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Abnormal Activity Recognition Based on Deep learning in Crowd

Abnormal Activity Recognition Based on Deep Learning in Crow...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Yan Fu Tao Liu Ou Ye College of Computer Science and Technology Xi'an University of Science and Technology Xi'an China

Abnormal activity recognition is considered as the most challenging task in surveillance videos. Due to the traditional method depend on the computation of artificial features, and noise data has some influence on the extracted features. In this paper, a new hybrid deep learning structure was proposed to fuse the extracted features, which integrates convolutional neural network (CNN) and long short-term memory network (LSTM). Firstly, the video was preprocessed and extracted visual features by CNN. Next, LSTM was used to learn the temporal features of visual features and added attention mechanism to select important features. Finally, the video feature vector obtained layer by layer to judge abnormal activity. An experiment is used to test the ability of the model on the standard dataset UMN to recognize abnormal activity, the result shows that our experimental demonstrate high performance of recognition and outperform the state-of-art algorithms.

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Gastric Cancer Diagnosis with Mask R-CNN

Gastric Cancer Diagnosis with Mask R-CNN

引用

international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Guitao Cao Wenli Song Zhenwei Zhao MOE Research Center for Software/Hardware Co-Design Engineering East China Normal University Shanghai China School of electrical engineering Shandong University Jinan China

It is a reliable way to judge gastric cancer by pathological section. Using deep learning method to detect medical images, as an auxiliary diagnosis method, it can improve the speed and accuracy of doctors to diagnose gastric cancer, and reduce misdiagnosis and missed diagnosis. Mask R-CNN is the latest method in the related field at the beginning of the research. It is mainly used to segment the objects in daily life and achieve good results. the medical image is very different from the scene in life, and the detection effect is also weakened. We use the Mask R-CNN method to detect the pathological sections of gastric cancer, and segment the cancer nest, and then optimize it by adjusting parameters. the method finally allows it to obtain a test result with an AP value of 61.2 when detecting medical images.

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Workshop Heterogeneous Equipment Information Perception Analysis System

Workshop Heterogeneous Equipment Information Perception Anal...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Pengfei Wu Mengjia Qi Lingyan Gao Wei Zou Qiang Miao Li-lan Liu Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai Aerospace Systems Engineering Institute Shanghai China

In order to meet the high requirements of the shop management system for device data-aware interaction, in the traditional data-aware mode, this paper proposes a development platform based on Kepware as a perceptual software, through the combination of heterogeneous device network design and wireless network (AP), using OPC the interface technology realizes the parallel acquisition of multi-channel data of heterogeneous devices, and verifies the feasibility of the data acquisition method through experiments. the results of the research show that the information of real-time perception and production status monitoring based on Kepware can quickly process, analyze, real-time display and store the data of each channel. this scheme has certain reference value for the data collection of heterogeneous equipment in digital workshop.

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A deep learning based method for cutting parameter optimization for band saw machine 11

A deep learning based method for cutting parameter optimizat...

引用

11th Annual conference of the Prognostics and Health Management Society, PHM 2019

作者： Li, Pin Feng, Jianshe Zhu, Feng Davari, Hossein Chen, Liang-Yu Lee, Jay NSF I/UCR Center for Intelligent Maintenance Systems Department of Mechanical Engineering University of Cincinnati CincinnatiOH45221-0072 United States COSEN Mechatronics CO. LTD No. 309 Sec. 2 Houke Rd. Houli Dist. Taichung City421 Taiwan

ISBN: (纸本)9781936263295

throughout the rough machining stage, band saw machines are widely used to cut various raw materials into the required dimensions. the replacement of a blade due to the blades degradation accounts for a large part of the total cost of band saw machine usage. therefore, mitigating the blade degradation by dynamically optimizing the cutting parameters can produce great economic benefit and can also be a leading factor toward realizing smart manufacturing. this paper proposes the steps to achieve this goal, starting with applying the Convolutional Neural Network (CNN) model to map the intricate relationship between the cutting parameters and blade degradation. Next, a simulation technique is used to find the optimal cutting parameters based on the blade degradation estimation from the model output, which can extend the blade life. the proposed optimization method is then validated on the data collected during a real manufacturing process using band saw machines. the comparative results demonstrate that the obtained optimal cutting parameters can effectively extend the service life of the blade for a band saw machine. © 2019 Prognostics and Health Management Society. All rights reserved.

