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A Close Look at Matrix Completion with Noisy Data

A Close Look at Matrix Completion with Noisy Data

引用

international conference on robotics and automation Engineering (ICRAE)

作者： Ke Yang Shuai Wu Kai Wang Yi Wang Chaofei Wang College of Chemistry Beijing University of Chemical Technology Beijing China Center for Information Research Academy of Military Sciences Beijing China National Innovation Institute of Defense Technology Academy of Military Sciences Beijing China Department of Automation Tsinghua University Beijing China

Matrix completion is a technique that utilizes the low rank characteristic of a matrix to recover unknown elements of the matrix with some observed elements. In practical applications, the observed elements are often contaminated by noise, which makes the matrix completion more difficult. In this paper, we first establish the mathematical models of the matrix completion with noise, and investigate three classical methods including SVT, ADM and OptSpace. Particularly, we propose to add convergence criterion and restrain divergence criterion to strengthen the constraint of the stopping criterion, solving the problem of non-convergence caused by noise data when the relative recovery error is used as the stopping criterion. Several experiments are designed to estimate the performance of these three algorithms in the task of matrix completion with noise on the synthesized dataset and Jester Joke dataset. Experimental results show that it is risky to arbitrarily assume that one of these methods is optimal. Specifically, SVT has the advantage in cost, OptSpace has the advantage in anti-noise, and ADM has the better robustness to rank. Furthermore, we apply ADM algorithm in the image recovery task, and obtain excellent results.

关键词： Costs Pollution automation Robustness Mathematical models Matrix decomposition Noise measurement

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Region of Interest Loss for Anonymizing Learned Image Compression

Region of Interest Loss for Anonymizing Learned Image Compre...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Christoph Liebender Ranulfo Bezerra Kazunori Ohno Satoshi Tadokoro Graduate School of Information Sciences Tohoku University Japan Robotics Group of Julius Maximilian University Würzburg Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

the use of AI in public spaces continually raises concerns about privacy and the protection of sensitive data. An example is the deployment of detection and recognition methods on humans, where images are provided by surveillance cameras. this results in the acquisition of great amounts of sensitive data, since the capture and transmission of images taken by such cameras happens unaltered, for them to be received by a server on the network. However, many applications do not explicitly require the identity of a given person in a scene; An anonymized representation containing information of the person’s position while preserving the context of them in the scene suffices. We show how using a customized loss function on region of interests (ROI) can achieve sufficient anonymization such that human faces become unrecognizable while persons are kept detectable, by training an end-to-end optimized autoencoder for learned image compression that utilizes the flexibility of the learned analysis and reconstruction transforms for the task of mutating parts of the compression result. this approach enables compression and anonymization in one step on the capture device, instead of transmitting sensitive, nonanonymized data over the network. Additionally, we evaluate how this anonymization impacts the average precision of pre-trained foundation models on detecting faces (MTCNN) and humans (YOLOv8) in comparison to non-ANN based methods, while considering compression rate and latency. the code and model weights for this approach, including usage examples are publicly available on Github: https://***/BRN-Hub/anon-compression

关键词： Training Data privacy Image coding Surveillance Transform coding Transforms Cameras Information filtering Faces Information integrity

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Day-Ahead Forecasting for the Tropics with Numerical Weather Prediction and Machine Learning

Day-Ahead Forecasting for the Tropics with Numerical Weather...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Nigel Yuan Yun Ng Harish Gopalan Venugopalan S. G. Raghavan Chin Chun Ooi Department of Physics National University Singapore Singapore Department of Fluid Dynamics Institute of High Performance Computing Singapore Department of Fluid Dynamics Centre for Frontier AI Research Institute of High Performance Computing Singapore

ISBN: (纸本)9781665476881

Numerical weather prediction (NWP) and machine learning (ML) methods are popular for weather forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. this is further complicated when regional NWP models are used with global climate models, each with multiple possible parameterizations. In this study, a hybrid numerical-statistical approach is proposed and evaluated for four radiation models. Weather Research and Forecasting (WRF) model is run in both global and regional mode to provide an estimate for solar irradiance. this estimate is then post-processed using ML to provide a final prediction. Normalized root-mean-square error from WRF is reduced by up to 40-50% with this ML error correction model. Results obtained using CAM, GFDL, New Goddard and RRTMG radiation models were comparable after this correction, negating the need for WRF parameterization tuning. Other models incorporating nearby locations and an ensemble set-up are also evaluated, although they produced much smaller improvements.

