Channel Constraints Research Articles

Sliding mode control of Lur'e nonlinear switched systems under dynamic schedule protocol

ABSTRACT In this work, the sliding mode control (SMC) problem is investigated for a class of Lur'e nonlinear switched systems subject to finite channels constraints, which may lead to data collision. To solve this phenomenon, the Weighted Try-Once-Discard (WTOD) protocol is proposed on the sensor-to-controller (S/C) channels to regulate which one sensor node is permitted to send data via the communication network at each instant. A key issue of this work is how to integrate the WTOD protocol into the design of observer and the sliding mode controller for the switched systems. To this end, a token-dependent observer is constructed to estimate the unmeasured states, and then an observer-based SMC law is properly designed. Moreover, by constructing a Lur'e-type Lyapunov function interconnected with a cone-bounded nonlinearity, some sufficient conditions are established such that the reachability with a sliding domain around the specified sliding surface is achieved, meanwhile, the exponential stability of the resultant closed-loop switched systems is guaranteed. Finally, some numerical simulation results are provided.

Efficient deep-learning-based history matching for fluvial channel reservoirs

In history matching, the calibration of a prior reservoir model is computationally expensive because many forward reservoir simulation runs are required. Multiple posterior (or calibrated) reservoir models need to be sampled to consider high reservoir uncertainty, which increases the computational cost significantly. In this study, we propose a novel deep-learning-based history matching method that efficiently samples posterior reservoir models for fluvial channel reservoirs. Three convolution-based neural networks (NNs) are used in the proposed method to sample posterior models quickly without conventional calibration processes: convolutional autoencoder (CAE), convolutional neural network (CNN), and convolutional denoising autoencoder (CDAE). First, low-dimensional latent features are extracted from prior models using CAE because the dimensionality of static data is too high to find the relation between the prior models and corresponding simulated dynamic (production) data. Next, CNN is used to find the relation between the latent features of the prior models and the corresponding production data, which are the output and input data of CNN, respectively. The CNN output is refined using CDAE to improve the geological connectivity of the posterior models. The performance of the proposed method is compared with non-convolution-based methods that combine fully-connected NN structures (multi-layer perceptron (MLP)) and dimension-reduction techniques (principal component analysis (PCA) and stacked autoencoder (SAE)) in the benchmark egg model. The proposed method outperforms the other methods (MLP-PCA and MLP-SAE) in terms of geological constraints for fluvial channels and the computational cost of sampling posterior models. • CAE compresses heterogeneous rock properties efficiently. • Posterior models can be sampled quickly using CAE and CNN. • The geological connectivity of posterior models can be improved using CDAE.

Inertial swimming in a channel filled with a power-law fluid

We employ an immersed boundary-lattice Boltzmann (IB-LB) scheme to simulate a cylindrical (a classical self-propelled model) and a rod-shaped squirmer swimming in a channel filled with power-law fluids. The power-law index n, the channel blocking ratio κ (squirmer diameter/channel width), and the swimming Reynolds number Re are, respectively, set at 0.8 ≤ n ≤ 1.2, 0.2 ≤ κ ≤ 0.5 and 0.05 ≤ Re ≤ 5 to investigate the microswimmer' swimming speed, its power expenditure (P), and its hydrodynamic efficiency (η). The results show that increasing n yields a faster squirmer at a low Re (Re ≤ 0.5). On further increasing Re (Re ≥ 1), a larger n results in a slower pusher (a squirmer propelled from the rear), or a faster puller (a squirmer propelled from the front). Increasing the channel's width (decreasing κ) can lead to a slower puller or a puller rod squirmer. A definition of puller/pusher will be provided later. It is also found that, with shear-thinning, it is easier to unstabilize a puller than with shear-thickening, when increasing Re. Swimming in a shear-thinning fluid expends more power P than in a shear-thickening fluid, and P is scaled with Re according to P ∼ Ren-1 (0.05 ≤ Re ≤ 1). In addition, a stronger channel constraint (κ = 0.5) yields a higher η for the puller and the weak inertial pusher, whereas a weaker channel constraint (κ = 0.2) results in a higher η for the pusher with the increased fluid inertia.

