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Automated Condition Monitoring Using Artificial Intelligence 2020

Automated Condition Monitoring Using Artificial Intelligence

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proceedings of the 2020 4th international conference on Vision, Image and Signal Processing

作者： Ieuan Griffiths University of South Wales Treforest Pontypridd UK

ISBN: (纸本)9781450389532

machine maintenance is a major part of total operating costs for manufacturing and production plants, hence, having a cost-effective maintenance strategy is key to success. Most standard solutions operate on a time-driven approach where repairs or replacements are carried out after a given amount of time. this results in healthy machines being replaced unnecessarily incurring losses in both time, resource, and parts. Predictive maintenance utilises sensors capable of collecting real-time operating data and analytical techniques to predict when a machine will require maintenance. this research develops a vibration analysis tool that can detect and differentiate a machines operating conditions without any human analysis or intervention demonstrated via a case study. By using spectrograms in conjunction with image recognition and unsupervised learning, the tool is capable of being applied to many different machines without modification. Results show that features extracted using convolutional auto-encoding and t-SNE, are capable of being clustered in an unsupervised manner accurately based on case study test conditions. Future work looks at the application of this tool into a real-time data stream that can determine operating conditions in real-time.

关键词： Signal processing Condition monitoring Predictive maintenance Unsupervised learning machine learning Neural networks Artificial intelligence machine maintenance Fault detection

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Evolution of IPIN conference Paper from a Scientometrical Perspective 20

Evolution of IPIN Conference Paper from a Scientometrical Pe...

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4th international conference on computer Science and application Engineering, CSAE 2020

作者： An, Zhenpeng Zhang, Di Yang, Shufeng Chen, Zening Liu, Yuan Shenyang Fire Reaserch Institute of Mem Shenyang China

ISBN: (纸本)9781450377720

IPIN Indoor Positioning and Indoor Navigation is the top academic conference, this paper intends to address the developing history of IPIN conference papers from a bibliometrical perspective in order to analyze and grasp the status and trend of IPIN conference papers as a whole. 891 records were retried from the Web of Science and used as the dataset in this study. Bibliometrical methods were the employed to conduct an analysis from the aspects including annual publication numbers, research institutions, core authors, and keywords. the core authors and keywords were analyzed diachronically. We especially focus on the evolution of concepts in the papers of IPIN. It has been found that the papers in IPIN is at a stable developing stage. there are mainly 8 research areas, including indoor positioning system, human bodies, coarse model, laser spot, etc. Taken together with our data indicating, it can be found that since 2015, using big data and machine learning to solve problems is a new research hotspot in IPIN field. And 20% of the keywords have been used throughout the whole developing stage. this study provides a way to identify the emerging and dying concepts of a field from massive knowledge concepts, which could be of great significance to the development of IPIN research. © 2020 ACM.

关键词： Indoor positioning systems

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A Hybrid machine learning Framework for Prediction of Software Effort at the Initial Phase of Software Development 4th

A Hybrid Machine Learning Framework for Prediction of Softwa...

引用

4th international conference on Advances in Computing and Data Sciences, ICACDS 2020

作者： Rai, Prerana Kumar, Shishir Verma, Dinesh Kumar Department of Computer Science and Engineering Jaypee University of Engineering and Technology Raghogarh-Vijaypur India

ISBN: (纸本)9789811566332

In the era of software application, the prediction of the effort of software plays an essential role in the success of project software. the inconsistent, inaccurate, and unreliable prediction of software leads to failure. As the requirement and specification changes as per the software needs, accurate prediction of effort is a difficult task for developing software. this prediction of effort must be calculated accurately to avoid unpredicted results. At the early stages of development, these inaccurate, unreliability, and uncertainty are the drawback of previously developed models. the main aim of the study is to overcome the drawbacks and develop a model for the prediction of the effort of software. A combination of regression analysis and genetic algorithm has been used to develop the model. the model is trained and validated using the ISBSG dataset. the proposed model is compared for performance with a few baseline models. the results show that the proposed model outperforms most of the baseline models against different performance metrics. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A method of English-Chinese language recognition and its application in oral English learning system 20

A method of English-Chinese language recognition and its app...

