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Improvement of the mathematical models reduction principle by group elements deletion 2

Improvement of the mathematical models reduction principle b...

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2nd international conference on Smart automation and robotics for Future Industry, SMARTINDUSTRY 2025

作者： Matviychuk, Yaroslav Melnykova, Nataliya Department of Systems of Artificial Intelligence Lviv Polytechnic National University S. Bandera str. 12 Lviv79013 Ukraine

The classic principle of reduction suggests removing unnecessary elements of the model one at a time [2]. The reduction process can be significantly accelerated by group removal of elements. The article substantiates such removal and shows its effectiveness on the example of the identification of the Lorenz ODE. © 2025 Copyright for this paper by its authors.

关键词： identification incorrectness Mathematical model ODE reduction

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A Survey of the Various methods and models Used to Detect Long COVID 6

A Survey of the Various Methods and Models Used to Detect Lo...

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6th international conference on Computing, Communication, Control and automation, ICCUBEA 2022

作者： Parvathy, P.S. Subbanna, Nagesh Rao, Sethuraman Pathinarupothi, Rahul Krishnan Amritapuri India

ISBN: (纸本)9781665484510

Long-COVID or post-COVID is a phenomenon where people who have recovered from the COVID-19, suffer persisting symptoms for more than 4 weeks after the confirmed case of COVID-19 and they can last for months. Approximately 20% of the people affected by this Coronavirus disease (COVID-19) are suffering from mid and long term effects known as the Long COVID and it can affect multiple organs in the body and this can lead to death. To date, different studies and researches have been undertaken to understand about the Long COVID and make robust estimates on the predicting factors, symptoms and also to assess the various long term effects on the patients affected by it. Based on the available research articles and the papers published in mainstream journals on Long COVID, this survey paper aims at analyzing various methods and Machine learning models used to detect and predict Long COVID, to help clinicians and researchers working on early diagnosis of Long COVID. © 2022 IEEE.

关键词： COVID-19

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Classification of Skin Cancer from Dermatological Image by Multimodal Method

Classification of Skin Cancer from Dermatological Image by M...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Tinghao Wang, Yinglong Wang, Dezheng Liao, Shuhao Ming, Yang Zhang, Xinxin School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Banan Chongqing401320 China School of Automation Southeast University Nanjing210096 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

Skin cancer remains a critical health issue, with over 1.2 million new cases diagnosed annually. Early detection is crucial in reducing mortality rates, yet challenges in diagnosis persist due to variability in dermoscopic image quality. Traditional methods for skin lesion classification are cumbersome and involve significant manual preprocessing. This study introduces an innovative approach using deep learning to automate feature extraction and enhance diagnostic accuracy. We investigate an ensemble of advanced neural networks (VGG, ResNet, GoogleNet, Visiontransformer) combined with a multimodal method that integrates patient metadata and image features. Our dataset includes 9, 013 training images and 1, 002 testing images across seven diagnostic categories of pigmented skin lesions. The multimodal approach, mainly using the DenseNet121-Mul model, demonstrated superior performance over the ensemble method in precision, recall, and F1-score, achieving an F1-score of 0.91. Our findings highlight the potentiality of multiple deep-learning models combining with diverse data types to advance the accuracy and efficiency of computer-aided diagnostics in dermatology, paving the way for systems that could match dermatologist-level diagnostic capabilities. © 2025 SPIE.

关键词： Diseases

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Policy-Guided Lazy Search with Feedback for Task and Motion Planning

Policy-Guided Lazy Search with Feedback for Task and Motion ...

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IEEE international conference on robotics and automation (ICRA)

作者： Mohamed Khodeir Atharv Sonwane Ruthrash Hari Florian Shkurti Robot Vision and Learning Lab University of Toronto Robotics Institute Dept. of Computer Science University of BITS Pilani Dept. of Mathematical and Computational Sciences University of Toronto

PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems. Code can be found at https://***/learning-based-tamp.

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Fusion of Efficient Transformer-CNN Hybrid Network (ETCHNet) and Adaptive Deep Clustering Optimization Network (ADCONet) for Advanced IoMT-Based Surgical Monitoring With robotics and Artificial Intelligence 12

Fusion of Efficient Transformer-CNN Hybrid Network (ETCHNet)...

