooperative task space and exploits the dual quaternion logarithmic mapping of wrenches and corresponding elastic displacements. By describing the coupled motion exploiting the geometric features of dual quaternions, the desired compliant behaviour is enforced on the whole dual-arm system and not on the single manipulator separately. The solution is thereafter extended by means of an adaptive modulation of the impedance gains along the logarithmic space of the dual quaternion algebra for cooperative dual task-space variables. The overall scheme consists of a stiffness adapter, cooperative admittance controller, and wrench adapter complete with an inner motion controller along with the cooperative system. Stability proofs and conditions for the stiffness and damping adaptation of the cooperative variables is provided. Finally, we present a use case depicting a cooperative variable stiffness adaptation policy with respect to the relative elastic behaviour between arms to reduce internal stress whilst satisfying grasp feasibility under shear forces and varying load conditions. Our formulation is validated by thorough simulation studies on two Franka Emika Panda robots carrying a shared object under varying loading conditions. The experimental investigations reinforce our claim of robust performance when compared with fixed stiffness and standard width-adaptation solutions.
Cost-Effective and Deployment-Friendly L4 Load Balancers Based on Programmable Switches

Recent proposals leverage emerging programmable switches to implement high-throughput and low-latency load balancers in the datacenter. However, most of them store per-connection states in programmable switches to ensure consistent load balancing decisions, which is costly due to the limited on-chip memory. Other proposals avoid storing per-connection states but have difficulty in large-scale deployment due to the modification of running applications. We present CDLB, a cost-effective and deployment-friendly Layer-4 load balancer based on programmable switches in the datacenter. To be cost-effective, CDLB leverages an improved hash-based algorithm that can maintain per-connection consistency in a static environment. To be easier in deployment, we introduce a small state table and design a protocol between load balancers and the controller to maintain per-connection consistency in a dynamic environment without modifying running applications. The state table stores small amounts of connection states temporarily to ensure consistent load balancing decisions under device variations. We implemented CDLB with bmv2 in the mininet environment. The evaluation results show that CDLB greatly reduces overhead and achieves better performance compared with stateful load balancers which store per-connection states in programmable switches, and CDLB maintains per-connection consistency well in a dynamic environment.
Safe trajectory tracking using closed-form controllers based on control barrier functions

This paper considers the problem of guaranteeing avoidance of critical state space regions during tracking of reference trajectories for systems with dynamics equivalent to r-th order decoupled integrators. The necessity to avoid those critical regions during trajectory tracking may arise during the transient phase or because the reference trajectory was planned without taking into account the presence of those critical regions. A typical problem in mobile robotics, taken as reference in this paper, is the avoidance of obstacles in the robot workspace during tracking of reference state space trajectories. The proposed controller ensures a safety clearance from the forbidden regions by filtering out, when appropriate, the component of the tracking command that would eventually lead the system to enter the critical region. The method relies on the construction of first-order control barrier functions and closed-form controllers, with formal proof of safety and stability, and its effective application to wheeled mobile robots and quadrotors is demonstrated through simulation.
Locating Faulty Section in Tie-line Using Classification Based Methods

Wide-area system data acquired by wide-area monitoring systems can be utilized to gain knowledge about the system states and its conditions. Data analytic methods are used to extract the knowledge from the acquired data. One of the critical applications of a monitoring system is to effectively identify the faulty section by reducing the search area of the outage management team and effective utilization of time and resources to enhance the reliability. In this paper, the problem of locating the faulty section on a tie-line is formulated using two classification methods, i.e., Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). Further, these methods are tested on the Kundur two-area system to evaluate their performance in locating the faulty section on the tie-line. Eventually, a comparison between these methods is drawn based on the time consumption, design complexity, and accuracy achieved.
An IoT Traffic Modeling Framework and its Application to Autonomous Edge Scaling

