Artificial Intelligence and Multimedia Data Engineering: Volume 1 -  - E-Book

Artificial Intelligence and Multimedia Data Engineering: Volume 1 E-Book

0,0
35,66 €

-100%
Sammeln Sie Punkte in unserem Gutscheinprogramm und kaufen Sie E-Books und Hörbücher mit bis zu 100% Rabatt.
Mehr erfahren.
Beschreibung

This book explains different applications of supervised and unsupervised data engineering for working with multimedia objects. Throughout this book, the contributors highlight the use of Artificial Intelligence-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, automation in vehicle manufacturing, data science and automation in electronics industries.

The book presents seven chapters which present use-cases for AI engineering that can be applied in many fields. The book concludes with a final chapter that summarizes emerging AI trends in intelligent and interactive multimedia systems.

Key features:
- A concise yet diverse range of AI applications for multimedia data engineering
- Covers both supervised and unsupervised machine learning techniques
- Summarizes emerging AI trends in data engineering
- Simple structured chapters for quick reference and easy understanding
- References for advanced readers

This book is a primary reference for data science and engineering students, researchers and academicians who need a quick and practical understanding of AI supplications in multimedia analysis for undertaking or designing courses. It also serves as a secondary reference for IT and AI engineers and enthusiasts who want to grasp advanced applications of the basic machine learning techniques in everyday applications

Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:

EPUB

Seitenzahl: 190

Veröffentlichungsjahr: 2001

Bewertungen
0,0
0
0
0
0
0
Mehr Informationen
Mehr Informationen
Legimi prüft nicht, ob Rezensionen von Nutzern stammen, die den betreffenden Titel tatsächlich gekauft oder gelesen/gehört haben. Wir entfernen aber gefälschte Rezensionen.



