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Hydrogeomorphological Study in Bamanghaty Subdivision of Mayurbhanj District, Odisha an Integrated Remote Sensing and GIS Approach

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International Journal of Geosciences 2017年第11期8卷 1361-1373页

作者： debabrata nandi Pramod Chandra Sahu Shreerup Goswami Department of Remote Sensing and GIS North Orissa University Odisha India Department of Geology MPC Autonomous Colleges Baripada Odisha India Department of Earth Science Sambalpur University Sambalpur Odisha India

Remote Sensing (RS) and geographic information system (GIS) are now very essential tools for efficient planning and management and handling a range of data simultaneously in a time- and cost-efficient manner for targeting of groundwater, which assists in measuring, monitoring, and conserving groundwater resources. Survey of India toposheets, LISS-III and CARTOSAT DEM satellite imageries are used to prepare various thematic layers viz., geology, slope, lineament, drainage, and geomorphology, and were transformed to raster data using feature to raster conversion tool in ArcGIS spatial analysis, then we reclassify each raster map using reclassify tools. By using weight overlay analysis, each weighted thematic layer is statistically computed to get the ground water potential zones. Then, five different groundwater potential zones were identified, namely “very good”, “good”, “moderate”, “poor”, and “very poor”. The villages under poor groundwater potential zone and the villages under very good groundwater potential zone are finding out. The above study has clearly demonstrated the capabilities of Remote Sensing and GIS in demarcation of the different groundwater potential zones in hard rock terrain.

关键词： Hydrogeomorphology Remote Sensing GIS

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 932-945页

作者： Bin Yang Yaguo Lei Xiang Li Naipeng Li Asoke K.nandi the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System the School of Mechanical EngineeringXi’an Jiaotong UniveristyXi’an 710049 the Hunan Provincial Key Labortory of Health Maintenance for Mechanical Equipment Hunan Univeristy of Science and TechnologyXiangtan 411201China the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniveristyXi’an 710049China the Department of Electronic and Electrical Engineering Brunel University LondonUxbridge UB83PHUK

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source ***,in engineering scenarios,achieving such high-quality label annotation is difficult and *** incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled *** overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three ***,a residual network with dual classifiers is to learn features from cross-domain ***,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source *** the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised ***,the adaptation trajectories are designed for sample distributions across *** ensures that the target domain samples are only adapted with the pure-labeled *** LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related *** results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

关键词： Deep transfer learning domain adaptation incorrect label annotation intelligent fault diagnosis rotating machines

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1317-1330页

作者： Jiaxin Ren Jingcheng Wen Zhibin Zhao Ruqiang Yan Xuefeng Chen Asoke K.nandi School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China Department of Electronic and Electrical Engineering Brunel University LondonKingston LaneUxbridgeUB83PHUK IEEE

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art ***,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models *** this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called *** SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural *** on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault ***,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability ***,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

关键词： Out-of-distribution detection traceability analysis trustworthy fault diagnosis uncertainty quantification.

Investigation on FBG based optical sensor for pressure and temperature measurement in civil application

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Optoelectronics Letters 2024年第9期20卷 531-536页

作者： Somesh nandi Chethana K T Srinivas Preeta Sharan Department of Artificial Intelligence and Machine Learning Rashtreeya Vidyalaya College of EngineeringBangalore560059India Department of Electronics and Communications Jyothy Institute of TechnologyBangalore 560082India Department of Electrical Communication Engineering Indian Institute of ScienceBangalore 560012India Department of Electronics and Communications the Oxford College of EngineeringBangaloreIndia

Optical fiber Bragg grating(FBG)sensors have advanced significantly in the last several *** use of innovative FBG in temperature and pressure measurement is examined in this *** benefits of FBGs,such as their compact size,low weight,resilience to corrosion,immunity to electromagnetic interference,distributed sensing,and remote monitoring,have brought attention to the growing research in this field of structural health monitoring of civil infrastructures.

关键词： immunity civil application

Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model

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Journal of Communications and Information Networks 2024年第3期9卷 219-232页

作者： Wei Wang Xinlin Wang Yutong Liu Yulin Ren Maozhen Li Asoke K.nandi School of Information and Communication Engineering North University of ChinaTaiyuan 030051China Department of Electronic and Computer Engineering Brunel University LondonUxbridge UB83PHUK

To address the issues of unstable received signal strength indicator(RSSI)and low indoor positioning accuracy caused by walls and obstacles,the propagation conditions of the wireless communication system are categorized into two distinct environments:line-of-sight(LOS)and non-line-of-sight(NLOS).In the LOS environment,the traditional logarithmic path loss model is *** the NLOS environment,the impact of walls on signal transmission is considered,leading to the development of a multi-wall path loss model based on the T-RL method,with improvements made to the key parameter,the Fresnel coefficient *** breakpoint value d=2.3 m in the partitioned model is determined,and the positional coordinates of the unknown nodes are calculated using the trilateration *** results indicate that the T-RL based multi-wall model improves localization accuracy by 47%in NLOS environments compared to the traditional logarithmic path loss *** average localization error using the T-RL partitioned path loss model is 0.7021 m,representing a 55.9%improvement over the logarithmic path loss model and a 16.8%enhancement over the T-RL attenuation multi-wall model,thereby providing better environmental adaptability.

