Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system

<p>In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.</p>

HEXAPOD MOVEMENT ALGORITHMS TO AVOID INTERFERENCE. ANGULAR MOVEMENT

This paper considers the possibility of using a stepping robot - hexapod for research, monitoring the condition of technical dry channels, enclosed spaces and more. Compared to existing designs used today, the hexapod has a list of advantages that make it a more versatile tool, namely: autonomy, due to the power supply installed at work, design features that ensure its increased patency on uneven surfaces. Instead, this type of work requires the development of complex algorithms for movement than in the case of wheeled or tracked machines, ie. hexapod is a platform that moves the limbs, which in turn move with the help of servos. Therefore, the movement of the platform is provided by the control of each servo. In addition, environmental information is additionally processed from rangefinders, limb con-tact sensors with the surface, cameras, accelerometers, etc. Particular attention is paid to robot rotation algorithms, as the proposed scope imposes restrictions on the ability to maneuver freely in space. An algorithm for rotating robots in confined spaces based on limb state matrices has been developed, which greatly simplifies the practical implementation and allows to easily change the type of stroke during the hexapod operation. It is also proposed to introduce a buffer state matrix, which allows you to remember the last position of the limbs of the robot in case of its failure, after the elimination of which, it is possible to continue moving from any last state. Or return to the starting position and change the route. The versatility of the algorithm allows its use not only in the development of the software part of the hesapod, but also for other types of walking robots. Since the developed algorithm allows you to easily modify the types of moves at each iteration of the step. In the future, it is planned to test this algorithm on a model of a hexapod and supplement it with the necessary components for vertical movement, which is very important for passability in this area of application.

Response-Only Parametric Estimation of Structural Systems Using a Modified Stochastic Subspace Identification Technique

The present paper is a study of output-only modal estimation based on the stochastic subspace identification technique (SSI) to avoid the restrictions of well-controlled laboratory conditions when performing experimental modal analysis and aims to develop the appropriate algorithms for ambient modal estimation. The conventional SSI technique, including two types of covariance-driven and data-driven algorithms, is employed for parametric identification of a system subjected to stationary white excitation. By introducing the procedure of solving the system matrix in SSI-COV in conjunction with SSI-DATA, the SSI technique can be efficiently performed without using the original large-dimension data matrix, through the singular value decomposition of the improved projection matrix. In addition, the computational efficiency of the SSI technique is also improved by extracting two predictive-state matrixes with recursive relationship from the same original predictive-state matrix, and then omitting the step of reevaluating the predictive-state matrix at the next-time moment. Numerical simulations and experimental verification illustrate and confirm that the present method can accurately implement modal estimation from stationary response data only.

Markov Decision Process approach in the estimation of raw material quality in incoming inspection process

The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtime due to defective raw materials. Prediction of the raw material acceptance rate can regulate the raw material supplier selection and improve the manufacturing process by filtering out non-conformities. This paper presents a raw material acceptance prediction model (RMAP) developed based on the Markov analysis. RFID tags are used to track the parts throughout the process. A secondary dataset can be derived from the raw RFID data. In this study, a dataset is simulated to reflect a typical incoming inspection process consisting of six substations (Packaging Inspection, Visual Inspection, Gauge Inspection, Rework1, and Rework2) are considered. The accepted parts are forwarded to the Pack and Store station and stored in the warehouse. The non-conforming parts are returned to the supplier. The proposed RMAP model estimates the probability of the raw material being accepted or rejected at each inspection station. The proposed model is evaluated using three test cases: case A (lower conformities), case B (higher conformities) and case C (equal chances of being accepted and rejected). Based on the outcome of the limiting matrix for the three test cases, the results are discussed. The steady-state matrix forecasts the probability of the raw material in a random state. This prediction and forecasting ability of the proposed model enables the industries to save time and cost.

