Implementation of Legendre Neural Network to Solve Time-Varying Singular Bilinear Systems

Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Their importance lies in their real world application such as economic,ecological,and socioeconomic processes.They are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,etc.Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor systems.The current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact solution.The results were obtained from two methods namely the RK-Butcher algorithm and the Runge Kutta Arithmetic Mean(RKAM)method.Compared with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be accurate.As such,this research article established that the proposed Legendre Neural Network could be easily implemented in MATLAB.One can obtain the solution for any length of time from this method in time-varying singular bilinear systems.

Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is essential to diagnose at the beginning.Notwithstanding,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor diagnoses.Similarly,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and location.Therefore,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment.Several computer models adapted to diagnose the tumor,but the accuracy of the model needs to be tested.Considering all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception V3.Data augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and performance.An accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch.With the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

Region Centric GL Feature Approximation Based Secure Routing for Improved QoS in MANET

Secure routing in Mobile Adhoc Network(Manet)is the key issue now a day in providing secure access to different network services.As mobile devices are used in accessing different services,performing secure routing becomes a challenging task.Towards this,different approaches exist whichfind the trusted route based on their previous transmission details and behavior of different nodes.Also,the methods focused on trust measurement based on tiny information obtained from local nodes or with global information which are incomplete.How-ever,the adversary nodes are more capable and participate in each transmission not just to steal the data also to generate numerous threats in degrading QoS(Quality of Service)parameters like throughput,packet delivery ratio,and latency of the network.This encourages us in designing efficient routing scheme to max-imize QoS performance.To solve this issue,a two stage trust verification scheme and secure routing algorithm named GL-Trust(Global-Local-Trust)is presented.The method involves in route discovery as like popular AODV(Adaptive On-demand Distance Vector)which upgrades the protocol to collect other information like transmission supported,successful transmissions,energy,mobility,the num-ber of neighbors,and the number of alternate route to the same destination and so on.Further,the method would perform global trust approximation to measure the value of global trust and perform local trust approximation to measure local trust.Using both the measures,the method would select a optimal route to perform routing.The protocol is designed to perform localized route selection when there is a link failure which supports the achievement of higher QoS performance.By incorporating different features in measuring trust value towards secure routing,the proposed GL-Trust scheme improves the performance of secure routing as well as other QoS factors.

混合铝基复合材料钻削工艺参数的优化(英文)

采用Taguchi方法和灰色关联分析对Al356/SiC-云母混合金属基复合材料的钻削工艺参数进行优化。实验采用L18正交阵列,在计算机的控制立式机床上进行。考察的钻削性能指标包括轴向力、表面粗糙度、刀具磨损和毛刺高度,对影响这些性能的钻削工艺参数进行了优化,包括轴转数、给进速度、钻头类型和云母质量分数。结果表明:给进速度和钻头类型是影响钻削过程的重要因素,通过这种方法可以有效地改进钻削工艺的性能。

PVD硬质合金刀具加工NimonicC-263合金表面粗糙度的建模和分析(英文)

研究了加工Nimonic C-263合金时,切削参数(切削速度、给进速度、切削深度等)对表面粗糙度的影响。采用正交方法设计实验。评估了切削参数对表面粗糙度的影响,获得最小粗糙度的最优切削条件。采用响应面方法建立了切削参数和表面粗糙度的二次多项式模型。实验结果表明:在所考察的切削参数中,给进速度对表面粗糙度的影响最为显著,其次是切削速度。模型预测结果与实际结果吻合较好,表明所建立的模型能够用来有效地预测加工Nimonic C-263合金时的表面粗糙度。最后,对预测结果用加成定律进行了验证。