Table of Contents
BENTHAM SCIENCE PUBLISHERS LTD.
End User License Agreement (for non-institutional, personal use)
Usage Rules:
Disclaimer:
Limitation of Liability:
General:
PREFACE
List of Contributors
A Quantum-assisted Diagnostics Method for Intelligent Manufacturing
Abstract
INTRODUCTION
Methodology
Feature Selection
Results
CONCLUSION
REFERENCES
Evaluation of Bio-inspired Computational Methods for Measuring Cognitive Workload
Abstract
INTRODUCTION
Cognitive Workload
Definition and Applications
Task Models
Cognitive Task Model
Operational Task Model
Machine Learning
Traditional Machine Learning Techniques to Detect Cognitive Workload
Datasets
EEGLearn
EEGMAT
Hybrid EEG-NIRS
WM-EEG
STEW
Data Preprocessing
Feature Extraction
Time Domain
Frequency Domain
Spatial Domain
Linear Domain
Nonlinear Dynamics
Functional Connectivity Features
Feature Selection
Filtering Methods
Wrapper Techniques
Embedded Techniques
Ensemble Feature Selection Techniques
Optimization Techniques
Classification
Deep Learning Models
Performance Evaluation
Conclusion
REFERENCES
Managing Libraries and Information Centres using Cloud Computing
Abstract
INTRODUCTION
Definition of cloud computing
Characteristics of cloud computing
Types of Cloud
Public Cloud
Private Cloud
Hybrid Cloud
Services provided by cloud computing
Platform as a Service
Infrastructure as a Service
Software as a Service
Types of Computing Techniques
Cluster Computing
Distributed Computing
Invasive Sensors
Grid Computing
Utility Computing
Latest Initiatives of Cloud Computing by Various Companies/Organizations
Amazon Web Services
Microsoft Azure
Google Apps
Need for cloud computing in Libraries
Cloud computing services and its applications in the library
Library Automation
Digital Library and Repository
Website Hosting
Searching Scholarly Content
Storage and Retrieval of Information
CLOUD COMPUTING PLATFORMS IN LIBRARY AND INFORMATION SCIENCE FIELD
DuraCloud by Duraspace
Webscale by OCLC
Ex-Libris Cloud
OSS Labs
Enhancing various services provided by the libraries and Information Centres using Cloud computing
E-Learning
File/Document Sharing
Interaction with the Users
Collection Development
Information Search and Discovery
Lending of E-books
Shared Catalogue/Union Catalogue/OPAC
Document Download and Delivery Service
Current Awareness Service and Information Literacy/Orientation
Role of Librarian in the Cloud Environment
Advantages of Cloud Computing in Library Services
Cloud OPAC
Less Cost Involved
Scalability
Accessibility
Higher Level Security
Portability
Reduced Risk Rate
Adjustable Storage
Conclusion
REFERENCES
Biometric Voting using IoT to Transfer Vote to Centralized System: A Bibliometric
Abstract
INTRODUCTION
Literature review
Review methodology
Results and Discussion
Publications by Year
Keyword Analysis
Geographical Analysis of Publications
Publications Analysis by Organization
Analysis of Citations
Analysis by Author
Analysis of Research Opportunities
Discussion
Conclusion
References
Face Recognition using Convolutional Neural Network Algorithms
Abstract
Introduction
Proposed Methodology
Viola Jones
Deep Reinforcement
Convolutional Neural Network (CNN)
Experimental Results
Conclusion
References
Multimedia Security in Audio Signal
Abstract
Introduction
Related Work
Proposed Methodology
FFT Decomposition of Audio Signal
QR-Cordic Decomposition
Watermark Embedding Technique
Experimental Results
Conclusion
References
Recent Advancements and Impact of Multimedia in Education
Abstract
INTRODUCTION
MULTIMEDIA
MULTIMEDIA LEARNING ENVIRONMENT
E-LEARNING AND EDUCATIONAL TECHNOLOGY
Innovative Teaching and Learning Methods
Role of ICT
Blockchain Technology Impact
Importance of Big Data
Advent of Artificial Intelligence(AI)
Informatics for Learning
STEAM
Use of Social Media
RECENT ADVANCEMENTS AND BENEFITS OF MULTIMEDIA LEARNING
CONCLUSION
REFERENCES
Emerging AI Trends in Intelligent and Interactive Multimedia Systems
Abstract
INTRODUCTION
Role of DL, ML in intelligent and interactive multimedia systems
Specific applications of AI
Enhance the naturalness, scalability, and customization of intelligent and interactive multimedia systems
Future Scopes
Visual Turing test
Recent Developments made in the Turing Test for Multimedia
An Explanation of Justification in Multimedia Formats
Automated Forms of Both Machine and Meta-Learning
Digital Retinas
The First Part of the Multimedia Turing Test
Meta-Learning and Automatic Machine Learning
CONCLUSION
REFERENCES
Artificial Intelligence and Multimedia Data Engineering
Volume 1
Edited by
Suman Kumar Swarnkar
Shri Shankaracharya Institute of Professional
Management and Technology
Raipur, Chhattisgarh, India
Sapna Singh Kshatri
Shri Shankaracharya Institute of Professional
Management and Technology
Raipur, Chhattisgarh, India
Virendra Kumar Swarnkar
Bharti University
Durg Chhattisgarh, India
&
Tien Anh TranVietnam Maritime University
Haiphong, Vietnam

BENTHAM SCIENCE PUBLISHERS LTD.

End User License Agreement (for non-institutional, personal use)

This is an agreement between you and Bentham Science Publishers Ltd. Please read this License Agreement carefully before using the book/echapter/ejournal (“Work”). Your use of the Work constitutes your agreement to the terms and conditions set forth in this License Agreement. If you do not agree to these terms and conditions then you should not use the Work.

Bentham Science Publishers agrees to grant you a non-exclusive, non-transferable limited license to use the Work subject to and in accordance with the following terms and conditions. This License Agreement is for non-library, personal use only. For a library / institutional / multi user license in respect of the Work, please contact: [email protected].

Usage Rules:

All rights reserved: The Work is the subject of copyright and Bentham Science Publishers either owns the Work (and the copyright in it) or is licensed to distribute the Work. You shall not copy, reproduce, modify, remove, delete, augment, add to, publish, transmit, sell, resell, create derivative works from, or in any way exploit the Work or make the Work available for others to do any of the same, in any form or by any means, in whole or in part, in each case without the prior written permission of Bentham Science Publishers, unless stated otherwise in this License Agreement.You may download a copy of the Work on one occasion to one personal computer (including tablet, laptop, desktop, or other such devices). You may make one back-up copy of the Work to avoid losing it.The unauthorised use or distribution of copyrighted or other proprietary content is illegal and could subject you to liability for substantial money damages. You will be liable for any damage resulting from your misuse of the Work or any violation of this License Agreement, including any infringement by you of copyrights or proprietary rights.