Antidisturbance Controllability Analysis and Enhanced Antidisturbance Controller Design With Application to Flexible Spacecraft

For a practical engineering system, disturbances usually have a negative impact on system performance. Especially, when the system physical constraints (e.g., actuator amplitude saturation and input channel constraint) are present, the study for antidisturbance controllability (ADC) is of paramount importance. This article presents the concepts of disturbance estimability, disturbance compensability, and an enhanced antidisturbance control (EADC). From the viewpoint of control system design, an example of spacecraft attitude control is given. The control constraints and multiple disturbances are explicitly analyzed for the sake of attitude control system design of flexible spacecraft. Subsequently, an EADC approach with integration of the disturbance observer-based control (DOBC) and active disturbance rejection control (ADRC) is proposed, based on the characteristics of disturbances exposed. Moreover, prescribed performance approach is incorporated into the spacecraft attitude tracking control. In consequence, the presented control can not only achieve superior antidisturbance capability than DOBC or ADRC, but also quantify the control performances. Numerical simulations and hardware-in-the-loop tests exemplify the applicability of the proposed scheme.

Hierarchical Reinforcement Learning for Integrated Recommendation

Integrated recommendation aims to jointly recommend heterogeneous items in the main feed from different sources via multiple channels, which needs to capture user preferences on both item and channel levels. It has been widely used in practical systems by billions of users, while few works concentrate on the integrated recommendation systematically. In this work, we propose a novel Hierarchical reinforcement learning framework for integrated recommendation (HRL-Rec), which divides the integrated recommendation into two tasks to recommend channels and items sequentially. The low-level agent is a channel selector, which generates a personalized channel list. The high-level agent is an item recommender, which recommends specific items from heterogeneous channels under the channel constraints. We design various rewards for both recommendation accuracy and diversity, and propose four losses for fast and stable model convergence. We also conduct an online exploration for sufficient training. In experiments, we conduct extensive offline and online experiments on a billion-level real-world dataset to show the effectiveness of HRL-Rec. HRL-Rec has also been deployed on WeChat Top Stories, affecting millions of users. The source codes are released in https://github.com/modriczhang/HRL-Rec.

Moving Horizon Estimation of Networked Nonlinear Systems With Random Access Protocol

This paper is concerned with the moving horizon (MH) estimation issue for a type of networked nonlinear systems (NNSs) with the so-called random access (RA) protocol scheduling effects. To handle the signal transmissions between sensor nodes and the MH estimator, a constrained communication channel is employed whose channel constraints implies that at each time instant, only one sensor node is permitted to access the communication channel and then send its measurement data. The RA protocol, whose scheduling behavior is characterized by a discrete-time Markov chain (DTMC), is utilized to orchestrate the access sequence of sensor nodes. By extending the robust MH estimation method, a novel nonlinear MH estimation scheme and the corresponding approximate MH estimation scheme are developed to cope with the state estimation task. Subsequently, some sufficient conditions are established to guarantee that the estimation error is exponentially ultimately bounded in mean square. Based on that the main results are further specialized to linear systems with the RA protocol scheduling. Finally, two numerical examples and the corresponding figures are provided to verify the effectiveness/correctness of the developed MH estimation scheme and approximate MH estimation scheme.

Dynamical Formation Scenarios for GW190521 and Prospects for Decihertz Gravitational-wave Astronomy with GW190521-like Binaries

Abstract The gravitational-wave (GW) detection of GW190521 has provided new insights on the mass distribution of black holes and new constraints for astrophysical formation channels. With independent claims of GW190521 having significant premerger eccentricity, we investigate what this implies for GW190521-like binaries that form dynamically. The Laser Interferometer Space Antenna (LISA) will also be sensitive to GW190521-like binaries if they are circular from an isolated formation channel. However, GW190521-like binaries that form dynamically may skip the LISA band entirely. To this end, we simulate GW190521 analogs that dynamically form via post-Newtonian binary–single scattering. From these scattering experiments, we find that GW190521-like binaries may enter the LIGO-Virgo band with significant eccentricity as suggested by recent studies, though well below an eccentricity of e 10 Hz ≲ 0.7. Eccentric GW190521-like binaries further motivate the astrophysical science case for a decihertz GW observatory, such as the kilometer-scale version of the Midband Atomic Gravitational-wave Interferometric Sensor. We carry out a Fisher analysis to estimate how well the eccentricity of GW190521-like binaries can be constrained with such a decihertz detector. These eccentricity constraints would also provide additional insights into the possible environments that GW190521-like binaries form in.

