Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Allocation in Information-Centric Wireless Networking With D2D-Enabled MEC: A Deep Reinforcement Learning Approach

Recently, information-centric wireless networks (ICWNs) have become a promising Internet architecture of the next generation, which allows network nodes to have computing and caching capabilities and adapt to the growing mobile data traffic in 5G high-speed communication networks. However, the design of ICWN is still faced with various challenges with respect to capacity and traffic. Therefore, mobile edge computing (MEC) and device-to-device (D2D) communications can be employed to aid offloading the core networks. This paper investigates the optimal policy for resource allocation in ICWNs by maximizing the spectrum efficiency and system capacity of the overall network. Due to unknown and stochastic properties of the wireless channel environment, this problem was modeled as a Markov decision process. In continuous-valued state and action variables, the policy gradient approach was employed to learn the optimal policy through interactions with the environment. We first recognized the communication mode according to the location of the cached content, considering whether it is D2D mode or cellular mode. Then, we adopt the Gaussian distribution as the parameterization strategy to generate continuous stochastic actions to select power. In addition, we use softmax to output channel selection to maximize system capacity and spectrum efficiency while avoiding interference to cellular users. The numerical experiments show that our learning method performs well in a D2D-enabled MEC system.
Residential Energy Use Prediction across different Time Scales with Advanced Machine Learning Techniques

As a significant part of total energy consumption, predictive modeling of residential energy use is critically important and highly desired. Previous efforts have proposed a number of statistical models for the prediction of residential energy consumption, while the accuracy and predictability of different models are still highly uncertain. In this study, we explore the effective temporal scale of residential energy use prediction, using the-state-of-the-art machine learning techniques: a fully connected Artificial Neutral Network (ANN) and a Recurrent Neural Network (RNN). For RNN modeling, the Long-Short-Term-Memory (LSTM) realization is employed. We find that ANN model in general has higher predictability than LSTM. Specifically, neither ANN nor LSTM is able to well predict high frequency fluctuation of residential energy use (~10 minutes) due to short-term random error. While, across a relatively longer time frame (from 24 hours to 48 hours), ANN model performs reasonably well and works much better than LSTM. From the perspective of dominating factors, room temperature and humidity are the most relevant ones to predict the building residential energy use. This work will facilitate the energy use prediction and decision-making within the framework of smart grid.
A Regularized System Identification Approach to Subject-Specific Physiological Modeling with Limited Data* Research supported by National Science Foundation CAREER Award (Grant No. 1748762) and Arcos, Inc. (Contract No. 29882).

This paper investigates a novel regularized system identification approach to physiological modeling using limited data. The proposed approach operates in two steps: 1) limited data from individual subjects are consolidated and leveraged to determine a population-average physiological model; then, 2) a subject-specific model for an individual subject is derived from a regularized system identification procedure whose objective is to reconcile the model's capability to predict individual-specific behavior and to retain typical population-representative trends. This is achieved by embedding a regularizing condition into the cost function for system identification that enforces parsimony in parametric deviation from the population-average model. A few unique advantages of the proposed approach are that 1) it offers superior predictive accuracy in both measured as well as unmeasured physiological system responses when compared to a standard system identification approach; and 2) it provides high-sensitivity parameters in the model associated with each individual subject, thus potentially eliminating the necessity for post-hoc parametric sensitivity analysis. Merits and limitations of the proposed regularized approach are illustrated with a real world case study on physiological modeling of hemodynamics in response to burn injury and resuscitation.
Formation control synthesis in local frames under communication delays and switching topology: An LMI approach

This paper presents a formation control synthesis method for a multiagent system to reach a prescribed rigid formation under communication delays and time-varying switching communication topology. The proposed control scheme only requires the knowledge of relative measurements of some neighbor agents, expressed in each agent's local frame, to be implemented. The presence of communication delays and switching topology are critical factors in the control design that could lead the system to slow convergence or even instability. To cope with this problem, we give sufficient conditions based on Linear Matrix Inequalities (LMI) and convex sum relaxation techniques which allow finding the control parameters that maximize the worst-case delay whilst keeping a minimum speed of convergence. Finally, simulation results are provided to show the effectiveness of the proposed approach.
Stabilization of Nonlinear Networked Control Systems under Denial-of-Service Attacks: A Linearization Approach

In this paper, we consider the stability analysis of nonlinear networked control systems under Denial-of-Service (DoS) attacks. In particular, we investigate local stabilization through a linearization approach. To formulate the networked control problem subject to such attacks, a switched system representation is employed with the controlled and uncontrolled modes. We provide a characterization of the frequency and duration of DoS attacks under which local stability is guaranteed. Moreover we derive a sufficient condition for convergence that makes the state remain within the linearization region even in the presence of DoS attacks. Our results are demonstrated with a numerical example.
On the Importance of the Azimuth Offset in a Combined 1P and 2P SISO IPC Implementation for Wind Turbine Fatigue Load Reductions