Disclaimer:

Bentham Science Publishers does not guarantee that the information in the Work is error-free, or warrant that it will meet your requirements or that access to the Work will be uninterrupted or error-free. The Work is provided "as is" without warranty of any kind, either express or implied or statutory, including, without limitation, implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the results and performance of the Work is assumed by you. No responsibility is assumed by Bentham Science Publishers, its staff, editors and/or authors for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products instruction, advertisements or ideas contained in the Work.

Limitation of Liability:

In no event will Bentham Science Publishers, its staff, editors and/or authors, be liable for any damages, including, without limitation, special, incidental and/or consequential damages and/or damages for lost data and/or profits arising out of (whether directly or indirectly) the use or inability to use the Work. The entire liability of Bentham Science Publishers shall be limited to the amount actually paid by you for the Work.

General:

Any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims) will be governed by and construed in accordance with the laws of Singapore. Each party agrees that the courts of the state of Singapore shall have exclusive jurisdiction to settle any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims).Your rights under this License Agreement will automatically terminate without notice and without the need for a court order if at any point you breach any terms of this License Agreement. In no event will any delay or failure by Bentham Science Publishers in enforcing your compliance with this License Agreement constitute a waiver of any of its rights.You acknowledge that you have read this License Agreement, and agree to be bound by its terms and conditions. To the extent that any other terms and conditions presented on any website of Bentham Science Publishers conflict with, or are inconsistent with, the terms and conditions set out in this License Agreement, you acknowledge that the terms and conditions set out in this License Agreement shall prevail.

Bentham Science Publishers Pte. Ltd. 80 Robinson Road #02-00 Singapore 068898 Singapore Email: [email protected]

PREFACE

Welcome to "Artificial Intelligence and Multimedia Data Engineering Vol. 1". In this book, we embark on a captivating journey through the cutting-edge realms of artificial intelligence (AI) and multimedia data engineering, exploring the remarkable synergies that exist between these two rapidly evolving fields. This fusion of AI and multimedia data engineering has opened up unprecedented opportunities for innovation and has profoundly impacted various industries, making it essential for researchers, practitioners, and enthusiasts alike to stay at the forefront of this dynamic landscape.

Advancements in AI, coupled with the explosive growth of multimedia data, have revolutionized the way we interact with technology and perceive the world around us. From computer vision and natural language processing to deep learning and intelligent systems, AI has become an indispensable part of our lives, shaping our experiences in ways we could have only imagined a few decades ago. Furthermore, multimedia data, including images, videos, audio, and other sensor-generated content, has become an integral part of our digital existence, leading to the creation of a vast ocean of information that needs to be efficiently processed and harnessed.

The primary aim of this book is to present a comprehensive overview of the interdisciplinary domain that intertwines AI and multimedia data engineering. Our endeavor is to provide a well-rounded understanding of the fundamental concepts, techniques, and applications that form the bedrock of this exciting field. Whether you are a seasoned professional seeking to expand your knowledge or a newcomer eager to explore the frontiers of AI and multimedia data engineering, this book caters to a wide audience with diverse interests and backgrounds.

Suman Kumar Swarnkar Shri Shankaracharya Institute of Professional Management and Technology Raipur, Chhattisgarh, IndiaSapna Singh Kshatri Shri Shankaracharya Institute of Professional Management and Technology Raipur, Chhattisgarh, IndiaVirendra Kumar Swarnkar Bharti University Durg Chhattisgarh, India &Tien Anh Tran Vietnam Maritime University

List of Contributors

Ankit KumarDepartment of Information Technology, Babu Banarasi Das Institute of Technology, Management, Lucknow, IndiaAnil Kumar SinghDepartment of Information Technology, Babu Banarasi Das Institute of Technology, Management, Lucknow, IndiaC.A. HarikrishnanDepartment of Computer Science and Engineering, Trivandrum, Kerala, IndiaDarpan AnandPadampat Singhania University, Udaipur, Udaipur, IndiaDeepak AsraniDepartment of Computer Science and Engineering, BN College of Engineering and Technology, Lucknow, IndiaEram FatimaDepartment of Information Technology, Babu Banarasi Das Institute of Technology, Management, Lucknow, IndiaGausiya YasmeenDepartment of Computer Application, Lucknow, IndiaIsaac Atta Senior AmpofoKwame Nkrumah University of Science and Technology, Kumasi, GhanaJayashree PadmanabhanAnna University, Chennai, IndiaMohd FaisalDepartment of Computer Application, Lucknow, IndiaP. DevisivasankariCMR Institute of Technology, Bengaluru, IndiaR.K. Kapila VaniDepartment of Computer Science and Engineering, Sri Venkateswara College of Engineering, , Valarpuram, Tamil Nadu, IndiaR. VijayakumarCMR Institute of Technology, Bengaluru, IndiaRichard EssahDepartment of Computer Science and Engineering, Chandigarh University, Chandigarh, IndiaRitesh DiwakerDepartment of Computer Science and Engineering, BN College of Engineering and Technology , Lucknow, IndiaSaman UzmaCubeight Solutions Sydney, Sydney, AustraliaSurender SinghApex Institute of Technology, Department of Computer Science and Engineering, Chandigarh University, Chandigarh, IndiaSyed Adnan AfaqDepartment of Computer Application, Lucknow, IndiaVishal SharmaDepartment of Computer Science and Engineering, WILP Faculty, BITS Pilani, Jhunjhunu, Rajasthan, India