Session Recommendation Model Based on Context-Aware and Gated Graph Neural Networks

The graph neural network (GNN) based approach has been successfully applied to session-based recommendation tasks. However, in the face of complex and changing real-world situations, the existing session recommendation algorithms do not fully consider the context information in user decision-making; furthermore, the importance of context information for the behavior model has been widely recognized. Based on this, this paper presents a session recommendation model based on context-aware and gated graph neural networks (CA-GGNNs). First, this paper presents the session sequence as data of graph structure. Second, the embedding vector representation of each item in the session graph is obtained by using the gated graph neural network (GGNN). In this paper, the GRU in GGNN is expanded to replace the input matrix and the state matrix in the conventional GRU with input context captured in the session (e.g., time, location, and holiday) and interval context (representing the proportion of the total session time of each item in the session). Finally, a soft attention mechanism is used to capture users’ interests and preferences, and a recommendation list is given. The CA-GGNN model combines session sequence information with context information at each time. The results on the open Yoochoose and Diginetica datasets show that the model has significantly improved compared with the latest session recommendation methods.

Negative imaginariness and positive realness analysis of state-space symmetric systems with interval uncertainty

In this article, the state-space symmetric systems with symmetrical interval uncertainty that have positive real and negative imaginary properties are studied. First, a necessary and sufficient test in view of a state matrix is derived for a state-space symmetric system to be negative imaginary, which allows having poles at the origin. Second, bounds on symmetrical interval uncertainty that guarantee the positive realness and negative imaginariness of state-space symmetric systems are provided. Finally, the main results are illustrated by a resistor–capacitor network and a numerical design example.

External knowledge transfer deployment inside a simple double agent Viterbi algorithm

We consider in this paper deploying external knowledge transfer inside a simple double agent Viterbi algorithm which is an algorithm firstly introduced by the author in his preprint "Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text". The key challenge of this work lies in discovering the reason why our old model does have bad performances when it is confronted with estimating ingredient state for unknown words and see if deploying external knowledge transfer directly on calculating state matrix could be the solution instead of deploying it only on back propagating step.

Indoor Positioning Algorithm Based on Maximum Correntropy Unscented Information Filter

In view of the fact that indoor positioning systems are usually affected by non-Gaussian noise in complex indoor environments, this paper tests the data in the actual scene and analyzes the distribution characteristics of noise, and proposes a new indoor positioning algorithm based on maximum correntropy unscented information filter (MCUIF). The proposed indoor positioning algorithm includes three steps: First, the estimation of the state matrix and the corresponding covariance matrix are predicted through the unscented transformation (UT). Second, the observed information is reconstructed by using a nonlinear regression method on the basis of the maximum correntropy criterion (MCC). Third, the contribution of information vector is gained by non-Gaussian measurement and the predicted information vector is corrected by the contribution of information vector. Finally, the gain of information filtering is got by the information entropy state matrix and the information entropy measurement matrix to calculate the position coordinates of the unknown nodes. This algorithm enhances the robustness of the MCUIF to non-Gaussian noise in complex indoor environments. The results from the indoor positioning experiments show that MCUIF is better than the traditional methods in state estimation and position location of the unknown nodes.

Matrix Analysis of Synchronous Boolean Networks

The synchronous Boolean network (SBN) is a simple and powerful model for describing, analyzing, and simulating cellular biological networks. This paper seeks a complete understanding of the dynamics of such a model by employing a matrix method that relies on relating the network transition matrix to its function matrix via a self-inverse state matrix. A recursive ordering of the underlying basis vector leads to a simple recursive expression of this state matrix. Hence, the transition matrix is computed via multiplication of binary matrices over the simplest finite (Galois) field, namely the binary field GF(2), i.e., conventional matrix multiplication involving modulo-2 addition, or XOR addition. We demonstrate the conceptual simplicity and practical utility of our approach via an illustrative example, in which the transition matrix is readily obtained, and subsequently utilized (via its powers, characteristic equation, minimal equation, 1-eigenvectors, and 0-eigenvectors) to correctly predict both the transient behavior and the cyclic behavior of the network. Our matrix approach for computing the transition matrix is superior to the approach of scalar equations, which demands cumbersome manipulations and might fail to predict the exact network behavior. Our approach produces result that exactly replicate those obtained by methods employing the semi-tensor product (STP) of matrices, but achieves that without sophisticated ambiguity or unwarranted redundancy.