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检索条件"任意字段=8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019"

共 336 条记录，以下是261-270 订阅

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Tag-based video retrieval with social tag relevance learning 8

Tag-based video retrieval with social tag relevance learning

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8th ieee Global conference on Consumer Electronics, GCCE 2019

作者： Takeda, Hiroshi Yoshida, Soh Muneyasu, Mitsuji Graduate School of Science and Engineering Kansai University 3-3-35 Yamate-cho Suita-shi Osaka564-8680 Japan

ISBN: (纸本)9781728135755

High-quality tags play an important role in many applications such as multimedia information retrieval. this paper proposes a social tag relevance learning method using a data-driven approach to improving tag-based video retrieval performance. the tag relevance means how a tag is relevant to multimedia content. To learn the tag relevance, we apply a well-known tag neighbor voting algorithm, which accumulates votes from visual neighbors. However, an imbalance in the number of tags among the datasets causes a loss in the accuracy of tag voting. therefore, in the proposed method, we examine a formula for calculating the tag relevance score considering the tag occurrence frequency imbalance. We conduct experiments on the YouTube-8M dataset, and the results show that our approach is effective and efficient. © 2019 ieee.

关键词： learning systems

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Efficient Fault Detection and Diagnosis in Photovoltaic System Using Deep learning Technique* 8

Efficient Fault Detection and Diagnosis in Photovoltaic Syst...

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8th International conference on control, Decision and Information Technologies (CoDIT)

作者： Marweni, Manel Fezai, Radhia Hajji, Mansour Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Natl Engn Sch Monastir Res Lab Automat Signal Proc & Image Monastir 5019 Tunisia Texas A&M Univ Qatar Dept Elect Doha 23874 Qatar Texas A&M Univ Qatar Comp Engn Program Doha 23874 Qatar Prince Sultan Univ Dept Math & Sci Riyadh 11586 Saudi Arabia Texas A&M Univ Qatar Dept Chem Engn Program Doha 23874 Qatar

ISBN: (纸本)9781665496070

PV systems are subject to failures during their operation due to the aging effects and external/environmental conditions. these faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). the most well-known data-driven methods are Deep learning (DL) approaches. the biggest advantage of DL algorithms, in diagnosis, are that they try to learn high- level features from PV data in a high-order, non-linear and adaptive manners. then, the fault is classified using soft-max activation function. this work therefore presents a comparative study of FDD based DL techniques. these techniques include Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). the DL techniquesbased fault diagnosis are implemented using an emulated Grid-Connected PV (GCPV) system. the classification results for the pretrained DL models is exhibited and performance of the models are evaluated.

关键词： Deep learning Photovoltaic systems Fault diagnosis Recurrent neural networks Fault detection Robustness Classification algorithms

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Building Load Prediction Model Based on Integration of Mechanism and data in District Heating systems 8

Building Load Prediction Model Based on Integration of Mecha...

引用

8th International conference on Power and Renewable Energy, ICPRE 2023

作者： Zhang, Ning Lin, Xiaojie Zhong, Wei Du-Ikonen, Liuliu Zhejiang University Polytechnic Institute Hangzhou China Zhejiang University College of Energy Engineering Hangzhou China Sustainablity Science LUT School of Energy Systems Lappeenranta Finland

ISBN: (纸本)9798350328813

For the building load prediction in district heating systems, the traditional methods cover the mechanism models and the data-driven model. However, the data of the district heating systems was often of low quality or partly missing, and thus the traditional models cannot present high accuracy in load prediction due to the insufficient data available for training. this paper proposed a building load prediction model based on the integration of mechanism and data in district heating systems. the proposed model considered the heat transfer mechanism process and was trained by historical data. As for the training results in the scenario of insufficient data, the proposed prediction model presented a remarkable improvement by decreasing the MAPE and RMSE to 7.34% and 7.74%, respectively, on the same test dataset, compared with the data-driven model. the mechanism process integrated into the proposed model served as an additional feature and enhanced the data quality. © 2023 ieee.

关键词： data-driven model deep learning district heating systems load prediction

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Suitable ASP U-Net training algorithms for grasping point detection of nontrivial objects 8

Suitable ASP U-Net training algorithms for grasping point de...

