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Comparison of Deep Learning Models for Body Cavity Fluid Cytology Images Classification

Comparison of Deep Learning Models for Body Cavity Fluid Cyt...

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2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022

作者： Ucan, Murat Kaya, Buket Kaya, Mehmet Dicle University Diyarbakir Department of Computer Technologies Turkey Firat University Elaziǧ Department of Electronics and Automation Turkey Firat University Elaziǧ Department of Computer Engineering Turkey

ISBN: (纸本)9781665490580

Disease detection is successfully performed in many medical fields using medical images and deep learning architectures. Deep learning architectures used in many fields such as brain images, gastroenterological images and chest x-rays support doctors in the detection of diseases. By using deep learning architectures, diseases can be detected more accurately and faster. With faster detection of diseases, treatment can be started more quickly. Early and accurate diagnosis of cytology images is also very important. After the cytology samples are taken from the patients in general health screenings, they should be interpreted by specialist doctors. The aim of this study is to detect benign and malignant cells from cytology images quickly and accurately. In this study, the Body Cavity Fluid Cytology Images dataset, which includes the data of a total of 21 patients with 14 malignant and 7 benign samples, was used. The dataset contains 693 images of 256x192 size. The images in the dataset are trained using the still popular ResNet50, GoogleNet and AlexNet classification architectures. The classification results obtained were compared with other studies using the same data set. As a result of the test processes, AlexNet architecture achieved 97.26%, GoogleNet architecture 98.12% and ResNet50 architecture 99.13% classification accuracy. The ResNet50 architecture with the best classification result achieved 99.25% specificity, 98.75% sensitivity and 99.13% accuracy. Training and testing in this study showed that the ResNet50 architecture classifies cytology images most successfully. © 2022 IEEE.

关键词： Cytology

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Diagnosis of Faults in Edge-PLC using Optimized Deep Learning models in Industrial IoT 9

Diagnosis of Faults in Edge-PLC using Optimized Deep Learnin...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kumar, A. Kishore Rajesh Kumar, T. Balamanigandan, R. Kumar, A. Sasi Mahaveerakannan, R. Department of Robotics and Automation Sri Ramakrishna Engineering College Tamilnadu Coimbatore641022 India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Surathkal India

ISBN: (纸本)9798350397376

Industrial Internet of Things (IIoT) may allow for the state of equipment to be monitored and defects to be detected before they become serious, helping smart factories keep up with the rising demands for safety and efficiency brought on by worldwide competition. In the case of Cloud Computing, data-driven approaches implemented in the cloud are used to first train the model using a vast quantity of raw data, and then end-machines send their real-time readings for processing at the cloud data centre. It is possible, however, that this will impose a significant delay and a high computational cost. In this study, we take into account a hierarchical structure in which edge-PLCs are used to collect sensed data locally and lessen the burden on long-distance connections. Due to the fact that a single responsibility may be associated with multiple swaying topographies, we aim to first reduce the sum of features required to identify a fault, and then identify the smallest possible set of edge-PLCs that still accounts for all of the most important ones, all while keeping deployment costs to a minimum. To address this, we present an Enhanced Harris Hawkins Optimization (EHHO) approach to tackling the features selection issue, followed by the assortment of edge-PLCs through the set coverage problematic. The model named Residual Attention Neural Network (RANN) is proposed the in this study to forecast the fault detection procedure. Residual Neural Network (ResNet) is used by RANN to help diagnose problems, alongside the knowledge of experts. We evaluate the performance of our technique in comparison to existing AI-based approaches, such as the lregression model and its expansions, by simulating on a real data trail. The findings confirm competence and presentation of the suggested approach, which meets or even surpasses the correctness of approaches employing the entire collection of data. © 2023 IEEE.

