Light Weight Cryptographic Address Generation(LW-CGA) Using System State Entropy Gathering for IPv6 Based MANETs

In IPv6 based MANETs, the neighbor discovery enables nodes to self-configure and communicate with neighbor nodes through autoconfiguration. The Stateless address autoconfiguration(SLAAC) has proven to face several security issues. Even though the Secure Neighbor Discovery(SeND) uses Cryptographically Generated Addresses(CGA) to address these issues, it creates other concerns such as need for CA to authenticate hosts, exposure to CPU exhaustion attacks and high computational intensity. These issues are major concern for MANETs as it possesses limited bandwidth and processing power. The paper proposes empirically strong Light Weight Cryptographic Address Generation(LW-CGA) using entropy gathered from system states. Even the system users cannot monitor these system states; hence LW-CGA provides high security with minimal computational complexity and proves to be more suitable for MANETs. The LW-CGA and SeND are implemented and tested to study the performances. The evaluation shows that LW-CGA with good runtime throughput takes minimal address generation latency.

Analytical Verification of Performance of Deep Neural Network Based Time-synchronized Distribution System State Estimation

Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements.It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.

PFL-DSSE:A Personalized Federated Learning Approach for Distribution System State Estimation

A centralized framework-based data-driven framework for active distribution system state estimation(DSSE)has been widely leveraged.However,it is challenged by potential data privacy breaches due to the aggregation of raw measurement data in a data center.A personalized federated learningbased DSSE method(PFL-DSSE)is proposed in a decentralized training framework for DSSE.Experimental validation confirms that PFL-DSSE can effectively and efficiently maintain data confidentiality and enhance estimation accuracy.

Four-Electron Systems in the Impurity Hubbard Model. Second Triplet State. Spectra of the System in the ν-Dimensional Lattice Zν

We consider an energy operator of four-electron system in the Impurity Hubbard model with a coupling between nearest-neighbors. The spectrum of the systems in the second triplet state in a ν-dimensional lattice is investigated. For investigation the structure of essential spectra and discrete spectrum of the energy operator of four-electron systems in an impurity Hubbard model, for which the momentum representation is convenient. In addition, we used the tensor products of Hilbert spaces and tensor products of operators in Hilbert spaces and described the structure of essential spectrum and discrete spectrum of the energy operator of four-electron systems in an impurity Hubbard model for the second triplet state of the system. The investigations show that the essential spectrum of the system consists of the union of no more than sixteen segments, and the discrete spectrum of the system consists of no more than eleven eigenvalues.

Finite-Time H∞ Control of Switched Nonlinear Systems under State-Dependent Switching

This paper investigates the finite-time H∞ control problem of switched nonlinear systems via state-dependent switching and state feedback control. Unlike the existing approach based on time-dependent switching strategy, in which the switching instants must be given in advance, the state-dependent switching strategy is used to design switching signals. Based on multiple Lyapunov-like functions method, several criteria for switched nonlinear systems to be finite-time H∞ control are derived. Finally, a numerical example with simulation results is provided to show the validity of the conclusions.

Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

Modern power systems are incorporated with distributed energy sources to be environmental-friendly and costeffective.However,due to the uncertainties of the system integrated with renewable energy sources,effective strategies need to be adopted to stabilize the entire power systems.Hence,the system operators need accurate prediction tools to forecast the dynamic system states effectively.In this paper,we propose a Bayesian deep learning approach to predict the dynamic system state in a general power system.First,the input system dataset with multiple system features requires the data pre-processing stage.Second,we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow model.Third,by incorporating the state matrix with the system features,we propose a Bayesian long short-term memory(BLSTM)network to predict the dynamic system state variables accurately.Simulation results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

Cyber-attack Detection Strategy Based on Distribution System State Estimation

Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system state estimation(DSSE).The uncertainty of the distribution network is represented by the interval of each state variable.A three-phase interval DSSE model is proposed to construct the interval of each state variable.An improved iterative algorithm(IIA)is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval.A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE.To validate the proposed cyber-attack detection strategy,the basic principle of the cyber-attack is studied,and its general model is formulated.The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems.Comparative experiments of the proposed IIA,Monte Carlo simulation algorithm,and interval Gauss elimination algorithm prove the validation of the proposed method.