关键词： Systematics Wind energy Weather forecasting Machine learning Predictive models Numerical models Error correction

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FDNet: Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Fourier Series and DenseNet

FDNet: Remaining Useful Life Prediction for Lithium-Ion Batt...

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Machine Learning and Computer Application (ICMLCA), international conference on

作者： Siwen Chen Xiujuan Zheng Shanghai United Imaging Healthcare Co. Ltd Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

To enhance the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, this study proposes a novel RUL prediction model, termed FDNet, which integrates Discrete Fourier Series (DFS) and Densely Connected Convolutional Networks (DenseNet). In contrast to traditional methods that rely on one-dimensional (1D) numerical representations of time-series data, this model utilizes a two-dimensional (2D) visual representation for direct RUL prediction. First, the DFS is employed to convert 1D time-series signals into a 2D time-frequency domain, enabling the extraction of high-dimensional features from the original data. then, these 2D images are subsequently input into DenseNet, which learns the mapping between the image features and the RUL targets. Finally, the proposed model is validated using the NASA lithium-ion battery aging dataset, and the results indicate that FDNet achieves superior prediction accuracy and exhibits notable robustness.

关键词： Lithium-ion batteries Time-frequency analysis Accuracy NASA Predictive models Feature extraction Robustness Numerical models Fourier series Convolutional neural networks

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Use of Yolo Algorithm for Traffic Sign Detection in Autonomous Vehicles and Improvement Using Data Replication methods

Use of Yolo Algorithm for Traffic Sign Detection in Autonomo...

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international conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Serkan Budak Firat Bozkaya Mustafa Yasin Akmaz Şükrücan Tiğlioğlu Cansel Boynukara Okan Kazanci Zülal Hilal Yildiz Budak Akif Durdu Cemil Sungur Electrical Electronics Engineering Konya Technical University Konya Türkiye Computer Engineering Konya Technical University Konya Türkiye Electrical Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Türkiye

Autonomous vehicles use many technologies and methods to detect and act on surrounding objects. the most common among these technologies is an algorithm called YOLO (You Only Look Once). this algorithm quickly detects objects in an image and classifies these objects accurately. this study examines the use of the YOLO algorithm for signage detection in autonomous vehicles and how this algorithm can be improved. First of all, the basic principles and working mechanisms of the YOLO algorithm are explained. then, it is explained in detail how this algorithm can be used for plate detection in autonomous vehicles. Various models were trained using the YOLO algorithm and the data set created with real data, and the trained models were tested on real-time systems. Finally, suggestions for the improvement of the YOLO algorithm are presented and how this algorithm can be improved further in the future is discussed.

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Uncertainty-aware Navigation in Crowded Environment

Uncertainty-aware Navigation in Crowded Environment

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Emmanuel Alao Philippe Martinet ACENTAURI team - Centre Inria d‘Universite Cĉte d’ Azur

ISBN: (纸本)9781665476881

Robots are now widely used around humans, in homes and public places like the museums, all due to their many benefits. these autonomous robots are called social or service robots and they always find it difficult to navigate in crowded environments; largely because of the high level of uncertainty in observing and predicting human behaviours in a highly dynamic environment. Uncertainty is propagated during prediction and might grow to levels that renders the whole environment unsafe for the robot leading to the so-called Freezing Robot Problem - FRP. this work presents our proposed approach to proactively account for various uncertainties during the robot's motion planning using a stochastic Nonlinear Model Predictive Controller (SNMPC). Additionally, using numerical optimization methods enables the planner to compute new control commands in realtime.