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proceedings of the 2020 4th international conference on Electronic Information Technology and computer Engineering

作者： XinGuang Li Shuai Chen ZhiChao Zhou XiaoLan Long ZeMing Chen WeiYuan Wu Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China Guangdong University of Foreign Studies Guangzhou China South China University of Technology Guangzhou China

ISBN: (纸本)9781450387811

In this paper, Gammatone Frequency Cepstrum Coefficients (GFCC) and Shifted Delta Cepstra (SDC) hybrid model were used to extract the speech feature parameters, and Gaussian Mixture Model-Universal Background Model (GMM-UBM) was used for language recognition. Taking oral English speeches of Chinese students as a corpus, we developed an English-Chinese language recognition module and applied it to the oral English learning system. the module could identify whether the student's answer language was English, which could increase the intelligence of the learning system. the experiment results showed the language recognition system introduced in this paper had a higher recognition accuracy and enhanced the function of the learning system.

关键词： GMM-UBM language recognition oral English learning system GFCC feature parameters SDC feature parameters

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Detection of Malicious Requests on Web Logs Using Data Mining Techniques 4

Detection of Malicious Requests on Web Logs Using Data Minin...

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4th international conference on computer Science and Engineering (UBMK)

作者： Sahin, Mehmet Emin Ozdemir, Suat Gazi Univ Bilisim Enstitusu Ankara Turkey Gazi Univ Muhendislik Fak Ankara Turkey

ISBN: (纸本)9781728139647

the rapid development and deployment of Internet-based applications has created many problems related to web security. Although the number and the variety of the methods that have been developed in order to maintain security of web applications are increasing, every passing day new types of attacking methods that are targeted at these systems emerge. In order to provide a supplement method to rule and signature based security systems for web application security, it becomes more important to detect and classify the malicious web requests using machine learning techniques. In this work, we used various machine learning techniques to discover the patterns on web requests, and classified the malicious requests while comparing different techniques.

关键词： malicious web requests web application security web access logs data mining machine learning

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Intelligent Traffic Management System Using machine learning and Traffic Signal Control Algorithms for Optimizing Vehicular Flow in Lima 6th

Intelligent Traffic Management System Using Machine Learning...

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6th international conference on international conference on Applied Technologies, ICAT 2024

作者： Toribio, Julio Huarcaya, Frank Huarcaya, Nelly Universidad Peruana de Ciencias Aplicadas Lima Peru

ISBN: (纸本)9783031897597

A 2023 TomTom study identifies Lima as the most congested city in Latin America and the fifth worldwide, causing significant impacts such as increased stress, long travel times, and lower productivity. Despite various approaches, none fully address the unique challenges of developing cities like Lima. To address this, the proposed system uses the Max Pressure (MP) algorithm for traffic signal control and a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network for traffic prediction. the MP algorithm dynamically adjusts signal timing to optimize traffic flow, while the Bi-LSTM predicts future traffic patterns. Validated through simulations on Javier Prado Avenue, the system achieved a 13.8% increase in vehicle throughput and an 18% reduction in travel times. these findings highlight its potential to enhance urban traffic management. the developed web app offers a practical tool, bridging research and real-world application to address Lima's traffic congestion effectively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Travel time

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application of Fisheye Image Correction in Intelligent Retail Containers 4

Application of Fisheye Image Correction in Intelligent Retai...

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4th Annual international conference on Data Science and Business Analytics, ICDSBA 2020

作者： Zeng, Min Wu, Shengjian Li, Fang Hu, Guosheng Shanghai Technical Institute of Electronics Information Shanghai201411 China FinVolution Group Shanghai201203 China

ISBN: (纸本)9781728181646

In recent years, image detection based on deep learning has become one of the main technologies of intelligent retail container (IRC). Fisheye lens is widely adopted as the imaging equipment of the IRC due to its short focal length, large viewing angle and small volume. Aiming at the distortion of fisheye lens imaging, an innovative "center coordinate correcting and clustering algorithm (CCCCA)"based on spherical double longitude model is proposed to correct the classification error of the hard sample in fisheye image predicted by neural network model. First, the YOLOv4Tiny model is used to detect the fisheye image of the IRC to gain the bounding boxes ("bboxes"). Second, the center and radius of the fisheye image are obtained by using the Hough circle algorithm in OpenCV, and the fisheye image is orthogonally mapped to the target plane by means of the spherical double longitude model so as to get the correction center coordinates of the bboxes. Finally, the x-axis coordinates of the correction bbox's centers are clustered to determine the categories of the hard samples ("HardSampleSet"). Experimental results show that the CCCCA can reduce the top-1 error rate ("top-1 err.") of the HardSampleSet in our project by 5.57%. © 2020 IEEE.

关键词： Containers

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Using machine learning to predict the sequences of optimization passes 4th

Using machine learning to predict the sequences of optimizat...

