arXiv subjects

Yue Chen

Publications and source records attributed to Yue Chen.

At least 289 records · Page 16Linked to original sources

arXiv2019

Forecasting Megaelectron-Volt Electrons inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms

Here we present the recent progress in upgrading a predictive model for Megaelectron-Volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMevE 2.0, is demonstrated to make much improved forecasts, particularly at outer Lshells, by including upstream solar wind speeds to the model's input parameter list. Furthermore, based on several kinds of linear and artificial machine learning algorithms, a list of models were constructed, trained, validated and tested with 42-month MeV electron observations from Van Allen Probes. Out-of-sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1-day (2-day) forecasts with Lshell-averaged performance efficiency values ~ 0.87 (~0.82). Interestingly, the linear regression model is often the most successful one when compared to other models, which indicates the relationship between 1 MeV electron dynamics and precipitating electrons is dominated by linear components. It is also shown that PreMevE 2.0 can reasonably predict the onsets of MeV electron events in 2-day forecasts. This improved PreMevE model is driven by observations from longstanding space infrastructure (a NOAA satellite in low-Earth-orbit, the solar wind monitor at the L1 point, and one LANL satellite in geosynchronous orbit) to make high-fidelity forecasts for MeV electrons, and thus can be an invaluable space weather forecasting tool for the future.

physics.space-ph↗

arXiv2019

An Energy Sharing Game with Generalized Demand Bidding: Model and Properties

This paper proposes a novel energy sharing mechanism for prosumers who can produce and consume. Different from most existing works, the role of individual prosumer as a seller or buyer in our model is endogenously determined. Several desirable properties of the proposed mechanism are proved based on a generalized game-theoretic model. We show that the Nash equilibrium exists and is the unique solution of an equivalent convex optimization problem. The sharing price at the Nash equilibrium equals to the average marginal disutility of all prosumers. We also prove that every prosumer has the incentive to participate in the sharing market, and prosumers' total cost decreases with increasing absolute value of price sensitivity. Furthermore, the Nash equilibrium approaches the social optimal as the number of prosumers grows, and competition can improve social welfare.

math.OC↗

arXiv2019

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

math.OC↗

arXiv2019

Non-Orthogonal Multiple Access in UAV-to-Everything (U2X) Networks

This article investigates the non-orthogonal multiple access (NOMA) enhanced unmanned aerial vehicle (UAV)-to-Everything (U2X) frameworks. A novel 3-Dimension framework for providing wireless services to randomly roaming NOMA receivers (Rxs) in the sphere space is proposed by utilizing stochastic geometry tools. In an effort to evaluate the performance of the proposed framework, we first derive closed-form expressions for the outage probability and the ergodic rate of paired NOMA Rxs. For obtaining more insights, we investigate the diversity order and the high signal-to-noise (SNR) slope of NOMA enhanced U2X frameworks. We also derive the spectrum efficiency in both NOMA and orthogonal multiple access (OMA) enhanced U2X frameworks. Our analytical results demonstrate that the diversity order and the high SNR slope of the proposed framework are $m$ and one, respectively. Numerical results are provided to confirm that: i) the proposed NOMA enhanced U2X frameworks have superior outage performance and spectrum efficiency compared with the OMA-enhanced U2X frameworks; and ii) for the case of fixed LoS probability, the outage performance of paired NOMA Rxs mainly depends on users with poor channel conditions.

cs.IT↗

arXiv2019

Exploiting NOMA for UAV Communications in Large-Scale Cellular Networks

This paper advocates a pair of strategies in non-orthogonal multiple access (NOMA) in unmanned aerial vehicles (UAVs) communications, where multiple UAVs play as new aerial communications platforms for serving terrestrial NOMA users. A new multiple UAVs framework with invoking stochastic geometry technique is proposed, in which a pair of practical strategies are considered: 1) the UAV-centric strategy for offloading actions and 2) the user-centric strategy for providing emergency communications. In order to provide practical insights for the proposed NOMA assisted UAV framework, an imperfect successive interference cancelation (ipSIC) scenario is taken into account. For both UAV-centric strategy and user-centric strategy, we derive new exact expressions for the coverage probability. We also derive new analytical results for orthogonal multiple access (OMA) for providing a benchmark scheme. The derived analytical results in both user-centric strategy and UAV-centric strategy explicitly indicate that the ipSIC coefficient is a dominant component in terms of coverage probability. Numerical results are provided to confirm that i) for both user-centric strategy and UAV-centric strategy, NOMA assisted UAV cellular networks is capable of outperforming OMA by setting power allocation factors and targeted rate properly; and ii) the coverage probability of NOMA assisted UAV cellular framework is affected to a large extent by ipSIC coefficient, target rates and power allocations factors of paired NOMA users.