关键词： Sawing

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Prediction of the Technical Status of Highway Pavement Based on Charging data 11

Prediction of the Technical Status of Highway Pavement Based...

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11th international conference on intelligent Computation Technology and Automation (ICICTA)

作者： Lv Peijian Charge & Commun Networking Serv Ltd Co Henan Prov Expressway Monitoring Zhengzhou 450016 Henan Peoples R China

ISBN: (纸本)9781538683088

On the highway, the rapid growth of the freight transportation leads to a sharp increase in actual traffic loads, and then directly causes serious damage to the pavement. Considering pavement maintenance, it is necessary to study the relationship between the truck axle load and the damage of pavement so as to providing data for the highway pavement maintenance. In China, for transport vehicles on highways, they are generally charged by weight. Besides, the damage condition of pavement can be detected periodically by manual work or the rapid testing equipment. these two points provide basic data for the study of the relationship between axle load of highway trucks and the pavement damage. In this paper, first, extract standard axle load that can represent the traffic load and the technical status of the pavement that can represent road damage based on the data of the loading-based toll collection data and the pavement's technical status. then, the machine-learning algorithm was used to study the relationship between the traffic load and the pavement damage to predict the technical status of the pavement.

关键词： highway traffic load pavement damage machine learning algorithm

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"Smart Entity" – How to Build DEVS Models from Large Amount of data and Small Amount of Knowledge? 11th

"Smart Entity" – How to Build DEVS Models from Large Amount...

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11th EAI international conference on Simulation Tools and Techniques, SIMUTools 2019

作者： Antoine-Santoni, thierry Poggi, Bastien Vittori, Evelyne Van Hieux, Ho Delhom, Marielle Aiello, Antoine University of Corsica UMR CNRS SPE Corte France

ISBN: (纸本)9783030322151

University of Corsica and CNRS are working on a scientific program called "Smart Paesi". this project focus on a sustainable rural territories development using advanced artificial intelligence concepts in order to adapt smart city concept (including sustainable development, ICT with by example wireless sensors network, education, e-citizenship, governance) to rural territories and their specificities. In this paper, we introduce a new approach combining discrete event modelling concepts and machine learning methods. this work is a first step towards the conception of a generic and scalable framework allowing model generation from large amount of data. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Internet of things

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ECG-derived blood pressure classification using complexity analysis-based machine learning 11

ECG-derived blood pressure classification using complexity a...

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11th international conference on Health Informatics, HEALthINF 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Simjanoska, Monika Gjoreski, Martin Bogdanova, Ana Madevska Koteska, Bojana Gams, Matjaž Tasič, Jurij Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Rugjer Boshkovikj 16 Skopje1000 Macedonia Department of Intelligent Systems Jozef Stefan Institute International Postgraduate School Jamova cesta 39 Ljubljana1000 Slovenia Faculty of Electrical Engineering University of Ljubljana Trzaska cesta 25 Ljubljana1000 Slovenia

ISBN: (纸本)9789897582813

the recent advancement on wearable physiological sensors supports the development of real-time diagnosis in preventive medicine that demands various signal processing techniques to enable the extraction of the vital signs (e.g., blood pressure). Blood pressure estimation from physiological sensors data is challenging task that usually is solved by a combination of multiple signals. In this paper we present a novel complexity analysis-based machine-learning perspective on the problem of blood pressure class estimation only from ECG signals. We show that high classification accuracy of 96.68% can be achieved by extracting information via complexity analysis on the ECG signal followed by applying a stack of machine-learning classifiers. In addition, the proposed stacking approach is compared to a traditional machine-learning approaches and feature analysis is performed to determine the influence of the different features on the classification accuracy. the experimental data was gathered by daily monitoring of 20 subjects with two different ECG sensors. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Electrocardiograms

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Graph theory for Dimensionality Reduction: A Case Study to Prognosticate Parkinson's

Graph Theory for Dimensionality Reduction: A Case Study to P...

