Local Information Exchange Research Articles

Distributed fault estimation of nonlinear dynamical systems over sensor networks: a non-fragile approach

PurposeThe purpose of this paper is to investigate the fault estimation issue of nonlinear dynamical systems via distributed sensor networks. Furthermore, based on the communication topology of sensor networks, the nonfragile design strategy considering the gain fluctuation is also adopted for distributed fault estimators.Design/methodology/approachBy means of intensive dynamical model transformation, sufficient conditions with disturbance attenuation performance are established to design desired fault estimator gains with the help of convex optimization.FindingsA novel distributed fault estimation framework for a class of nonlinear dynamical systems is established over a set of distributed sensor networks, where sampled data of sensor nodes via local information exchanges can be used for more efficiency.Originality/valueThe proposed distributed fault estimator gain fluctuations are taken into account for the nonfragile strategy, such that the distributed fault estimators are more applicable for practical sensor networks implementations. In addition, an illustrative example with simulation results are provided to validate the effectiveness and applicableness of the developed distributed fault estimation technique.

Online LiDAR-camera extrinsic parameters self-checking and recalibration

Abstract During production, smart cars are equipped with calibrated LiDARs and cameras. However, due to the vibrations that inevitably occur during driving, the sensors’ extrinsic parameters may change slightly over time. It is a significant challenge to ensure the ongoing security of these systems throughout the car’s lifetime. To address this issue, we propose a self-checking and recalibration algorithm that can continuously detect the sensor data of intelligent vehicles. If the sensor’s miscalibration is detected, the data can be repaired promptly to ensure the vehicle’s reliability. Our self-checking algorithm extracts features from the point cloud and image and performs pixel-wise comparisons. To improve feature quality, we utilize the patch-wise transformer to enhance local information exchange, which also benefits the subsequent extrinsic recalibration. To facilitate the study, we generate two customized datasets from the KITTI dataset and the Waymo Open Dataset. The experiments conducted on these datasets demonstrate the effectiveness of our proposed method in accurately calibrating the LiDAR and camera systems throughout the car’s lifetime. This study is the first to highlight the importance of continually checking the calibrated extrinsic parameters for autonomous driving. Our findings contribute to the broader goal of improving safety and reliability in autonomous driving systems. The dataset and code are available at https://github.com/OpenCalib/LiDAR2camera_self-check.

Consensus control and vibration suppression for multiple flexible nonlinear Timoshenko manipulators under undirected communication topology

In this paper, consensus control and vibration suppression problems are considered for the flexible nonlinear Timoshenko manipulator multi-agent system. The multi-agent system comprises multiple identical flexible Timoshenko manipulators, which can realize cooperation utilizing local information exchange between various agents. The bending deformation and shear deformation generated by the practical robotic manipulators during motion both affect the control effect, so the flexible Timoshenko manipulator is used to describe the dynamic characteristics. The flexible Timoshenko manipulator is a typical distributed parameter system, the deformation states are not only related to time but also to position, and the partial differential equations (PDEs) models are studied in this paper. Based on the PDEs models, the consensus control scheme is developed under the undirected communication topology structure, which can realize joint angle consensus tracking and vibration suppression of all the flexible Timoshenko manipulator agents simultaneously. Finally, the control effect is verified by the numerical simulations.

Skin lesion segmentation via Neural Cellular Automata

Skin melanoma is one of the most dangerous tumor lesions. In recent years, the number of cases and deaths caused by melanoma has been increasing. The discovery and segmentation of the lesion area are crucial for the timely diagnosis and treatment of melanoma. However, the lesion area is often similar to the healthy area, and the size scale changes greatly, which makes the segmentation of the skin lesion area a very challenging task. Neural Cellular Automata (NCA) is a model that can be described as a recurrent convolutional network. It achieves global consistency through multiple local information exchanges, thereby completing the processing of information. Recent research on NCA shows that such a local interactive model can segment low-resolution images well. However, for high-resolution images, direct use of NCA for processing is limited by high memory requirements and difficulty in model convergence. In this paper, in order to overcome these limitations, we propose a new NCA-based segmentation model, named UNCA. UNCA is a model with a U-shaped structure. The high-resolution image is down-sampled to obtain high-dimensional low-resolution features, which are then input into the NCA for information processing. Finally, the image size is restored by upsampling. The experimental results show that the UNCA proposed in this paper has achieved good results on the ISIC2017 dataset, surpassing most of the current methods.