cs.IT↗

arXiv2019

Modal-based Kinematics and Contact Detection of Soft Robots

Soft robots offer an alternative approach to manipulate inside the constrained space while maintaining the safe interaction with the external environment. Due to its adaptable compliance characteristic, external contact force can easily deform the robot shapes and lead to undesired robot kinematic and dynamic properties. Accurate contact detection and contact position estimation are of critical importance for soft robot modeling, control, trajectory planning, and eventually affect the success of task completion. In this paper, we focus on the study of 1-DoF soft pneumatic bellow bending actuator, which is one of the fundamental components to construct complex, multi-DoF soft robots. This 1-DoF soft robot is modeled through the integral representation of the spacial curve. The direct and instantaneous kinematics are calculated explicitly through a modal method. The fixed centrode deviation (FCD) method is used to to detect the external contact and estimate contact location. Simulation results indicate that the contact location can be accurately estimated by solving a nonlinear least square optimization problem.

cs.RO↗

arXiv2019

Buy or Sell? Energy Sharing of Prosumers on Constrained Networks

The advent of intelligent agents who produce and consume energy by themselves has led the smart grid into the era of "prosumer", offering the energy system and customers a unique opportunity to revaluate/trade their spot energy via a sharing initiative. To this end, designing an appropriate sharing mechanism is an issue with crucial importance and has captured great attention. This paper addresses the prosumers' demand response problem via energy sharing. Under a general supply-demand function bidding scheme, a sharing market clearing procedure considering network constraints is proposed, which gives rise to a generalized Nash game. The existence and uniqueness of market equilibrium are proved in non-congested cases. When congestion occurs, infinitely much equilibrium may exist because the strategy spaces of prosumers are correlated. A price-regulation procedure is introduced in the sharing mechanism, which outcomes a unique equilibrium that is fair to all participants. Properties of the improved sharing mechanism, including the individual rational behaviors of prosumers and the components of sharing price, are revealed. When the number of prosumers increases, the proposed sharing mechanism approaches social optimum. Even with fixed number of resources, introducing competition can result in a decreasing social cost. Illustrative examples validate the theoretical results and provide more insights for the energy sharing research.

math.OC↗

arXiv2019

Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach

A novel non-orthogonal multiple access (NOMA) based cache-aided mobile edge computing (MEC) framework is proposed. For the purpose of efficiently allocating communication and computation resources to users' computation tasks requests, we propose a long-short-term memory (LSTM) network to predict the task popularity. Based on the predicted task popularity, a long-term reward maximization problem is formulated that involves a joint optimization of the task offloading decisions, computation resource allocation, and caching decisions. To tackle this challenging problem, a single-agent Q-learning (SAQ-learning) algorithm is invoked to learn a long-term resource allocation strategy. Furthermore, a Bayesian learning automata (BLA) based multi-agent Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions. More specifically, a BLA based action select scheme is proposed for the agents in MAQ-learning to select the optimal action in every state. We prove that the BLA based action selection scheme is instantaneously self-correcting and the selected action is an optimal solution for each state. Extensive simulation results demonstrate that: 1) The prediction error of the proposed LSTMs based task popularity prediction decreases with increasing learning rate. 2) The proposed framework significantly outperforms the benchmarks like all local computing, all offloading computing, and non-cache computing. 3) The proposed BLA based MAQ-learning achieves an improved performance compared to conventional reinforcement learning algorithms.

eess.SP↗

arXiv2019

Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design

A novel framework is proposed for quality of experience (QoE)-driven deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex three-dimensional (3D) deployment and dynamic movement of the UAVs is formulated for maximizing the sum mean opinion score (MOS) of ground users, which is proved to be NP-hard. In the aim of solving this pertinent problem, a three-step approach is proposed for attaining 3D deployment and dynamic movement of multiple UAVs. Firstly, genetic algorithm based K-means (GAK-means) algorithm is utilized for obtaining the cell partition of the users. Secondly, Q-learning based deployment algorithm is proposed, in which each UAV acts as an agent, making their own decision for attaining 3D position by learning from trial and mistake. In contrast to conventional genetic algorithm based learning algorithms, the proposed algorithm is capable of training the direction selection strategy offline. Thirdly, Q-learning based movement algorithm is proposed in the scenario that the users are roaming. The proposed algorithm is capable of converging to an optimal state. Numerical results reveal that the proposed algorithms show a fast convergence rate after a small number of iterations. Additionally, the proposed Q-learning based deployment algorithm outperforms K-means algorithms and Iterative-GAKmean (IGK) algorithms with a low complexity.