关键词： Internet of things

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Behavioral-based circular formation control for robot swarms

Behavioral-based circular formation control for robot swarms

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IEEE International Conference on Robotics and automation (ICRA)

作者： Jesús Bautista Héctor García de Marina Department of Computer Engineering Automation and Robotics University of Granada (UGR-CITIC) Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper focuses on coordinating a robot swarm orbiting a convex path without collisions among the individuals. The individual robots lack braking capabilities and can only adjust their courses while maintaining their constant but different speeds. Instead of controlling the spatial relations between the robots, our formation control algorithm aims to deploy a dense robot swarm that mimics the behavior of tornado schooling fish. To achieve this objective safely, we employ a combination of a scalable overtaking rule, a guiding vector field, and a control barrier function with an adaptive radius to facilitate smooth overtakes. The decision-making process of the robots is distributed, relying only on local information. Practical applications include defensive structures or escorting missions with the added resiliency of a swarm without a centralized command. We provide a rigorous analysis of the proposed strategy and validate its effectiveness through numerical simulations involving a high density of unicycles.

关键词： Robot kinematics MIMICs Numerical simulation Fish Formation control Vectors Orbits

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

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32nd Mediterranean Conference on Control and automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement 63

Distributed Optimization with Finite Bit Adaptive Quantizati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Artificial Intelligence Thrust of the Information Hub Guangzhou China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Clear Water Bay Hong Kong Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland University of Cyprus School of Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Kth Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics. © 2024 IEEE.

关键词： Bandwidth

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Multi-Class Gastrointestinal Images Classification Using EfficientNet-B0 CNN Model

Multi-Class Gastrointestinal Images Classification Using Eff...

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2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022

作者： Ucan, Murat Kaya, Buket Kaya, Mehmet Dicle University Diyarbakir Department of Computer Technologies Turkey Firat University Elazǧ Department of Electronics and Automation Turkey Firat University Elaziǧ Department of Computer Engineering Turkey

ISBN: (纸本)9781665490580

Many diseases and cancerous cells can be detected using images taken by gastroenterology specialists. Accurate and rapid detection of gastroenterological diseases is very important for the treatment processes to be applied and for the patient's recovery. In this study, a data set containing data from 8 different diseases (Esophagitis, Dyed and Lifted Polyps, Dyed Resection Margins, Cecum, Pylorus, Z-line, Polyps, Ulcerative colitis) was used. A deep learning network was trained using the EfficientNet architecture and the test results were given in the study. In addition, comparisons were made with other studies using the same data set and the same parameters in the literature. Studies have shown that gastrological images can be successfully classified with an accuracy of 0.935. Class-based classification results are also shared in detail for 8 diseases in the results section of the study. The results showed that the trained architecture would contribute to minimizing human error in disease detection. © 2022 IEEE.

关键词： Convolutional neural networks

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Observer based Descriptor Approach for Model Reference Tracking Control of Quadrotor 6

Observer based Descriptor Approach for Model Reference Track...

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6th International Conference on Control, Robotics and Informatics, ICCRI 2023

作者： Bezzaoucha Rebai, Souad EIGSI- La Rochelle Department of Electrical Computer Engineering and Automation 26 Rue François de Vaux de Foletier La Rochelle17041 France

ISBN: (纸本)9798350323283

In this paper, an algorithm based on a polytopic - fuzzy-TS Takagi-Sugeno PDC (Parrallel Distributed Control) control to ensure the stability and model reference tracking of a quadrotor is considered. After giving the nonlinear model of the UAV, its representation by a polytopic model is first discussed. Next, an observer and PDC controller are synthesized which guarantee desired control performances (stability and L2 reference tracking performance). The given observer and controller gains are designed using numerical tools (Linear Matrix Inequalities-LMI). The simulation results show effectiveness and robustness of the proposed method. © 2023 IEEE

关键词： Unmanned aerial vehicles (UAV)

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Unifying Generation and Prediction on Graphs with Latent Graph Diffusion 38

Unifying Generation and Prediction on Graphs with Latent Gra...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Cai Wang, Xiyuan Zhang, Muhan Department of Automation Tsinghua University China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Institute for Artificial Intelligence Peking University China School of Intelligence Science and Technology Peking University China

