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20th IFAC Symposium on System Identification (SYSID)

作者： Busetto, R. Breschi, V. Forgione, M. Piga, D. Formentin, S. Politecn Milan Dipartimento Elettron Bioingn & Informaz Milan Italy Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands IDSIA Dalle Molle Inst Artificial Intelligence US Lugano Switzerland

State estimation has a pivotal role in several applications, including but not limited to advanced control design. Especially when dealing with nonlinear systems state estimation is a nontrivial task, often entailing approximations and challenging fine-tuning phases. In this work, we propose to overcome these challenges by formulating an in-context state-estimation problem, enabling us to learn a state estimator for a class of (nonlinear) systems abstracting from particular instances of the state seen during training. To this end, we extend an in-context learning framework recently proposed for system identification, showing via a benchmark numerical example that this approach allows us to (i) use training data directly for the design of the state estimator, (ii) not requiring extensive fine-tuning procedures, while (iii) achieving superior performance compared to state-of-the-art benchmarks. Copyright (c) 2024 The Authors.

关键词： In-context learning machine learning and data mining Filtering and Smoothing

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Attributes of Big data Analytics for data-Driven Decision Making in Cyber-Physical Power Systems 14

Attributes of Big Data Analytics for Data-Driven Decision Ma...

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14th International Conference on Protection and Automation of Power Systems (IPAPS)

作者： Moradi, Jalal Shahinzadeh, Hossein Nafisi, Hamed Marzband, Mousa Gharehpetian, Gevork B. Islamic Azad Univ Khomeinishahr Branch Young Researchers & Elite Club Esfahan Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran Northumbria Univ Dept Elect Engn Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781728161891

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to localscale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions.

关键词： Big data analytics machine learning and data mining Future power systems Artificial intelligence Clustering and classification Smart grids

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Intelligent Graph Review System Based on Knowledge Map 18

Intelligent Graph Review System Based on Knowledge Map

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18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Zhu, Liangsheng Bian, Wenwen Wu, Bin Feng, Wanli Zhu, Quanyin Song, Houhou Hu, Lingyu Huaiyin Inst Technol Fac Comp & Software Engn Huaian Peoples R China

ISBN: (纸本)9781728128658

In order to meet the background of the digital map review of the rapid development of information technology such as "Internet +", big data, machine learning, etc., a smart graph review system based on knowledge map is proposed. The system includes a data processing module, a knowledge map building module, and a comment recommendation module. At present, the knowledge map is constructed through the Neo4j database, and the accuracy of the entity recognition algorithm in this project reaches 95.7%. The survey results show that the application of the system can meet the requirements of intelligent review.

关键词： machine learning and data mining Knowledge Map Visualization Building Standard

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learning and Online Prediction of Weld Quality in Robotic GMAW

Learning and Online Prediction of Weld Quality in Robotic GM...

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作者： Ayoade, Adewole Adekola Colorado School of Mines

学位级别：Ph.D.

We have come a long way in the automation of robotic welding. But with the decline in qualified welders, there has been a steadily growing demand for robotic solutions with capabilities that match that of human welders. Among these capabilities, a major missing element is the ability of the robotic systems to extract actionable information about the quality of their welds, in real time. While this is a challenging problem, we have been able to show that we can extract this information through-the-arc that captures the controlled dynamics leading to defect occurrence and at the same time identify the variation in the process leading to that defect. Our methodology involves a unique approach to gathering synchronized measurements of the welding process mapped to an objective evaluation of the resulting weld bead at a resolution of 1 mm. Demonstrating a system that can achieve this was at the heart of this work, after which we gathered measurements on voltage, current, weld bead profile, visual images, welding torch pose and radiographic images. These measurements were gathered under experiments involving induced porosity and geometric defects under process variations such as the presence of primer and oil, loss of shielding gas, erratic wire feeding, out of joint and the presence of gap between plates. From these measurements, we created a dataset and identified discriminative features that are indicative of weld quality deviations from process variations. Particularly, we proposed a specialized feature extraction methodology, "Bag-of-Pulses" as informed by the domain expertise knowledge based on "Bag-of-Words" approach. In this feature extraction methodology, we were able to represent the data concisely with new building blocks that retain the characteristic nature of the signal source (i.e., pulsed GMAW) such that we can feed them into a classification algorithm to identify defects and make diagnosis.

关键词： gas metal arc welding online weld quality assessment time series analysis machine learning and data mining defect detection and diagnosis robotics

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Personalized Professional Recommendation System Based on Undergraduate Questionnaires 17

Personalized Professional Recommendation System Based on Und...

