scholarly journals A Novel of New 7D Hyperchaotic System with Self-Excited Attractors and Its Hybrid Synchronization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ahmed S. Al-Obeidi ◽  
Saad Fawzi Al-Azzawi ◽  
Abdulsattar Abdullah Hamad ◽  
M. Lellis Thivagar ◽  
Zelalem Meraf ◽  
...  
Keyword(s):  
Lyapunov Stability Theory ◽  
Control Technique ◽  
Hyperchaotic System ◽  
The Novel ◽  
Nonlinear Feedback ◽  
New Class ◽  
Proposed Model ◽  
Quadratic Nonlinearities ◽  
Hybrid Synchronization ◽  
Novel Model

In this study, a novel 7D hyperchaotic model is constructed from the 6D Lorenz model via the nonlinear feedback control technique. The proposed model has an only unstable origin point. Thus, it is categorized as a model with self-excited attractors. And it has seven equations which include 19 terms, four of which are quadratic nonlinearities. Various important features of the novel model are analyzed, including equilibria points, stability, and Lyapunov exponents. The numerical simulation shows that the new class exhibits dynamical behaviors such as chaotic and hyperchaotic. This paper also presents the hybrid synchronization for a novel model via Lyapunov stability theory.

scholarly journals Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

2018 ◽  
Vol 13 (3) ◽  
pp. 408-428 ◽  
Author(s):  
Phu Vo Ngoc
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
The Novel ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Novel Model ◽  
Better Than

We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

scholarly journals A New Enhanced Template Protection Algorithm on Iris Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 286-289
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
The Novel ◽  
Compression Technique ◽  
Novel Technology ◽  
Proposed Model ◽  
Template Protection ◽  
Comparative Results ◽  
Networked Society ◽  
Novel Model

Over the past few years, biometric systems have become prominent in terms of verification of the user identity due to increased demand of security in the networked society. Iris recognition system is a novel technology for the verification of user which is considered as the most secure, reliable and stable technique. It is generally accepted in the areas with high security. Though, security is major concern in this field, a significant number of approaches have been proposed to secure iris biometrics, But still, there is a scope to improve these techniques. Thus, in this work, a novel model is proposed which employs a bitmask compression technique to secure the template obtained for iris by compressing its actual size. In addition; SVM is used for the classification process. Mean Square Error, Bit Error Rate, PSNR, and GAR are different parameters which are used for measuring the effectiveness of the proposed model. The simulation results are carried out in MATLAB software and the comparative results validated the efficacy of the novel model with respect to security, efficacy and accuracy.

scholarly journals Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts

2019 ◽  
Vol 4 (4) ◽  
pp. 42-55
Author(s):  
Gaihong Yu ◽  
Zhixiong Zhang ◽  
Huan Liu ◽  
Liangping Ding
Keyword(s):  
Contextual Information ◽  
English Literature ◽  
Language Model ◽  
Classification Model ◽  
The Novel ◽  
Model Based ◽  
Contextual Features ◽  
Proposed Model ◽  
Novel Model ◽  
Fine Tune

Abstract Purpose Move recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units. To improve the performance of move recognition in scientific abstracts, a novel model of move recognition is proposed that outperforms the BERT-based method. Design/methodology/approach Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences. In this paper, inspired by the BERT masked language model (MLM), we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition. Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps. Then, we compare our model with HSLN-RNN, BERT-based and SciBERT using the same dataset. Findings Compared with the BERT-based and SciBERT models, the F1 score of our model outperforms them by 4.96% and 4.34%, respectively, which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-the-art results of HSLN-RNN at present. Research limitations The sequential features of move labels are not considered, which might be one of the reasons why HSLN-RNN has better performance. Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed, which is a typical biomedical database, to fine-tune our model. Practical implications The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences. Originality/value T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way. The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.

scholarly journals Hyperchaos, adaptive control and synchronization of a novel 4-D hyperchaotic system with two quadratic nonlinearities

2016 ◽  
Vol 26 (4) ◽  
pp. 471-495 ◽  
Author(s):  
Sundarapandian Vaidyanathan
Keyword(s):  
Adaptive Control ◽  
Lyapunov Exponents ◽  
Research Work ◽  
Adaptive Controller ◽  
Phase Portraits ◽  
Hyperchaotic System ◽  
The Novel ◽  
System Parameters ◽  
Quadratic Nonlinearities ◽  
Unknown System

