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INFLUENCE OF SCALING AND UNFOLDING IN PCA BASED MONITORING OF NUTRIENT REMOVING BATCH PROCESS

IFAC Proceedings Volumes 2006年第13期39卷 114-119页

作者： Magda Ruiz Kris Villez Gurkan Sin Joan Colomer Peter Vanrrolleghem 1Control Engineering and Intelligent Systems Group –eXiT Department of Electronics Computer Science and Automatic Control University of Girona Campus Montilivi CP 17071 Building PIV Girona - Spain Department of Applied Mathematics Biometrics and Process Control Ghent University Coupure Links 653 B-9000 Ghent – Belgium

The data set of batch biological and biotechnological processes can be organized in a three-way data matrix. In this paper the usefulness of different PCA approaches for monitoring is analyzed. Different ways of unfolding and scaling of data have been applied to a pilot-scale SBR data. PCA is used to reduce the dimensionality and to remove the non-linearity dynamic of the data. Moreover, a new method to select the number of principal components is proposed. Loadings graphics are used to determinate the predominant variables for each one. The results show that whatever model can be applied depending on the goal of the monitoring, however the models implicate possible false alarms or faults omission.

关键词： Biological Process Process monitoring Multiway Principal Component Analysis (MPCA)

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Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy

arXiv

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arXiv 2024年

作者： Huo, Wei Liu, Changxin Ding, Kemi Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck. We introduce a federated learning algorithm called Differentially Private Federated Cubic Regularized Newton (DP-FCRN). By leveraging second-order techniques, our algorithm achieves lower iteration complexity compared to first-order methods. We also incorporate noise perturbation during local computations to ensure privacy. Furthermore, we employ sparsification in uplink transmission, which not only reduces the communication costs but also amplifies the privacy guarantee. Specifically, this approach reduces the necessary noise intensity without compromising privacy protection. We analyze the convergence properties of our algorithm and establish the privacy guarantee. Finally, we validate the effectiveness of the proposed algorithm through experiments on a benchmark dataset. © 2024, CC BY.

关键词： Federated learning

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Frequency-domain approach to robust iterative learning controller design for uncertain time-delay systems

Frequency-domain approach to robust iterative learning contr...

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Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Deyuan Meng Yingmin Jia Junping Du Fashan Yu the Department of Systems and Control Beihang University (BUAA) the Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and TechnologyBeijing University of Posts and Telecommunications the School of Electrical Engineering and Automation Henan Polytechnic University

This paper deals with the robust iterative learning control(ILC) for time-delay systems(TDS) with both model and delay *** ILC algorithm with anticipation in time is considered,and a frequency-domain approach to its design is *** shows that a necessary and sufficient convergence condition can be provided in terms of three design parameters:the lead time,the learning gain,and the performance weighting *** particular,if the lead time is chosen as just the delay estimate,then the convergence condition is derived independent of the delay and the *** this case,with the selection of the performance weighting function,the perfect tracking can be achieved,or the least upper bound of the L2-norm of the limit tracking error can be guaranteed less than the least upper bound of the L2-norm of the initial tracking error.

关键词： Iterative learning control time-delay systems frequency-domain approach anticipation in time

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Distributed Bayesian posterior sampling via moment sharing 14

Distributed Bayesian posterior sampling via moment sharing

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Proceedings of the 28th International Conference on Neural Information Processing systems - Volume 2

作者： Minjie Xu Balaji Lakshminarayanan Yee Whye Teh Jun Zhu Bo Zhang State Key Lab of Intelligent Technology and Systems Tsinghua National TNList Lab and Department of Computer Science and Technology Tsinghua University Beijing China Gatsby Unit University College London London UK Department of Statistics University of Oxford Oxford UK

We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation. We assume that the dataset is partitioned and stored across nodes of a cluster. Our procedure involves an independent MCMC posterior sampler at each node based on its local partition of the data. Moment statistics of the local posteriors are collected from each sampler and propagated across the cluster using expectation propagation message passing with low communication costs. The moment sharing scheme improves posterior estimation quality by enforcing agreement among the samplers. We demonstrate the speed and inference quality of our method with empirical studies on Bayesian logistic regression and sparse linear regression with a spike-and-slab prior.

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Multi-Scale Recurrent Neural Network for Sound Event Detection

Multi-Scale Recurrent Neural Network for Sound Event Detecti...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rui Lu Zhiyao Duan Changshui Zhang Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Beijing P.R.China Department of Electrical and Computer Engineering University of Rochester Rochester NY USA

ISBN: (纸本)9781538646595

Sound event detection (SED) in real life is an interesting but challenging task due to the polyphonic and long-term dependent nature of sound events. Recently, multi-label recurrent neural networks (RNNs) have shown promises. However, even equipped with long short-term memory (LSTM) or gated recurrent unit (GRU) cells, RNNs are still limited to model the long-term dependency. In this paper, we propose a multiscale RNN to address this issue. By integrating information from different time resolutions, we can better capture both the fine-grained and long-term dependencies of sound events. We experiment on the development sets of Task3 of DCASE2016 and DCASE2017. Compared to our previously proposed single-scale RNN that won the third place among the 13 teams in Task3 of DCASE2017, the proposed multiscale model achieves statistically significantly better performance on the development datasets of both DECASE2016 and DCASE2017.