Wind turbines experience periodic loads causing fatigue damage to blades and structural parts. The application of individual pitch control (IPC) using the multi-blade coordinate (MBC) transformation enables the attenuation of these loads. Often, the transformed fixed-frame load signals are subject to a single-input single-output controller design. However, the coupling between the transformed fixed-frame axes increases for higher load harmonics, increased actuator phase lag, and larger rotors with more flexible blades, posing a need for multivariable controller designs or decoupling strategies. It has been shown earlier that the coupling for the 1P harmonic is minimized by introduction of an azimuth offset in the MBC transformation. This paper extends the previous work and demonstrates the consequences and importance of including the azimuth offset for mitigation of higher load harmonics. A multivariable sensitivity and fatigue load analysis is performed using different pitch actuator models. Results from high-fidelity simulations show near-perfect attenuation of 1P and $\pmb{2}\mathbf{P}$ periodic loads by including the azimuth offset, whereas by excluding the offset, the performance worsens with respect to the baseline case without IPC.
A New Framework for Network Flow Queuing Delay Prediction Based on Stream Computing

In the network, accurately predicting network flow queuing delay is important for congestion control, network bandwidth allocation, and network performance improvement. In order to improve QoS (quality of service) and predict fine-grained queuing delay in advance, A framework is proposed in this paper. We firstly perform real-time preprocessing on fine-grained data packet, which is acquired using In-band Network Telemetry (INT) technology, based on the Storm platform. Then we use Spark Streaming to perform online prediction based on LSTM short-term prediction model which is obtained by training off-line data on the Spark platform. The online prediction and off-line training technology framework proposed in this paper will provide effective information for congestion avoidance or dynamic routing planning in real time.
Integrated Aircraft and Propulsion System Simulation for Control and Performance Optimization

Commercial airliners need to meet stringent regulations and are subject to intense commercial competitive pressure for fuel economy. During an aircraft and engine program, the matching of engine to aircraft is a complex task that covers multiple domains. In particular, the optimization of flight controls, flight trajectories with respect to fuel economy, noise footprint and pollutant emissions must be considered. Besides performance metrics, safety and fault cases must be analysed, such as engine faults during take-off. Historically, each domain would conduct simulations focusing on their own domain with a limited data exchange between domains. We introduce here a simulation method that integrates several key domains: flight dynamics, flight controls, engine controls, engine dynamics, engine exhaust, aircraft noise, fuel economy, flight planning. In “one-pass”, a complete flight route can be simulated, from departure gate to arrival gate, and corresponding performance metrics can be obtained.
Predictive Trajectory Planning in Situations with Hidden Road Users Using Partially Observable Markov Decision Processes

State of the art emergency brake assistant systems solely based on sensor measurements reduced the number of traffic accidents and casualties drastically in recent years. In order to be able to react on road users who elude a vehicle's field of view because of sensor limits or occlusions, this paper presents an approach to anticipate potential hidden traffic participants in occluded areas in the decision making process of an autonomous vehicle. A Partially Observable Markov Decision Process is used to determine the vehicle's longitudinal motion. Observations are made using the vehicle's field of view. Therefore the field of view is calculated with a generic model of a sensor setup in dependence of the current or the predicted environment. In this way, the vehicle can either observe that it detects a previously hidden road user or receives information that the road is clear. In total, that allows the vehicle to better anticipate future developments. Therefore, assumptions about vehicles that may be located in hidden areas need to be made. We demonstrate the approach in two scenarios. Firstly in a scenario, where the vehicle has to move cautiously into the intersection with a minimum number of actions and secondly in a typical scenario for urban traffic. Evaluation shows, that the approach is able to anticipate hidden road users correctly and act accordingly.
Optimal Quadratic Regulation of Nonlinear System Using Koopman Operator

In this paper, we study the optimal quadratic regulation problem for nonlinear systems. The linear operator theoretic framework involving the Koopman operator is used to lift the dynamics of nonlinear control system to an infinite dimensional bilinear system. Optimal quadratic regulation problem for nonlinear system is formulated in terms of the finite dimensional approximation of the bilinear system. A convex optimization-based approach is proposed for solving the quadratic regulator problem for bilinear system. Simulation results are presented to demonstrate the application of the developed framework.
$2p$ -th Moment Global Exponential Stability of a Unique $2P$ -th Mean Almost Periodic Oscillation for Semi-Discrete Takagi-Sugeno Fuzzy Stochastic Cellular Neural Networks With Time Delays

