Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst themselves.There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by WSNs.There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data.Towards the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efficient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artificial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy utilization.There is avoidance of the local optima problem at the time of solution space search in this proposed technique.Experimentations have been conducted on a large WSN network that has issues with mobility of nodes.

Mobility Aware Zone-Based Routing in Vehicle Ad hoc Networks Using Hybrid Metaheuristic Algorithm

Vehicle Ad hoc Networks(VANETs)have high mobility and a rando-mized connection structure,resulting in extremely dynamic behavior.Several challenges,such as frequent connection failures,sustainability,multi-hop data transfer,and data loss,affect the effectiveness of Transmission Control Protocols(TCP)on such wireless ad hoc networks.To avoid the problem,in this paper,mobility-aware zone-based routing in VANET is proposed.To achieve this con-cept,in this paper hybrid optimization algorithm is presented.The hybrid algo-rithm is a combination of Ant colony optimization(ACO)and artificial bee colony optimization(ABC).The proposed hybrid algorithm is designed for the routing process which is transmitting the information from one place to another.The optimal routing process is used to avoid traffic and link failure.Thefitness function is designed based on Link stability and Residual energy.The validation of the proposed algorithm takes solution encoding,fitness calculation,and updat-ing functions.To perform simulation experiments,NS2 simulator software is used.The performance of the proposed approach is analyzed based on different metrics namely,delivery ratio,delay time,throughput,and overhead.The effec-tiveness of the proposed method compared with different algorithms.Compared to other existing VANET algorithms,the hybrid algorithm has proven to be very efficient in terms of packet delivery ratio and delay.

基于GA-BPANN的钻井机械钻速预测模型

在钻井过程中,优化钻井技术可以降低钻井成本和减少施工事故,而钻速预测是优化钻井的基础。为了提高机械钻速(ROP)预测模型的准确性,开发了一种遗传算法优化的BP人工神经网络(GA-BPANN)的ROP预测模型。首先,采用最大信息系数(MIC)方法进行特征选择降低模型冗余,并将数据进行标准化处理。其次,利用遗传算法(GA)对BPANN的初始权重和偏置进行优化,建立ROP预测新模型。最后,将新模型与BPANN、支持向量回归(SVR)模型进行对比分析。研究结果表明,GA-BPANN模型具有较高的预测精度,同时为钻井过程中提高ROP提供科学依据。

Hybrid Color Texture Features Classification Through ANN for Melanoma

Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.

A Novel Approach to Design Distribution Preserving Framework for Big Data

In several fields like financial dealing,industry,business,medicine,et cetera,Big Data(BD)has been utilized extensively,which is nothing but a collection of a huge amount of data.However,it is highly complicated along with time-consuming to process a massive amount of data.Thus,to design the Distribution Preserving Framework for BD,a novel methodology has been proposed utilizing Manhattan Distance(MD)-centered Partition Around Medoid(MD–PAM)along with Conjugate Gradient Artificial Neural Network(CG-ANN),which undergoes various steps to reduce the complications of BD.Firstly,the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function;subsequently,the missing data are handled by substituting or by ignoring the missed values.After that,the data are transmuted into a normalized form.Next,to enhance the classification performance,the data’s dimensionalities are minimized by employing Gaussian Kernel(GK)-Fisher Discriminant Analysis(GK-FDA).Afterwards,the processed data is submitted to the partitioning phase after transmuting it into a structured format.In the partition phase,by utilizing the MD-PAM,the data are partitioned along with grouped into a cluster.Lastly,by employing CG-ANN,the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user.To analogize the outcomes of the CG-ANN with the prevailing methodologies,the NSL-KDD openly accessible datasets are utilized.The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN.The proposed work outperforms well in terms of accuracy,sensitivity and specificity than the existing systems.

Improving Power Quality by DSTATCOM Based DQ Theory with Soft Computing Techniques

The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic current,undesired voltage regulation,and extreme reactive power demand.To overcome this issue,Distributed STATicCOMpensator(DSTATCOM)is implemented.DSTATCOM is a shunt-connected Voltage Source Converter(VSC)that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor.DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation.A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply.The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink.The DQ theory is used to produce the reference signal for the Pulse Width Modulation(PWM)generator.In addition,a traditional Propor-tional Integral Derivative(PID)controller is designed and compared with pro-posed soft computing approaches such as Fuzzy–PID and Artificial Neural Network(ANN-PID)and compared accurate reference current determination for Direct Current(DC)bus through DC link.An Analytical explores the pro-posed control strategies given to establish superior outcomes.Finally,total harmo-nic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.

Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

水下声速场构建方法综述

实时、精确地构建区域声速场是建设水下综合定位、导航、授时与通信体系的重要组成部分。本文梳理并分析了水下声速场构建领域的研究现状,总结了声速剖面直接测量方法与反演方法。声速反演法相比于直接测量法具有更高的便捷性和可接受的精度性能。然而,声速反演方法依赖于声呐观测数据,因此难以适用于无水下观测系统覆盖的地区,并且无法对未来时刻的声速分布进行预测。如何在无声场观测数据情况下,综合利用历史先验信息进行智能化、高精度地全海深声速场构建,弹性化为水下用户提供不同精度、实时性需求的声速分布估计服务,是未来声速场构建研究主流趋势。