Future wireless networks will exhibit heterogeneity of traffic generating sources originated by numerous Internet of Things (IoT) nodes as well as traditional mobile phones. Moreover, the space of novel IoT services is expanding the simple monitoring tasks of IoT nodes to more complex services in which a node can be in a monitoring state and transition autonomously to an alarm state when predefined conditions are detected. The complexity of the envisioned future wireless networks is indeed new to the community with challenges affecting many aspects such as protocol design and network operation mechanisms. Traffic modeling lies at the core of these issues. As the advancement of technologies continues, faithful performance evaluation measures are dependent on the underlying traffic model. In this scope, we propose a Tiered Markov Modulated Poisson Process (TMMPP) that is capable of capturing IoT traffic characteristics, e.g. patterns and seasonality, which occur in long time spans, e.g days, with the flexibility of modeling different IoT service behaviors. Moreover, we study an autonomous edge scaling mechanism as a use case illustrating the benefits of the proposed TMMPP traffic model.
Flexcatter: Low-Power Signal Emulation for Software-Defined Backscatter Modulation

In this paper, we design and implement a flexible backscatter system, i.e., Flexcatter, which can support various baseband modulation schemes to be compatible with active radios. The key technique employed by Flexcatter is signal emulation, where the tag can vary the reflection coefficient in the time domain to emulate desired baseband signals. To meet the low-power requirements of Flexcatter, we first carefully design a cost-effective impedance network, which can provide up to 16 reflection coefficients for different signal amplitudes and phases. Moreover, we present an effective emulation strategy for desired baseband signals. Next, we introduce the oversampling method in the baseband modulation pipeline to mitigate the effect of emulation errors caused by hardware deficiency. We further build the prototype hardware, including an FPGA platform and the radio frequency (RF) module. Experiment results show that OFDM transmissions from Flexcatter can achieve a throughput of 21.1 Mbps at the backscatter range of 12 m. To the best of our knowledge, we are the first to generate OFDM transmission with 64 subcarriers using low-power RF switches. Our design has the potential to accept other types of existing active radios as the receiver to reduce the deployment cost significantly.
A High-Precision Folding Time-to-Digital Converter Implemented in Kintex-7 FPGA

Time-to-digital converters (TDCs) based on field-programmable gate array (FPGA) generally use a tapped delay line (TDL) to propagate the hit signal for time interpolation within one system clock cycle. The length of TDL is normally required to provide the total delay time larger than one system clock period, and all taps of TDL are sampled for encoding, which causes unnecessary logic resource consumption. In this article, we propose to interpolate the system clock cycle with a folding TDL. The shorter TDL with fewer sampled taps can significantly save logic resource usage. We will also demonstrate that the multiedge measurement technique is still available to this folding TDC for precision improvement. In a Xilinx Kintex-7 FPGA, three pairs of such folding TDCs with folding factors of 2–4 are implemented and performance evaluated. The average rms precision is measured as 4.6, 5.6, and 6.4, respectively, over time intervals from 0 to 50 ns. Compared with the normal TDL-TDC with similar performance, the proposed folding TDC can save more than 50% of logic resources. This advantage makes the TDC helpful to applications requiring multiple TDC channels.
Detection of Pulmonary Embolism Based on Receptive Field Amplification and Attention Mechanism

Pulmonary Embolism (PE) is a serious threat to human life and health due to its high incidence rate and mortality. It is important to detect PE in time for the treatment of the disease and recovery of patients. Computed Tomography (CT) images are often used for disease diagnosis in clinical practice. Existing lung CT image disease classification algorithms only focus on local information, resulting in low accuracy, To solve this problem, a network model (DR-SENet) based on receptive field amplification and attention mechanism is proposed to detect PE. Specifically, Resnet network is used as the backbone network to slow down gradient explosion and gradient disappearance. Channel attention mechanism is used to extract the weight information between feature channels to guide the network to focus on important feature information, At the same time, the receptive field amplification structure is introduced to enhance the feature extraction ability of the network, obtain more comprehensive features, and make up for the limitation of convolution operation focusing on local features. The method is tested on the open PE dataset–FUMPE. Through experiments, we found that our method obtained better index and improved the auxiliary diagnosis performance of pulmonary embolism.
Students’ Classroom Behavior Detection Based on Human-Object Interaction Model

Existing classroom behavior detection methods for students are mainly based on the network model to extract key common features to directly determine behavior types, which cannot provide a higher fine-grained understanding of interaction relationships in the classroom. This paper proposes a classroom behavior detection method for students based on the Human-Object Interaction (HOI) model, which further utilizes human-object relationship features to infer interaction relationships based on object detection. In the study, the cell phone is selected as the detected object to interact with the students, and the HOI model is trained and tested for two types of behaviors—Use and No interaction. The results show that the average accuracy of the trained HOI model reaches about 83.4% in the test, which promotes a higher fine-grained perception and understanding of classroom behavior detection and provides a new perspective for building smart classrooms and exploring personalized teaching and learning paths.
On simple design of a robust finite-time observer