A Quantum-assisted Diagnostics Method for Intelligent Manufacturing

Vishal Sharma1,*
1 Department of Computer Science and Engineering, WILP Faculty, BITS Pilani, Jhunjhunu, Rajasthan, India

Abstract

Present manufacturing machines have few methods to investigate machine health. To minimize issues and enhance the correctness of machine decisions and automation, machine health conditions require to be investigated. Therefore, the evolution of a fresh investigating and diagnostics approach for additive manufacturing machines is needed for better productivity in Industry 4.0. In the current chapter, an intelligent technique for the condition monitoring of additive manufacturing (AM) is described, where an accelerometer fitted on the extruder assembly is used to receive vibration signals. The process errors with the printer were the worn-out timing belts driving the extruder assembly. Quantum-based Support Vector Machine was simulated to identify the 3D-printer status. The simulation outcomes presented here show that this approach has better correctness as compared to the previous Support Vector Machine techniques.

Keywords: 3D Printer, Additive Manufacturing, Industry 4.0, Support Vector Machine.
*Corresponding author Vishal Sharma: Department of Computer Science and Engineering, WILP Faculty, BITS Pilani, Jhunjhunu, Rajasthan, India; E-mail: [email protected]

INTRODUCTION

3D printer is one of the important fields of research under the Industry 4.0. This technique provides many benefits. Therefore, it is essential to confirm feasible and safety equipment functioning. If mechanical equipment fail, it can create many issues [1-3]. Several scientists have done a lot of innovation, and proposed many impactful fault diagnosis approaches [4-9]. The recent research work accomplished in the domain of quantum technologies [10-19] showed a significant improvement in terms of speed, accuracy, security, and parallel processing with minimum resources.

3D printing is a suitable term to detail the techniques of additive manufacturing. The term 3D printing covers many techniques [17]. 3D printing techniques have

the strength to make better science, technology, and engineering as well as to speed up manufacturing techniques. While the possible uses of 3D printing have been enhancing over time, a number of problems continue to stop its widespread acceptance [19]. The main difficulties in 3D printing are increased manufacturing time as compared to standard methods, dimensional correctness, non-linearity (many resolutions for X, Y and Z axes, wall thickness), material properties and system cost. All these are being highlighted by the machine manufacturers for improvement in the manufacturing steps [20].

Even though additive manufacturing has been present since the 1980s, it was not until recently that 3D printing was deployed in commercial manufacturing [19]. Hence, a diagnostics model could be framed for a 3D printer in case of unsuccessful timing belts. Acoustic emissions of 3D printers were also analysed [17, 19]. The printer was run at many nozzle temperatures. The experiments were carried out to analyse the condition monitoring of the nozzle through the deployment of a vibration sensor [21].

Here we try to construct a real-time diagnostic approach for condition monitoring of the machine, in order to find out and preclude breakdowns and process failures. The comprehensive target is to get better process reliability, dimensional correctness of the product, and automation of Additive manufacturing. Mainly, the concentration is on the health status of the belts driving the extruder. They are important parts of a 3D printer device which impact the overall feature and efficiency of the product. In the current chapter, an analysis of the reliability of PHM-based vibration signal analysis is described and based on the results from the signal, a diagnosis model for a 3D printer fault detection is constructed [22].

Methodology

With the demand of AM, its health status observation has become an important and untouched field of research [23]. The complete working procedure is shown in Fig. (1).

Feature Selection

It is required to know important parameters and remove repeated ones.

Fig. (1) likely illustrates the steps or stages involved in diagnosing issues or problems related to a 3D printer. It visually represents the diagnostic workflow, showing the sequential or parallel steps involved in identifying and resolving printer malfunctions or errors.