AbstractThis research work announces an eleven-term novel 4-D hyperchaotic system with two quadratic nonlinearities. We describe the qualitative properties of the novel 4-D hyperchaotic system and illustrate their phase portraits. We show that the novel 4-D hyperchaotic system has two unstable equilibrium points. The novel 4-D hyperchaotic system has the Lyapunov exponents L1= 3.1575, L2= 0.3035, L3= 0 and L4= −33.4180. The Kaplan-Yorke dimension of this novel hyperchaotic system is found as DKY= 3.1026. Since the sum of the Lyapunov exponents of the novel hyperchaotic system is negative, we deduce that the novel hyperchaotic system is dissipative. Next, an adaptive controller is designed to stabilize the novel 4-D hyperchaotic system with unknown system parameters. Moreover, an adaptive controller is designed to achieve global hyperchaos synchronization of the identical novel 4-D hyperchaotic systems with unknown system parameters. The adaptive control results are established using Lyapunov stability theory. MATLAB simulations are depicted to illustrate all the main results derived in this research work.

scholarly journals Dynamical Analysis and FPGA Implementation of a Novel Hyperchaotic System and Its Synchronization Using Adaptive Sliding Mode Control and Genetically Optimized PID Control

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Karthikeyan Rajagopal ◽  
Laarem Guessas ◽  
Sundarapandian Vaidyanathan ◽  
Anitha Karthikeyan ◽  
Ashokkumar Srinivasan
Keyword(s):  
Sliding Mode ◽  
Dynamic Properties ◽  
Lyapunov Stability Theory ◽  
Dynamical Analysis ◽  
Adaptive Sliding Mode Control ◽  
Pid Controllers ◽  
Hyperchaotic System ◽  
The Novel ◽  
Unknown Parameters ◽  
Theory Use

We announce a new 4D hyperchaotic system with four parameters. The dynamic properties of the proposed hyperchaotic system are studied in detail; the Lyapunov exponents, Kaplan-Yorke dimension, bifurcation, and bicoherence contours of the novel hyperchaotic system are derived. Furthermore, control algorithms are designed for the complete synchronization of the identical hyperchaotic systems with unknown parameters using sliding mode controllers and genetically optimized PID controllers. The stabilities of the controllers and parameter update laws are proved using Lyapunov stability theory. Use of the optimized PID controllers ensures less time of convergence and fast synchronization speed. Finally the proposed novel hyperchaotic system is realized in FPGA.

scholarly journals Malicious traffic detection combined deep neural network with hierarchical attention mechanism

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoyang Liu ◽  
Jiamiao Liu
Keyword(s):  
Data Flow ◽  
Detection Efficiency ◽  
False Positive Rate ◽  
Attention Mechanism ◽  
The Novel ◽  
Malicious Attack ◽  
Detection Model ◽  
Proposed Model ◽  
Traffic Detection ◽  
Novel Model

AbstractGiven the gradual intensification of the current network security situation, malicious attack traffic is flooding the entire network environment, and the current malicious traffic detection model is insufficient in detection efficiency and detection performance. This paper proposes a data processing method that divides the flow data into data flow segments so that the model can improve the throughput per unit time to meet its detection efficiency. For this kind of data, a malicious traffic detection model with a hierarchical attention mechanism is also proposed and named HAGRU (Hierarchical Attention Gated Recurrent Unit). By fusing the feature information of the three hierarchies, the detection ability of the model is improved. An attention mechanism is introduced to focus on malicious flows in the data flow segment, which can reasonably utilize limited computing resources. Finally, compare the proposed model with the current state of the method on the datasets. The experimental results show that: the novel model performs well in different evaluation indicators (detection rate, false-positive rate, F-score), and it can improve the performance of category recognition with fewer samples when the data is unbalanced. At the same time, the training of the novel model on larger datasets will enhance the generalization ability and reduce the false alarm rate. The proposed model not only improves the performance of malicious traffic detection but also provides a new research method for improving the efficiency of model detection.

