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Eleventh international conference on Digital Image Processing, ICDIP 2019

Eleventh International Conference on Digital Image Processin...

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11th international conference on Digital Image Processing, ICDIP 2019

ISBN: (纸本)9781510630758

the proceedings contain 167 papers. the topics discussed include: computer image analysis in evaluating the quality of dried meat. Case study: poultry meat;sparse 3D point clouds segmentation considering 2D image feature extraction with deep learning;health properties and evaluation of quality of dried strawberry fruit produced using the convective drying method with neural image analysis;anchored neighborhoods search based on global dictionary atoms for face photo-sketch synthesis;velodyne LiDAR and monocular camera data fusion for depth map and 3D reconstruction;analysis of the effects of scrambling and diffusion of logistic chaotic map on image encryption;and performance analysis of anti-noise interference of hemorrhagic cerebral stroke image reconstruction in chirp-pulse microwave CT (CP-MCT).

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Deep learning Based Prediction Towards Designing A Smart Building Assistant System

Deep Learning Based Prediction Towards Designing A Smart Bui...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Ankur Sarker Fan Yao Haiying Shen Huiying Zhao Haoran Zhu Haroon Lone Laura Barnes Brad Campbell Mitchel Rosen University of Virginiaa Beijing Jiaotong University

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

Nowadays, smart building infrastructures are equipped with hundreds of sensors to monitor building environments and provide smart solutions for occupant comfortability and energy efficiency. Ideally, an automated system can predict and adjust the physical features (e.g., lighting, air quality, temperature, and so on) in a person's office based on his/her personalized preferences and activities. However, since the data is from one person, there may not be sufficient data for machine learning model training, and the data's quality may be low (e.g., with noises). then, it is a challenge to conduct accurate predictions to provide personalized environment adjustment. To handle this problem, in this paper, we propose a smart building assistance system consisting of different sensor data analysis approaches and a deep neural network (DNN)-based prediction model to make a more accurate prediction despite low-quality sensor data. First, we collected a year-long smart building dataset from four different data sources (i.e., sensors, calendar, weather, and survey). Second, we perform different feature engineering approaches (i.e., concretization, one-hot encoding, and multiple feature combination) on the data as inputs for the prediction models. third, we identify a support vector regression-based prediction model and propose a hybrid DNN model consisting of several recurrent neural network blocks and a feed-forward DNN block to predict different preferred physical features considering different activities of a person (e.g., meeting, lunch, research activities). Finally, we conduct experimental studies to evaluate the performance of the proposed prediction models compared to other existing machine learning models in terms of accuracy. Our predicted preferred physical features match the occupant's preferred ranges of different physical features during a specific activity. We also open-sourced our code on Github.

关键词： Temperature sensors Smart buildings Predictive models Sensor systems data models Robustness intelligent sensors

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A Pervasive Community Detection Method through Modular Decomposition of Markov Chain for Social Media Analysis

A Pervasive Community Detection Method Through Modular Decom...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Takeshi Matsui Antonio O. N. Rene Faculty of Social and Information Studies Gunma University Maebashi Japan Faculty of Engineering Toyama Prefectural University Imizu Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

In a network structure, a subset with a dense link is called a community. Many networks in the real world are composed of loosely linked multiple communities. Individual communities are compatible with a functional unit constituting a complex system represented by the network. therefore, if the community structure of the network is clarified, it can be seen how the entire complex system is composed of individual functional units. Clarifying the community structure is a fundamental and essential task for understanding complex systems expressed in networks. Many researchers have been studying to detect communities from networks effectively and efficiently. Meanwhile, the connection density of water pipes affects the failure rate. therefore, the connection of water pipes is regarded as a network, it is possible to estimate the trouble spot through an application of community detection. Clarifying a community of members is to demonstrate a link between members. However, autonomously formed communities in the real world have a ubiquitous (pervasive) structure. If a community has a ubiquitous structure, it implies pervasive overlapping between communities. though studies on the community detection method (modularity maximization and infomap method divide communities into the partition that do not overlap with each other. It follows that each member belongs to only one community. Under these circumstances, in this research, we propose a new community detection method through the modular decomposition of Markov chain method for social network activity.

关键词： Social networking (online) Markov processes Sociology Physics Graph theory data visualization data mining

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Self-learning calculation for selective laser melting 11

Self-learning calculation for selective laser melting

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1th CIRP conference on intelligent Computation in Manufacturing engineering (CIRP ICME)

作者： Rudolph, Jan-Peer Emmelmann, Claus LZN Laser Zentrum Nord GmbH Schleusengraben 14 D-21029 Hamburg Germany Hamburg Univ Technol Inst Laser & Syst Technol Denickestr 17 D-21073 Hamburg Germany

Selective laser melting (SLM) is increasingly used in the industrial production of metallic parts. this creates the need for an efficient and accurate quotation costing. the manufacturing costs of a part mainly result from the machine running time for coating and exposure. At the time of the offer calculation the final orientation of the part in the build chamber and the composition of the build job are typically not known. Addressing this need, this paper presents and evaluates different statistical based methods for an automated and self-learning calculation for SLM given a part's CAD data. (C) 2017 the Authors. Published by Elsevier B.V.