Survey on Content Forwarding and Caching Schemes in Vehicular Named Data Networks

For the past few decades, Vehicular Ad-hoc Networks (VANETs) has been one of the major topics for the industries, academia and researchers. Since the IP based architecture is not able to satisfy the demands of the current technology, researchers have started to explore different extensions for VANETs. Named Data Networking (NDN) and Software Defined Networking (SDN) are the two alternatives strongly focussed by the research fraternity. The NDN architecture provides an edge because of the fundamental working principle, wherein the content is addressed rather than the host. Although, Vehicular Named Data Network (VNDN) is a relatively new field, the advantage it brings about has urged the researchers to explore it further. There are issues such as content/data forwarding, routing strategies, naming schemes, channel constraints and adding security to the content without increasing overhead, creating gateways and standardization. Efficient content forwarding plays an important role in reducing the number of duplicate copies of interest and data packets. Caching the content reduces the path traversed by interest packets. These factors aid in improving the overall network performance. A survey and comparison of content forwarding strategies and caching policies with an aim to help the researchers to select the appropriate content forwarding strategies and caching policies for their research is presented in this paper.

Nonlinear uncertain systems with nonlinear control channel and unilateral input constraints

ABSTRACT In this paper, we study the constraints of control inputs, which is important to system performance, but rarely noticed. To deal with the constraints in practical situations, a new approach is proposed based on using diffeomorphism, which is the first introduced. Based on this transformation, the control input can be regarded as a function of an auxiliary variable. As a result, the value of control input is limited to a prescribed bound by choosing the proper bounded function. That is, it provides a new aspect to deal with the inequality control input constraints. The range of the input bounds is related to the uncertainties and can be designed according to the actual situation. This novel control is proven to guarantee uniform boundedness and uniform ultimate boundedness. In the simulation, we show this control renders a superior performance at a lower control cost, comparing with the LQR control.

Assessment of Vertical Ozone Profiles from INSAT-3D Sounder Over The Central Himalaya

Vertical distribution of ozone has been obtained for the first time using INSAT-3D for the period 2013-2017 over the central Himalaya and validated utilizing balloon-borne observations from a high-altitude site in Nainital (29.4°N, 79.5°E, 1793 m amsl). The INSAT-3D retrieved ozone profiles captured ozone gradient and ozone peak altitude successfully, despite only one IR channel for ozone. This demonstrates the capability of the INSAT-3D Sounder in capturing the observed features, with a smaller bias in the stratosphere and somewhat larger bias in the troposphere. Total ozone column from INSAT-3D showed maximum difference of 8% with ozonesonde-derived total ozone column. Larger ozone bias in the lower troposphere could be attributed to lower reliability of regression coefficient and INSAT-3D channel constraints itself, whereas high variability near the tropopause is possibly due to low ozone, poor temperature retrieval near the tropo-pause and stratosphere–troposphere transport process in the Himalayan region.

Lag quasi-synchronization for periodic neural networks with unreliable redundant communication channels

This work studies lag quasi-synchronization (LQS) for discrete-time master–slave (MS) periodic neural networks (NNs) with the communication channel (CC) constraint. A logarithmic quantizer is used to overcome the CC constraint, and a redundant CC is introduced to transmit more information to improve the system performance. Two independent bernoulli processes are considered to model the packet dropouts of the main and the redundant CCs, respectively. A sufficient condition, ensuring LQS of MS NNs, is achieved, which also gives the boundary of the LQS error. Finally, a controller is designed on the basis of the obtained sufficient condition and the derived result is proved by a numerical example.