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17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Wan, Jin Sun, Qiang Li, Xiang Ding, Jin Zhu, Quanyin Huaiyin Inst Technol Fac Comp & Software Engn Huaian Peoples R China

ISBN: (纸本)9781538674451

In order to satisfy the majority of the undergraduate students who desire their major, a personalized professional recommendation system based on students' interests and learning ability is proposed. data transfer module, Professional recommendation module, and Visual graph analysis module are contained in this system. The basic information such as hobbies, learning abilities are fitted a series of professional characteristics by users passing respectively. Parts of personalized recommendation professional health directions are unfolded through a visual map, which is conducive to his professional selection. At present, the correct rate of Text Segmentation reaches 98% by analyzing users' preferences, the accuracy rate of making professional predictions by using random forest algorithm is up to 97%. The investigation results demonstrate that the application of the proposed system catch the students' mind of personalized professional recommendations.

关键词： Hobbies and interests machine learning and data mining Recommendation algorithm Visualization

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Intelligent Graph Review System Based on Knowledge Map

Intelligent Graph Review System Based on Knowledge Map

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第十八届分布式计算及其应用国际学术研讨会

作者： Liangsheng Zhu Wenwen Bian Bin Wu Wanli Feng Quanyin Zhu Houhou Song Lingyu Hu Faculty of Computer & Software Engineering Huaiyin Institute of Technology

In order to meet the background of the digital map review of the rapid development of information technology such as "Internet+",big data,machine learning,etc.,a smart graph review system based on knowledge map is *** system includes a data processing module,a knowledge map building module,and a comment recommendation *** present,the knowledge map is constructed through the Neo4 j database,and the accuracy of the entity recognition algorithm in this project reaches 95.7%.The survey results show that the application of the system can meet the requirements of intelligent review.

关键词： machine learning and data mining Knowledge Map Visualization Building Standard

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A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2017年 320卷 633-667页

作者： Bessa, M. A. Bostanabad, R. Liu, Z. Hu, A. Apley, Daniel W. Brinson, C. Chen, W. Liu, Wing Kam CALTECH 1200 East Calif Blvd Pasadena CA 91125 USA Northwestern Univ Dept Mech Engn Evanston IL 60208 USA Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Northwestern Univ Theoret & Appl Mech Evanston IL 60208 USA

A new data-driven computational framework is developed to assist in the design and modeling of new material systems and structures. The proposed framework integrates three general steps: (1) design of experiments, where the input variables describing material geometry (microstructure), phase properties and external conditions are sampled;(2) efficient computational analyses of each design sample, leading to the creation of a material response database;and (3) machine learning applied to this database to obtain a new design or response model. In addition, the authors address the longstanding challenge of developing a data-driven approach applicable to problems that involve unacceptable computational expense when solved by standard analysis methods - e.g. finite element analysis of representative volume elements involving plasticity and damage. In these cases the framework includes the recently developed "self-consistent clustering analysis" method in order to build large databases suitable for machine learning. The authors believe that this will open new avenues to finding innovative materials with new capabilities in an era of high-throughput computing ("big-data"). (C) 2017 Elsevier B.V. All rights reserved.

关键词： Design of experiments Reduced order model Self-consistent clustering analysis machine learning and data mining Plasticity

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Fecal glucocorticoid metabolite relates to social rank in Sichuan snub-nosed monkeys

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ITALIAN JOURNAL OF ZOOLOGY 2016年第1期83卷 15-25页

作者： Yang, L. L. Huettmann, F. Brown, J. L. Liu, S. Q. Wang, W. X. Yang, J. Y. Hu, D. F. Beijing Forestry Univ Coll Nat Conservat Lab Noninvas Res Technol Endangered Wildlife Beijing Peoples R China Univ Alaska Fairbanks Inst Arctic Biol EWHALE Lab Dept Biol & Wildlife Fairbanks AK USA Smithsonian Inst Natl Zool Pk Ctr Species Survival Smithsonian Conservat Biol Inst Front Royal VA USA Sichuan Snub Nosed Monkeys Res Inst Hu Bei Prov Changsha Hunan Peoples R China

