Studies on electroless Ni-P-Cr_2O_3 and Ni-P-SiO_2 composite coatings

本文对化学镀镍及化学镀镍磷基质中SiO2与Cr2O3的共沉积进行了研究。微粒在不断生长的膜层中共沉积引起了新的化学复合镀层的出现,这些复合镀层许多都具有优异的耐磨及耐蚀性能。通过选取镀层合金/复合微粒/金属基体的组分可改进镀层,获得所需的性能,以满足特别的需求。在对这些复合镀层的应用需求正在迫近与增长的同时,其市场正在迅速扩张。本文开发出了一种合适的复合化学镀镍液,并通过维氏硬度法对化学复合镀镍层进行了表征。采用动电位极化及交流阻抗法测定了镀层的Taber耐磨性能及耐蚀性能。采用SEM及XRD对复合镀层的表面形貌进行了分析。

Effect of axial force on microstructure and tensile properties of friction stir welded AZ61A magnesium alloy

The influences of axial force on tensile properties of friction stir welded AZ61A magnesium alloy were studied. Five different values of axial forces ranging from 3 to 7 kN were used to fabricate the joints. Tensile properties of the joints were evaluated and correlated with the stir zone microstructure and hardness. From this investigation, it is found that the joint fabricated with an axial force of 5 kN exhibits superior tensile properties compared to other joints. The formation of finer grains in the stir zone and higher hardness of the stir zone are the main reasons for the superior tensile properties of these joints.

化学镀Ni-P-石墨复合镀层的特性(英文)

与化学镀Ni-P镀层比较,化学镀Ni-P-石墨复合镀层的机械性能得到了改进,适合于工程方面的应用。Ni-P-石墨复合镀层是从含有硫酸镍、次磷酸镍、丁二酸、表面活性剂和稳定剂的镀液中制备的。当镀液中石墨含量为15g/L时,镀层中石墨的复合量最大。采用维氏硬度测试仪、泰伯磨耗实验机、交流阻抗和SEM等手段对复合了石墨颗粒的Ni-P化学镀层进行了测定,其硬度,耐磨、耐蚀性随镀层中石墨含量的增加而增强。

Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

In several countries,the ageing population contour focuses on high healthcare costs and overloaded health care environments.Pervasive health care monitoring system can be a potential alternative,especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care,mobile care and home care.In this aspect,we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation.It facilitates better healthcare assistance,especially for COVID’19 patients and quarantined people.It identies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model.Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identication.Linguistics rules are framed based on the fuzzy set attributes belong to different context types.The fuzzy semantic rules are used to identify the relationship among the attributes,and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation.Outcomes are measured using a fuzzy logic-based context reasoning system under simulation.The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.

Experimental investigation and multi-objective optimization of wire electrical discharge machining(WEDM) of 5083 aluminum alloy

The experimental analysis presented aims at the selection of the most optimal machining parameter combination for wire electrical discharge machining （WEDM） of 5083 aluminum alloy. Based on the Taguchi experimental design （L9 orthogonal array） method, a series of experiments were performed by considering pulse-on time, pulse-off time, peak current and wire tension as input parameters. The surface roughness and cutting speed were considered responses. Based on the signal-to-noise （S/N） ratio, the influence of the input parameters on the responses was determined. The optimal machining parameters setting for the maximum cutting speed and minimum surface roughness were found using Taguchi methodology. Then, additive model was employed for prediction of all （34） possible machining combinations. Finally, a handy technology table has been reported using Pareto optimality approach.

Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision impairment.Symptoms of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked eye.In this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses.The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy.The fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent layers.The feature loss factor increases the label value to identify the patterns with the kernel-based matching.The performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and SVM.Experimental results show that the proposed CNN performs better.

Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.