关键词： Multi-scale model deep learning recurrent neural network sound event detection recurrent neural nets multiscale models Event detection Machine Learning SOUND Vibration Hearing temporal resolution

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Super-duper-activation of the zero-error quantum capacity

Super-duper-activation of the zero-error quantum capacity

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IEEE International Symposium on Information Theory

作者： Jianxin Chen Toby S. Cubitt Aram W. Harrow Graeme Smith State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Department of Mathematics University of Bristol Bristol UK IBM Thomas J. Watson Research Center Yorktown Heights NY USA

ISBN: (纸本)9781424478903

The zero-error classical capacity of a quantum channel is the asymptotic rate at which it can be used to send classical bits perfectly, so that they can be decoded with zero probability of error. The study of zero-error capacities dates right back to Shannon and the early days of information theory. We show that there exist pairs of quantum channels, neither of which individually have any zero-error capacity whatsoever (even if arbitrarily many uses of the channels are available), but such that access to even a single copy of both channels allows classical information to be sent perfectly reliably. In other words, we prove that the zero-error classical capacity can be superactivated. This result is the first example of superactivation of a classical capacity of a quantum channel. We further strengthen this result to show that there exist pairs of channels, neither of which have any zero-error classical capacity (as before), yet for which access to one copy of the joint channel even allows far more delicate quantum information to be transmitted perfectly. This subsumes the first result, and also implies that the quantum zero-error capacity can be superactivated. But it is strictly stronger than either of these. Indeed, this is the strongest conceivable form of superactivation, and nothing similar is possible for standard Shannon capacities of quantum channels or for zero-error capacities of classical channels.

关键词： Capacity planning Channel capacity Information theory Quantum mechanics Error probability Quantum computing Laboratories intelligent systems computer science Mathematics

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AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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Research on the applications of the genetic algorithm to sensor registration

Research on the applications of the genetic algorithm to sen...

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Congress on Evolutionary Computation

作者： Ling Wu FaXing Lu PeiFa Jia Electronic Engineering Collage Naval University of Engineering China The State Key Laboratory of Intelligent Technology and Systems Department of Computer Science Tsinghua University Beijing China Electronic Engineering Collage Naval University of Engineering Wuhan China

Sensor registration is a basis for well-organized sensor network, and a precondition for data fusion. In cases of constant registration errors, batch processing methods are always applied, where the registration is actually viewed as an optimization problem. Such methods are fast convergent, but sometimes they are not flexible in different cases and the optimal techniques used in batch processing methods may provide the suboptimum as solution. What's more, when dealing with a large number of sensors, the batch processing methods may come across numeric problems. To address the registration problem in some practical cases, the evolutionary algorithm based method can be explored. A method based on genetic algorithm, as well as the least squares method, are developed for sensor registration in different simulation scenarios and compared. Simulation results are analyzed to make clear the advantages and disadvantages of the methods.

关键词： Genetic algorithms Evolutionary computation

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A Systematic Procedure for Topological Path Identification with Raw Data Transformation in Electrical Distribution Networks

arXiv

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arXiv 2024年

作者： Vassallo, Maurizio Bahmanyar, Alireza Duchesne, Laurine Leerschool, Adrien Gerard, Simon Wehenkel, Thomas Ernst, Damien Department of Electrical Engineering and Computer Science Liège Belgium Intelligent Systems Solutions Haulogy Neupré Belgium RESA Liège Belgium LTCI Télécom Paris Institut Polytechnique de Paris France

This paper introduces a systematic approach to address the topological path identification (TPI) problem in power distribution networks. Our approach starts by listing the DSO’s raw information coming from several sources. The raw information undergoes a transformation process using a set of transformation functions. This process converts the raw information into well-defined information exploitable by an algorithm. Then a set of hypothetical paths is generated, considering any potential connections between the elements of the power distribution system. This set of hypothetical paths is processed by the algorithm that identifies the hypothetical paths that are compatible with the well-defined information. This procedure operates iteratively, adapting the set of transformation functions based on the result obtained: if the identified paths fail to meet the DSO’s expectations, new data is collected, and/or the transformation functions found to be responsible for the discrepancies are modified. The systematic procedure offers practical advantages for DSOs, including improved accuracy in path identification and high adaptability to diverse network configurations, even with incomplete or inaccurate data. Consequently, it emerges as a useful tool for the construction of digital twins of power distribution networks that aligns with DSO expectations. © 2024, CC0.

关键词： Power distribution networks

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YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion

arXiv

引用

arXiv 2024年

作者： Chen, Junzhou Huang, Heqiang Zhang, Ronghui Lyu, Nengchao Guo, Yanyong Dai, Hong-Ning Yan, Hong Guangdong Provincial Key Laboratory of Intelligent Transport System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China School of Transportation Southeast University Nanjing210097 China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. While current methods show effectiveness, they often compromise between speed and accuracy. To address this issue, we present a novel real-time and efficient road sign detection network, YOLO-TS. This network significantly improves performance by optimizing the receptive fields of multi-scale feature maps to align more closely with the size distribution of traffic signs in various datasets. Moreover, our innovative feature-fusion strategy, leveraging the flexibility of Anchor-Free methods, allows for multiscale object detection on a high-resolution feature map abundant in contextual information, achieving remarkable enhancements in both accuracy and speed. To mitigate the adverse effects of the grid pattern caused by dilated convolutions on the detection of smaller objects, we have devised a unique module that not only mitigates this grid effect but also widens the receptive field to encompass an extensive range of spatial contextual information, thus boosting the efficiency of information usage. Evaluation on challenging public datasets, TT100K and CCTSDB2021, demonstrates that YOLO-TS surpasses existing state-of-the-art methods in terms of both accuracy and speed. The code for our method will be available. Copyright © 2024, The Authors. All rights reserved.

关键词： Advanced driver assistance systems

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