A mechanical antenna for long-wave communication by integrating piezoelectric material and magnets

The Chu’s limit imposes a significant challenge for traditional antennas operating at extremely low frequencies, as they require a large size due to the long operating wavelength, thus limiting their applicability. To reduce the antenna size, this paper proposes a vibrating beam system for long-wave communication that leverages the inverse piezoelectric effect, vibration theory, and Maxwell’s equations. A prototype utilizing beam structures is developed and examined experimentally. Furthermore, the frequency modulation of the vibrating beam system and signal transfer protocol are investigated in detail. The experimental results demonstrate that exciting different vibration modes of the vibrating beam system leads to varied electromagnetic signals in specific rules, enabling long-wave communications and ensuring confidentiality. This work offers valuable insights into the potential of local information exchange among close-range platforms. It also highlights the promising approach of integrating piezoelectric material and magnets within the vibrating beam system, showcasing their potential towards practical applications in long-wave communication.

Differentially private consensus and distributed optimization in multi-agent systems: A review

In the past few decades, distributed multi-agent system (MAS) control has received growing attention due to its numerous advantages. Nonetheless, the substantial reliance on local information exchange in distributed MAS control has given rise to significant privacy concerns. Differential privacy (DP), a mathematically rigorous privacy notion, has gained popularity as a means of safeguarding privacy across multiple fields, including distributed MAS control. In this paper, we present an in-depth overview of the techniques for preserving DP in distributed MAS control, concentrating on consensus and distributed optimization. We begin by outlining the defining features and modeling of MASs from the control theory perspective. Then, we illustrate the motivation for adopting differentially private mechanisms to protect the privacy of distributed MAS control and present the fundamental principles of DP. Based on them, we investigate the cutting-edge techniques designed to preserve DP in consensus and distributed optimization. This review sheds light on the current landscape of DP applications in distributed MAS control and lays the groundwork for future progress in this essential field.

Dynamic event‐triggered fixed‐time average consensus for general linear multi‐agent systems under switching topologies

AbstractThis article studies the practical fixed‐time average consensus problem of general linear multi‐agent systems under switching topologies. First, a distributed fixed‐time consensus control protocol based on dynamic event‐triggered mechanism is designed by using the local information exchange between neighboring agents. The protocol introduces an internal dynamic variable and the triggering condition changes in real time based on the dynamic variable obtained online, which can significantly reduce the number of triggering events and effectively decrease the energy consumption of the multi‐agent system. Then, the sufficient conditions for the multi‐agent system to solve the practical fixed‐time average consensus problem are given, and it is proved that there is no Zeno behavior in the system. Furthermore, a self‐triggered control scheme is proposed such that the neighboring agents communicate with each other only when one of them is triggered. In other words, this scheme does not require continuous communication between neighboring agents and thus reduces communication cost of the system. Finally, the simulation results verify the effectiveness of the proposed method.

Distributed Stochastic Proximal Algorithm With Random Reshuffling for Nonsmooth Finite-Sum Optimization.

The nonsmooth finite-sum minimization is a fundamental problem in machine learning. This article develops a distributed stochastic proximal-gradient algorithm with random reshuffling to solve the finite-sum minimization over time-varying multiagent networks. The objective function is a sum of differentiable convex functions and nonsmooth regularization. Each agent in the network updates local variables by local information exchange and cooperates to seek an optimal solution. We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution with an O((1/T)+(1/√T)) convergence rate, where T is the total number of iterations. Finally, some comparative simulations are provided to verify the convergence performance of the proposed algorithm.

A distributed MPC approach — Local agents decide network convergence

In an earlier work, the Limited Communication-Distributed Model Predictive Control (LC-DMPC) scheme for controlling networks with dynamically coupled and locally constrained linear systems is presented. The scheme has an iterative and cooperative structure in which the systemwide optimum point is achieved by the distributed controllers requiring only coupled agents to cooperate. For assessing the network convergence, it is essential to possess complete information pertaining to all subsystems which has to be available to a central monitor. The current work endeavors to investigate this challenging point by distributing the network convergence within the local agents. With the new version of the algorithm, the convergence of the network is now guaranteed through the dissipativity of the local information exchange dynamics in the iteration domain. This is accomplished by introducing a set of free design variables into the distributed problems which are utilized by the agents to fulfill a simple local LMI problem employing local information only. Despite that the new approach is eliminating the necessity for a centralized observer, it may result in suboptimal local solutions. This is because the convergence of the information sharing loop between the coupled subsystems is insured by the small gain theorem. The new algorithm exhibits enhanced modularity due to the novel introduced convergence condition, implying that any updates to a subsystem physicals or design parameters do not require corresponding updates to neighboring subsystems or the network. The presented concepts are demonstrated by simulating a network of eight interconnected tanks.