关键词： WSN indoor positioning loss model reflection transmission RSSI

基于地理探测器的元阳梯田地质灾害影响因素及滑坡成因分析

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中国地质调查 2024年第3期11卷 92-100页

作者：苏航卢楠迪伏小双李奇云南省有色地质局三O八队云南昆明650200

元阳梯田受其特殊的地理位置、地表植被和农耕文化影响,表生地质灾害频发,对其生态价值和文化价值造成极大损害,梯田区域的地质灾害防治显得尤为关键。在详细野外调查基础上,结合元阳梯田区15 m分辨率遥感影像数据,利用空间叠加分析和... 详细信息

元阳梯田受其特殊的地理位置、地表植被和农耕文化影响,表生地质灾害频发,对其生态价值和文化价值造成极大损害,梯田区域的地质灾害防治显得尤为关键。在详细野外调查基础上,结合元阳梯田区15 m分辨率遥感影像数据,利用空间叠加分析和地理探测器模型对研究区地质灾害影响因素开展了详细研究。结果表明:元阳梯田区地质灾害(隐患)点中滑坡灾害(隐患)点数量占比为93.39%,小型规模灾害(隐患)点数量占比为87.69%,总体表现为以滑坡性质的小型灾害为主;地质灾害分布的影响因素主要为地形坡度,其次为地质构造,受地貌单元影响最弱;滑坡灾害形成的影响因素具有复合性特点,其中强烈的河流侵蚀作用、地壳抬升作用和活跃的断裂构造活动是其成灾的主要地质环境背景,丰富的降雨量和人类工程活动是其成灾的主要触发因素。通过研究可进一步认识元阳梯田地质灾害影响因素及形成条件,可更好地助力于地方防灾减灾体系提升,也可为区域地质灾害风险分析提供一定的参考。

关键词：元阳梯田地质灾害地理探测器影响因素成因机制

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Ra数对停输管道原油相变特征的影响研究

工程热物理学报 2024年第4期45卷 1055-1061页

作者：刘晓燕周正李从范玉泽张楠迪徐颖李晓庆东北石油大学机械科学与工程学院大庆163318 中国石油塔里木油田分公司库尔勒841000 常熟理工学院常熟215506

本文探究了Ra数对停输原油相变特征的影响,为管道停输时间控制提供参考依据。建立了基于焓法的原油相变传热数学模型,将固液模糊区处理为两相流区,利用格子Boltzmann方法求解原油相变传热控制方程组。将原油相变传热过程分为三个不同阶... 详细信息

本文探究了Ra数对停输原油相变特征的影响,为管道停输时间控制提供参考依据。建立了基于焓法的原油相变传热数学模型,将固液模糊区处理为两相流区,利用格子Boltzmann方法求解原油相变传热控制方程组。将原油相变传热过程分为三个不同阶段。发现Ra数对流场的影响会随时间不断减弱,对于凝油层的影响不断增强导致液相率场产生明显的形态变化。在换热第一阶段提高Ra数会使原油下沉速度的增长幅度远大于原油上升速度的增长幅度。

关键词：相变流动原油数值研究格子Boltzmann方法 Ra数

Discrete Event System Framework for Fault Diagnosis with Measurement Inconsistency:Case Study of Rogue DHCP Attack

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 789-806页

作者： Mayank Agarwal Santosh Biswas Sukumar nandi IEEE the Department of Computer Science and Engineering Indian Institute of Technology Guwahati

Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system *** to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.

关键词： Fault diagnosis instrasion detection system (IDS) measurement inconsistent discrete event system (DES) rogue dynamic host configuration protocol (DHCP) server

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 73-93页

作者： Hosameldin O.A.Ahmed Asoke K nandi Department of Electronic and Electrical Engineering Brunel University LondonUxbridge UB83PHUK School of Mechanical Engineering Xi’an Jiaotong UniversityXi’anChina

Roller bearing failure is one of the most common faults in rotating *** techniques for bearing fault diagnosis based on faults feature extraction have been *** feature extraction from fault signals requires expert prior information and human ***,deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input *** its robust performance in image recognition,the convolutional neural network(CNN)architecture has been widely used to learn automatically discriminative features from vibration images and classify health *** paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault *** first stage in the proposed method is to generate the RGB vibration images(RGBVIs)from the input vibration *** begin this process,first,the 1-D vibration signals were converted to 2-D grayscale vibration *** the conversion was completed,the regions of interest(ROI)were found in the converted 2-D grayscale vibration ***,to produce vibration images with more discriminative characteristics,an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images(RGBVIs)with sets of colours and texture *** the second stage,with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health *** cases of fault classification of rolling element bearings are used to validate the proposed *** results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high ***,several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accura

关键词： Bearing fault diagnosis Image representation of vibrations Deep learning Convolutional neural networks