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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhong, Weiheng Mallick, Tanwi MacFarlane, Jane Meidani, Hadi Balaprakash, Prasanna University of Illinois at Urbana-Champaign Department of Civil Environmental Engineering ChampaignIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Sustainable Energy Systems Group Lawrence Berkeley National Laboratory BerkeleyCA United States Computing and Computational Sciences Directorate Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350345346

Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. To that end, we develop Graph Pyramid Autoformer (GPA), an attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. We demonstrate the efficacy of the GPA using two benchmark traffic datasets: Los Angeles' METR-LA and the Bay Area's PEMS-BAY. Notably, our model has outperformed a range of existing state-of-the-art methods, delivering up to a 25 % improvement in the accuracy of long-term traffic forecasts. Our code is available at: https://***/WeiheneZlExplainable-Graph-Autoformer. © 2023 IEEE.

关键词： Graph Neural Network Long-term forecasting Pyramid Autocorrelation attention

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Quality Process Control and Preventive Maintenance Optimization of a Grinding Unit: A Case Study

Quality Process Control and Preventive Maintenance Optimizat...

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International Conference on Optimization and Applications (ICOA)

作者： Youssef Sadraoui Mohamed Er-Ratby Moulay Saddik Kadiri Abdessamad Kobi LIPIM Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco LASTI Laboratory of Science and Engineering Techniques National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco LARIS Angevin Laboratory for Systems Engineering Research Polytech Angers University of Angers France

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

The studied process is a grinding and hydroclassification unit designed to ensure that the grain size it processes is in line with the specifications for the pipe's transport mode. Obstacles to the unit's operational process include frequent stops, plant's low reliability, product delivery with an inappropriate grain size, and a lack of control over operating parameters. These factors have a significant impact on the unit's relevance and, consequently, its performance. We created a quality culture among operators with “0 grain quality loss” through the implementation of the quality and processes control study, which was essential to improve the situation. Thus, by completing the seven project stages, we were able to enhance and optimize the grinding and hydroclassification unit's overall performance and capability to regulate the grain size quality. We noticed a notable improvement in regulating the several parameters that affect the targeted particle size after we established tools for unit quality control, such as control and monitoring charts, reaction matrices, intervention flow charts and operational standards. The unit has seen a great deal of satisfaction from the project in terms of quality, process control and resources optimization. Moreover, the incorporation of preventive maintenance into this process has shown to be crucial in guaranteeing the reliability and durability of the installation, thereby substantially improving the unit's overall efficiency.

关键词： Grain size Process control Quality control Particle measurements Product delivery Total quality management Time factors Optimization Preventive maintenance Standards

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High-Gain, Low-Profile, Integrable Planar Lens Antenna at 275 GHz 11

High-Gain, Low-Profile, Integrable Planar Lens Antenna at 27...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Li, Mingxiang Stephen Ako, Rajour Tanyi Sriram, Sharath Fumeaux, Christophe Withayachumnankul, Withawat Rmit University Functional Materials and Microsystems Research Group and the Micro Nano Research Facility Arc Centre of Excellence for Transformative Meta-Optical Systems MelbourneVIC3000 Australia The University of Adelaide Terahertz Engineering Laboratory AdelaideSA5005 Australia The University of Queensland School of Electrical Engineering and Computer Science BrisbaneQLD4072 Australia

ISBN: (纸本)9798350326277

Terahertz communications require compact, high-gain antennas that can be integrated with sources, as alternative to traditional lenses, which have bulky sizes and lack design flexibility. This paper introduces a high-gain, low-profile silicon cavity antenna integrated with a WR-3 waveguide feed. The antenna consists of a 2-mm thick silicon wafer with non-uniform hole arrays designed to locally control the effective permittivity. The holes are laser-etched on both sides of the silicon wafer, to overcome phase coverage limitations caused by the tapered side walls of the air hole during fabrication. Experimental results confirm a gain of 19 dBi at 275 GHz, with a 3-dB bandwidth of approximately 29%. We further demonstrate that the high-gain antenna can be integrated with a quarter-wave plate for circularly polarized (CP) radiation. © 2023 IEEE.

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Adaptive Methods for Kernel Initialization of Convolutional Neural Network Model Applied to Plant Disease Classification

Adaptive Methods for Kernel Initialization of Convolutional ...