By using semi-discrete and Takagi-Sugeno fuzzy methods, a new version of discrete analogue of stochastic fuzzy cellular neural networks is formulated, which gives a more accurate characterization for continuous-time stochastic model than that by Euler scheme. Firstly, the <inline-formula> <tex-math notation="LaTeX">$2p$ </tex-math></inline-formula>-th moment global exponential stability for the obtained semi-discrete stochastic Takagi-Sugeno fuzzy model is studied with the help of Minkowski inequality and Hölder inequality. Secondly, the <inline-formula> <tex-math notation="LaTeX">$2p$ </tex-math></inline-formula>-th mean almost periodic outputs of the model is investigated by using Krasnoselskii’s fixed point theorem. Finally, illustrative examples and numerical simulations are given to demonstrate that our results are feasible.
Learning Interaction-Aware Probabilistic Driver Behavior Models from Urban Scenarios

Human drivers have complex and individual behavior characteristics which describe how they act in a specific situation. Accurate behavior models are essential for many applications in the field of autonomous driving, ranging from microscopic traffic simulation, intention estimation and trajectory prediction, to interactive and cooperative motion planning. Designing such models by hand is cumbersome and inaccurate, especially in urban environments, with their high variety of situations and the corresponding diversity in human behavior. Learning how humans act from recorded scenarios is a promising way to overcome these problems. However, predicting complete trajectories at once is challenging, as one needs to account for multiple hypotheses and long-term interactions between multiple agents. In contrast, we propose to learn Markovian action models with deep neural networks that are conditioned on a driver's route intention (such as turning left or right) and the situational context. Step-wise forward simulation of these models for the different possible routes of all agents allows for multi-modal and interaction-aware scene predictions at arbitrary road layouts. Learning to predict only one time step ahead given a specific route reduces learning complexity, such that simpler and faster models are obtained. This enables the integration into particle-based algorithms such as Monte Carlo tree search or particle filtering. We evaluate the learned model both on its own and integrated into our previously presented dynamic Bayesian network for intention estimation and show that it outperforms our previous hand-tuned rule-based model.
Rapid Recognition and Localization Based on Deep Learning and Random Filtering

Occlusion is a challenge for accuracy of recognition and localization for picking robot in harvesting. The algorithm developed in this paper combing deep learning and random filtering, which can be effectively eliminate the disturbing of obstacle. Firstly, apple images under occluded are as training input for neural network YOLO. Recognition result was signed by rectangular after YOLO. Secondly, for eliminate the interference of occlusion by random sampling filtered the depth values of obstacle points. Experiments shown that, this algorithm can provide real-time and accurate location information for picking. The difference between real and detection is limited in 4mm by the measure range from 600mm to 1500mm. The algorithm proposed in this paper could be flexible applied in other fruits, and it has a widely application values.
SLAM Based on Double Layer Unscented Kalman Filter

The unscented Kalman filter based SLAM algorithm (UKF-SLAM) has the problem of the inaccurate estimation and the system instability. A novel SLAM algorithm based on double layer unscented Kalman filter (DLUKF-SLAM) is presented in this paper to overcome the important drawbacks of the previous frameworks. The proposed algorithm first uses a set of weighted sampling points to represent the prior distribution of states, and then the inner layer UKF-SLAM is performed to achieve the states prediction, finally, the estimations of the robot pose and landmark position at each time are obtained by the update mechanism of the outer layer UKF-SLAM. Simulation results demonstrate the superiority of the DLUKF-SLAM algorithm in the aspect of estimation accuracy and stability, compared with the EKF-SLAM algorithm and the UKF-SLAM algorithm.
Data Encryption and Fragmentation in Autonomous Vehicles Using Raspberry Pi 3

Abstract-Autonomous vehicles have huge potential in improving road safety and congestion. Towards the road map of full autonomy, each vehicle will be able to communicate with other vehicles within the network of vehicles to improve congestion and notify emergencies. Many architectures for communication between vehicles are centralised, typically using cloud servers. The security and trust of that communication is paramount. Therefore, the aim of this paper is to propose a novel method for encrypting and fragmenting data in various cloud providers in order to protect the anonymity and increase the uncertainty for an attacker having access to the data on cloud. Our experimental results seem promising and we were able to achieve good results with low overhead in transmission.
On the Design and Analysis of Full-Duplex Non-Orthogonal Multiple Access Systems