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8th International conference on control, Decision and Information Technologies (CoDIT)

作者： Dolezel, Petr Stursa, Dominik Kopecky, Dusan Univ Pardubice Fac Elect Engn & Informat Pardubice 53210 Czech Republic

ISBN: (纸本)9781665496070

Robotic manipulation with nontrivial or irregular objects, which provide various types of grasping points, is of both academic and industrial interest. Recently, a powerful data-driven ASP U-Net deep neural network has been proposed to detect feasible grasping points of manipulated objects using RGB data. the ASP U-Net showed the ability to detect feasible grasping points with exceptional accuracy and more than acceptable inference times. So far, the network has been trained using an Adam optimizer only. However, in order to optimally utilize the potential of ASP U-Net, it was necessary to perform a systematic investigation of suitable training algorithms. therefore, the aim of this contribution was to extend the impact of ASP U-Net by recommending suitable training algorithms and their parameters based on the result of training experiments.

关键词： Training Deep learning Systematics Service robots Neural networks Grasping Inference algorithms

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Quality Prediction in Semiconductor Manufacturing processes Using Multilayer Perceptron Feedforward Artificial Neural Network 8

Quality Prediction in Semiconductor Manufacturing processes ...

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8th International conference on systems and control (ICSC)

作者： Al-Kharaz, Mohammed Ananou, Bouchra Ouladsine, Mustapha Combal, Michel Pinaton, Jacques Aix Marseille Univ Univ Toulon CNRS LIS Marseille France STMicroelectronics Proc Control Dept Rousset France

ISBN: (纸本)9781728119380

Physical measurements and inspection tests of products quality in semiconductor manufacturing processes are carried out on specific equipment that often expensive and usually separated from production, which is time-consuming and production throughput decrease. Instead, predicting quality state using the available processes data has now become possible through mathematical models where this enables quick decision making in regards to product health state based on the prognosticated information about product quality. In this context, data-driven techniques based on Multilayer Perceptron Artificial Neural Network (MLP-ANN) have proposed to reveal the relationship between products end quality state and processes alarm events. Within this framework, data transformation and adaptation have discussed and model parameters selection was accomplished using the cross-validation technique. the results show a reasonable performance of the selected model that was effectively proven on a data-set collected over the whole semiconductor fabrication facility.

关键词： Multilayer neural networks

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From data to learning: the Scientific Approach to AI-Enhanced Online Course Design 8

From Data to Learning: The Scientific Approach to AI-Enhance...

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8th International conference on Big data, Knowledge and control systems Engineering, BdKCSE 2023

作者： Blagoev, Ivaylo Vassileva, Gergana Monov, Vladimir Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9798350313246

this article explores a transformative approach to online education, driven by the integration of Generative Artificial Intelligence (AI). We present a comprehensive methodology for the creation of interactive online courses, emphasizing four essential steps: content generation powered by AI, division of content into manageable knowledge bites, diversification of content types, and the automated design of course screens with interactive elements and visual assets. the journey begins with AI-driven content creation, leveraging advanced algorithms to generate educational materials efficiently. these materials are then segmented into bite-sized, self-contained knowledge bites, following the principles of microlearning. the diverse array of content types within each knowledge bite enhances engagement and comprehensive understanding. © 2023 ieee.

关键词： Curricula

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Personalized Adaptation of learning Environments 8

Personalized Adaptation of Learning Environments

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8th ieee Int Conf on Advanced Optoelectron and Lasers (CAOL) / Sci Workshop on data Sci in Modern Optoelectronics and Laser Engineering (DSMOLE) / 16th Sci Workshop on Measurement Uncertainty - Scientific, Normative, Appl and Methodical Aspects (UM)

作者： Filatov, Valentin Zolotukhin, Oleh Yerokhin, Andriy Kudryavtseva, Maryna Kharkiv Natl Univ Radio Elect Artificial Intelligence Dept Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Software Engn Dept Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Informat Control Syst Dept Kharkiv Ukraine

ISBN: (纸本)9781728118147

this work is devoted to the development of personalized training systems. A major problem in learning environmens is applying the same approach to all students: teaching materials, time for their mastering, and a training program that is designed in the same way for everyone. Although, each student is individual has his own skills, ability to assimilate the material, his preferences and other. Recently, recommendation systems, of which the system of personalized learning is a part, have become widespread in the learning environment. On the one hand, this shift is due to mathematical approaches, such as machine learning and data mining, that are used in such systems while, on the other hand, the requirements of technological standards "validated" by the World Wide Web Consortium (W3C). According to this symbiosis of mathematical methods and advanced technologies, it is possible to implement a system that has several advantages: identifying current skill levels, building individual learning trajectories, tracking progress, and recommending relevant learning material. the conducted research demonstrates how to make learning environment more adaptive to the users according to their knowledge base, behavior, preferences, and abilities. In this research, a model of a learning ecosystem based on the knowledge and skills annotations is presented. this model is a general model of the all life learning. Second, this thesis focuses on the creation of tools for personalized assessment, recommendation, and advising.

