Multiple Independent Channels Research Articles

Combined Fault Diagnosis Scheme Based on Recurrent Neural Network and H− Observer for Satellite Attitude Control System

This study proposes a combined fault diagnosis scheme based on a recurrent neural network (RNN) and an H− observer for satellite attitude control systems (ACSs) in the presence of model uncertainties, external disturbances, and measurement noise. The ACS is decoupled into multiple independent channels, such that the residuals generated by the observers respond to the corresponding faults. A novel multilayer adaptive Gaussian recurrent neural network (MAGRNN) structure is developed as an approximator to estimate the lumped disturbance; a robust term is introduced to improve accuracy. Considering the actuator fault in the finite frequency domain and the fault-sensitive index, a set of H− unknown input observers (UIOs) is designed using the output of the MAGRNN-based approximator as compensation. The existence conditions of the approximator and observer are proposed and proved. The fault diagnosis results for three cases verify that the proposed method can be used for small fault detection and isolation.

Plant and buffer state estimation for networked predictive control over multiple erasure channels

This paper investigates optimal estimation for networked control systems composed of linear, time-variant, and multi-input multi-output (MIMO) plants that communicate with the controller over multiple independent erasure channels. In the controller-to-plant link, we cope with data losses by considering a networked predictive control strategy in which the controller not only sends the current control inputs to the actuators, but also sequences of predicted control inputs at every time step. These are stored in buffers located at the corresponding actuator side, with the aim of being used in case of data loss. Additionally, for losses occurring in the plant-to-controller link, we assume that the controller contains a data-dropout compensation mechanism capable of replacing the missing measurements with their optimal estimates. It is assumed that acknowledgment packages are sent from the plant to the controller, to indicate if the previous sequence was received or not. For this setup, we compute optimal time-varying estimates of the plant and buffer state, and for the lost measurements as well. Finally, we provide the optimal stationary estimator, which serves as a simpler alternative to the time-varying one.

Periodic Event-Triggered Networked Control Systems Subject to Large Transmission Delays

This article studies periodic event-triggered networked control for nonlinear systems, where the plants and controllers are connected by multiple independent communication channels. Several network-induced imperfections are considered simultaneously, including time-varying intersampling times, sensor node scheduling, and especially, large time-varying transmission delays, where the transmitted signal may arrive at the destination node after the next transmission occurs. A new hybrid system approach is provided to model the closed-loop system that contains all communication related behavior. Then, by constructing new storage functions on the system state and updating errors, the relationship between the maximum allowable sampling period and maximum allowable delay number in sampling is analyzed, where the latter denotes how many inter-sampling periods can be included in one transmission delay. Moreover, to efficiently reduce unnecessary transmissions, a new dynamic event-triggered control scheme is proposed, where the event-triggering conditions are detected only at aperiodic and asynchronous sampling instants. From emulation-based method, where the controllers are initially designed by ignoring all the network-induced imperfections, sufficient conditions on the dynamic event-triggered control are given to ensure closed-loop input-to-state stability with respect to external disturbances. Finally, two nonlinear examples are simulated to illustrate the feasibility and efficiency of the theoretical results.

A Dueling Deep Recurrent Q -Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks

This paper investigates a deep reinforcement learning algorithm based on dueling deep recurrent Q -network (Dueling DRQN) for dynamic multichannel access in heterogeneous wireless networks. Specifically, we consider the scenario that multiple heterogeneous users with different MAC protocols share multiple independent channels. The goal of the intelligent node is to learn a channel access strategy that achieves high throughput by making full use of the underutilized channels. Two key challenges for the intelligent node are (i) there is no prior knowledge of spectrum environment or the other nodes’ behaviors; (ii) the spectrum environment is partially observable, and the spectrum states have complex temporal dynamics. In order to overcome the aforementioned challenges, we first embed the long short-term memory layer (LSTM) into the deep Q -network (DQN) to aggregate historical observations and capture the underlying temporal feature in the heterogeneous networks. And second, we employ the dueling architecture to overcome the observability problem of dynamic environment in neural networks. Simulation results show that our approach can learn the optimal access policy in various heterogeneous networks and outperforms the state-of-the-art policies.

Single-wavelength transmission at 1.1-Tbit/s net data rate over a multi-modal free-space optical link using commercial devices.

We employ commercial mode-selective photonic lanterns to implement mode multiplexing and demultiplexing for high-capacity free-space optical communications. Moreover, we design a time-division-multiplexed frame structure to efficiently emulate multiple independent transmitters with channelized precoding using only one transmitter. To maximize the throughput of the system, we optimize the modes selected for carrying data, and apply adaptive loading to different channels. By leveraging mode- and polarization-division multiplexing, the free-space optical data link comprising multiple independent channels provides an aggregate net data rate of 1.1 Tbit/s and net spectral efficiency of 28.35 bit/s/Hz. Different from many previous demonstrations based on delayed or partially delayed copies of identical data streams, to the best of our knowledge, ours is a record-high net data rate and net spectral efficiency achieved by a single-wavelength mode-division multiplexed free-space optical communication system with fully independent channels. Moreover, all key devices used in this work, including optical transponder, multiplexer, and demultiplexer are commercially available.