Decision Diagram Decomposition for Quadratically Constrained Binary Optimization

In recent years the use of decision diagrams within the context of discrete optimization has proliferated. This paper continues this expansion by proposing the use of decision diagrams for modeling and solving binary optimization problems with quadratic constraints. The model proposes the use of multiple decision diagrams to decompose a quadratic matrix so that each individual diagram has provably limited size. The decision diagrams are then linked through channeling constraints to ensure that the solution represented is consistent across the decision diagrams and that the original quadratic constraints are satisfied. The resulting family of decision diagrams are optimized over by a dedicated cutting-plane algorithm akin to Benders decomposition. The approach is general, in that commercial integer programming solvers can readily apply the technique. A thorough experimental evaluation on both benchmark and synthetic instances exhibits that the proposed decision diagram reformulation provides significant improvements over current methods for quadratic constraints in state-of-the-art solvers.

Optimal Benefit Strategy of Clean Energy Consumption Based on Transmission Channel Constraints

With the continuous development of social economy, energy and environmental issues deserve more and more attention. In order to improve the new energy’s consumption, this paper proposes an optimized social income strategy for clean energy consumption based on transmission channel constraints. The main method combines the method of cooperative game to maximize the value of social income, so as to determine the optimal combination of power generation, then taking into account the constraints of transmission channels and other issues, and then solving the problem of cross-regional clean energy trade with the goal of maximal social benefits. At the same time, the proceeds from the cooperative game can be distributed to each power transaction entity through the method of nuclear solution.

Coded Cooperative Data Exchange in Multichannel Multihop Wireless Networks

This article investigates the coded cooperative data exchange (CCDE) problem, where a set of nodes initially hold a subset of packets and wish to retrieve all desired packets via direct wireless communication with neighbors. The CCDE problem in multihop wired networks has seen significant research recently and is proved to be NP-hard. The CCDE problem in multihop wireless networks (MWNs) must additionally consider half-duplex constraint, interference constraint, and channel constraint. Channel assignment brings new challenges to the CCDE problem in MWN, since it is also a well-known NP-hard problem even with one channel. In this article, we study the CCDE problem in MWN with multiple channels. We first construct a path network to evaluate the priority for each possible transmission and then construct a conflict graph (CG). This graph depicts the half-duplex, interference, and channel constraints. After that, a greedy channel assignment algorithm is proposed to obtain the nonconflict transmissions based on the constructed CG and assign a channel for each selected transmission. Finally, based on the selected transmissions, we construct a single-source multicast network exploiting the time expanded network, with which the network encoding and decoding schemes can be computed within polynomial time. Extensive simulations are conducted to demonstrate the efficiency of the proposed algorithm.

Single color image super-resolution using sparse representation and color constraint

Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., L 1 or L 2 ). These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the L 2 / 3 sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The L 2 / 3 sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed high-resolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation.

R-Accelerator: An RRAM-Based CGRA Accelerator With Logic Contraction

In this paper, a novel RRAM-based reconfigurable accelerator (R-accelerator) design is proposed, which makes special use of existing RRAM device for high-efficient reconfigurable application-specific computing. The proposed R-accelerator design consists of RRAM-based arithmetic unit (AU) array, fully integrated into commercial EDA design tools. A significant area saving is achieved compared with conventional digital counterpart due to the proposed logic contraction technique, as well as saving of storage space and routing congestions. For enabling the optimization of the AU array, this paper also proposes a systematical method on the synthesis of the AU array under routing channel constraint for application-specific designs. Two automatic mapping algorithms, including simultaneous mapping and incremental mapping algorithms, are proposed and compared. The experiments using 45-nm CMOS technology on a case study of dynamic time warping example and general benchmark programs show up to 49% area reduction and 28% performance enhancement using the proposed R-accelerator technique compared with conventional application-specified integrated circuit (ASIC) design.

RETRACTED ARTICLE: Research on application of the Internet of things technology in financial leasing of intelligent manufacturing enterprises

In 2015, the State Council issued “Made in China 2025,” stating that the deep integration of information technology and manufacturing should be accelerated, and intelligent manufacturing should be the main direction of integration. In this context, China’s manufacturing-led cities have implemented the “machine substitution” program to upgrade traditional machinery and equipment to intelligent machinery and equipment, which has led to the development of traditional financial leasing models in China, such as capital. Constraints, financing channel constraints, risk constraints, and information constraints have severely constrained the advancement of the intelligent manufacturing process. In this context, the paper discusses the use of the Internet of things as a technical means to help the existing smart manufacturing enterprises to access the financing channels and open up new financing channels.