Enhancing public sector enterprise risk management through interactive information processing.

Federal agencies are increasingly expected to adopt enterprise risk management (ERM). However, public sector adoption of ERM has typically focused on the economic efficiency of tax-financed activities based on control-based practices. This reflects an emphasis on quantifiable concerns that invariably directs attention to risk, that (by definition) relates to identifiable and measurable events, thereby downplaying uncertain and unknown aspects of public exposures. This is a potentially serious shortcoming as government entities often act as society's risk managers of last resort. When extreme events happen what were previously considered private matters can quickly turn into public obligations. Hence, there is a need for proactive assessments of the evolving public risk landscape to discern unpredictable-even unknowable-developments. The article reviews recent empirical studies on public risk management practices, effects of digitalization in public sector institutions, current strategic management research, and insights uncovered from a recent study of risk management practices in federal agencies. On this basis, the article explains how the ability to generate value from ERM can be enhanced when it intertwines with local responsive initiatives and central strategic risk analyses. It can form a dynamic adaptive risk management process where insights from dispersed actors inform updated risk analyses based on local autonomy and open exchange of information. This approach builds on specific structural features embedded in culture-driven aspirations to generate collaborative solutions. Its functional mode is an interactive control system with open discussions across levels and functions in contrast to conventional diagnostic controls that monitor predetermined key performance indicators (KPIs) and key risk indicators (KRIs). Backed by theoretical rationales and empirical research evidence, it is found that applications of ERM frameworks can produce positive results but is unable to deal with a public risk landscape characterized by uncertain unpredictable conditions with potentially extreme outcome effects. It is shown how interactive exchange of fast local insights and slow integrated strategic risk analyses supported by digitized data processing can form a dynamic adaptive system that enable public sector institutions to deal with emergent high-scale exposures. It is explained how the requirement for conducive organizational structures and supportive values require a new strategic risk leadership approach, which is contrasted to observed practices in federal agencies that are constrained by prevailing public governance requirements. The need to deal with uncertainty and unknown conditions demands a cognitive shift in current thinking from a primary focus on risk to also appraise complexity and prepare for the unexpected where data-driven methods can uncover emergent exposures through dynamic information processing. This requires strategic risk leaders that recognize the significance of complex public exposures with many unknowns and a willingness to facilitate digitalized information processing rooted in a collaborative organizational climate. If handled properly, adoption of ERM in public risk management can consider emergent dimensions in complex public exposures applying interactive information processing as a dynamic adaptive risk management approach incorporating digitized methods to solicit collective intelligence for strategic risk updating.

Efficient Underground Tunnel Place Recognition Algorithm Based on Farthest Point Subsampling and Dual-Attention Transformer.

An autonomous place recognition system is essential for scenarios where GPS is useless, such as underground tunnels. However, it is difficult to use existing algorithms to fully utilize the small number of effective features in underground tunnel data, and recognition accuracy is difficult to guarantee. In order to solve this challenge, an efficient point cloud position recognition algorithm, named Dual-Attention Transformer Network (DAT-Net), is proposed in this paper. The algorithm firstly adopts the farthest point downsampling module to eliminate the invalid redundant points in the point cloud data and retain the basic shape of the point cloud, which reduces the size of the point cloud and, at the same time, reduces the influence of the invalid point cloud on the data analysis. After that, this paper proposes the dual-attention Transformer module to facilitate local information exchange by utilizing the multi-head self-attention mechanism. It extracts local descriptors and integrates highly discriminative global descriptors based on global context with the help of a feature fusion layer to obtain a more accurate and robust global feature representation. Experimental results show that the method proposed in this paper achieves an average F1 score of 0.841 on the SubT-Tunnel dataset and outperforms many existing state-of-the-art algorithms in recognition accuracy and robustness tests.