Learning Control for Networked Stochastic Systems With Random Fading Communication

The learning control strategy is studied for networked stochastic systems, where the output and input data are transmitted through multiple independent fading channels. The traditional P-type learning control scheme is revised according to the specific fading positions, where the constant learning gain is replaced by a variable one to suppress the effect of various uncertainties. Strong convergence of the proposed scheme is established under random fading phenomena and system noise. The input error is shown convergent to zero as the cycle number increases. Two numerical examples demonstrate the applications of the proposed scheme.

Mid-lateral cerebellar complex spikes encode multiple independent reward-related signals during reinforcement learning

Although the cerebellum has been implicated in simple reward-based learning recently, the role of complex spikes (CS) and simple spikes (SS), their interaction and their relationship to complex reinforcement learning and decision making is still unclear. Here we show that in a context where a non-human primate learned to make novel visuomotor associations, classifying CS responses based on their SS properties revealed distinct cell-type specific encoding of the probability of failure after the stimulus onset and the non-human primate’s decision. In a different context, CS from the same cerebellar area also responded in a cell-type and learning independent manner to the stimulus that signaled the beginning of the trial. Both types of CS signals were independent of changes in any motor kinematics and were unlikely to instruct the concurrent SS activity through an error based mechanism, suggesting the presence of context dependent, flexible, multiple independent channels of neural encoding by CS and SS. This diversity in neural information encoding in the mid-lateral cerebellum, depending on the context and learning state, is well suited to promote exploration and acquisition of wide range of cognitive behaviors that entail flexible stimulus-action-reward relationships but not necessarily motor learning.

Learning graph attention-aware knowledge graph embedding

The knowledge graph, which utilizes graph structure to represent multi-relational data, has been widely used in the reasoning and prediction tasks, attracting considerable research efforts recently. However, most existing works still concentrate on learning knowledge graph embeddings straightforwardly and intuitively without subtly considering the context of knowledge. Specifically, recent models deal with each single triple independently or consider contexts indiscriminately, which is one-sided as each knowledge unit (i.e., triple) can be derived from its partial surrounding triples. In this paper, we propose a graph-attention-based model to encode entities, which formulates a knowledge graph as an irregular graph and explores a number of concrete and interpretable knowledge compositions by integrating the graph-structured information via multiple independent channels. To measure the correlation between entities from different angles (i.e., entity pair, relation, and structure), we respectively develop three attention metrics. By making use of our enhanced entity embeddings, we further introduce several improved factorization functions for updating relation embeddings and evaluating candidate triples. We conduct extensive experiments on downstream tasks including entity classification, entity typing, and link prediction to validate our methods. Empirical results validate the importance of our introduced attention metrics and demonstrate that our proposed method can improve the performance of factorization models on large-scale knowledge graphs.

A compact AAA-compatible multispectral solar simulator based on spherical cap chamber

Rapidly developing thin-film solar cells prompts the demand for artificial narrow-band spectral illumination. Here, an LED-based compact spherical cap solar simulator is proposed to provide multiple independent color channels, while compatible with the traditional broadband IEC60904-9 standard in terms of illumination non-uniformity, stability and solar-like spectrum. Firstly, a one-dimensional model is established and then validated to analyze locations of maximum power point which may degenerate the spatial homogeneity. Secondly, the LED layout is optimized by Monte Carlo ray tracing, and it is found that a good illumination uniformity can be achieved if they are rotational symmetrically distributed. Thirdly, another two structural parameters, including output slot size and cavity height, are simulated to balance the output uniformity and output power. Finally, an instance simulator based on the structure is developed for performance verification. In the implementation, heat dissipation strategies are introduced to stabilize the system, including the use of heat sinks and flip-connected printed circuit boards to keep heat sources away from the optical chamber, thereby ensuring 0.052% short-term and 1.13% long-term stability. In addition, seven types of LEDs are adopted to simulate the solar spectrum, and its maximum deviation between the AM 1.5G is −8.82%. A control system consisting of a graphical user interface, STM32 microprocessor and constant current sources is developed to configure the current of different color channels. As a result, in predominant area of the entire output slot, six narrow-band and broadband illuminations can achieve 2% non-uniform, which is of interest for testing thin film solar cells.