Machine Learning-Based Channel Analysis for User Concentric Optical Switching Networks

Optical switching networks (OSN) rely on both hardware components and signals for performing efficient switching operations achieving service level requirements of the end-user. In this manuscript, an incremental learning-based wavelength assignment is introduced to minimize asynchronous channel selection and dispersion-free switch-over in OSN. This method accounts on the wavelength dispersion characteristics of the light path for establishing and reconnecting communications between end-to-end devices. The learning process segregates favorable and conflict channels based on blocking probability and channel capacity assigned to the optical users. A conventional wavelength division multiplexing technique is employed for improving data transmission rates. This method is appropriate for evading blocking rates in optical networks to improve throughput rate and to leverage OSN performance. Machine learning (ML) paradigm is designed for OSN leverages the communication rate influenced by channel conflicts and utilization capacity. Therefore, the number of transmissions likely requires optimal switch-over with the knowledge of channel constraints to improve the performance of OSN.

Characterizing Land Use Impacts on Channel Geomorphology and Streambed Sedimentological Characteristics

Land use can radically degrade stream physical habitat via alterations to channel geomorphology and sedimentological characteristics. However, independent and combined influences such as those of agricultural and urban land use practices on channel geomorphology and substrate composition remain poorly understood. To further understanding of mixed land use influence on stream physical habitat, an intensive, 56 km hydrogeomorphological assessment was undertaken in a representative mixed land use watershed located in Midwestern USA. Sub-objectives included quantitative characterization of (1) channel geomorphology, (2) substrate frequency and embeddedness, and (3) relationships between land use, channel geomorphology, and substrate frequency and embeddedness. Channel geomorphology, and stream substrate data were directly measured at survey transects (n = 561) every 100 m of the entire 56 km distance of the reference stream. Observed data were averaged within five sub-basins (Sites #1 to #5) nested across an agricultural-urban land use gradient. Multiple regression results showed agricultural and urban land use explained nearly all of the variance in average width to depth ratios (R2 = 0.960; p = 0.020; n = 5), and maximum bank angle (R2 = 0.896; p = 0.052; n = 5). Streambed substrate samples of pools indicated significantly (p < 0.001) increased substrate embeddedness at agricultural Site #1 (80%) located in the headwaters and urban Site #5 (79%) located in the lower reaches compared to rural-urban Sites #2 to #4 (39 to 57%) located in the mid-reaches of the study stream. Streambed substrate embeddedness samples of riffles that ranged from 51 to 72% at Sites #1 and #5, and 27 to 46% at Sites #2 to #4 were significantly different between sites (p = 0.013). Percent embeddedness increased with downstream distance by 5% km−1 with the lower urban reaches indicating symptoms of urban stream syndrome linked to degraded riffle habitat. Collectively, observed alterations to channel morphology and substrate composition point to land use alterations to channel geomorphology metrics correlated with increased substrate embeddedness outside of mid-reaches where bedrock channel constraints accounted for less than 3% of substrate frequency. Results from this study show how a hydrogeomorphological assessment can help elucidate casual factors, target critical source areas, and thus, guide regional stream restoration efforts of mixed-land-use watersheds.

Flavor constraints on electroweak ALP couplings

We explore the signals of axion-like particles (ALPs) in flavor-changing neutral current (FCNC) processes. The most general effective linear Lagrangian for ALP couplings to the electroweak bosonic sector is considered, and its contribution to FCNC decays is computed up to one-loop order. The interplay between the different couplings opens new territory for experimental exploration, as analyzed here in the ALP mass range 0<m_a lesssim 5 GeV. When kinematically allowed, Krightarrow pi nu {bar{nu }} decays provide the most stringent constraints for channels with invisible final states, while B-meson decays are more constraining for visible decay channels, such as displaced vertices in Brightarrow K^{(*)} mu ^+ mu ^- data. The complementarity with collider constraints is discussed as well.