For efficient resource utilization, we adopt an efficiency measure, namely the received signal-to-interference-plus-noise ratio (SINR) per unit power for a full duplex (FD) multiuser communications system employing non-orthogonal multiple access (NoMA) transmission scheme. Accordingly, we formulate an optimization problem for a FD NoMA system to maximize the power-normalized SINR (PN-SINR), which involves two stages: 1) smart power allocation to suppress the residual self-interference and 2) smart user clustering through game theory at a stage prior to the power allocation phase to mitigate the co-channel interference. Based on the suppression level of self-interference and co-channel interference, numerical results demonstrate the superiority of FD NoMA over other schemes.
Operation and Control Strategies for Wind Energy Conversion Systems: Review and Simulation Study

Wind energy systems have become an essential part of nowadays electricity supply and are increasingly competitive with conventional fossil fuel based power plants. Despite their contribution towards a more sustainable electricity supply, large-scale integration of wind power plants as well as Distributed renewable Energy Resources (DER) in general, poses a major challenge for the operation of future power grids. Besides increased system complexity and uncertainties, DERs are required to provide grid operation support and ancillary system services. This paper deals with operation and control strategies for wind energy systems, focusing on wind farm management but also individual wind turbines, and gives a review on state-of-art industrial solutions as well as recent research findings. A simulation study shows the prospects of advanced wind farm operation strategies to achieve a globally optimized energy yield. The developed simulation models include the emulation of aerodynamics and wind turbine wakes. Novel wind farm operation strategies are discussed in contrast to conventional approaches.
Tactical Level Decision-Making for Platoons of Autonomous Vehicles Using Auction Mechanisms

This paper proposes the application of market-based mechanisms to establish cooperative behavior within traffic scenarios involving autonomous vehicles. The aim is to understand the suitability of commonly used auction rules as a potential mechanism for tactical-level collective decision-making in platoon applications considering near-reliable communications. As a proof of feasibility, two auction clearing rules are compared, namely the first-price and the second-price sealed bid auctions. Both rules are tested according to their impact on coalition welfare and communication. To realistically assess the system performance, we designed an integrated simulation platform composed of an agent-based platform, a microscopic traffic model, and a vehicular network simulator. Results show the viability of two rules to maintain high satisfaction among platoon members, that can lead to stable formations and consequently better traffic conditions. From a communication perspective, a non-negligible delay is present, and should be taken into account when implementing an auctioning mechanism for real-world deployment.
Wireless Power Transmission System via Magnetic Resonance Coupling Platform

Aiming at the problem of lacking systematic research on the change rules of efficiency characteristics over coil parameters toward magnetically coupled resonant wireless power transmission technology, and combining with engineering practice, it is difficult to guide design of transmission characteristics in practical engineering. For this reason, based on the mutual inductance analysis model of magnetically coupled resonant system, the hardware of the magnetic coupling resonant drive circuit and the full-bridge inverter are designed. And a variety of resonant coils are wound in certain constraining condition. The experimental platform of magnetic coupling resonant wireless energy transmission is built, and the transmission characteristics of each resonant coil model are tested. It research the variation of system power and transmission efficiency with coil radius, number of turns and wire diameter and optimal design parameters of coil. By comparing with the simulation results of mutual inductance theory, it is found that the change law of output power and efficiency of the simulation and experiment are the same, which verifies the correctness of the experimental results. It shows by experiment that the resonant coil satisfies the system transmission requirement under the condition of the coil radius is 157mm, the number of coil turns is 16 turns, and the wire diameter is around 3.65mm. At this time, the output power is 309.8W and the transmission efficiency is up to 80%. Therefore, the resonant system is more reasonable and scientific than other design parameters under the coil structure design.
Crosstalk-Aware Wavelength-Switched All-to-All Optical Interconnect Using Sub-Optimal AWGRs

We propose a crosstalk (XT)-aware wavelength (<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>)-detuning scheme to overcome the non-optimal in-band XT limiting all-to-all optical interconnects based on sub-optimal arrayed waveguide grating routers (AWGR). Experimental validation is demonstrated employing an <inline-formula> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> O-band cyclic Si-AWGR with 10 nm channel spacing and a 5.5 nm channel bandwidth, where the in-band XT limits the simultaneous use of the same <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> in only 2 out of the 8 possible AWGR I/O routing scenarios, yielding a 10 <sup>−7</sup> error-floor when >3 channels at the same <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> are launched. We present experimentally an <inline-formula> <tex-math notation="LaTeX">$8 \times 25$ </tex-math></inline-formula> Gb/s <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>-switched interconnect exploiting slightly detuned <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> s around the central <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> for realizing all 8 possible I/O routing scenarios, enabling an error-free all-to-all interconnect even for sub-optimal AWGRs. Error-free simultaneous transmission via the AWGR of all 8 channels using <inline-formula> <tex-math notation="LaTeX">$4\lambda \text{s}$ </tex-math></inline-formula> and a <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> utilization factor 2 is shown, with a max. power penalty of 4.64 dB at 10 <sup>−9</sup> error-rate.