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Electrical, Electronic and Information sciences (eStream), Open Conference of

作者： Saloua Lagnaoui Khaoula Boumais Saad El Fallah Zakariae En-Naimani Khalid Haddouch Dalius Matuzevičius Laboratory Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory AI Data Science and Emerging Systems Sidi Mohammed ben Abdallah University ENSA Fez Morocco Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences Private University of Fez Fez Morocco Laboratory Computer Science AI and Cyber Security ENSET University of Hassan II Casablanca Mohammedia Morocco Department of Electronic Systems Faculty of Electronics Vilnius Gediminas Technical University Vilnius Lithuania

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

Convolutional Neural Networks are instrumental in artificial intelligence, especially in image processing, where their ability to autonomously learn hierarchical features has led to significant breakthroughs. However, the success of these models is intricately tied to the judicious choice of hyperparameters, which include the configuration of convolutional layers, activation functions, and kernel initialization methods. This research explores kernel initialization methods in Convolutional Neural Network models, seven diverse initialization methods (Glorot Uniform, Ones initialization, Zero initialization, Constant initialization, Random initialization, HeNormal initialization, and Orthogonal initialization) are comprehensively compared. The primary objective is to showcase the sensitivity of Convolutional Neural Networks to these various initialization techniques. The study not only aims to reveal the nuanced impact of kernel initialization but also introduces an adaptive method to enhance model performance. By delving into the intricacies of initialization methods, this research contributes to the improvement of Convolutional Neural Networks effectiveness, especially in critical applications like Plant Disease classification.

关键词： Training Plant diseases Adaptation models Sensitivity Neural networks Convolutional neural networks Kernel

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Prediction Aided by Surrogate Training

arXiv

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arXiv 2024年

作者： Xia, Eric Wainwright, Martin J. Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering & Computer Sciences Massachusetts Institute of Technology CambridgeMA United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

We study a class of prediction problems in which relatively few observations have associated responses, but all observations include both standard covariates as well as additional "helper" covariates. While the end goal is to make high-quality predictions using only the standard covariates, helper covariates can be exploited during training to improve prediction. Helper covariates arise in many applications, including forecasting in time series;incorporation of biased or mis-calibrated predictions from foundation models;and sharing information in transfer learning. We propose "prediction aided by surrogate training" (PAST), a class of methods that exploit labeled data to construct a response estimator based on both the standard and helper covariates;and then use the full dataset with pseudo-responses to train a predictor based only on standard covariates. We establish guarantees on the prediction error of this procedure, with the response estimator allowed to be constructed in an arbitrary way, and the final predictor fit by empirical risk minimization over an arbitrary function class. These upper bounds involve the risk associated with the oracle data set (all responses available), plus an overhead that measures the accuracy of the pseudo-responses. This theory characterizes both regimes in which PAST accuracy is comparable to the oracle accuracy, as well as more challenging regimes where it behaves poorly. We demonstrate its empirical performance across a range of applications, including forecasting of societal ills over time with future covariates as helpers;prediction of cardiovascular risk after heart attacks with prescription data as helpers;and diagnosing pneumonia from chest X-rays using machine-generated predictions as helpers. © 2024, CC BY.

关键词： Prediction models

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Online Task Scheduling Algorithm with Complex Dependencies in Edge Computing 12

Online Task Scheduling Algorithm with Complex Dependencies i...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Shi, Lei Ma, Zhaoxing Fan, Yuqi Shi, Yi Ding, Xu Li, Zhehao School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Procince Hefei University of Technology Hefei 230009 China Virginia Tech Dept. of ECE BlacksburgVA24061 United States

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. © 2022 The Authors. Published by Elsevier B.V.

关键词： Dependent task Edge computing Online scheduling

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Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs

arXiv

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arXiv 2023年

作者： Wang, Zhiyang Ruiz, Luana Ribeiro, Alejandro Department of Electrical and Systems Engineering University of Pennsylvania PA United States Computer Science & Artificial Intelligence Laboratory MIT MA United States

This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information. We consider convolutional MNNs and GNNs where the manifold and the graph convolutions are respectively defined in terms of the Laplace-Beltrami operator and the graph Laplacian. Using the appropriate kernels, we analyze both dense and moderately sparse graphs. We prove non-asymptotic error bounds showing that convolutional filters and neural networks on these graphs converge to convolutional filters and neural networks on the continuous manifold. As a byproduct of this analysis, we observe an important trade-off between the discriminability of graph filters and their ability to approximate the desired behavior of manifold filters. We then discuss how this trade-off is ameliorated in neural networks due to the frequency mixing property of nonlinearities. We further derive a transferability corollary for geometric graphs sampled from the same manifold. We validate our results numerically on a navigation control problem and a point cloud classification task. © 2023, CC0.

关键词： Graph neural networks

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Geospatial exploration of geothermal reservoirs in the Souss Massa regions of the anti-atlas and Moroccan Sahara 5

Geospatial exploration of geothermal reservoirs in the Souss...