Strategies to improve the quality and competitiveness of coastal tourism areas

Strategies to improve the quality and competitiveness of coastal tourism areas

The Application of Deep Reinforcement Learning to Distributed Spectrum Access in Dynamic Heterogeneous Environments With Partial Observations

This papera <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> investigates deep reinforcement learning (DRL) based on a Recurrent Neural Network (RNN) for Dynamic Spectrum Access (DSA) under partial observations, referred to as a Deep Recurrent Q-Network (DRQN). Specifically, we consider a scenario with multiple independent channels and multiple heterogeneous Primary Users (PUs). Two key challenges in our problem formulation are that we assume our DRQN node does not have any prior knowledge of the other nodes' behavior patterns and attempts to predict the future channel state based on previous observations. The goal of the DRQN is to learn a channel access strategy with a low collision rate but a high channel utilization rate. With proper definitions of the state, action and rewards, our extensive simulation results show that a DRQN-based approach can handle a variety of communication environments including dynamic environments. Further, our results show that the DRQN node is also able to cope with multi-rate and multi-agent scenarios. Importantly, we show the following benefits of using recurrent neural networks in DSA: (i) the ability to learn the optimal strategy in different environments under partial observations; (ii) robustness to imperfect observations and (iii) the ability to utilize multiple channels, and (iv) robustness in the presence of multiple agents. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> A parton of this work was presented at MILCOM 2018 in [1].

Off-Chip-Controlled Droplet-on-Demand Method for Precise Sample Handling.

We present a simple, stable, and highly reproducible off-chip-controlled method for generating droplets-on-demand. To induce the droplet generation, externally pre-programmed positive pressure pulses are applied to the dispersed phase input while the continuous phase channel remains at constant input pressure. By controlling solely one fluid phase, the method allows for connecting multiple independent dispersed-phase channels to a single continuous channel. Experimental results show that the method allows for a droplet generation frequency of 33 Hz and a high reproducibility of droplets with standard deviations less than 5% of the mean value. Moreover, utilization of the off-chip-controlled method results in the simplicity in chip design and allows rapid (∼5 min) and cost-efficient (0.5 USD) prototyping of the device.

Optimal Jamming Attack Scheduling of Interactive Channels

Recently, public attention is thoroughly aroused as to the security threats of Cyber-Physical Systems (CPS), which can seriously disrupt the system operation. In order to analyze the impact of potential cyber-attacks, one needs to investigate the attack scheduling strategy from the attacker's perspective. In this paper, we investigate the optimal attack schedules to degrade the system performance through multiple interactive channels against remote state estimation, which is different from the studies through multiple independent channels. Specifically, a channel is affected when the attacker launches an attack on another one, causing two different loss rates of the data package. Based on this, we represent the estimation error covariance of the remote estimator. Then we give the optimal jamming attack schedule theoretically. Finally, the effectiveness of the theoretical results is shown by numerical simulations.

Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission

Compressed sensing is well known for its superior compression performance, in existing schemes, in lossy compression. Conventional research aims to reach a larger compression ratio at the encoder, with acceptable quality reconstructed images at the decoder. This implies looking for compression performance with error-free transmission between the encoder and the decoder. Besides looking at compression performance, we applied block compressed sensing to digital images for robust transmission. For transmission over lossy channels, error propagation or data loss can be expected, and protection mechanisms for compressed sensing signals are required for guaranteed quality of the reconstructed images. We propose transmitting compressed sensing signals over multiple independent channels for robust transmission. By introducing correlations with multiple-description coding, which is an effective means for error resilient coding, errors induced in the lossy channels can effectively be alleviated. Simulation results presented the applicability and superiority of performance, depicting the effectiveness of protection of compressed sensing signals.

Multi‐frequency power system for renewable source integration in smart grid

This study presents a new power system enabled by power-electronic technology for renewable source integration in micro-grid. The micro-grid is decoupled into multiple independent power channels. These power channels are used for any source to any load-independent power transfer over the common power transmission line. This system works on the principle of frequency-selective orthogonal power transfer and superposition theorem. To demonstrate the proposed power system, a laboratory-scale prototype of an interconnected two-source two-load system is developed. The two sources independently transfer power to their respective loads at power transmission frequencies of 500 and 0 Hz (DC is also a valid frequency for power transmission), respectively, over the common power transmission path. Thus, a two-source two-load system is decoupled into two independent power channels. The proposed system is modular and scalable in structure and can be extended for any number of sources and loads. Finally, the effectiveness of the proposed system is verified through simulations and experimental results which demonstrates independent transmission of 100 W power in each of the two power channels of the two source-two load interconnected system.

