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Research on intelligent Linkage Server Switch in Case of Power Loss in Computer Room

Research on Intelligent Linkage Server Switch in Case of Pow...

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IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Tao Zeng Shibing Wang Shuying Liu Fuyang Normal University Anhui Fuyang China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

the computer room of the information center is a centralized place for university data. Once a power failure occurs, it will have a great impact on the entire school. In this paper, an intelligent management scheme is designed for the situation of power loss and call in the computer room. Firstly, the IP address and data type correspondence of each server in the university are calculated to provide conditions for the intelligent opening of the server. then, according to the importance of the server, it is divided into three categories. When the computer room is powered off, the power supply is provided to different types of servers according to the remaining power of the UPS power supply. When the computer room comes in, the main server controls the automatic startup of the other three types of servers, the theory has proved that the scheme has a certain feasibility and can effectively avoid the loss of important data in the first time when the power is cut off.

关键词： Power supplies Switches Maintenance engineering Servers Personnel Uninterruptible power systems Software engineering

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Concept of a Machine learning supported Cross-Machine Control Loop in the Ramp-Up of Large Series Manufacturing

Concept of a Machine Learning supported Cross-Machine Contro...

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international conference on Mechanical and intelligent Manufacturing Technologies (ICMIMT)

作者： Moritz Meiners Jörg Franke Institute for Factory Automation and Production Systems (FAPS) Friedrich-Alexander-University Erlangen-Nuremberg Nuremberg Germany

ISBN: (数字)9781728153322

ISBN: (纸本)9781728153339

With the advancing digitalization of production plants, it becomes possible to use process data across machine boundaries. A machine can adapt its parameters to another machine-measured parameter to increase product quality. the present paper describes the design of an inter-machine control loop with machine learning techniques in order to improve the final quality output. the production ramp-up represents a special application case for this since at this point of time there is only limited knowledge about cause-effect relationships. For this purpose, the paper presents a method for analyzing these interrelations. On the one hand, simple linear regression is used to analyze the linear relationships; on the other hand, machine learning algorithms are used to analyze non-linear relationships. Two independent control loops form the overall control loop, which is capable of deriving holistic prognoses on upstream or downstream process effects.

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Frost Prediction for Vineyard Using Machine learning

Frost Prediction for Vineyard Using Machine Learning

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Yosuke Tamura Liya Ding Kosuke Noborio Kazuki Shibuya School of Science and Technology Meiji University Kawasaki Japan School of Agriculture Meiji University Kawasaki Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

Frost damage in vineyard is severe. In this study, we use the concept of causal modeling proposed in our previous study to predict the frost in vineyards located in Hokkaido, Japan. It is possible to use a trained model for forecasting frost in the next few hours based on recent observations of temperature and vapor pressure. Several methods of input granulation are examined, and it is verified that methods with exponential moving average outperform those with simple moving average in terms of F1 measure.

关键词： Predictive models data models Atmospheric modeling Temperature measurement Support vector machines Labeling Forecasting

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Discharge Fault Simulation System for High Voltage SF6 Gas Insulated Switch-gear and Its intelligent Pattern Recognition 19

Discharge Fault Simulation System for High Voltage SF6 Gas I...

引用

11th international conference on Machine learning and Computing (ICMLC)

作者： Zhang Shiling State Grid Chongqing Elect Power Co Chongqing Elect Power Res Inst Chongqing 401123 Peoples R China

ISBN: (纸本)9781450366007

In this paper, the defect simulator for high voltage sulfur hexafluoride gas insulated composite electrical apparatus is developed. the device consists of four parts: sulfur hexafluoride gas chamber, solid insulator, defect simulator, observation and measurement device. the defect simulator can effectively simulate free metal particle discharge, tip discharge, suspension discharge and air gap discharge. A real-type integrated defect simulator based on GIS is developed, and the partial discharge signal is tested on the simulator, the change trend of decomposed gas with time is detected. Based on this, an artificial intelligence classification method combining fuzzy ISOdata algorithm and ant colony algorithm is proposed, and the structure parameters of the two algorithms are optimized by PSO algorithm. the field application results of HV combined electrical appliances show that the proposed method is effective. the fault type diagnosis method can effectively judge the fault mode intelligently according to time series of SF6 micro-decomposition gas and typical micro-decomposition gas. this paper not only collects the original classification data from the hardware platform of the defect simulator, but also develops an artificial intelligence classification algorithm software system which is easy to be programmed. It can be directly and effectively used to diagnose and evaluate the type of insulation defect in the field practical engineering of GIS. It has certain theoretical guidance value for GIS equipment fault diagnosis and pattern recognition.