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international conference and Workshop on Computing and Communication (IEMCON)

作者： Shithi Maitra Tonmoy Hossain Khan Md. Hasib Fairuz Shadmani Shishir DS(Ai) Sheba.xyz Dhaka Bangladesh Ahsanullah University of Science and Technology Dhaka Bangladesh Software Engineering Team InsideMaps Inc. Dhaka Bangladesh

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

In the present world, the commotion centering Big data is somewhat obscuring the craft of mining information from smaller samples. Populations with limited examples but huge dimensionality are a common phenomenon, otherwise known as the curse of dimensionality-especially in the health sector-thanks to the recently-discovered potential of data mining and the enthusiasm for feature engineering. Correlated, noisy, redundant features are byproducts of this tendency, which makes learning algorithms converge with greater efforts. this paper proposes a novel feature-pruning technique relying on computational graph theory. Restoring the appeal of pre-AI conventional computing, the paper applies Disjoint Set Union (DSU) on unidirectional graphs prepared basis thresholded Spearman's rank correlation coefficient, r. Gradual withdrawal of leniency on Spearman's r caused a greater tendency in features to form clusters, causing the dimensionality to shrink. the results-extracting out finer, more representative roots as features-have been k-fold cross-validated on a case study examining subjects for Parkinson's. Qualitatively, the method overcomes Principal Component Analysis's (PCA) limitation of inexplicit merging of features and Linear Discriminant Analysis's (LDA) limitation of inextendibility to multiple classes. Statistical inference verified a significant rise in performance, establishing an example of conventional hard computing reinforcing modern soft computing.

关键词： Sociology Merging Mobile communication Graph theory data mining Noise measurement Principal component analysis

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Quantitative Analysis of Influence of Morphological Feature Selection on Neuron Classification

Quantitative Analysis of Influence of Morphological Feature ...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Jianyang Zheng Xianghong Lin College of Computer Science and Engineering Northwest Normal University Lanzhou China

the neuron classification problem is significant for understanding structure-function relationships in computational neuroscience. By solving neuronal classification problem, we can further understand the characteristic of neurons and the process of information transmission. In the training process of the neuron geometry classifier, selecting the appropriate neuron feature data is the premise of obtaining good classification results. First, a data set of 200 three-dimensional neurons was constructed, and there were 10 different types of neurons. Secondly, according to the difference of the extracted neuron characteristic parameters, it is divided into two categories: global features and distribution features. Finally, the machine learning methods such as Logistic Regression, K-Nearest Neighbor, Naive Bayes, Decision Tree, Random Forest, Extremely Randomized Trees and Extreme Gradient Boosting are used to classify neurons, and the effects of different features on neuron classification results are analyzed. the experimental results show: (1) For the K-Nearest Neighbor and Naive Bayes, the distribution feature of the neuron is more critical than the global feature, and even if the effect of the combination of the two will not be improved; (2) the ensemble classifier based on decision trees, such as Random Forests, Extremely Randomized Trees and Extreme Gradient Boosting have better accuracy and stability for neuron classification.

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Analysis on the Strategy for Construction Quality Optimization Management of Municipal Road engineering Based on Fast Mining Algorithm for Large Cloud data 11

Analysis on the Strategy for Construction Quality Optimizati...

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11th international conference on intelligent Computation Technology and Automation (ICICTA)

作者： Xie, Wenguang Guiyang Water Resources & Transportat Dev Investm Guiyang 550003 Guizhou Peoples R China

ISBN: (纸本)9781538683088

In view of the insufficient in the existing researches that the non-linearity of subcontracting resource system for the municipal road engineering construction is ignored, combined with the theory of road construction quality optimization management in the context of cloud data, a kind of strategy for construction quality optimization management of municipal road engineering based on fast mining algorithm for large cloud data (referred to as FMALCD for short) is put forward to achieve the effective analysis on the construction quality optimization management of the municipal road engineering. Firstly, after the identification of the conditions under the dynamic constraints of the municipal road engineering construction, the serial number of the first construction node is calculated. Reordering is carried out to meet the optimization characteristics of the construction quality optimization management. In the FMALCD, the nonlinear characteristics of a kind of fast mining for the cloud data is combined in the application to further optimize the issue of the municipal road engineering construction quality optimization management. In addition, analysis is carried out on the performance of the algorithm through simulation experiment. the analysis is conducted mainly from the perspective of effectiveness, relative advantages and so on.

关键词： Fast Mining Algorithm for Large Cloud data Construction Quality Optimization Management Municipal Road engineering Nonlinear

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