eess.SP↗

arXiv2019

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach

A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users' mobility information. The problem of joint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed which is based on machine learning techniques to obtain both the position information of users and the trajectory design of UAVs. Firstly, a multi-agent Q-learning based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Secondly, in an effort to determine the mobility information of users based on a real dataset, their position data is collected from Twitter to describe the anonymous user-trajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Thirdly, a multi-agent Q-learning based algorithm is conceived for predicting the position of UAVs in each time slot based on the movement of users. In this algorithm, multiple UAVs act as agents to find optimal actions by interacting with their environment and learn from their mistakes. Additionally, we also prove that the proposed multi-agent Q-learning based trajectory design and power control algorithm can converge under mild conditions. Numerical results are provided to demonstrate that as the size of the reservoir increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that throughput gains of about 17% are achieved.

eess.SP↗

arXiv2019

Coexistence of polar displacements and conduction in doped ferroelectrics: an ab initio comparative study

Polar metals are rare because free carriers in metals screen electrostatic potential and eliminate internal dipoles. Degenerate doped ferroelectrics may create an approximate polar metallic phase. We use first-principles calculations to investigate $n$-doped LiNbO$_3$-type oxides (LiNbO$_3$ as the prototype) and compare to widely studied perovskite oxides (BaTiO$_3$ as the prototype). In the rigid-band approximation, substantial polar displacements in $n$-doped LiNbO$_3$ persist even at 0.3 $e$/f.u. ($\simeq$ 10$^{21}$ cm$^{-3}$), while polar displacements in $n$-doped BaTiO$_3$ quickly get suppressed and completely vanish at 0.1 $e$/f.u. Furthermore, in $n$-doped LiNbO$_3$, Li-O displacements decay more slowly than Nb-O displacements, while in $n$-doped BaTiO$_3$, Ba-O and Ti-O displacements decay approximately at the same rate. Supercell calculations that use oxygen vacancies as electron donors support the main results from the rigid-band approximation and provide more detailed charge distributions. Substantial cation displacements are observed throughout LiNbO$_{3-δ}$($δ= 4.2\%$), while cation displacements in BaTiO$_{3-δ}$($δ= 4.2\%$) are almost completely suppressed. We find that conduction electrons in LiNbO$_{3-δ}$ are not as uniformly distributed as in BaTiO$_{3-δ}$, implying that the rigid-band approximation should be used with caution in simulating electron doped LiNbO$_3$-type oxides. Our work shows that polar distortions and conduction can coexist in a wide range of electron concentration in $n$-doped LiNbO$_3$, which is a practical approach to generating an approximate polar metallic phase. Combining doped ferroelectrics and doped semiconductors may create new functions for devices.

physics.comp-ph↗

arXiv2019

Learning Automata Based Q-learning for Content Placement in Cooperative Caching

An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised feed-forward back-propagation connectionist model based neural network (SFBC-NN) is invoked for user mobility and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of mobility prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked for Q-learning to obtain an optimal action selection in a random and stationary environment. It is proven that the LA-based action selection scheme is capable of enabling every state to select the optimal action with arbitrarily high probability if Q-learning is able to converge to the optimal Q value eventually. To characterize the performance of the proposed algorithms, the sum MOS of users is applied to define the reward function. Extensive simulations reveal that: 1) The prediction error of SFBC-NN lessen with the increase of iterations and nodes; 2) the proposed LAQL achieves significant performance improvement against traditional Q-learning; 3) the cooperative caching scheme is capable of outperforming non-cooperative caching and random caching of 3% and 4%.

eess.SP↗

arXiv2019

Optimal Rate of Convergence for Quasi-Stochastic Approximation

The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic frameworks for reinforcement learning (RL), and often an efficient approach to solving (or approximating the solution to) complex optimal control problems. However, in many cases practitioners are unable to apply these techniques because of an inherent high variance. This paper aims to provide a general foundation for "quasi-stochastic approximation," in which all of the processes under consideration are deterministic, much like quasi-Monte-Carlo for variance reduction in simulation. The variance reduction can be substantial, subject to tuning of pertinent parameters in the algorithm. This paper introduces a new coupling argument to establish optimal rate of convergence provided the gain is sufficiently large. These results are established for linear models, and tested also in non-ideal settings. A major application of these general results is a new class of RL algorithms for deterministic state space models. In this setting, the main contribution is a class of algorithms for approximating the value function for a given policy, using a different policy designed to introduce exploration.