关键词： SF6 gas insulated AC GIS pattern recognition fuzzy ISOdata algorithm ant colony algorithm parameter optimization

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A Genetic Algorithm-based AutoML Approach for Large-scale Traffic Speed Prediction

A Genetic Algorithm-based AutoML Approach for Large-scale Tr...

引用

IEEE international conference on intelligent Transportation engineering (ICITE)

作者： Junwei You Rutgers University Newark Institute at NENU Northeast Normal University Jinzhong China

ISBN: (数字)9781728194097

ISBN: (纸本)9781728194103

With the continuous innovation of computer science as well as the big data acquisition technology, machine learning (ML), developed as a state-of-art framework, now has been comprehensively applied in speed prediction tasks. However, ML methods usually require intensive hyper-parameter tuning, which hinders the practical deployment of ML models. In view of this, this paper proposes an automated machine learning (AutoML) framework for speed prediction, which enables the prediction work to be accomplished in a much more timesaving and convenient way as well as in high prediction accuracy. the proposed framework utilizes the Genetic Algorithm (GA) following its four major procedures: Genome coding, Crossover, Mutation and Selection to automatically search for the optimal neural network architectures and hyperparameters. the proposed framework is examined on a real-world large-scale dataset in the city of Berlin, Germany. the experimental results demonstrate that the proposed method outperforms other benchmarking methods by a significant margin. Sensitivity analysis is also conducted to show the robustness of the proposed method. this study demonstrates the great penitential of using AutoML in traffic speed prediction and other related transportation applications.

关键词： data models Biological system modeling Transportation Genetic algorithms Deep learning Urban areas Predictive models

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Building Energy Management Methods based on Fuzzy Logic and Expert Intelligence

Building Energy Management Methods based on Fuzzy Logic and ...

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international conference on Information, Intelligence, Systems and Applications (IISA)

作者： Peter P. Groumpos Vassiliki Mpelogianni Dimitrios Tsipianitis Aimilia Papagiannaki John Gionas Elan Roy Amit Aflalo Electrical and Computer Engineering Departement University of Patras Patras Greece Meazon S.A.) Patras Greece Green Power Solutions Shoham Israel

ISBN: (数字)9781665422284

ISBN: (纸本)9781665422291

Buildings consume a significant percentage of the world's energy resources. the rapid depletion of energy resources, has imparted researchers to focus on energy conservation and wastage. the next generation of intelligent buildings is becoming a trend to cope with the needs of energy and environmental ease in buildings. this advances the intelligent control of building to fulfill the occupants' need. intelligent system control for sustainable buildings is dynamic and highly complex.

关键词： Energy resources Heuristic algorithms Buildings Market research data mining intelligent systems Next generation networking

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A Novel Recommendation System for Next Feature in Software 20th

A Novel Recommendation System for Next Feature in Software

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Prata, Victor R. Moreira, Ronaldo S. Cordeiro, Luan S. Maia, Atilla N. Martins, Alan R. Leao, Davi A. Cavalcante, C. H. L. Souza Junior, Amauri H. Rocha Neto, Ajalmar R. Fed Inst Ceara Fortaleza Ceara Brazil Fortes Tecnol Rua Antonio Fortes 330 BR-60813460 Fortaleza Ceara Brazil

ISBN: (纸本)9783030336073;9783030336066

Software that needs to fulfill many tasks requires a large number of components. Users of these software need a lot of time to find the desired functionality or follow a particular workflow. Recommendation systems can optimize a user's working time by recommending the next features he/she needs. Given that, we evaluate the use of three algorithms (Markov Chain, IndRNN, and LSTM) commonly applied in sequence recommendation/classification in a dataset that reflects the use of the accounting software from Fortes Tecnologia. We analyze the results under two aspects: accuracy for top-5 recommendations and training time. the results show that the IndRNN achieved the highest accuracy, while the Markov Chain reached the lowest training time.

