Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.     

A recursive state estimator in the presence of state inequality constraints

This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness of the proposed estimator, several simulation results are presented and discussed.     

Absolute Stability of Nonlinear Systems with Two Additive Time-varying Delay Components

In this paper, we present a new sufficient condition for absolute stability of Lure system with two additive time-varying delay components. This criterion is expressed as a set of linear matrix inequalities (LMIs), which can be readily tested by using standard numerical software. We use this new criterion to stabilize a class of nonlinear time-delay systems. Some numerical examples are given to illustrate the applicability of the results using standard numerical software.     

Absolute stability of nonlinear systems with two additive time-varying delay components

In this paper, we present a new sufficient condition for absolute stability of Lure system with two additive time-varying delay components. This criterion is expressed as a set of linear matrix inequalities (LMIs), which can be readily tested by using standard numerical software. We use this new criterion to stabilize a class of nonlinear time-delay systems. Some numerical examples are given to illustrate the applicability of the results using standard numerical software.     

Designing genetic regulatory networks using fuzzy Petri nets approach

In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition (HFT) to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.     

Decision making in dynamic stochastic Cournot games

In this paper, the Cournot competition is modeled as a stochastic dynamic game. In the proposed model, a stochastic market price function and stochastic dynamic decision functions of the rivals are considered. Since the optimal decision of a player needs the estimation of the unknown parameters of the market and rivals’ decisions, a combined estimation-optimization algorithm for decision making is proposed. The history of the rivals’ output quantities (supplies) and the market clearing price (MCP) are the only available information to the players. The convergence of the algorithm (for both estimation and decision making processes) is discussed. In addition, the stability conditions of the equilibrium points are analyzed using the converse Lyapunov theorem. Through the case studies, which are performed based on the California Independent System Operator (CA-ISO) historical public data, the theoretical results and the applicability of the proposed method are verified. Moreover, a comparative study among the agents using the proposed method, naïve expectation and adaptive expectation in the market is performed to show the effectiveness and applicability of the proposed method.     

Distributed topology control algorithm based on one- and two-hop neighbors’ information for ad hoc networks

In this paper, we present a topology control algorithm for ad hoc networks. By considering the weight of the links, each node orders its one-hop neighbors in an ordered list and then the ordered lists are exchanged between the neighbors. This information enables the nodes to compute their transmission radius on the basis of its one- and two-hop neighbors’ information. We demonstrate that compared to the best known algorithms, the degree and transmission radius of the nodes in the topology produced by the proposed algorithm are smaller. In addition to Euclidean graphs, the algorithm works correctly on general weighted graphs. Also an extension of our algorithm is proposed which adapts its topology to network changes. Finally, we use the four metrics, node degree, transmission radius, the power stretch factor and, packet loss ratio to measure the performance improvements of the presented algorithms through simulations.     

Design, fabrication and analysis of micromachined high sensitivity and 0% cross-axis sensitivity capacitive accelerometers

This paper presents the design and fabrication of three MEMS based capacitive accelerometers. The first design illustrates the achievement of an accelerometer with 0% cross-axis sensitivity and has been fabricated using PolyMUMPs, a multi-user surface-micromachining process. A unidirectional parallel plate configuration is utilized in this design to illustrate the achievement of 0% cross-axis sensitivity and an acceptable performance range. In addition, a method is introduced to improve the sensitivity of a capacitive sensor employing a transverse configuration based on the relationship of initial gaps setup in comb-finger arrangements. A design based on this technique and the PolyMUMPs fabrication process is illustrated which demonstrates a sensitivity value of 4.07 fF/μm, with a nonlinearity of 2.05% for a ±3 μm sensor operating range. The last design based on this method and the SOIMUMPs fabrication process exhibits a sensitivity of 3.45 pF/μm for ±1 μm operating range of the sensor.