Fig. (1)) 3D Printer Diagnostic Process [2].

Results

In Fig. (2), 3D-Printer Test Rig likely refers to a figure depicting a test setup or apparatus specifically designed for testing and evaluating 3D printers.

Fig. (2)) 3D-Printer Test Rig [2].

Typically, a 3D-Printer Test Rig is a controlled environment that allows researchers or technicians to assess various aspects of 3D printers, such as their performance, accuracy, reliability, and functionality. The test rig is designed to simulate real-world conditions and scenarios to ensure consistent and standardized testing.

Fig. (2) illustrates the physical structure of the test rig, including its components and subsystems. It includes a 3D printer, sensors for measuring different parameters, data acquisition systems, control mechanisms, and other relevant equipment.

The purpose of a 3D-Printer Test Rig is to provide a controlled and reproducible environment for evaluating the performance and capabilities of 3D printers. It allows researchers, manufacturers, or quality control personnel to conduct systematic tests, identify potential issues or limitations, and make improvements to enhance the overall quality and efficiency of 3D printing processes [23].

Table 1 provides the values of different condition indicators and their corresponding scores obtained through two evaluation techniques: Univariate Selection and Feature Importance. The indicators listed in the table include Kurtosis, RMS (Root Mean Square), Skewness, Median, Standard Deviation, and Entropy. Each indicator is associated with a numerical value, representing its strength or importance in the given context. The scores provided for Univariate Selection and Feature Importance indicate the relative significance of each indicator for the task at hand [24].

Table 1Values of condition indicator.FeatureUnivariate SelectionFeature ImportanceKurtosis146.440.166RMS99.440.2685Skewness78.80.171Median41.240.2768Standard Deviation14.270.1174Entropy0.000.1053

Table 2 presents various approaches or algorithms' correctness or accuracy rates in different scenarios. The table shows the performance of three algorithms: Random Forest, SVC (Support Vector Classifier), and ANN (Artificial Neural Network). The correctness rates are reported for different comparisons: Fresh vs.

Train-Set, Fresh vs. Test-Set, and specific belt comparisons (e.g., All Belts, Both Y-Belts, X Belt, etc.).

Table 2Correctness of various approaches.AlgorithmsRandom ForestSVCANNFresh v/sTrain-SetTest-SetTrain-SetTest-SetTrain-SetTest-SetAll Belts100%100%98.35%98.88%98.35%98.51%Both Y-Belts100%98.76%99.18%98.76%98.57%99.59%X Belt100%97.9%97.93%97.9%97.76%96.86%X and Y-Belts100%94.11%95.63%94.48%91.45%88.6%One Y-Belt100%99.59%99.19%99.59%98.6%98.37%Multi-Class100%85.51%77.03%82.55%72.43%74.45%

For each comparison, the table displays the correctness rates achieved by each algorithm. The percentages provided indicate the corresponding algorithm's accuracy in correctly classifying the data. For example, if an algorithm achieves 100% correctness, it accurately classifies all the instances or samples in the given scenario.

Overall, Table 2 compares the performance of different algorithms across various scenarios, demonstrating their effectiveness in correctly classifying the data based on other criteria [25].

The experimental process for 3D Printer Diagnosis likely illustrates the experimental process or workflow undertaken for diagnosing issues or problems in a 3D printer. It is easier to explain the figure precisely with specific details about the content of Fig. (3). However, the figure could include a graphical representation or diagram outlining the steps involved in the experimental process for diagnosing 3D printer issues.

The experimental process involves various stages, such as data collection, measurement, analysis, and testing. It includes preparing the 3D printer, identifying the specific issue or malfunction, conducting tests or experiments, gathering relevant data, analyzing the results, and ultimately diagnosing the problem [26].

Fig. (3)) Considered experimental process for 3D printer diagnosis [2].

Additionally, Fig. (4) depicts the equipment, tools, or techniques utilized during the experimental process. It highlights the different components involved, the connections or interactions between them, and the flow of the diagnostic process.

Fig. (4)) Sensor deployed to receive vibration signals [2].

CONCLUSION

Loosening and damage of belts are some of the processing errors in AM which impact the correctness of the prototype. In this chapter, a novel approach to the application of an accelerometer to describe this process error is highlighted. Normal and aberrant conditions were detected. The necessary features were detected, which were used in Random Forest, SVM, and ANN classifier. The practical outcomes recommend that vibration sensors validate authenticity for condition monitoring of 3D printers.