This paper presents a novel observer that estimates the state of a linear system in the observer canonical form in finite time. Due to the specially selected dynamical gain, the proposed observer is easy to tune and, at the same time, it is robust to external perturbations (e.g., state disturbances and measurement noises). The tuning algorithm consists in solving a simple system of linear matrix inequalities with only two adjustable parameters. Theoretical results and a comparison with homogeneous and prescribed-time observers are illustrated by numerical simulation.
Network-based deployment of multi-agents without communication of leaders with multiple followers: a PDE approach

Recently, a PDE-based deployment of multi-agents was suggested that employed transmission of control signals from leaders to other agents which may be expensive and not secure. In this paper we propose a solution to the deployment problem that avoids the latter transmission. In our consideration, only two boundary agents (leaders) measure their absolute positions that they send through communication network to the controller and receive their control signals from the controller via communication network. Our design employs network-based finite-dimensional control of 1D heat equation under two Neumann actuations by using two boundary measurements.
Evolution of Electricity Distribution Control Room Data Streams - UK Case Study

The move towards ‘Net Zero’, the balance of greenhouse gas emissions produced and removed from the atmosphere, is increasing the complexity of managing distribution networks, with more deployments of low carbon technologies, new markets for flexibility services, and greater monitoring and visibility of the network itself. This has driven the UK's Distribution Network Operators who manage the network, to evolve towards becoming Distribution System Operators, taking on a role more akin to the traditionally more complex Transmission System Operator. In parallel with this development, the data streams utilised by the distribution network control room have also evolved. This paper sets out this evolution by detailing the existing data streams within a typical distribution control room, and how these data streams will need to evolve, as well what new sources of data a future control room will need to accommodate, and finally by identifying novel ways that that value can be created from this data through the use of new ‘smart’ tools.
IMU-based Online Kinematic Model Parameter Estimation for Four-Wheeled Skid-Steering Mobile Robot Desired Motion Tracking on Different Terrains

This work presents an online adaptive tracking control for a 4-wheel skid-steering mobile robot (SSMR). The objective is to develop an online kinematic model parameter estimation for an equivalent differential drive kinematic model, enabling motion control on the various terrains’ conditions. Combined with an equivalent differential drive kinematic model, the angular velocity of robot motion analyzed by the inertial measurement unit (IMU) sensor is used as the observation quantity, and the steering efficiency parameters in the kinematic model are automatically adjusted by a proportional derivative (PD) controller to improve the desired motion tracking control performance for the robot in the face of different terrains. The execution of tracking control is achieved by the SSMR with torque-adjusted 4-wheel independent velocity control. The proposed strategy is validated experimentally, where the SSMR performs the desired motion tracking in 10 sets with four types of terrains with different levels of flatness. The results demonstrate the high potential in overground navigation settings, due to its fast adaptation and motionfollowing capacity.
Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data

The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square‐specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square‐specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square‐specific covariates and illustrate this by introducing deterministic spatial changes.
Derivation and Application of the Fokker‐Planck Equation

The Fokker‐Planck equation is derived and applied to discrete nonlinear dynamic systems subjected to white‐noise excitation. For the class of problems in which the nonlinearities involve only the displacements of the system, it is shown that exact solutions can be constructed for the stationary Fokker‐Planck equation. It is further shown that, if steady‐state solutions exist. they are unique.
A Backbone-Listener Relative Localization Scheme for Distributed Multi-agent Systems

Reliable and accurate localization awareness is of great importance for the distributed multi-agent system (DMAS). Instead of global information, measurements only between neighbors pose locatability and accuracy challenges for distributed systems, which leads to the development and application of relative localization. In this paper, we put forward a backbone-listener localization scheme for the D-MAS. Agents switch backbone-listener modes through a node selection strategy. Position and orientation angle of agents are jointly estimated by range and angle information fusion. A distributed multidimensional scaling method is proposed for backbone agents to maintain the topology estimation. And listener agents ensure the localization capacity through a least square range and angle fusion algorithm. Extensive simulation and real-world experiments validate that our method achieves decimeter-level accuracy relative localization.
A Vehicle and Charging Scheduling Framework for Campus Shuttle Electric Buses