Studies suggest there is a connection between adrenal cortisol production and social rank in many non-human primates. Behavioral observations have confirmed that Sichuan snub-nosed monkeys (Rhinopithecus roxellana) have obvious social ranks. Thus, the goal of this study was to investigate whether there is a relationship between fecal glucocorticoid metabolite (FGM) concentrations as an indicator of social stress and dominance rank in Sichuan snub-nosed monkeys. Fecal samples were collected about every 5days for 1year from 10 Sichuan snub-nosed monkeys (> 7years old;n=5 males, 5 females), and analysed for FGM with a corticosterone radioimmunoassay using machine learning and open access data. Results showed that: (1) yearly mean FGM levels were negatively correlated with individual animal behavioral social rank in both males and females;(2) we divided the whole year into warm and cold seasons (seasonally) and breeding and nonbreeding seasons physiologically. Except for cold seasons, FGM levels have a negative correlation with behavioral social ranks in males and females;(3) female rank does not clearly relate to FGM levels;(4) social ranks of snub-nosed monkeys are more collaborative traits than aggressive ones. Our ?ndings have important implications for understanding the different physiological consequences of dominant and subordinate social status on Sichuan snub-nosed monkey societies, and it quantifies how physiological stress differs during seasons and phases, and by individuals.

关键词： Sichuan snub-nosed monkey (Rhinopithecus roxellana) machine learning and data mining open access dominance rank and stress fecal glucocorticoid metabolites (FGM)

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Big Vehicular Traffic data mining: Towards Accident and Congestion Prevention 12

Big Vehicular Traffic Data Mining: Towards Accident and Cong...

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12th International Wireless Communications & Mobile Computing Conference (IWCMC)

作者： Al Najada, Hamzah Mahgoub, Imad Florida Atlantic Univ Comp & Elect Engn & Comp Sci Dept 777 Glades Rd Boca Raton FL 33431 USA

ISBN: (纸本)9781509003044

In 2013, 32,719 people died in traffic crashes in the USA. Almost 90 people on average lose their lives every day and more than 250 are injured every hour. Road safety could be enhanced by decreasing the traffic crashes. Traffic crashes cause traffic congestion as well, which has become unbearable, especially in mega-cities In addition, direct and indirect loss from traffic congestion only is over $124 billion. The existence of the Big data of traffic crashes, as well as the availability of Big data analytics tools can help us gain useful insights to enhance road safety and decrease traffic crashes. In this paper we use H2O and WEKA mining tools. We apply the feature selection techniques to find the most important predictors. In addition, we tackle the problem of class imbalance by employing bagging and using different quality measures. Furthermore, we evaluate the performance of five classifiers to: (1) Conduct Big data analysis on a big traffic accidents dataset of 146322 examples, find useful insight and patterns from the data, and forecast possible accidents in advance (2) Conduct Big data analysis on a big vehicular casualties dataset of 194477 examples, to study the driver's behavior on the road. From the driver's behavior mining we can predict the driver age, sex as well as the accident severity. The aforementioned analyses, can be used by decision makers and practitioners to develop new traffic rules and policies, in order to prevent accidents, and increase roadway safety.

关键词： Intelligent Transportation System (ITS) Vehicular Ad-hoc Network (VANET) Traffic Engineering Big data machine learning and data mining

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Anticipation and Alert System of Congestion and Accidents in VANET Using Big data Analysis for Intelligent Transportation Systems

Anticipation and Alert System of Congestion and Accidents in...

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IEEE Symposium Series on Computational Intelligence

作者： Hamzah Al Najada Imad Mahgoub Florida Atlantic University 777 Glades Road Boca Raton FL 33431 USA

ISBN: (纸本)9781509042418

Vehicular Networks (VN) have a huge potential to increase roadway safety and traffic efficiency. Big data analysis can be instrumental in realizing this potential and enhancing the Intelligent Transportation Systems (ITSs). We study the causes of road accidents using big real-time accidents data obtained from Florida Department of Transportation (FDOT) - District 4. The ultimate goal is to prevent or at least decrease traffic accidents and congestions. Our approach is based on dividing the roadway into segments, based on the infrastructure availability and the secondary accidents factors. We design a real-time Big data system that receives online streamed data from vehicles on the road in addition to real-time average speed data from vehicles detectors on the road side to (1) Provide accurate Estimated Time of Arrival (ETA) using a Linear Regression (LR) model (2) Predict accidents and congestions before they happen using Naive Bayes (NB) and Distributed Random Forest (DRF) classifiers (3) Update ETA if an accident or a congestion takes place by predicting accurate clearance time. To make this system fast, accurate, and reliable we have implemented Lambda Architecture (LA) in our framework because of its speed, scalability, and fault tolerance. Furthermore, we have optimized the efficiency, the speed, and the accuracy of the designed model by securely selecting the most relevant and significant set of features required for the analysis.

关键词： Big data machine learning and data mining Vehicular Ad-hoc Network (VANET) Intelligent Transportation Systems (ITSs) Lambda Architecture (LA) Traffic Crashes Prediction Traffic Congestions Prediction

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