math.OC↗

arXiv2019

A Unified Framework for Non-Orthogonal Multiple Access

This paper proposes a unified framework of non-orthogonal multiple access (NOMA) networks. Stochastic geometry is employed to model the locations of spatially NOMA users. The proposed unified NOMA framework is capable of being applied to both code-domain NOMA (CD-NOMA) and power-domain NOMA (PD-NOMA). Since the detection of NOMA users mainly depend on efficient successive interference cancellation (SIC) schemes, both imperfect SIC (ipSIC) and perfect SIC (pSIC) are taken into account. To characterize the performance of the proposed unified NOMA framework, the exact and asymptotic expressions of outage probabilities as well as delay-limited throughput for CD/PD-NOMA with ipSIC/pSIC are derived. In order to obtain more insights, the diversity analysis of a pair of NOMA users (i.e., the $n$-th user and $m$-th user) are provided. Our analytical results reveal that: i) The diversity orders of the $m$-th and $n$-th user with pSIC for CD-NOMA are $mK$ and $nK$, respectively; ii) Due to the influence of residual interference (RI), the $n$-th user with ipSIC obtains a zero diversity order; and iii) The diversity order is determined by the user who has the poorer channel conditions out of the pair. Finally, Monte Carlo simulations are presented to verify the analytical results: i) When the number of subcarriers becomes lager, the NOMA users are capable of achieving more steep slope in terms of outage probability; and ii) The outage behavior of CD-NOMA is superior to that of PD-NOMA.

cs.IT↗

arXiv2019

Modeling and Analysis of Two-Way Relay Non-Orthogonal Multiple Access Systems

A two-way relay non-orthogonal multiple access (TWR-NOMA) system is investigated, where two groups of NOMA users exchange messages with the aid of one half-duplex (HD) decode-and-forward (DF) relay. Since the signal-plus-interference-to-noise ratios (SINRs) of NOMA signals mainly depend on effective successive interference cancellation (SIC) schemes, imperfect SIC (ipSIC) and perfect SIC (pSIC) are taken into account. In order to characterize the performance of TWR-NOMA systems, we first derive closed-form expressions for both exact and asymptotic outage probabilities of NOMA users' signals with ipSIC/pSIC. Based on the derived results, the diversity order and throughput of the system are examined. Then we study the ergodic rates of users' signals by providing the asymptotic analysis in high SNR regimes. Lastly, numerical simulations are provided to verify the analytical results and show that: 1) TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR regimes; 2) Due to the impact of interference signal (IS) at the relay, error floors and throughput ceilings exist in outage probabilities and ergodic rates for TWR-NOMA, respectively; and 3) In delay-limited transmission mode, TWR-NOMA with ipSIC and pSIC have almost the same energy efficiency. However, in delay-tolerant transmission mode, TWR-NOMA with pSIC is capable of achieving larger energy efficiency compared to TWR-NOMA with ipSIC.

cs.IT↗

arXiv2019

Convolution Forgetting Curve Model for Repeated Learning

Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated. In the paper, a convolution model of forgetting curve is proposed to simulate the memory process during learning. In this model, the memory ability (i.e. the central procedure in the working memory model) and learning material (i.e. the input in the working memory model) is regarded as the system function and the input function, respectively. The status of forgetting (i.e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material. The model is applied to simulate the forgetting curves in different situations. The results show that the model is able to simulate the forgetting curves not only in one time learning condition but also in multi-times condition. The model is further verified in the experiments of Mandarin tone learning for Japanese learners. And the predicted curve fits well on the test points.

q-bio.NC↗

arXiv2018

Efficient Reduced-Order Models for Soft Actuators

Soft robotics have gained increased attention from the robotic community due to their unique features such as compliance and human safety. Impressive amount of soft robotic prototypes have shown their superior performance over their rigid counter parts in healthcare, rehabilitation, and search and rescue applications. However, soft robots are yet to capitalize on their potential outside laboratories and this could be attributed to lack of advanced sensing capabilities and real-time dynamic models. In this pilot study, we explore the use of high-accuracy, high-bandwidth deformation sensing via fiber optic strain sensing (FOSS) in soft bending actuators (SBA). Based on the high density sensor feedback, we introduce a reduced order kinematic model. Together with cubic spline interpolation, this model is able to reconstruct the continuous deformation of SBAs. The kinematic model is extended to derive an efficient real-time equation of motion and validated against the experimental data.

cs.RO↗

arXiv2018

Dynamic Control of Pneumatic Muscle Actuators

Pneumatic muscle actuators (PMA) are easy-to-fabricate, lightweight, compliant, and have high power-to-weight ratio, thus making them the ideal actuation choice for many soft and continuum robots. But so far, limited work has been carried out in dynamic control of PMAs. One reason is that PMAs are highly hysteretic. Coupled with their high compliance and response lag, PMAs are challenging to control, particularly when subjected to external loads. The hysteresis models proposed to-date rely on many physical and mechanical parameters that are difficult to measure reliably and therefore of limited use for implementing dynamic control. In this work, we employ a Bouc-Wen hysteresis modeling approach to account for the hysteresis of PMAs and use the model for implementing dynamic control. The controller is then compared to PID feedback control for a number of dynamic position tracking tests. The dynamic control based on the Bouc-Wen hysteresis model shows significantly better tracking performance. This work lays the foundation towards implementing dynamic control for PMA-powered high degrees of freedom soft and continuum robots.

cs.RO↗