Electric buses (e-buses) are one of the promising drivers of the transition towards electrified public transportation to facilitate efficient and environmentally friendly mobility. In this regard, several universities across the globe are deploying e-buses for their campus shuttle services. However, enabling reliable and cost-effective operation of e-bus fleets is a complex task, mainly due to the limited driving range and charging costs of the vehicles. In this work, we propose a vehicle and charging scheduling framework to enable cost-optimal and feasible operation of campus shuttle electric buses. First, we develop a three-stage multi-depot vehicle scheduling problem (VSP) to allocate the trips to e-buses such that the daily energy requirement per each bus is levelized. Then, we introduce a charging scheduling problem (CSP) to minimize the electricity cost of charging, which consists of demand and time-of-use energy charges. Numerical case studies on the shuttle bus network of National University of Singapore reveal that the charging costs can be decreased by up to 63.33% when compared to an overnight charging strategy. Furthermore, with the proposed approach, it is found out that the average battery degradation of e-bus fleet is reduced by up to 11.78%.
A Novel Navigation-Communication Integrated Waveform for LEO Network

For the increasingly strong demand for low-orbit navigation enhancement, a novel navigation and communication integrated (NAVCOM) waveform for the LEO constellation is proposed, which can conduct communication and navigation simultaneously. Zadoff-Chu (ZC) sequence with controllable power is superimposed on the communication signal in the frequency domain as the positioning signal. Furthermore, positioning can be conducted with both time-domain correlation detection (TDCD) and frequency-domain phase estimation (FDPE) for higher ranging accuracy benefits from the Fourier invariance of ZC sequence. Interference between positioning and communication components is analyzed, and the Cramer-Rao low bound (CRLB) of ranging error is given. The performance evaluations show that the novel waveform can achieve high-precision positioning without signifilcantly affecting communication performance.
Handover Game for Data Transportation over Dynamic UAV Networks with Predictable Channels

This paper studies a decentralized transmission strategy for delay-tolerant wireless data transportation over unmanned aerial vehicle (UAV) networks, where a team of UAVs is employed for data transportation as a secondary task without altering the preset courses of the UAVs from their primary tasks. The key novelty yet challenge is to exploit the predicted channel knowledge among the UAVs to optimize the transmission strategy. Much prior work focuses on multi-UAV cooperation from a centralized perspective, or in a distributed manner but requiring intensive message exchange. This paper develops a team strategy following a game-theoretical approach, where the data packets are passed from one node to the other according to the handover time that is negotiated in a fully distributive way. A Handover Game is thus formulated and is proven that the Nash equilibriums (NEs) are the stationary points of an energy minimization problem for coordinated data transportation. A neighbor coordinate response strategy is designed, which is shown to converge faster than a classical asynchronous response in a multi-player game. Numerical experiments show that the proposed scheme substantially reduces transmission energy from classical relay or data ferry schemes.
Task-Invariant Centroidal Momentum Shaping for Lower-Limb Exoskeletons

Task-invariant approaches are desirable in exoskeleton control design as they have the potential of providing consistent assistance across locomotor tasks. Different from traditional trajectory-tracking approaches that are specific to tasks and users, task-invariant control approaches do not replicate normative joint kinematics, which could eliminate the need for task detection and allow more flexibility for human users. In this paper, we propose a task-invariant control paradigm for lower-limb exoskeletons to alter the human user's centroidal momentum, i.e., a sum of projected limb momenta onto the human's center of mass. We design a virtual reference model based on human user's self-selected gaits to provide a reference centroidal momentum for the exoskeleton to track and make it adaptable to changes in gait patterns. Mathematically, the proposed approach reduces the control design problem into a lower-dimensional space. With the number of actuators being greater than the dimension of the centroidal momentum vector, we can guarantee the existence of a centroidal momentum shaping law for underactuated systems through optimization. Simulation results on a human-like biped show that the proposed shaping strategy can produce beneficial results on assisting human locomotion, such as metabolic cost reduction.