Deep Residual Network Based on Image Priors for Single Image Super Resolution in FFA Images

Diabetic retinopathy,aged macular degeneration,glaucoma etc.are widely prevalent ocular pathologies which are irreversible at advanced stages.Machine learning based automated detection of these pathologies facilitate timely clinical interventions,preventing adverse outcomes.Ophthalmologists screen these pathologies with fundus Fluorescein Angiography Images(FFA)which capture retinal components featuring diverse morphologies such as retinal vasculature,macula,optical disk etc.However,these images have low resolutions,hindering the accurate detection of ocular disorders.Construction of high resolution images from these images,by super resolution approaches expedites the diagnosis of pathologies with better accuracy.This paper presents a deep learning network for Single Image Super Resolution(SISR)of fundus fluorescein angiography images,modeled on residual learning,gridded interpolation and Swish activation functions.The image prior for this network is constructed by gridded interpolation which provides better image fidelity compared to other priors.Evaluation of the performance of this network and comparative analysis with benchmark architectures,on a standard dataset shows that the proposed network is superior with respect to performance metrics and computational time.

Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification

With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus disease 2019,and other diseases.In particular,our algorithm focuses on integrated datasets as compared with other existing works.In this study,parallel-based SVM(P-SVM)andmulticlass-basedmultiple submodels(MMSM-SVM)were used to analyze the optimal classification of lung diseases.This analysis aimed to find the optimal classification of lung diseases with id and stages,such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses,respectively.For nonlinear classification,kernel clustering-based SVM embedded with multiple submodels was developed.Both algorithms were developed using Apache spark environment,and data for the analysis were retrieved from microscope lab,UCI,Kaggle,and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 GB.Performance measures were conducted using a 5 GB dataset with five nodes.Dataset size was finally increased,and task analysis and CPU utilization were measured.

Energy Based Random Repeat Trust Computation in Delay Tolerant Network

As the use of mobile devices continues to rise,trust administration will significantly improve security in routing the guaranteed quality of service(QoS)supply in Mobile Ad Hoc Networks(MANET)due to the mobility of the nodes.There is no continuance of network communication between nodes in a delay-tolerant network(DTN).DTN is designed to complete recurring connections between nodes.This approach proposes a dynamic source routing protocol(DSR)based on a feed-forward neural network(FFNN)and energybased random repetition trust calculation in DTN.If another node is looking for a node that swerved off of its path in this situation,routing will fail since it won’t recognize it.However,in the suggested strategy,nodes do not stray from their pathways for routing.It is only likely that the message will reach the destination node if the nodes encounter their destination or an appropriate transitional node on their default mobility route,based on their pattern of mobility.The EBRRTC-DTN algorithm(Energy based random repeat trust computation)is based on the time that has passed since nodes last encountered the destination node.Compared to other existing techniques,simulation results show that this process makes the best decision and expertly determines the best and most appropriate route to send messages to the destination node,which improves routing performance,increases the number of delivered messages,and decreases delivery delay.Therefore,the suggested method is better at providing better QoS(Quality of Service)and increasing network lifetime,tolerating network system latency.

Classification of Glaucoma in Retinal Images Using EfficientnetB4 Deep Learning Model

Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma.Glaucoma is an incurable and unavoidable eye disease that damages the vision ofoptic nerves and quality of life. Classification of Glaucoma has been an active fieldof research for the past ten years. Several approaches for Glaucoma classification areestablished, beginning with conventional segmentation methods and feature-extraction to deep-learning techniques such as Convolution Neural Networks (CNN). Incontrast, CNN classifies the input images directly using tuned parameters of convolution and pooling layers by extracting features. But, the volume of training datasetsdetermines the performance of the CNN;the model trained with small datasets,overfit issues arise. CNN has therefore developed with transfer learning. The primary aim of this study is to explore the potential of EfficientNet with transfer learning for the classification of Glaucoma. The performance of the current workcompares with other models, namely VGG16, InceptionV3, and Xception usingpublic datasets such as RIM-ONEV2 & V3, ORIGA, DRISHTI-GS1, HRF, andACRIMA. The dataset has split into training, validation, and testing with the ratioof 70:15:15. The assessment of the test dataset shows that the pre-trained EfficientNetB4 has achieved the highest performance value compared to other models listedabove. The proposed method achieved 99.38% accuracy and also better results forother metrics, such as sensitivity, specificity, precision, F1_score, Kappa score, andArea Under Curve (AUC) compared to other models.

Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

This research investigates the dielectric performance of Natural Ester(NE)using the Partial Differential Equation(PDE)tool and analyzes dielectric performance using fuzzy logic.NE nowadays is found to replace Mineral Oil(MO)due to its extensive dielectric properties.Here,the heat-tolerant Natural Esters Olive oil(NE1),Sunflower oil(NE2),and Ricebran oil(NE3)are subjected to High Voltage AC(HVAC)under different electrodes configurations.The break-down voltage and leakage current of NE1,NE2,and NE3 under Point-Point(P-P),Sphere-Sphere(S-S),Plane-Plane(PL-PL),and Rod-Rod(R-R)are measured,and survival probability is presented.The electricfield distribution is analyzed using the Partial Differential Equation(PDE)tool.NE shows better HVAC with stand capacity under all the electrodes configuration,especially in the S-S shape geometry.The exponential function is developed for the oils under different elec-trode geometry;NE shows a higher survival probability.Likewise,the most influ-ential dielectric properties such as breakdown voltage,kinematic viscosity,and water content are used to develop a Mamdani-based control system model that combines two variables to produce the surface model.The surface model indi-cates that the NE subjected for investigation is less susceptible to moisture effect and higher kinematic viscosity.Based on the surface models of PDE and fuzzy,it is concluded that NE possesses a high survival rate since its breakdown voltage does not react to changes in water content.Hence the application of NE in the transformer application is highly safe and possesses extended life.

Secured Health Data Transmission Using Lagrange Interpolation and Artificial Neural Network

In recent decades,the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services.The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations.This necessitates the medical field to be redesigned by cloud technology to preserve information about patient’s critical diseases,electrocardiogram(ECG)reports,and payment details.The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)resources over the private and public cloud.The stored data are categorized as significant and non-significant by Artificial Neural Networks(ANN).The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer.Upon receiving the request from a secondary user,the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user.Once the key matches the key in the hidden layer,the preserved information will be shared between the users.Due to the enhanced privacy preserving key generation,the proposed work prevents the tracking of keys by malicious users.The outcomes reveal that the introduced work provides improved success rate with reduced computational time.

Intelligent Deployment Model for Target Coverage in Wireless Sensor Network

Target coverage and continuous connection are the major recital factors for Wireless Sensor Network(WSN).Several previous research works studied various algorithms for target coverage difficulties;however they lacked to focus on improving the network’s life time in terms of energy.This research work mainly focuses on target coverage and area coverage problem in a heterogeneous WSN with increased network lifetime.The dynamic behavior of the target nodes is unpredictable,because the target nodes may move at any time in any direction of the network.Thus,target coverage becomes a major problem in WSN and its applications.To solve the issue,this research work is motivated to design and develop an intelligent model named Distributed Flexible Wheel Chain(DFWC)model for efficient target coverage and area coverage in WSN applications.More number of target nodes is covered by minimum number of sensor nodes that can improve energy efficiency.To be specific,DFWC motivated at obtaining lesser connected target coverage,where every target is available in the monitoring area is covered by a smaller number of sensor nodes.The simulation results show that the proposed DFWC model outperforms the existing models with improved performance.

Deep Learning-Based Hookworm Detection in Wireless Capsule Endoscopic Image Using AdaBoost Classifier

Hookworm is an illness caused by an internal sponger called a roundworm.Inferable from deprived cleanliness in the developing nations,hookworm infection is a primary source of concern for both motherly and baby grimness.The current framework for hookworm detection is composed of hybrid convolutional neural networks;explicitly an edge extraction framework alongside a hookworm classification framework is developed.To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework,pooling layers are proposed.The hookworms display different profiles,widths,and bend directions.These challenges make it difficult for customized hookworm detection.In the proposed method,a contourlet change was used with the development of the Hookworm detection.In this study,standard deviation,skewness,entropy,mean,and vitality were used for separating the highlights of the each form.These estimations were found to be accurate.AdaBoost classifier was utilized to characterize the hookworm pictures.In this paper,the exactness and the territory under bend examination in identifying the hookworm demonstrate its scientific relevance.