关键词： Uncertainty Navigation Service robots Computational modeling Dynamics Stochastic processes Predictive models

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Fully-Automated Packaging Structure Recognition in Logistics Environments 25

Fully-Automated Packaging Structure Recognition in Logistics...

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25th IEEE international conference on Emerging Technologies and Factory automation (ETFA)

作者： Doerr, Laura Brandt, Felix Pouls, Martin Naumann, Alexander FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9781728189567

Within a logistics supply chain, a large variety of transported goods need to be handled, recognized and checked at many different network points. Often, huge manual effort is involved in recognizing or verifying packet identity or packaging structure, for instance to check the delivery for completeness. We propose a method for complete automation of packaging structure recognition: Based on a single image, one or multiple transport units are localized and, for each of these transport units, the characteristics, the total number and the arrangement of its packaging units is recognized. Our algorithm is based on deep learning models, more precisely convolutional neural networks for instance segmentation in images, as well as computer vision methods and heuristic components. We use a custom data set of realistic logistics images for training and evaluation of our method. We show that the solution is capable of correctly recognizing the packaging structure in approximately 85% of our test cases, and even more (91 %) when focusing on most common package types.

关键词： Heuristic methods

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Improving Hazy Image Recognition by Unsupervised Domain Adaptation

Improving Hazy Image Recognition by Unsupervised Domain Adap...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Zhiyu Yuan Yuhang Li Jianfei Yang Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor School of Electrical and Electronic Engineering Nanyang Technological University

ISBN: (纸本)9781665476881

Deep learning has achieved excellent performance in computer vision tasks, like image recognition, natural language processing, etc. However, in real-world applications, special circumstances brought about by the external world may create domain bias caused by distribution discrepancy between training and testing data, leading to degrading model performance. For example, when auto-driving meets hazy weather, the model performance will drop significantly. In this paper, we explore to solve this problem by utilizing modern Domain Adaptation (DA) methods, which generalizes from the source domain to the target domain by minimizing the distribution difference caused by dataset bias. We firstly propose the cross-domain haze image datasets and benchmark the five classic DA methods. the experiments show that DA methods can mitigate the negative effect of haze and significantly improves the model performance for visual recognition.

关键词： Training Deep learning Adaptation models Visualization Image recognition Computational modeling Benchmark testing

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Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning

Network Parameter Control in Cellular Networks through Graph...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Albin Larsson Forsberg Alexandros Nikou Aneta Vulgarakis Feljan Jana Tumova Ericsson Research Research Area Artificial Intelligence (AI) Stockholm Sweden Department of Electrical Engineering and Computer Science Division of Robotics Perception and Learning KTH Stockholm Sweden

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. the question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. the results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective.

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Determination of Basic Probability Assignment Based on Membership Function and PCA

Determination of Basic Probability Assignment Based on Membe...

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international conference on robotics and automation Engineering (ICRAE)

作者： Aoxiang Zhao Junwei Li Huanyu Liu School of Artificial Intelligence Henan University Zhengzhou China

When using evidence theory to identify targets, how to generate basic probability assignment (BPA) based on the collected data is still an open issue. Based on this problem, a novel method to generate BPA based on membership function and principal component analysis (PCA) is proposed. First, this paper proposed a novel membership function and divide the data set into the training set and testing set, the membership model of each attribute is constructed based on the training set data. Secondly, the testing set sample is input to obtain the initial BPA of each attribute. thirdly, the contribution rate of each attribute is used by PCA. Finally, the final BPA is obtained by discounting the initial BPA according to the contribution rate. the results of the experiment have demonstrated the classification accuracy under the Iris dataset is higher than other methods, and the average recognition rate of the Iris dataset is 97.3%.

关键词： Training automation Evidence theory Data models Principal component analysis Iris recognition Testing

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