关键词： personalized learning adaptive learning learning environment Semantic Web recommendation system academic advising

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Less (data) Is More: Why Small data Holds the Key to the Future of Artificial Intelligence 8

Less (Data) Is More: Why Small Data Holds the Key to the Fut...

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8th International conference on data Science, Technology and Applications (data)

作者： Greco, Ciro Polonioli, Andrea Tagliabue, Jacopo Tooso Labs San Francisco CA 94102 USA

ISBN: (纸本)9789897583773

the claims that big data holds the key to enterprise successes and that Artificial Intelligence (AI) is going to replace humanity have become increasingly more popular over the past few years, both in academia and in the industry. However, while these claims may indeed capture some truth, they have also been massively oversold, or so we contend here. the goal of this paper is two-fold. First, we provide a qualified defence of the value of less data within the context of AI. this is done by carefully reviewing two distinct problems for big data driven AI, namely a) the limited track record of Deep learning (DL) in key areas such as Natural Language Processing (NLP), b) the regulatory and business significance of being able to learn from few data points. Second, we briefly sketch what we refer to as a case of "A.I. with humans and for humans", namely an AI paradigm whereby the systems we build are privacy-oriented and focused on human-machine collaboration, not competition. Combining our claims above, we conclude that when seen through the lens of cognitively inspired A.I., the bright future of the discipline is about less data, not more, and more humans, not fewer.

关键词： Artificial Intelligence Big data Probabilistic Programming Concept learning Machine learning

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Methods for Improving the Accuracy of the Virtual Assessment of Autonomous Driving 8

Methods for Improving the Accuracy of the Virtual Assessment...

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8th ieee International conference on Connected Vehicles and Expo (ICCVE)

作者： Notz, Dominik Sigl, Martin Kuhbeck, thomas Wagner, Sebastian Groh, Korbinian Schtutz, Christoph Watzenig, Daniel BMW North Amer 2606 Bayshore Pkwy Mountain View CA 94043 USA BMW AG Petuelring 130 D-80788 Munich Germany Tech Univ Munich Dept Informat Chair Robot Artificial Intelligence & Embedded Sy Munich Germany Graz Univ Technol Inst Automat & Control Inffeldgasse 21-B-I A-8010 Graz Austria

ISBN: (纸本)9781728101422

Assessing the safety of an autonomous vehicle is an open problem within the research domain for autonomous vehicles. Next to real-world driving tests, simulation and reprocessing of recordings play a crucial role in validating the correct and safe behavior. Current state-of-the-art methods for function reprocessing suffer from several sources of error and hence, might lead to incorrect results. In this work, an overview of the most recent reprocessing methods is given and their shortcomings are described. We suggest the derivation of explicit sensor models and the learning of behavior models for traffic objects. An overview of different levels of sensor and different kinds of agent models is given along with a discussion for the need for statistical and machine learning based models. Furthermore, a novel method, based on infrastructure sensors, to collect the data needed for the derivation of the models is presented.

关键词： Autonomous Vehicles Machine learning Intelligent Agents Vehicle Detection Sensor systems data Collection Simulation data Reprocessing

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Geometry-agnostic Melt-pool Homogenization of Laser Powder Bed Fusion through Reinforcement learning

Geometry-agnostic Melt-pool Homogenization of Laser Powder B...

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ieee/ASME International conference on Advanced Intelligent Mechatronics (AIM)

作者： Park, Bumsoo Mishra, Sandipan Rensselaer Polytech Inst Dept Mech Aerosp & Nucl Engn 110 8th St Troy NY 12180 USA

ISBN: (纸本)9781665476331

this work explores the feasibility of a geometry-agnostic laser power control strategy for laser powder bed fusion (L-PBF) using reinforcement learning. the controller is designed to anticipate and compensate geometry-induced process inhomogeneities, as well respond to in-process uncertainty through feedback control. To train the reinforcement learning controller, first a reduced-order simulation model is fit to experimental data. then, the optimal control strategy is found through reinforcement learning on this reduced-order model. After the training, we demonstrate that the learned control strategy can reduce up to 55% of the error 2-norm and 59% of the standard deviation with respect to a given reference value. Moreover, the learned control strategy is applicable to novel build geometries without any additional tuning, or modification of the controller, in which we find that the controller attenuated 2-norm error by 62% and variation levels by 60% when deployed on a new (test) geometry, presenting the efficacy of the proposed controller. Finally, the experimental validation of the algorithm in a 'playback' setting resulted in a 24% reduction of both 2-norm error and variation levels, highlighting its potential in an industrial L-PPBF system.

关键词： Geometry

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