关键词： Calculation Quotation costing Self-learning Selective laser melting (SLM) Additive manufacturing (AM)

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Deep learning in Gait Recognition for Drone Surveillance System 11

Deep Learning in Gait Recognition for Drone Surveillance Sys...

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11th Curtin University Technology, Science and engineering international conference, CUTSE 2018

作者： Sien, Jonathan Phang then Lim, King Hann Au, Pek-Ing CDT 250 Sarawak Miri98009 Malaysia

As implementation of video surveillance system becomes mainstream, tremendous amount of data is produced. A robust and efficient recognition measure is required for enforcement of public safety in smart city projects. One of a demanded bio-metric recognition measures is gait recognition. With gait recognition, automatic indexing of individual's action in surveillance area allow identification of abnormal activities, hence keeping the area in order. Besides, with advancement in online computing, gait recognition is enabled in remote computing that promotes hybrid of static-mobile surveillance system. Essentially in this research project, a deep learning pipeline to detect suspicious human actions is proposed to recognize individual human's gait via untrimmed continuous streaming video. It consists of Single Shot Multi-box Detector (SSD) for human detection, Inception-V3 based transferred learning convolutional neural networks (CNNs) to extract spatial features. Subsequently, the spatial features are integrated with temporal information by sequentially inserted into Long Short-Term Memory (LSTM) deep architecture for action recognition. the pipeline is trained with Kth human action dataset consists of six action classes, i.e. walking, running, hand clapping, hand waving and boxing. the proposed pipeline achieved 99.51% detection rate to spot the suspicious gait action for surveillance system. © Published under licence by IOP Publishing Ltd.

关键词： Security systems

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Cross-modal Retrieval Based on Stacked Bimodal Auto-Encoder 11

Cross-modal Retrieval Based on Stacked Bimodal Auto-Encoder

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11th international conference on Advanced Computational Intelligence (ICACI)

作者： Qiang, Bao-hua Zhao, Tian Wang, Yu-feng Tao, Lin Xie, Wu Zheng, Hong Chen, Xian-jun Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Peoples R China China Elect Technol Grp Corp Res Inst 54 Shijiazhuang Hebei Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China

ISBN: (纸本)9781538677322

Deep learning (DL) has achieved excellent results in dealing with various types of single-modal problems, and many researchers have applied DL in the field of cross-modal retrieval, in which the popular approaches are based on two-stage learning. the first stage obtains a separate representation of each modality, and the second stage is responsible for learning the inter-modal correlation, which is the key to retrieval. the traditional solutions to the second stage obtain the inter-modal shared representation through a shallow network structure, which cannot effectively learn the intermodal multi-level correlation. Motivated by this, in this paper, a novel hybrid deep structure combining two stages was presented for cross-modal retrieval task. To learn the intermodal correlation, we incorporated a Stacked Bimodal Auto-Encoder (Stacked-BAE) to the third layer. On the one hand, our model introduced Stacked-BAE to learn the rich cross-modal correlation further and enhanced learning ability of the model. On the other hand, we utilized the layer-wise learning method to obtain the inter-modal multi-level correlation, which improves the accuracy of cross-modal retrieval. Extensive experiments on several cross-modal datasets show that our model is superior to the baseline correlation analysis and three common multi-modal deep models regarding cross-modal retrieval tasks.

关键词： deep learning cross-modal retrieval correlation

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Application of MOOCs in China's Vocational Education and Training: Feasibilities and Difficulties 19

Application of MOOCs in China's Vocational Education and Tra...

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Proceedings of the 11th international conference on Education Technology and Computers

作者： Jianzhen Zhang Yuyan Niu Jiehong Long Shanxi Institute of Mechanical & Electrical Engineering Changzhi China Agricultural Bank of China Changzhi Branch Office Changzhi China

ISBN: (纸本)9781450372541

Massive Open Online Courses (MOOCs) are one of the most important events in the history of education in the 21st century, which not only tearing down the walls of universities, but also achieving greatly the sharing of high-quality educational resources and providing opportunities for everyone who wants to improve themselves. this paper explored the feasibilities of MOOCs in China's vocational education and training from three aspects, including national economic strategy, enterprise market competition and individual career planning. After analyzing the difficulties, such as how to select the typical work tasks, how to draw private professional MOOC plan, how to develop professional ability, and how to get social recognition for MOOCs, four approaches were proposed, including that MOOC platform and enterprises build and share the professional MOOCs collectively, getting private MOOCs plan by big data analysis technology and data mining technology, learning professional MOOCs with online and offline, adding online virtual simulation to MOOCs learning. Approaches above will enhance MOOCs feasibility in China's Vocational education and training.