Image Captioning Using Detectors and Swarm Based Learning Approach for Word Embedding Vectors

IC(Image Captioning)is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements.However,in existing works,because of the complexity in images,neglecting major relation between the object in an image,poor quality image,labelling it remains a big problem for researchers.Hence,the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC.So in this research work the main contribution deals with the framework consists of three phases that is image understanding,textual understanding and decoding.Initially,the image understanding phase is initiated with image pre-pro-cessing to enhance image quality.Thereafter,object has been detected using IYV3MMDs(Improved YoloV3 Multishot Multibox Detectors)in order to relate the interrelation between the image and the object,and then it is followed by MBFOCNNs(Modified Bacterial Foraging Optimization in Convolution Neural Networks),which encodes and providesfinal feature vectors.Secondly,the tex-tual understanding phase is performed based on an image which is initiated with preprocessing of text where unwanted words,phrases,punctuations are removed in order to provide a healthy text.It is then followed by MGloVEs(Modified Glo-bal Vectors for Word Representation),which provides a word embedding of fea-tures with the highest priority towards the object present in an image.Finally,the decoding phase has been performed,which decodes the image whether it may be a normal or complex scene image and provides an accurate text by its learning ability using MDAA(Modified Deliberate Adaptive Attention).The experimental outcome of this work shows better accuracy of shows 96.24%when compared to existing and similar methods while generating captions for images.

Theoretical Analysis of Simulating the Locked-In Stress in Rock Pore by Thermal Expansion of Hard Rubber

Rocks are composed of mineral particles and micropores between mineral which has a great influence on the mechanical properties of rocks. In this paper, based on the theory of locked-in stress developed by academician Chen Zongji, the locked-in stress problem in underground rock is simulated by the thermal expansion of hard rubber particles. The pore inclusion in rock is assumed to be uniformly distributed spherical cavities. Using the thermal stress theory, the stress of rock with a spherical pore inclusion is equivalent to the thermal stress generated by the spherical hard rubber inclusion. The elastic theory formula of the temperature increment and the equivalent pore pressure of the spherical hard rubber inclusion is derived. The numerical simulation of the rock mass model with a spherical hard rubber inclusion is carried out and compared to the theoretical calculation results; the results show that they are consistent. The method proposed by this paper for simulating stress distribution in rock by thermal stress is reasonable and feasible; it has a positive meaning for further study of mechanic phenomenon of rock with micropore inclusion.

Implementation of an Efficient Light Weight Security Algorithm for Energy-Constrained Wireless Sensor Nodes

In-network data aggregation is severely affected due to information in transmits attack. This is an important problem since wireless sensor networks (WSN) are highly vulnerable to node compromises due to this attack. As a result, large error in the aggregate computed at the base station due to false sub aggregate values contributed by compromised nodes. When falsified event messages forwarded through intermediate nodes lead to wastage of their limited energy too. Since wireless sensor nodes are battery operated, it has low computational power and energy. In view of this, the algorithms designed for wireless sensor nodes should be such that, they extend the lifetime, use less computation and enhance security so as to enhance the network life time. This article presents Vernam Cipher cryptographic technique based data compression algorithm using huff man source coding scheme in order to enhance security and lifetime of the energy constrained wireless sensor nodes. In addition, this scheme is evaluated by using different processor based sensor node implementations and the results are compared against to other existing schemes. In particular, we present a secure light weight algorithm for the wireless sensor nodes which are consuming less energy for its operation. Using this, the entropy improvement is achieved to a greater extend.

Hybrid Fuzzy Controller Based Frequency Regulation in Restructured Power System

This paper discusses the implementation of Load Frequency Control (LFC) in restructured power system using Hybrid Fuzzy controller. The formulation of LFC in open energy market is much more challenging;hence it needs an intelligent controller to adapt the changes imposed by the dynamics of restructured bilateral contracts. Fuzzy Logic Control deals well with uncertainty and indistinctness while Particle Swarm Optimization (PSO) is a well-known optimization tool. Abovementioned techniques are combined and called as Hybrid Fuzzy to improve the dynamic performance of the system. Frequency control of restructured system has been achieved by automatic Membership Function (MF) tuned fuzzy logic controller. The parameters defining membership function has been tuned and updated from time to time using Particle Swarm Optimization (PSO). The robustness of the proposed hybrid fuzzy controller has been compared with conventional fuzzy logic controller using performance measures like overshoot and settling time following a step load perturbation. The motivation for using membership function tuning using PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes is tested on a two-area restructured power system.