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12th international conference on Intelligent Systems and Embedded Design, ISED 2024

作者： Valivarthi, Dharma Teja Peddi, Sreekar Narla, Swapna Kethu, Sai Sathish Natarajan, Durai Rajesh Swain, Jyotiranjan Tek Leaders TX United States Tek Yantra Inc CA United States NeuraFlash GA United States Estrada Consulting Inc CA United States School of Comuting and Information Technology REVA University Bangalore India

ISBN: (纸本)9798331522612

Traditional health care systems face the challenge of real-time data processing and decision-making, particularly in the surgery domain. Health gadgets based on advanced technologies like IoMT, blockchain, and AI-based systems are used for improving surgical precision and real-time monitoring. While these systems aim to enhance surgical monitoring accuracy, scalability, and efficiency, handling high-dimensional data and real-time analysis still present challenges. This study focuses on the development of a robust and scalable enhanced IoMT-based surgical monitoring system that improves precision. The framework enhances feature extraction, clustering, and decision-making using real-time feedback and blockchain for secure data storage. The proposed framework integrates ETCHNet and ADCONet with robotic automation. The first algorithm, ADCONet, clusters dynamically and builds decisions based on optimised approaches and ETCHNet extracts hybrid features using transformers and CNNs. Robotic automation provides real-time feedback and a fluid surgical interaction. The proposed method achieved 96% classification accuracy and 95% clustering accuracy, which exceeded the existing methods. It was so effective that the method detected 94.5% of anomalies and prevented 97% of collisions. Even more impressively, it did so with an inference latency of only 120 milliseconds. As a result, the solution is still ideal upon surgical real time monitoring where split second decisions are needed. These impressive results clearly prove that the system improves healthcare accuracy and effectiveness. © 2024 IEEE.

关键词： ADCONet Blockchain Data Clustering ETCHNet Healthcare Systems IoMT Machine Learning Real-Time Feedback Robotic automation Surgical Monitoring

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Predicting voltage fluctuation of photovoltaic generation system by deep learning and its control method

Predicting voltage fluctuation of photovoltaic generation sy...

引用

2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Dong, Yanhao Wang, Yihan Li, Peishuai School of Automation Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Photovoltaic generation system (PGS) is affected by many factors, such as weather conditions, equipment aging and power grid load change, which leads to voltage fluctuation and affects power quality and system stability. Traditional forecasting methods, such as physical model and statistical model, have limited effects because of inaccurate parameter acquisition, simplified assumptions of the model and nonlinear complexity of PGS. The study utilizes a ConvLSTM model that integrates Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) to extract features and model the temporal sequence of PGS historical data. The ConvLSTM model not only inherits the advantages of LSTM in handling long-term dependence of time series data but also incorporates the robust spatial feature extraction capability of CNN, thereby significantly enhancing prediction accuracy. Experimental results indicate that the ConvLSTM model exhibits commendable predictive performance under sunny, cloudy, and rainy weather conditions. Moreover, it outperforms traditional LSTM, CNN, and linear regression models in terms of mean square error (MSE) and mean absolute error (MAE). Specifically, the ConvLSTM model achieves an MSE index of 0.0085 and an MAE index of 0.060, underscoring its efficacy in predicting voltage fluctuations. Building on these predictions, this paper introduces a voltage fluctuation control strategy for PGS. This strategy encompasses proactive adjustments to the inverter's operating point, optimization of the battery panel's tilt angle, and refinement of the energy storage equipment's charging and discharging approach, all aimed at bolstering system stability and energy efficiency. This research offers a novel approach for the precise forecasting and intelligent management of PGS voltage fluctuations, holding significant promise for enhancing the stability and efficiency of PGS. © 2025 SPIE.

关键词： Long short-term memory

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Improving robot navigation in crowded environments using intrinsic rewards

Improving robot navigation in crowded environments using int...

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IEEE international conference on robotics and automation (ICRA)

作者： Diego Martinez-Baselga Luis Riazuelo Luis Montano Robotics Perception and Real Time Group Aragon Institute of Engineering Research (I3A) Universidad de Zaragoza Zaragoza Spain

Autonomous navigation in crowded environments is an open problem with many applications, essential for the coexistence of robots and humans in the smart cities of the future. In recent years, deep reinforcement learning approaches have proven to outperform model-based algorithms. Nevertheless, even though the results provided are promising, the works are not able to take advantage of the capabilities that their models offer. They usually get trapped in local optima in the training process, that prevent them from learning the optimal policy. They are not able to visit and interact with every possible state appropriately, such as with the states near the goal or near the dynamic obstacles. In this work, we propose using intrinsic rewards to balance between exploration and exploitation and explore depending on the uncertainty of the states instead of on the time the agent has been trained, encouraging the agent to get more curious about unknown states. We explain the benefits of the approach and compare it with other exploration algorithms that may be used for crowd navigation. Many simulation experiments are performed modifying several algorithms of the state-of-the-art, showing that the use of intrinsic rewards makes the robot learn faster and reach higher rewards and success rates (fewer collisions) in shorter navigation times, outperforming the state-of-the-art.