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4th international conference on New Trends in Information and Communications Technology applications, NTICT 2020

作者： Alhasnawy, Laith H. Alwan, Esraa H. Fanfakh, Ahmed B. M. Department of Computer Science College of Science for Women University of Babylon Hillah Iraq

ISBN: (纸本)9783030553395

the manual tuning for the sequence of optimization passes in modern compilers was impractical, where this sequence was not general to all benchmark programs in achieving optimal performance. therefore, the process of selecting a set of passes manually them over a particular program to achieve optimal performance is a very difficult problem. Moreover, choosing the order for these passes will add another problem called phase order problem. In this paper, the proposed approach provides auto tuning optimization sequences instead of manual tuning by building a prediction scheme. the proposed framework used machine learning to find a sequence for each program by collecting these passes based on the features of program. K-Nearest Neighbors classifier algorithm (KNN) is used in the prediction process and improved by that the reduction algorithm that work after it. the reduction algorithm eliminated passes that have a negative impact on program execution time. the proposed approach was evaluated using the LLVM (Low Level Virtual machine) compiler under Linux Ubuntu. the obtained results showed that this approach outperforms standard optimization level-O2 of LLVM compiler in improving the execution time byan average of 21% through building a prediction scheme by using KNN algorithm. Consequently, the execution time is improved byan average of 23% due to the application of the reduction algorithm on the sequences of optimization passes resulting from KNN algorithm. © Springer Nature Switzerland AG 2020.

关键词： Benchmarking

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A Projected Stochastic Gradient Algorithm for Estimating Shapley Value Applied in Attribute Importance 1

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4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 international Cross-Domain conference for machine learning and Knowledge Extraction, CD-MAKE 2020

作者： Simon, Grah Vincent, thouvenot Thales SIX GTS 4 Avenue des Louvresses Gennevilliers France SINCLAIR AI Lab 7 boulevard Gaspard Monge Palaiseau France

ISBN: (数字)9783030573218

ISBN: (纸本)9783030573201

machine learning is enjoying an increasing success in many applications: medical, marketing, defence, cyber security, transportation. It is becoming a key tool in critical systems. However, models are often very complex and highly non-linear. this is problematic, especially for critical systems, because end-users need to fully understand the decisions of an algorithm (e.g. why an alert has been triggered or why a person has a high probability of cancer recurrence). One solution is to offer an interpretation for each individual prediction based on attribute relevance. Shapley Values allow to distribute fairly contributions for each attribute in order to understand the difference between a predicted value for an observation and a base value (e.g. the average prediction of a reference population). they come from cooperative game theory. While these values have many advantages, including their theoretical guarantees, they are however really hard to calculate. Indeed, the complexity increases exponentially with the dimension (the number of variables). In this article, we propose two novel methods to approximate these Shapley Values. the first one is an optimization of an already existing Monte Carlo scheme. It reduces the number of prediction function calls. the second method is based on a projected gradient stochastic algorithm. We prove for the second approach some probability bounds and convergence rates for the approximation errors according to the learning rate type used. Finally, we carry out experiments on simulated datasets for a classification and a regression task. We empirically show that these approaches outperform the classical Monte Carlo estimator in terms of convergence rate and number of prediction function calls, which is the major bottleneck in Shapley Value estimation for our application. © 2020, IFIP international Federation for Information Processing.

关键词： Classification (of information)

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An In-class Teaching Comprehensive Evaluation Model Based on Statistical Modelling and Ensemble learning 4

An In-class Teaching Comprehensive Evaluation Model Based on...

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4th IEEE international conference on Smart Internet of things, SmartIoT 2020

作者： Bai, Ludi Yu, Zehui Zhang, Shifeng Hu, Kangying Chen, Zhan Guo, Junqi Beijing Normal University College of Artificial Intelligence Beijing China Hangzhou Hikvision Digital Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9781728165141

Education is the foundation of our cities' and society's development. the realization of Smart City is inseparable from the construction of Smart Education. the development of science and technology has promoted the popularity and booming of information technology in the education field. With the rise of Smart Education, intelligent learning and evaluation have provided new ideas for in-class teaching evaluation. the model proposed in this article is based on existing multi-dimensional and multi-modal data set from in-class audio and video recognition, as well as movement perception and interaction analysis. We firstly designed a statistical model and an ensemble learning model for in-class teaching evaluation, which are based on Analytic Hierarchy Process-Entropy Weight Method and AdaBoost algorithm respectively. then, we designed experiments to assess the performance of the proposed statistical model and ensemble learning model. Finally, we compared and selected better models through experiments in different evaluation indicators and combined them into our In-class Teaching Comprehensive Evaluation Model with outstanding performance. © 2020 IEEE.

关键词： learning systems

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