Measurement Sensitivity and Estimation Error in Distribution System State Estimation using Augmented Complex Kalman Filter

Distribution state estimation(DSE)is an essential part of an active distribution network with high level of distributed energy resources.The challenges of accurate DSE with limited measurement data is a well-known problem.In practice,the operation and usability of DSE depend on not only the estimation accuracy but also the ability to predict error variance.This paper investigates the application of error covariance in DSE by using the augmented complex Kalman filter(ACKF).The Kalman filter method inherently provides state error covariance prediction.It can be utilized to accurately infer the error covariance of other parameters and provide a method to determine optimal measurement locations based on the sensitivity of error covariance to measurement noise covariance.This paper also proposes a generalized formulation of ACKF to allow scalar measurements to be incorporated into the complex-valued estimator.The proposed method is simulated by using modified IEEE 34-bus and IEEE 123-bus test feeders,and randomly generates the load data of complex-valued Wiener process.The ACKF method is compared with an equivalent formulation using the traditional weighted least squares(WLS)method and iterated extended Kalman filter(IEKF)method,which shows improved accuracy and computation performance.

Outlier Reconstruction Based Distribution System State Estimation Using Equivalent Model of Long Short-term Memory and Metropolis-Hastings Sampling

The accuracy of distribution system state estimation(DDSE)is reduced when phasor measurement unit(PMU)measurements contain outliers because of cyber attacks or global positioning system spoofing attacks.Therefore,to enhance the robustness of DDSE to measurement outliers,approximate the target distribution of Metropolis-Hastings(MH)sampling,and judge the prediction of the long short-term memory(LSTM)network,this paper proposes an outlier reconstruction based state estimation method using the equivalent model of the LSTM network and MH sampling(E-LM model),motivated by the characteristics of the chronological correlations of PMU measurements.First,the target distribution of outlier reconstruction is derived using a kernel density estimation function.Subsequently,the reasons and advantages of the E-LM model are explained and analyzed from a mathematical point of view.The proposed LSTM-based MH sampling can approximate the target distribution of MH sampling to decrease the number of the futile iterations.Moreover,the proposed MH-based forecasting of the LSTM can judge each LSTM prediction,which is independent of its true value.Finally,simulations are conducted to evaluate the performance of the E-LM model by integrating the LSTM network and the MH sampling into the outlier reconstruction based DDSE.

Coping with the Impact of Climate Change: A Dive into Precision Agriculture in the United States

With the continued increase in the number of people that are food insecure globally, which could be increasing because of the ongoing Ukraine-Russia war, leading to reduction in international agribusinesses, coupled with drastic climate change exacerbating the problem of food insecurity, there is a constant need to come up with innovative approaches to solve this global issue. In this article, we articulated how precision agriculture can be a tool for ensuring food security in the United States. This study aims to reiterate the significance of precision agriculture in solving global food insecurity.

Audio Signal Generator System Based On State Machines

A state machine can make program designing quicker,simpler and more efficient. This paper describes in detail the model for a state machine and the idea for its designing and gives the design process of the state machine through an example of audio signal generator system based on Labview. The result shows that the introduction of the state machine can make complex design processes more clear and the revision of programs easier.

On Asymptotic Stabilization of Linear Discrete-time Systems with Saturated State Feedback

As saturation is involved in the stabilizing feedback control of a linear discrete-time system, the original global-asymptotic stabilization (GAS) may drop to region-asymptotic stabilization (RAS). How to test if the saturated feedback system is GAS or RAS? The paper presents a criterion to answer this question, and describes an algorithm to calculate an invariant attractive ellipsoid for the RAS case. At last, the effectiveness of the approach is shown with examples.