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Real-Time Human Pose Estimation on Embedded Devices Based on Deep Learning 20

Real-Time Human Pose Estimation on Embedded Devices Based on...

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20th international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Caishi, H.E. Sijia, W.U. Yan, Lu Zihao, Dou Feng, Yang School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Human posture detection plays an important role in areas such as human behavior analysis and human-computer interaction. Deep learning based methods are one of the mainstream approaches. In practice, detection models are often deployed in resource-constrained edge devices for privacy protection, increased realtime, etc. However, most of the pose detection is for dynamic scenes, which requires high detection speed and accuracy, and thus models are mostly complex in structure and decentralized computation. Aiming at the above difficulties, this paper proposes a multi-person pose detection method, implements a lightweight detection network-RepPose, and deploys it on an embedded device. Specifically, this paper proposes a branch pruning scheme based on the reparameterized structure;and designs a high-performance post-processing operator for the post-processing part;in addition, we also propose a multi-threaded parallel accelerated inference scheme. Finally, it is deployed on Huawei Atlas200DK, and the result reaches 26FPS in the multi-target detection experiment, and achieves more than 90% classification accuracy in pedestrian state detection experiment in simple traffic scenarios. © 2023 IEEE.

关键词： Human computer interaction

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SLURP! Spectroscopy of Liquids Using Robot Pre-Touch Sensing

SLURP! Spectroscopy of Liquids Using Robot Pre-Touch Sensing

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IEEE international conference on robotics and automation (ICRA)

作者： Nathaniel Hanson Wesley Lewis Kavya Puthuveetil Donelle Furline Akhil Padmanabha Taşlan Padir Zackory Erickson Institute for Experiential Robotics Northeastern University Boston Massachusetts USA Robotics Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

Liquids and granular media are pervasive throughout human environments. Their free-flowing nature causes people to constrain them into containers. We do so with thousands of different types of containers made out of different materials with varying sizes, shapes, and colors. In this work, we present a state-of-the-art sensing technique for robots to perceive what liquid is inside of an unknown container. We do so by integrating Visible to Near Infrared (VNIR) reflectance spectroscopy into a robot's end effector. We introduce a hierarchical model for inferring the material classes of both containers and internal contents given spectral measurements from two integrated spectrometers. To train these inference models, we capture and open source a dataset of spectral measurements from over 180 different combinations of containers and liquids. Our technique demonstrates over 85% accuracy in identifying 13 different liquids and granular media contained within 13 different containers. The sensitivity of our spectral readings allow our model to also identify the material composition of the containers themselves with 96% accuracy. Overall, VNIR spectroscopy presents a promising method to give household robots a general-purpose ability to infer the liquids inside of containers, without needing to open or manipulate the containers.

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Analyzing Risk Level from RoI and VoI for Robust Data Privacy using Fuzzy Inference System

Analyzing Risk Level from RoI and VoI for Robust Data Privac...

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international conference on robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Rabab Khan Rongon M. Akhtaruzzaman Krishna Das Shekh Tanjil Sharif Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh International University of Business Agriculture and Technology (IUBAT) Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

In the modern digital environment, securing data privacy has become increasingly critical as organizations confront rising threats from cyber-attacks and data breaches. To safeguard sensitive information, businesses must invest in robust data protection measures, yet balancing these investments with their potential benefits remains challenging. This research addresses this issue by proposing a novel framework that integrates Return on Investment (RoI) and Value of Information (VoI) within a Fuzzy Inference System (FIS) to assess data privacy risks effectively. RoI measures the financial return relative to the cost of data protection investments, indicating the economic value derived from such measures. VoI, on the other hand, evaluates the worth of the information protected by these investments, reflecting its impact on decision-making and operational efficiency. By employing a FIS, which accommodates uncertainties and imprecise data, this framework provides a nuanced approach to risk assessment, enabling organizations to optimize their data privacy strategies. The integration of RoI and VoI within the FIS allows for a comprehensive evaluation of risk levels, facilitating more informed decision-making and improved risk management. This research aims to enhance data privacy practices by offering a methodical and adaptable tool for evaluating and balancing investments in data protection.

关键词： Fuzzy logic Analytical models Uncertainty Decision making Data protection Signal processing algorithms Organizations Mathematical models Risk management Investment

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