Trochoidal patterns generation using generalized consensus strategy for double-integrator dynamic agents

This article studies a distributed consensus law to achieve a coordinated trochoidal formation among a team of mobile agents. The mobile agents are modeled as double integrators. We generalize an existing consensus algorithm for double-integrator dynamics (1) to achieve trochoidal patterns under a general network topology and (2) to extend the pattern formation in a 3-D plane. Since the trochoidal patterns are 2-D curves, the proposed control law allows us to specify the 2-D plane in the 3-D space on which the patterns will be formed. We modify the standard consensus protocol and manipulate the gains to create trochoidal motion through local information exchange by appropriately placing the eigenvalues of the system. Trochoids have been used in multifaceted robotic applications such as area coverage, hurricane sampling, and orbit designs for satellites, apart from their aesthetic appeal. Computer simulations and multi-robot experiments are presented to validate the theoretical findings.

Fully Distributed Event-Driven Adaptive Consensus of Unknown Linear Systems.

This article considers the consensus problem of unknown linear multiagent systems (MASs) through adaptive event-driven control in leader-follower and leaderless networks. The proposed event-driven algorithms do not involve any global information related to the network communication structure and rely only on local information exchange to achieve consensus on MASs and are therefore fully distributed. Furthermore, the constraint of continuous communication among the agents is eliminated in terms of control law updates and triggering state monitoring. Another desirable aspect of this article is that the design process of the control algorithms is independent of the parameters of each agent's dynamics and thus does not require precise information about the dynamics of MASs. We further exclude the Zeno behavior of each agent by proving the existence of a strict positive lower bound between any two adjacent events. Finally, the effectiveness of the proposed adaptive event-driven algorithms is verified by a simulation example.

Stability Region and Transmission Control of Multi-Cell Aloha Networks

As one of the most representative random-access schemes, slotted Aloha has been adopted in various wireless communication networks. A multi-cell Aloha network may easily become unstable as the inter-cell interference grows if the traffic input rates and transmission probabilities of nodes are not properly regulated. Yet how to stabilize a multi-cell Aloha network has remained largely unknown. To address the above open issue, an analytical framework is proposed in this paper for multi-cell Aloha networks to characterize the stability region of traffic input rates and operating region of transmission probabilities of nodes for achieving network stability. Specifically, the inter-cell interference level is captured by the overlapping ratio of each cell, and shown to be a key factor that determines the stability performance. For a two-cell Aloha network, the stability region of input rates and complete operating regions of transmission probabilities are obtained as functions of the overlapping ratios of cells. For the general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -cell case, a transmission control algorithm is further proposed to stabilize the network only based on the local information exchange between neighboring cells, with effectiveness demonstrated through simulations.

Distributed and Localized Model Predictive Control—Part II: Theoretical Guarantees

Engineered cyberphysical systems are growing increasingly large and complex. These systems require scalable controllers that robustly satisfy state and input constraints in the presence of additive noise – such controllers should also be accompanied by theoretical guarantees on feasibility and stability. In our companion paper, we introduced Distributed and Localized Model Predictive Control (DLMPC) for large-scale linear systems; DLMPC is a scalable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-loop</i> MPC scheme in which subsystems need only exchange local information in order to synthesize and implement local controllers. In this paper, we provide recursive feasibility and asymptotic stability guarantees for DLMPC. We leverage the System Level Synthesis framework to express the maximal positive robust invariant set for the closed-loop system and its corresponding Lyapunov function in terms of the closed-loop system responses. We use the invariant set as the terminal set for DLMPC, and show that this guarantees feasibility with minimal conservatism. We use the Lyapunov function as the terminal cost, and show that this guarantees stability. We provide fully distributed and localized algorithms to compute the terminal set offline, and also provide necessary additions to the online DLMPC algorithm to accommodate coupled terminal constraint and cost. In all algorithms, only local information exchange is necessary, and computational complexity is independent of the global system size – we demonstrate this analytically and experimentally. This is the first distributed MPC approach that provides minimally conservative yet fully distributed guarantees for recursive feasibility and asymptotic stability in the nominal and robust settings.

A Variation-Aware Quantum Circuit Mapping Approach Based on Multi-Agent Cooperation

Quantum circuit mapping is an essential process required by executing quantum circuits using a noisy intermediate-scale quantum (NISQ) device. Since qubits and quantum gates of a NISQ device are error-prone and variable in quality, it is crucial to choose qubits or quantum gates in a variation-aware manner to maximize the success rate of executing circuits. To this end, this paper proposes a variation-aware method for quantum circuit mapping through the cooperation of multiple agents. Each agent in the proposed method can gradually construct a physical circuit that respects the device's connectivity constraints by inserting a SWAP gate at each step. Moreover, at each step, the circuit information of each agent is shared within the agent population through a communication mechanism that combines global and local information exchange, so that agents with poor fitness can get an opportunity to improve their physical circuits. The experimental results on extensive benchmark circuits confirm that the proposed method can effectively and consistently improve the overall circuit fidelity compared with the state-of-the-art methods.