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5th Edition of the International Conference on Materials and Environmental science, ICMES 2022

作者： El Bouazouli, Abderrahim Baidder, Lahssen Bahi, Hicham Abdehamid, Fadil Rhouzlane, Said Moussaoui, Rabia Oukrich, Nadia Baite, Wissal Hlal, Mohammed Laboratory of Geosciences Faculty of Sciences Aïn Chock University Hassan II of Casablanca Morocco School of Architecture Planning and Design Mohammed VI Polytechnic University Benguerir Morocco Morocco Polytechnic School of Montreal Canada Laboratory of Industrial Equipe Architecture de Systems ENSEM Morocco Research Laboratory in Computer Science and Education The Mohammedia School of Engineering Rabat Morocco Laboratory of Industrial Engineering Sciences and Technologies Faculty Sultan Moulay Slimane University Beni Mellal Morocco Laboratory Dynamics of Arid Environments Mohamed Premier University Oujda Morocco

Geothermal reservoirs refer to any heat stored, naturally, in the earth’s crust;it is considered as resource coming from a natural complex depending on the geological, geophysical aspect and territories geochemistry. Although this natural resource remains distinguished by his characters to be a renewable, ecological, universal and sustainable mining energy, it stills not, yet, sufficiently exploitable worldwide (El Bouazouli et al., 2020). The same finding was brought up in Moroccan context, despite the presence of some hot resources and capitalizable scientific research to develop this potential. Prospecting for underground geothermal resources is an expensive project that deserves to mobilize scientific and technical methods in order to overcome this risky impasse. Thus, Geomatics has been used, drawing on Remote Sensing for geo-spatial exploration in order to guide prospecting through the development of geothermal deposits potential maps at very low and medium depth on Moroccan territories (El Bouazouli et al., 2019). Furthermore, we explore smart tools to support short and long-term decision-making in terms of creating of clean energy mix and complementary mining energy as well as investment in energy exploration operations. And, as an experiment, we chose for this study, areas distributed mainly in the Moroccan Sahara and the Souss Massa Region, known, probably, by a potentially high geothermal gradient, and therefore constituting geothermal energy reservoirs. As for the exploration methodology adopted, it consists in processing of the imageries: LANDSAT 8, MODIS AND ASTER, in order to extract exploitation indices such as the surface temperature, the layer of lineaments and cracks density, and evidence of hydrothermal alteration that may indicate geothermal activity. The correlation between these three factors, will lead us to conclude that the Moroccan Sahara area is considered as one of the most potential geothermal reservoir areas in Morocco. In short, the a

关键词： Geothermal prospecting

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Fine-Grained Recognition with Incremental Classes 22

Fine-Grained Recognition with Incremental Classes

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

作者： Yang-Zhun Zhou PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781450396899

This work focuses on dealing with fine-grained recognition problems when incremental classes emerge. The task is desirable to not only distinguish subordinate visual classes based on discriminative but subtle object parts, but also recognize new coming sub-classes without suffering from catastrophic forgetting. In this paper, we first propose to localize both object- and part-level image regions for capturing powerful fine-grained patterns. Then, these fine-grained regions are fed into a bilateral network consisting of a stable branch and a flexible branch for supporting observed and incremental sub-classes recognition respectively. Moreover, a cumulative adaptation strategy is further equipped to adjust the network training during the incremental sessions. Meanwhile, to better retain the modeling capability of observed classes, we also replay samples from previous classes by a hallucination approach. Experiments are conducted on three popular fine-grained recognition datasets and results of the proposed method can reveal its superiority over state-of-the-arts.

关键词： Attention mechanisms Fine-grained recognition Generative adversarial networks Incremental learning

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Short-Term Traffic Flow Prediction Method Based on Spatio-Temporal Characteristics of Complex Road Network

Short-Term Traffic Flow Prediction Method Based on Spatio-Te...

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2021 International Conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2021

作者： Zhu, Huawei Mao, Yihao Zhao, Yiming Shi, Liyuan Shao, Yanli School of Information and Electrical Engineering Zhejiang University City College Hangzhou310015 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781643682549

With the development of urbanization, the number of residents' motor vehicles has increased sharply, and traffic congestion problem has become increasingly serious. The construction of Intelligent Traffic System (ITS) has become the main means to alleviate traffic congestion. Short-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation, and has become one of the research hotspots in intelligent transportation ***, A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction cost. Firstly, a graph convolutional network (GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow data. Then, the long and short-term memory (LSTM) neural network is used to process the time characteristics. Finally, the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow data. Experimental results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method, and can provide a basis for intelligent traffic control and smart city construction. © 2022 The authors and IOS Press.

关键词： Traffic congestion

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