关键词： Recommendation system Independently Recurrent Neural Network Sequential recommendation Deep learning Commercial applications

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Non-stationary Noise Cancellation Using Deep Autoencoder Based on Adversarial learning 20th

Non-stationary Noise Cancellation Using Deep Autoencoder Bas...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Lim, Kyung-Hyun Kim, Jin-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030336073;9783030336066

Studies have been conducted to get a clean data from non-stationary noisy signal, which is one of the areas in speech enhancement. Since conventional methods rely on first-order statistics, the effort to eliminate noise using deep learning method is intensive. In the real environment, many types of noises are mixed with the target sound, resulting in difficulty to remove only noises. However, most of previous works modeled a small amount of non-stationary noise, which is hard to be applied in real world. To cope with this problem, we propose a novel deep learning model to enhance the auditory signal with adversarial learning of two types of discriminators. One discriminator learns to distinguish a clean signal from the enhanced one by the generator, and the other is trained to recognize the difference between eliminated noise signal and real noise signal. In other words, the second discriminator learns the waveform of noise. Besides, a novel learning method is proposed to stabilize the unstable adversarial learning process. Compared with the previous works, to verify the performance of the propose model, we use 100 kinds of noise. the experimental results show that the proposed model has better performance than other conventional methods including the state-of-the-art model in removing non-stationary noise. To evaluate the performance of our model, the scale-invariant source-to-noise ratio is used as an objective evaluation metric. the proposed model shows a statistically significant performance of 5.91 compared with other methods in t-test.

关键词： Speech enhancement Noise cancellation Non-stationary noise Generative adversarial networks Autoencoder

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Adaptive Dimensionality Adjustment for Online "Principal Component Analysis" 1

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Migenda, Nico Moeller, Ralf Schenck, Wolfram Bielefeld Univ Appl Sci Ctr Appl Data Sci Gutersloh Fac Engn & Math Bielefeld Germany Bielefeld Univ Comp Engn Grp Fac Technol Bielefeld Germany

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

Many applications in the Industrial Internet of things and Industry 4.0 rely on large amounts of data which are continuously generated. the exponential growth in available data and the resulting storage requirements are often underestimated bottlenecks. therefore, efficient dimensionality reduction gets more attention and becomes more relevant. One of the most widely used techniques for dimensionality reduction is "Principal Component Analysis" (PCA). A novel algorithm to determine the optimal number of meaningful principal components on a data stream is proposed. the basic idea of the proposed algorithm is to optimize the dimensionality adjustment process by taking advantage of several "natural" PCA features. In contrast to the commonly used approach to start with a maximal set of principal components and apply some sort of stopping rule, the proposed algorithm starts with a minimal set of principal components and uses a linear regression model in the natural logarithmic scale to approximate the remaining components. An experimental study is presented to demonstrate the successful application of the algorithm to noisy synthetic and real world data sets.

关键词： Principal Component Analysis Adaptive dimensionality adjustment Stopping rule Big data Industry 4.0 Industrial IoT

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Automating Visual Inspection of Lyophilized Drug Products With Multi-Input Deep Neural Networks 15

Automating Visual Inspection of Lyophilized Drug Products Wi...

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15th IEEE international conference on Automation Science and engineering (IEEE CASE)

作者： Tsay, Calvin Li, Zheng Genentech Inc 1 DNA Way San Francisco CA 94080 USA Univ Texas Austin McKetta Dept Chem Engn Austin TX 78712 USA

ISBN: (纸本)9781728103563

In the increasingly automated world of biotech manufacturing, the inspection of lyophilized drug products remains a cumbersome and manual process, relying on human operators to inspect finished vials for glass defects, contaminants, and other possible issues. Automating this procedure is challenging due to "fogging" on glass vials and low occurrence of defective samples for training decision models. In this work, we apply deep neural networks to automated classification of lyophlized product vials via computer vision. the proposed approach utilizes shared convolutional layers to account for multiple images acquired of the same vial at various rotations, and transfer learning is examined as a tool to partially overcome the lack of defective data in industrial manufacturing applications. the method is tested using a real-world industrial product as a representative case study. We find that 85-90% of defects can be detected from a single camera angle, demonstrating the potential of multi-input neural networks in lyophilized drug product inspection. Moreover, the results suggest that transfer learning enhances the generalization ability of learned models, even when using data designed for a very different task.

关键词： Automation in life sciences: biotechnology pharmaceutical and health care Computer vision for manufacturing Defect detection Deep learning

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