In this paper, we propose the first framework that enables solving graph learning tasks of all levels (node, edge and graph) and all types (generation, regression and classification) using one formulation. We first formulate prediction tasks including regression and classification into a generic (conditional) generation framework, which enables diffusion models to perform deterministic tasks with provable guarantees. We then propose Latent Graph Diffusion (LGD), a generative model that can generate node, edge, and graph-level features of all categories simultaneously. We achieve this goal by embedding the graph structures and features into a latent space leveraging a powerful encoder and decoder, then training a diffusion model in the latent space. LGD is also capable of conditional generation through a specifically designed cross-attention mechanism. Leveraging LGD and the "all tasks as generation" formulation, our framework is capable of solving graph tasks of various levels and types. We verify the effectiveness of our framework with extensive experiments, where our models achieve state-of-the-art or highly competitive results across a wide range of generation and regression tasks. © 2024 Neural information processing systems foundation. All rights reserved.

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Proposing Bus Adapter Connections in IEC 61499

Proposing Bus Adapter Connections in IEC 61499

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Annual Conference of Industrial Electronics Society

作者： Pranay Jhunjhunwala Valeriy Vyatkin Department of Electrical Engineering and Automation Aalto University Espoo Finland Department of Computer Science Computer and Space Engineering Lulea Tekniska Universitet Sweden

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper proposes an extension to IEC 61499 architecture to better support flexibility and reconfigurability of automation systems while reducing communication overhead. The extension concerns adapter connections between function blocks. It aims at improving the performance of popular "one-line engineering" design pattern. The current adapter connections are supporting one-to-one links between function blocks, through which a bi-direction flow of events and data takes place. The proposed extension, called bus, allows many-to-many connections, i.e., several function blocks can be connected to several function blocks sending and receiving events and data simultaneously. Before implementing the extension by means of compiler and editor, it was prototyped using an existing means of IEC 61499, as a composite function block with several inputs - adapter sockets and outputs - adapter plugs. The prototyping shows feasibility of the proposed extension.

关键词： Industrial electronics automation Sockets Bidirectional control IEC Standards Plugs

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Hydrogen Supply Infrastructure Planning Method Considering Indirect Network Effects

Hydrogen Supply Infrastructure Planning Method Considering I...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Deng, Haoran Yang, Bo Zhu, Dafeng Yao, Gang Guan, Xinping Srinivasan, Dipti Shanghai Jiao Tong University Department of Automation Shanghai China Kth Royal Institute of Technology Electric Power and Energy Systems Division Stockholm Sweden Shanghai Jiao Tong University Department of Electrical Engineering Shanghai China National University of Singapore Department of Electrical & Computer Engineering Singapore

ISBN: (纸本)9798350376067

At the early commercialization stage of hydrogen fuel cell vehicles (HFCVs), there is a strong causal relationship between HFCV parc and hydrogen refueling stations (HRSs) planning, also known as indirect network effects. This is a crucial factor that hasn't been considered and cannot be ignored in the hydrogen supply infrastructure planning (HSIP) problem. To model the indirect network effects, we quantify the effect of HRS planning on HFCV parc in detail by proposing the calculation method of drivers' fuel buffer, defining hydrogen refueling convenience, and incorporating coupled constraints between the hydrogen network and transportation network. Further, we characterize the effect of HFCVs on HRS planning by constructing a decision-dependent distributionally robust optimization problem considering the HFCV parc uncertainty. We innovatively quantify the upper bound of the ambiguity set of Wasserstein associated with both the endogenous and exogenous factors. We propose effective linearization methods to deal with the nonlinear constraints in the indirect network effects model and transform the two-stage optimization problem into a solvable MILP problem. The simulation results show that the proposed method can obtain more economical investment decisions and achieve higher energy utilization efficiency. More importantly, in this planning framework, investors can set model parameters more flexibly according to the situation of the HFCV market, thereby promoting the popularization of HFCVs. © 2024 IEEE.

关键词： Hydrogen fuels

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