H_∞ Control for 2-D Discrete State Delayed Systems in the Second FM Model

这份报纸涉及 H 控制的问题为二维(2-D ) 分离州的延期系统由第二 Fornasini 和 Marchesini (FM ) 描述了州空间当模特儿。为这个 2-D 系统有 H 噪音变细的一个足够的条件以某个线性矩阵不平等(LMI ) 被给。最佳的 H 控制器被解决一个凸的优化问题获得。最后，一个模拟例子被给说明建议结果的有效性。

State Feedback Stabilzation for a Class of Time-delay Nonlinear Systems

The problem of global stabilization by state feedback for a class of time-delay nonlinear system is considered. By constructing the appropriate Lyapunov-Krasovskii functionals (LKF) and using the backstepping design, a linear state feedback controller making the closed-loop system globally asymptotically stable is constructed.

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.

Robust Sliding-mode Filtering for a Class of Uncertain Nonlinear Discrete-time State-delayed Systems

This paper is concerned with the problem of robust sliding-mode filtering for a class of uncertain nonlinear discrete-time systems with time-delays. The nonlinearities are assumed to satisfy global Lipschitz conditions and parameter uncertainties are supposed to reside in a polytope. The resulting filter is of the Luenberger type with the discontinuous form. A sufficient condition with delay-dependency is proposed for existence of such a filter. And the desired filter can be found by solving a set of matrix inequalities. The resulting filter adapts for the systems whose noise input is real functional bounded and not be required to be energy bounded. A numerical example is given to illustrate the effectiveness of the proposed design method.

Steady-state responses of a belt-drive dynamical system under dual excitations

The stable steady-state periodic responses of a belt-drive system with a one-way clutch are studied. For the first time, the dynamical system is investigated under dual excitations. The system is simultaneously excited by the firing pulsations of the engine and the harmonic motion of the foundation. Nonlinear discrete-continuous equations are derived for coupling the transverse vibration of the belt spans and the rotations of the driving and driven pulleys and the accessory pulley. The nonlinear dynamics is studied under equal and multiple relations between the frequency of the fir- ing pulsations and the frequency of the foundation motion. Furthermore, translating belt spans are modeled as axially moving strings. A set of nonlinear piecewise ordinary differ- ential equations is achieved by using the Galerkin truncation. Under various relations between the excitation frequencies, the time histories of the dynamical system are numerically simulated based on the time discretization method. Further- more, the stable steady-state periodic response curves are calculated based on the frequency sweep. Moreover, the convergence of the Galerkin truncation is examined. Numer- ical results demonstrate that the one-way clutch reduces the resonance amplitude of the rotations of the driven pul- ley and the accessory pulley. On the other hand, numerical examples prove that the resonance areas of the belt spans are decreased by eliminating the torque-transmitting in the opposite direction. With the increasing amplitude of the foun- dation excitation, the damping effect of the one-way clutch will be reduced. Furthermore, as the amplitude of the firing pulsations of the engine increases, the jumping phenomena in steady-state response curves of the belt-drive system with or without a one-way clutch both occur.

A Composite State Convergence Scheme for Bilateral Teleoperation Systems

State convergence is a novel control algorithm for bilateral teleoperation of robotic systems. First, it models the teleoperation system on state space and considers all the possible interactions between the master and slave systems. Second, it presents an elegant design procedure which requires a set of equations to be solved in order to compute the control gains of the bilateral loop. These design conditions are obtained by turning the master-slave error into an autonomous system and imposing the desired dynamic behavior of the teleoperation system. Resultantly, the convergence of master and slave states is achieved in a well-defined manner. The present study aims at achieving a similar convergence behavior offered by state convergence controller while reducing the number of variables sent across the communication channel. The proposal suggests transmitting composite master and slave variables instead of full master and slave states while keeping the operator's force channel intact. We show that,with these composite and force variables;it is indeed possible to achieve the convergence of states in a desired way by strictly following the method of state convergence. The proposal leads to a reduced complexity state convergence algorithm which is termed as composite state convergence controller. In order to validate the proposed scheme in the absence and presence of communication time delays, MATLAB simulations and semi-real time experiments are performed on a single degree-of-freedom teleoperation system.

Adaptive State-feedback Stabilization for More General High-order Stochastic Nonlinear Systems

适应州反馈的稳定为在的高顺序的随机的非线性的系统的一个类被调查函数 fi 的上面的界限(?? 铄吗？？