基于AI技术的花境设计应用分析

人工智能(AI)在各个领域都展现出巨大的潜能,如何利用AI技术辅助风景园林领域正处于初步摸索与完善阶段。通过计算机视觉和机器学习算法,训练一种自动化的花境设计模型,以探索AI技术在花境设计领域中的应用。以河南省首届花境大赛——北龙湖湿地公园花境展为案例素材库,收集了大量的花境作品照片作为模型训练数据,利用计算机视觉算法对花境实景图像进行分析和特征提取;使用机器学习算法训练模型,根据语义分割图和输入的关键词生成新的花境设计方案。机器学习模型可以为不同类型的花境场景生成高质量和多样化的设计方案,并且可以识别和提取一些花卉植物特征,如植物种类、尺度、空间关系等。此外,对AI生成的花境配置方案效果进行了评价,验证AI技术在花境设计应用中的可行性及适用性。旨在为AI技术在植物景观设计领域的理论研究及设计实践应用提供创新研究视角及思路。

基于人工神经网络的大型电厂锅炉飞灰含碳量建模

飞灰含碳量是影响锅炉热效率的一个重要因素,但飞灰含碳量受煤种、锅炉设计结构、操作参数等多种因素影响,关系复杂。煤种变化往往导致燃烧工况偏离试验调整获得的最优值。在对某台300MW四角切圆燃煤电厂锅炉飞灰含碳量特性进行多工况热态测试的基础上,应用人工神经网络的非线性动力学特性及自学习特性,建立了大 型四角切圆燃烧锅炉飞灰含碳量特性的神经网络模型,并对此模型进行了校验。

基于小波和Radon变换的桥梁裂缝检测

为提高桥梁裂缝检测效率,提出了一种新型的桥梁裂缝检测方法。针对桥梁表面图像具有噪声污点干扰进行预处理,先后用中值滤波和频域滤波有效的减弱了噪点干扰并加强了裂缝区域;针对裂缝处的纹理特点,用小波变换突出图像的纹理特征,计算小波高频段的高幅值系数占比,即高幅小波系数比(HAWCP),高频能量比(HFEP),Radon变换最大值和概率统计参数作为特征值,这些特征值的结合有很好的区分度和容错能力;将误差反向传播算法多层前向神经网络作为桥梁裂缝分类器,并且只用9次迭代既能完成训练,分类效率高。实验结果表明,提出的方法对桥梁裂缝的识别率高(超过95%),泛化能力强。

基于人工智能的图书订购策略分析

随着对图书馆评估关注度的上升,采用有效途径实现图书采购变得越来越重要,而传统图书订购过程多依赖于图书馆订购者"经验"式决定,本文利用人工智能方法,建立基于荐购图书信息、读者喜好和价格等多种综合信息下的图书订购决策模型,实现图书订购件的客观确立,避免个人因素的干扰。仿真结果显示该模型的有效性,并提出进一步改善模型方法。

频域合成房间频率响应的人工混响方法

给出了一种频域合成房间频率响应的方法用于卷积法人工混响,基于频域内房间频率响应的后期部分为高斯随机过程的假设,用自回归滑动平均模型为其自协方差函数和功率谱密度进行参数化描述,在对自回归滑动平均模型中的参数求解后,通过逆滤波得到了房间频率响应后期部分,与房间频率响应前期部分组合后经过傅里叶反变换得到完整的房间脉冲响应。仿真结果表明该方法的混响效果与镜像源法接近,明显优于反馈延迟网络法,但其计算复杂度比镜像源法低,便于实时应用。

基于深度学习的人体肋骨骨折智能检测技术

目的将人工智能中的深度学习技术应用到人体肋骨骨折识别,实现人体肋骨骨折智能检测,提高法医肋骨骨折诊断效率。方法采集3143例人体胸部X线数字影像(2602例用于训练,541例用于测试),标注肋骨骨折特征点,通过多层网络堆叠,分层、分级主动学习原始数据高度抽象的特征表述,并将此特征反馈至检测器进行骨折检测,输出骨折位置及相应置信度。结果基于深度学习的人体肋骨骨折检测准确率在90%以上。结论基于深度学习的人体肋骨骨折检测准确率较高,可用于辅助法医进行肋骨骨折识别诊断、检验鉴定等,本研究可为人体其他部位骨骼损伤智能检测提供参考。

基于核主成分分析和粒子群优化神经网络的充填体强度预测

以磷石膏、粉煤灰、磷渣等为主要原材料制备磷石膏胶结充填体,分析了主要原材料的理化特性,测定了磷石膏胶结充填体28d单轴抗压强度。通过核主成分分析对磷石膏胶结充填体单轴抗压强度影响因子进行非线性特征提取,基于获得的主成分构建粒子群优化BP人工神经网络(PSO-BP-ANN)预测模型。结果表明,核主成分分析能较好地实现充填体单轴抗压强度影响因子非线性特征的提取和降维的目的,同时,PSO-BP-ANN模型训练和预测值可决系数分别为0.995和0.991,均方根误差分别为3.660E-4和5.805E-2,平均相对误差分别为1.699%和3.602%,总体性能表现优于传统BP-ANN模型,对矿山充填体的配比设计具有指导意义。

穿墙雷达人体探测技术的新进展

介绍了国内外穿墙雷达人体探测装备研制的现状,重点放在穿墙雷达人体探测技术的最新研究进展上,包括超宽带技术、微多普勒理论、人工神经网络技术,以及时间反转镜技术等。

面向6G的去中心化的人工智能理论与技术

去中心化网络架构和原生AI能力是未来6G网络的两个重要发展趋势,现有的依赖于云端服务器或者终端的中心化的AI模式将难以支持6G网络下多终端、多节点的分布式智能协作需求,这种新型的去中心网络环境给AI在模型的训练、数据的采集和处理、模型的部署和推理等方面带来了新的挑战,针对6G网络去中心化计算环境中海量终端设备异构、计算能力差异大、通信网络条件动态变化等特点,分析去中心化的人工智能发展趋势及相关的理论与技术,并提出相关的前瞻性的技术挑战和研究方向。