Further tests are required to increase the correctness of the multi-class classifier. Mounting of the sensor is very significant to receive correct readings from the data acquisition system. So, RNN models can be developed to speed up the power of the diagnostic system.

REFERENCES

[1]Xiong J., Zhang Q., Peng Z., Sun G., Xu W., Wang Q.. A diagnosis method for rotation machinery faults based on dimensionless indexes combined with-nearest neighbor algorithm.Mathematical Problems in Engineering, Hindawi2015[2]Zhang Q.H.. Fault diagnosis in unit based on artificial immune detectors system.China Petrochemical Press2008[3]Sharma V., Banerjee S.. Quantum communication using code division multiple access network.Opt. Quantum Electron.202052838110.1007/s11082-020-02494-3[4]Qiu G., Tang X., Zhuang L., Yang Z.. Application of neural network trained by chaos particle swarm optimization to fault diagnosis for rotating machinery.Zhongguo Jixie Gongcheng2008192126422645[5]Dou D.Y., Zhao Y.K.. A priority and diagnosis tree-based expert system for fault diagnosis of rotating machinery.Zhongguo Dianji Gongcheng Xuebao200828328289[6]Sharma V., Sharma R.. Analysis of spread spectrum in MATLAB.Int. J. Sci. Eng. Res.20145118991902[7]Sharma Vishal. Effect of noise on practical quantum communication systems.Defence Science Journal2016662[8]Sharma V., Bhardwaj A.. Analysis of differential phase shift quantum key distribution using single-photon detectors.2022 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD) 12-16 September 2022, Turin, Italy, pp. 17-18,. 10.1109/NUSOD54938.2022.9894772[9]Xiong J., Zhang Q., Sun G., Zhu X., Liu M., Li Z.. An information fusion fault diagnosis method based on dimensionless indicators with static discounting factor and KNN.IEEE Sens. J.20161672060206910.1109/JSEN.2015.2497545[10]Sharma V., Gupta S., Mehta G., Lad B.K.. A quantum-based diagnostics approach for additive manufacturing machine, IET Collaborative Intelligent Manufacturing.Wiley Online Library202132184192[11]Sharma V., Banerjee S.. Analysis of quantum key distribution based satellite communication In 2018 9th International Conference on Com- putingCommunication and Networking Technologies (ICCCNT)201815[12]Sharma V., Banerjee S.. Analysis of atmospheric effects on satellite-based quantum communication: A comparative study.Quantum Inform. Process.20191836710.1007/s11128-019-2182-0[13]Zhang Q, Qian Y., Xu B.. Control (Chic. Ill)20082818992[14]Konar P., Chattopadhyay P.. Bearing fault detection of induction motor using wavelet and support vector machines (SVMs).Appl. Soft Comput.20111164203421110.1016/j.asoc.2011.03.014[15]Wu H., Wang Y., Yu Z.. In situ monitoring of FDM machine condition via acoustic emissionInternational Journal of Advanced Manufacturing Technology, Springer2016845-814831495[16]Zhang Jian, Li YJ, Cao YY, Zhang Lina. Immune SVM used in wear fault diagnosis of aircraft engine.J. Beijing Univ. Aeronaut. Astron2017437018[17]Sharma V., Shukla C., Banerjee S., Pathak A.. Controlled bidirectional remote state preparation in noisy environment: A generalized view.Quantum Inform. Process.20151493441346410.1007/s11128-015-1038-5[18]Yoon J., He D., Van Hecke B.. A PHM approach to additive manufacturing equipment health monitoring, fault diagnosis, and quality controlProceedings of the Prognostics and Health Management Society Conference, Fort Worth, TX, USA, Citeseer, 29 (3): pp. 1–9 , 2014..[19]Sharma V., Thapliyal K., Pathak A., Banerjee S.. A comparative study of protocols for secure quantum communication under noisy environment: Single-qubit-based protocols versus entangled-state-based protocols.Quantum Inform. Process.201615114681471010.1007/s11128-016-1396-7[20]O’Callaghan J., Wells J., Richardson S., Holmes H., Yu Y., Walker-Samuel S., Siow B., Lythgoe M.F.. Is your system calibrated? MRI gradient system calibration for pre-clinical, high-resolution imaging.PLoS One201495e9656810.1371/journal.pone.009656824804737[21]ZHANG2019164Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoder.Computers in Industry201910516417610.1016/j.compind.2018.12.004[22]