Attention Markets of Blockchain-Based Decentralized Autonomous Organizations

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’attention.First,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is adopted.Then,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as followers.Its equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention pricing.Moreover,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is demonstrated.Finally,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different conditions.This study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

Designing a Secure and Scalable Data Sharing Mechanism Using Decentralized Identifiers(DID)

Centralized storage and identity identification methods pose many risks,including hacker attacks,data misuse,and single points of failure.Additionally,existing centralized identity management methods face interoperability issues and rely on a single identity provider,leaving users without control over their identities.Therefore,this paper proposes a mechanism for identity identification and data sharing based on decentralized identifiers.The scheme utilizes blockchain technology to store the identifiers and data hashed on the chain to ensure permanent identity recognition and data integrity.Data is stored on InterPlanetary File System(IPFS)to avoid the risk of single points of failure and to enhance data persistence and availability.At the same time,compliance with World Wide Web Consortium(W3C)standards for decentralized identifiers and verifiable credentials increases the mechanism’s scalability and interoperability.

On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault tolerance.However,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism.As a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network.To address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional PoA.This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus.First,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts.Second,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing throughput.In particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation variation.Moreover,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness.Finally,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems

In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning solutions.Identifying malicious contributions in DML systems is challenging,which has led to the exploration of blockchain technology.Blockchain leverages its transparency and immutability to record the provenance and reliability of training data.However,storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs.Additionally,current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data.However,less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair advantage.This paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system participants.First,we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS network.Second,we design a blockchain smart contract to transparently split training datasets into training and test datasets,respectively,without involving system participants.Under the system,transparent and accountable training data sharing can be achieved with attribute-based proxy re-encryption.We demonstrate the security analysis for the system,and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.

Decentralized Optimal Control and Stabilization of Interconnected Systems With Asymmetric Information

The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.

Finite-time decentralized event-triggered state estimation for coupled neural networks under unreliable Markovian network against mixed cyberattacks

This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks,in which the Markov process is used to model the mixed cyberattacks.To optimize the utilization of channel resources,a decentralized event-triggered mechanism is adopted during the information transmission.By establishing the augmentation system and constructing the Lyapunov function,sufficient conditions are obtained for the system to be finite-time bounded and satisfy the H∞ performance index.Then,under these conditions,a suitable state estimator gain is obtained.Finally,the feasibility of the method is verified by a given illustrative example.

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

Decentralized Semi-Supervised Learning for Stochastic Configuration Networks Based on the Mean Teacher Method

The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.

Construction and realization of the knowledge base and inference engine of an IDSS model for air-conditioning cooling/heating sources selection

The knowledge representation mode and inference control strategy were analyzed according to the specialties of air-conditioning cooling/heating sources selection. The constructing idea and working procedure for knowledge base and inference engine were proposed while the realization technique of the C language was discussed. An intelligent decision support system (IDSS) model based on such knowledge representation and inference mechanism was developed by domain engineers. The model was verified to have a small kernel and powerful capability in list processing and data driving, which was successfully used in the design of a cooling/heating sources system for a large-sized office building.

Application of water loop variable refrigerant flow air-conditioning system in large-scale buildings in northern China

A water loop variable refrigerant flow（WLVRF）air-conditioning system is designed to be applied in large-scale buildings in northern China.The system is energy saving and it is an integrated system consisting of a variable refrigerant flow（VRF）air-conditioning unit,a water loop and an air source heat pump.The water loop transports energy among different regions in the buildings instead of refrigerant pipes,decreasing the scale of the VRF air-conditioning unit and improving the performance.Previous models for refrigerants and building loads are cited in this investigation.Mathematical models of major equipment and other elements of the system are established using the lumped parameter method based on the DATAFIT software and the MATLAB software.The performance of the WLVRF system is simulated.The initial investments and the running costs are calculated based on the results of market research.Finally,a contrast is carried out between the WLVRF system and the traditional VRF system.The results show that the WLVRF system has a better working condition and lower running costs than the traditional VRF system.

A Decentralized Autonomous Control on Highly Redundant Robot Manipulators

The control method of highly redundant robot manipulators is introduced. A decentralized autonomous control scheme is used to guide the movement of robot manipulators so that the work done by manipulators is minimized. The method of computing pseudoinverse which needs too many complicated calculation can be avoided. Then the calculation and control of robots are simplified. At the same time system robustness/fault tolerance is achieved.

DECENTRALIZED ADAPTIVE CONTROL FOR LINEAR TIME INVARIENT SYSTEMS WITH FIRST ORDER INTERCONNECTIONS

A new decentralized adaptive control scheme is presented for linear time invariant systems with first order interconnections. The proposed control scheme with “proportional plus integral” terms is used to improve the convergence rate and the ultimate bound of the tracking error. It is important to note that the adaptive scheme uses lower adaptive gains and smaller control inputs to avoid input saturation and oscillatory behavior. Simulation results are illustrated for controlling a dual inverted pendulum and a multivariable turbofan engine using the proposed adaptive scheme. These simulations validate out conclusions.