关键词： National Economic Strategy Vocational MOOCs Practical Skills Vocational Education and Training Career Development Training Cost Typical Work Tasks

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DCS: A Policy Framework for the Detection of Correlated data Streams 9th

DCS: A Policy Framework for the Detection of Correlated Data...

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9th international Workshop on Business Intelligence for the Real-Time Enterprise, BIRTE 2015, 10th international Workshop on Enabling Real-Time Business Intelligence, BIRTE 2016 and 11th international Workshop on Real-Time Business Intelligence and Analytics, BIRTE 2017 held in conjunction with the international conference on Very Large data Bases, VLDB 2017

作者： Alseghayer, Rakan Petrov, Daniel Chrysanthis, Panos K. Sharaf, Mohamed Labrinidis, Alexandros University of Pittsburgh PittsburghPA United States Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9783030241230

there is an increasing demand for real-time analysis of large volumes of data streams that are produced at high velocity. the most recent data needs to be processed within a specified delay target in order for the analysis to lead to actionable result. To this end, in this paper, we present an effective solution for detecting the correlation of such data streams within a micro-batch of a fixed time interval. Our solution, coined DCS, for Detection of Correlated data Streams, combines (1) incremental sliding-window computation of aggregates, to avoid unnecessary re-computations, (2) intelligent scheduling of computation steps and operations, driven by a utility function within a micro-batch, and (3) an exploration policy that tunes the utility function. Specifically, we propose nine policies that explore correlated pairs of live data streams across consecutive micro-batches. Our experimental evaluation on a real world dataset shows that some policies are more suitable to identifying high numbers of correlated pairs of live data streams, already known from previous micro-batches, while others are more suitable to identifying previously unseen pairs of live data streams across consecutive micro-batches. © Springer Nature Switzerland AG 2019.

关键词： Artificial intelligence

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intelligent Perception of CNC Machine Tools Based on Human-Machine Collaboration

Intelligent Perception of CNC Machine Tools Based on Human-M...

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

作者： Ping Lou Shijie Wei Junwei Yan Jiwei Hu School of Information Engineering Wuhan University of Technology (WUT) Wuhan China Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan China

Condition sensing and understanding for CNC machine tools is an effective means to find hidden faults and to make their diagnosis. In this paper, an intelligent perception system of CNC machine tools is designed and implemented based on human-machine collaboration. With the application of wearable and mobile smart devices, such as Google Glasses and mobile phones, the system makes the information acquisition and data analysis capabilities of site-operator improved further more. A light deep learning model MobileNetV2 is used in this system, which can identify the key parts of the machine tools observed by the site-operator with the Google Glass. the various sensing data of the machine tools can be displayed visually via the screen of the Google Glass, so that site-operator can monitor them more conveniently. Finally, a fault response method for CNC machine tools based on human-machine collaboration is presented. the method improves the sensing capability and responding speed of site-operator by the collaboration of the smart glasses.

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Dissolved Oxygen (DO) Meter Hydrological Modelling Using Predictive Algorithms 11

Dissolved Oxygen (DO) Meter Hydrological Modelling Using Pre...

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11th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019

作者： Lorenzo, Aljay R. Dula, Allysa Y. Valeroso, Neil Aldrin C. David, John Supang, Brian Noli G. Victoria Padilla, Maria Madrigal, Gilfred Allen M. Amado, Timothy M. Tolentino, Lean Karlo S. Technological University of the Philippines Electronics Engineering Department Ermita Manila Philippines

ISBN: (纸本)9781728130446

Dissolved oxygen is one of the critical indicators of a body of water's health and water quality. It refers to the presence of free, non-compound oxygen found in water. It also influences the growth and survival of the aquatic organisms living in it. this study aims to develop a low-cost, multi-function device that could determine the value of the dissolved oxygen (DO) level through hydrological modelling of water parameters such as temperature, pH, and conductivity using Decision Tree, Decision forest, and Multi-layer Perceptron machine learning algorithms. Using various metrics, the most efficient model was built using Random Forest algorithm, for it yielded the most reliable metrics when compared to the other two algorithms. the evaluated model has the following metrics: the Coefficient of Determination, or how well a model explains and predicts future outcomes, is 0.99. the Mean Absolute Error, or the average magnitude of the errors in a set of predictions, is 0.32. the Mean Squared Error, utilized in order to measure the performance of an estimator, is 0.36. the Root Mean Squared Error, or how concentrated the data is around the line of best fit, is 0.60. Relative to Atlas Scientific's DO Sensor, the device can predict the dissolved oxygen level of a given water pond with 2.61% error. the final device is a handheld device consisting of the sensors for the highest- ranking parameters with respect to their relationship to DO: temperature, conductivity, and pH. © 2019 IEEE.

关键词： Python

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