Energy-efficient network planning and traffic provisioning in IP-over-elastic optical networks

In this paper, for IP-over-elastic optical networks (IP-over-EONs) we explore energy-efficient network planning and traffic provisioning methods that exploit the flexibility of sliceable bandwidth variable transponders (SBVTs) with reference to providing multiple transmission options and setting up multiple independent optical channels sourced (destined) from (to) a single SBVT. From the same SBVT, several optical channels with different modulation formats, transmission spectra, capacities, maximum transparent reach limits, and related power consumption (PC) values can be set up. For both the static and dynamic traffic conditions, with due consideration of efficiency in overall PC of network (comprising all major power-consuming equipment), the most suitable optical channels out of all the candidate options are set up and traffics are provisioned along the most suitable paths. We judiciously use electrical and optical layer traffic grooming to reduce PC. We present a mixed integer linear programming (MILP) model and an auxiliary graph (AG)-based heuristic scheme for energy-efficient network planning and static traffic provisioning in IP-over-EON. We validate through simulation the result of the heuristic scheme with the conclusion drawn from the result of the MILP model. We also present an AG-based heuristic scheme for energy-efficient traffic provisioning in dynamic traffic condition and show result through exhaustive simulations for realistic network setting.

Stabilization of MIMO Systems Over Multiple Independent and Memoryless Fading Noisy Channels

This paper investigates the state feedback stabilizability problem for multi-input multi-output systems over memoryless fading noisy channels under stationary signal-to-noise ratio constraints. The channel is modeled as a cascade of a multiplicative noise and an additive white Gaussian noise. The aim of the addressed problem is to find the minimum overall quality of service required to render the stabilization possible. The essential idea of our approach is to view the stabilization from the perspective of a demand/supply balance. Specifically, each control input is considered as the demand side for communication resource while the channels are considered as the supply side. The supply resource of the channels is characterized by their respective quality of service. The stabilization of the networked systems requires the demand/supply balance of the communication resource. Depending on whether the channel resource is configurable or not, two different approaches are adopted for realizing the required balance. If the channel resource is configurable, one can tailor the supply to meet the demand via channel resource allocation; otherwise, one can shape the demand to meet the supply via certain transceivers design mechanism. Explicit conditions on the minimum overall quality of service for networked stabilization are established in both scenarios.

Analysis, Design, and Experimental Verification of a Mixed High-Order Compensations-Based WPT System with Constant Current Outputs for Driving Multistring LEDs

Current imbalance in multistring light-emitting diodes (LEDs) is a critical issue. It may cause overcurrent in one or more LED strings, leading to rapid degradation. In this paper, a mixed high-order compensation networks-based wireless power transfer system is proposed to generate multiple constant current outputs. It is composed of an LCC resonant network in the transmitting side, a series resonant network, and multiple CLC resonant rectifiers in the receiving side. The CLC resonant rectifiers are connected in parallel to form multiple independent output channels, and each channel is then connected to an LED string. Based on the analysis of the T resonant circuit and the modeling of coupling coils, multiple constant output currents can be derived. As a result, current balance can be achieved, which is very suitable for driving multistring LEDs. The proposed system also offers a modular, scalable, and maintenance-free design, which can significantly reduce the construction cost and the control complexity. In addition, the inverter in the transmitting side can achieve zero phase angle. A laboratory prototype with dual independent output currents is built to verify the proposed method. The experimental results agree well with the theoretical analysis.

Focus on photonics, spectrometry, and spectroscopy

Focus on photonics, spectrometry, and spectroscopy

Controlled dissolution of freeform 3D printed carbohydrate glass scaffolds in hydrogels using a hydrophobic spray coating

Freeform 3D printing combined with sacrificial molding promises to lead advances in production of highly complex tubular systems for biomedical applications. Here we leverage a purpose-built isomalt 3D printer to generate complex channel geometries in hydrogels which would be inaccessible with other techniques. To control the dissolution of the scaffold, we propose an enabling technology consisting of an automated nebulizer coating system which applies octadecane to isomalt scaffolds. Octadecane, a saturated hydrocarbon, protects the rigid mold from dissolution and provides ample time for gels to set around the sacrificial structure. With a simplified model of the nebulizer system, the robotic motion was optimized for uniform coating. Using a combination of stimulated Raman scattering (SRS) microscopy and X-ray computed tomography, the coating was characterized to assess surface roughness and consistency. Colorimetric measurements of dissolution rates allowed optimization of sprayer parameters, yielding a decrease in dissolution rates by at least 4 orders of magnitude. High fidelity channels are ensured by surfactant treatment of the coating, which prevents bubbles from clinging to the surface. Spontaneous Raman scattering microspectroscopy and white light microscopy indicate cleared channels are free of octadecane following gentle flushing. The capabilities of the workflow are highlighted with several complex channel architectures including helices, blind channels, and multiple independent channels within polyacrylamide hydrogels of varying stiffnesses.