Optimal attitude consensus control for rigid spacecraft formation based on control Lyapunov functions

AbstractIn this article, the optimal attitude consensus control problem of distributed spacecraft formation through local information exchange is solved in the presence of parameter uncertainty and disturbance. The attitude dynamics model of rigid spacecraft formation is established based on unit quaternion under an undirected graph. Then, the optimal attitude consensus control scheme based on control Lyapunov functions is adopted for rigid spacecraft formation. The approach to construct an appropriate Lyapunov function for multiple spacecraft is developed. Then the lumped disturbance is estimated by an extended state observer (ESO) and using the sliding mode control approach improves the robustness of the controller. The proposed optimal controller can not only ensure the global consensus convergence of the system but also make the preset performance index function achieve the minimum value. In addition, the mathematical proof is given for the stability analysis of the system by the Lyapunov stability theory. Finally, some simulations and comparisons are presented to demonstrate the validity and advantages of the proposed controller.

Experiences with information blocking in the United States: a national survey of hospitals.

The 21st Century Cures Act Final Rule's information blocking provisions, which prohibited practices likely to interfere with, prevent, or materially discourage access, exchange, or use of electronic health information (EHI), began to apply to a limited set of data elements in April 2021 and expanded to all EHI in October 2022. We sought to describe hospital leaders' perceptions of the prevalence of practices that may constitute information blocking, by actor and hospital characteristics, following the rule's applicability date. Cross-sectional analysis of a national survey of hospitals fielded in 2021. The analytic sample included 2092 nonfederal acute care hospitals in the United States. We present descriptive statistics on the perception of the prevalence of information blocking and results of multivariate regression models examining the association between hospital, health information technology (IT) developer and market characteristics and the perception of information blocking. Overall, 42% of hospitals reported observing some behavior they perceived to be information blocking. Thirty-six percent of responding hospitals perceived that healthcare providers either sometimes or often engaged in practices that may constitute information blocking, while 17% and 19% perceived that health IT developers (such as EHR developers) and State, regional and/or local health information exchanges did the same, respectively. Prevalence varied by health IT developer market share, hospital for-profit status, and health system market share. These results support the value of efforts to further reduce friction in the exchange of EHI and support the need for continued observation to provide a sense of the prevalence of information blocking practices and for education and awareness of information blocking regulations.

대일항쟁군의 전통의학 이용에 관한 고찰

Over the course of centuries, traditional medicine has accumulated a wealth of medical knowledge and experience that has been passed down from generation to generation. In the context of Korea, the Japanese colonial rule of the early twentieth century posed a significant threat to the survival of traditional medicine. Despite the challenges, traditional medicine continued to be practiced by ordinary people and the righteous army against imperial Japanese army(RAIJA), and was even employed in anti-Japanese activities. This study seeks to explore the significance of traditional medicine for the RAIJA in Gando and Manchuria. Through an examination of the historical context and case studies of traditional medicine usage, this research reveals the multifaceted meaning of traditional medicine, including its safe and effective treatment methods, utilization of local resources, fundraising and information exchange methods. The ultimate goal of this study is to recognize the existence of a medical system within traditional medicine and its practical utility for military purposes.

Sublinear and Linear Convergence of Modified ADMM for Distributed Nonconvex Optimization

In this article, we consider distributed nonconvex optimization over an undirected connected network. Each agent can only access to its own local nonconvex cost function and all agents collaborate to minimize the sum of these functions by using local information exchange. We first propose a modified alternating direction method of multipliers (ADMM) algorithm. We show that the proposed algorithm converges to a stationary point with the sublinear rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(1/T)$</tex-math></inline-formula> if each local cost function is smooth and the algorithm parameters are chosen appropriately. We also show that the proposed algorithm linearly converges to a global optimum under an additional condition that the global cost function satisfies the Polyak–Łojasiewicz condition, which is weaker than the commonly used conditions for showing linear convergence rates including strong convexity. We then propose a distributed linearized ADMM (L-ADMM) algorithm, derived from the modified ADMM algorithm, by linearizing the local cost function at each iteration. We show that the L-ADMM algorithm has the same convergence properties as the modified ADMM algorithm under the same conditions. Numerical simulations are included to verify the correctness and efficiency of the proposed algorithms.