Decentralized robust H-infinity descriptor output feedback control for value-bounded uncertain descriptor large-scale systems

For a class of value-bounded uncertain descriptor large-scale interconnected systems, the decentralized robust H∞ descriptor output feedback control problem is investigated. A design method based on the bounded real lemma is developed for a decentralized descriptor dynamic output feedback controller, which is reduced to a feasibility problem for a nonlinear matrix inequality （NLMI）. It is proposed to solve the NLMI iteratively by the idea of homotopy, where some of the variables are fixed alternately at each iteration to reduce the NLMI to a linear matrix inequality （LMI）. A given example shows the efficiency of this method .

A New Soil Infiltration Technology for Decentralized Sewage Treatment:Two-Stage Anaerobic Tank and Soil Trench System

The low removal efficiency of total nitrogen （TN） is one of the main disadvantages of traditional single stage subsurface infiltration system, which combines an anaerobic tank and a soil filter field. In this study, a full-scale, two-stage anaerobic tank and soil trench system was designed and operated to evaluate the feasibility and performances in treating sewage from a school campus for over a one-year monitoring period. The raw sewage was prepared and fed into the first anaerobic tank and second tank by 60% and 40%, respectively. This novel process could decrease chemical oxygen demand with the dichromate method by 89%-96%, suspended solids by 91%-97%, and total phosphorus by 91%-97%. The denitrification was satisfactory in the second stage soil trench, so the removals of TN as well as ammonia nitrogen （NH4^＋-N） reached 68%-75% and 96% 99%, respectively. It appeared that the removal efficiency of TN in this two-stage anaerobic tank and soil trench system was more effective than that in the single stage soil infiltration system. The effluent met the discharge standard for the sewage treatment plant （GB18918-2002） of China.

Decentralized model reference adaptive sliding mode control based on fuzzy model

A new design scheme of decentralized model reference adaptive sliding mode controller for a class of MIMO nonlinear systems with the high-order interconnections is propcsed. The design is based on the universal approximation capability of the Takagi - Seguno （T-S） fuzzy systems. Motivated by the principle of certainty equivalenteontrol, a decentralized adaptive controller is designed to achieve the tracking objective without computafion of the T-S fuzz ymodel. The approach does not require the upper bound of the uncertainty term to be known through some adaptive estimation. By theoretical analysis, the closed-loop fuzzy control system is proven to be globally stable in the sense that all signalsinvolved are bounded, with tracking errors converging to zero. Simulation results demonstrate the effectiveness of the approach.

Decentralized Adaptive Strategies for Synchronization of Fractional-order Complex Networks

This paper focuses on synchronization of fractionalorder complex dynamical networks with decentralized adaptive coupling.Based on local information among neighboring nodes,two fractional-order decentralized adaptive strategies are designed to tune all or only a small fraction of the coupling gains respectively.By constructing quadratic Lyapunov functions and utilizing fractional inequality techniques,Mittag-Leffler function,and Laplace transform,two sufficient conditions are derived for reaching network synchronization by using the proposed adaptive laws.Finally,two numerical examples are given to verify the theoretical results.

DECENTRALIZED STABILIZATION OF A CLASS OF INTERCONNECTED SYSTEMS

This paper is concerned with the decentralized stabilization of continuous and discrete linear interconnected systems with the structural constraints about the interconnection matrices. For the continuous case,the main improvement in the paper as compared with the corresponding results in the literature is to extend the considered class of systems from S to S (both will be defined in the paper) without resulting in high decentralized gain and difficult numerical computation. The algorithm for obtaining decentralized state feedback control to stable the overall system is presented. The discrete case and some very useful results are discussed as well.

H_∞tracking design for a class of decentralized nonlinear systems via fuzzy adaptive observer

A novel H∞ tracking-based decentralized indirect adaptive output feedback fuzzy controller for a class of uncertain large-scale nonlinear systems is developed. By virtue of the proper filtering of the observation error dynamics, the observer-based decentralized indirect adaptive fuzzy control scheme is presented for a class of large-scale nonlinear systems using the combination of H∞ tracking technique, a fuzzy adaptive observer and fuzzy inference systems. The output feedback and adaptation mechanisms are both robust and implementable indeed owing to their freedom from the unavailable observation error vector. All the signals of the closed-loop largescale system are guaranteed to stay uniformly bounded and the output errors take on H∞ tracking performance. Simulation results substantiate the effectiveness of the proposed scheme.

Decentralized Sliding Mode Control for a Spacecraft Flexible Appendage Based on Finite Element Method

This paper is devoted to study the application of the decentralized sliding mode control method, which is used to reduce the vibration of large spacecraft flexible appendage. In the process of control design, the sliding surface of sliding mode control is determined by minimizing the optimal cost function, and the controller is the saturation controller. The controlled structure is subject to arbitrary, unmeasurable and uncertainty disturbance forces and initial displacement. The decentralized control method and the centralized control method are used to control vibration of the structure respectively. When the system is subjected to the initial displacement or external disturbance, the computer simulation shows that both of these control methods perform effectively, but the number of Riccati equation of the decentralized method is far smaller than that of centralized control method, especially in a large system.

Decentralized Control for Residential Energy Management of a Smart Users' Microgrid with Renewable Energy Exchange

This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.