Enhanced Composite Nonlinear Control Technique using Adaptive Control for Nonlinear Delayed Systems

2020 ◽  
Vol 13 (3) ◽  
pp. 396-404
Author(s):  
Sonal Singh ◽  
Shubhi Purwar
Keyword(s):  
Adaptive Control ◽  
Actuator Saturation ◽  
Input Saturation ◽  
Chemical Reactor ◽  
Control Technique ◽  
Control Law ◽  
Nonlinear Feedback ◽  
Composite Nonlinear Feedback ◽  
Delayed Systems ◽  
Analysis Of Stability

Background and Introduction: The proposed control law is designed to provide fast reference tracking with minimal overshoot and to minimize the effect of unknown nonlinearities and external disturbances. Methods: In this work, an enhanced composite nonlinear feedback technique using adaptive control is developed for a nonlinear delayed system subjected to input saturation and exogenous disturbances. It ensures that the plant response is not affected by adverse effect of actuator saturation, unknown time delay and unknown nonlinearities/ disturbances. The analysis of stability is done by Lyapunov-Krasovskii functional that guarantees asymptotical stability. Results: The proposed control law is validated by its implementation on exothermic chemical reactor. MATLAB figures are provided to compare the results. Conclusion: The simulation results of the proposed controller are compared with the conventional composite nonlinear feedback control which illustrates the efficiency of the proposed controller.

Analysis of a New Class of Impulsive Implicit Sequential Fractional Differential Equations

2020 ◽  
Vol 21 (6) ◽  
pp. 571-587 ◽  
Author(s):  
Akbar Zada ◽  
Sartaj Ali ◽  
Tongxing Li
Keyword(s):  
Differential Equations ◽  
Fractional Order ◽  
Fractional Differential Equations ◽  
Sufficient Conditions ◽  
Uniqueness Of Solutions ◽  
Integer Order ◽  
New Class ◽  
Proposed Model ◽  
Fractional Differential ◽  
Sequential Fractional Differential Equations

AbstractIn this paper, we study an implicit sequential fractional order differential equation with non-instantaneous impulses and multi-point boundary conditions. The article comprehensively elaborate four different types of Ulam’s stability in the lights of generalized Diaz Margolis’s fixed point theorem. Moreover, some sufficient conditions are constructed to observe the existence and uniqueness of solutions for the proposed model. The proposed model contains both the integer order and fractional order derivatives. Thus, the exponential function appearers in the solution of the proposed model which will lead researchers to study fractional differential equations with well known methods of integer order differential equations. In the last, few examples are provided to show the applicability of our main results.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

scholarly journals Novel Protein-Repellent and Antibacterial Resins and Cements to Inhibit Lesions and Protect Teeth

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Li Cao ◽  
Junling Wu ◽  
Qiang Zhang ◽  
Bashayer Baras ◽  
Ghalia Bhadila ◽  
...  
Keyword(s):  
Orthodontic Treatment ◽  
The Novel ◽  
Dental Resin ◽  
Biofilm Growth ◽  
New Class ◽  
Dental Resins ◽  
Wide Range ◽  
Oral Biofilms ◽  
One Year

Orthodontic treatment is increasingly popular as people worldwide seek esthetics and better quality of life. In orthodontic treatment, complex appliances and retainers are placed in the patients’ mouths for at least one year, which often lead to biofilm plaque accumulation. This in turn increases the caries-inducing bacteria, decreases the pH of the retained plaque on an enamel surface, and causes white spot lesions (WSLs) in enamel. This article reviews the cutting-edge research on a new class of bioactive and therapeutic dental resins, cements, and adhesives that can inhibit biofilms and protect tooth structures. The novel approaches include the use of protein-repellent and anticaries polymeric dental cements containing 2-methacryloyloxyethyl phosphorylcholine (MPC) and dimethylaminododecyl methacrylate (DMAHDM); multifunctional resins that can inhibit enamel demineralization; protein-repellent and self-etching adhesives to greatly reduce oral biofilm growth; and novel polymethyl methacrylate resins to suppress oral biofilms and acid production. These new materials could reduce biofilm attachment, raise local biofilm pH, and facilitate the remineralization to protect the teeth. This novel class of dental resin with dual benefits of antibacterial and protein-repellent capabilities has the potential for a wide range of dental and biomedical applications to inhibit bacterial infection and protect the tissues.

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