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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24667 条记录，以下是211-220 订阅

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

Predicting voltage fluctuation of photovoltaic generation sy...

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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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MEIC: Re-thinking RTL Debug automation using LLMs 24

MEIC: Re-thinking RTL Debug Automation using LLMs

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Xu, Ke Sun, Jialin Hu, Yuchen Fang, Xinwei Shan, Weiwei Wang, Xi Jiang, Zhe National Center of Technology Innovation for EDA School of Integrated Circuits Southeast University China Department of Computer Science University of York United Kingdom

ISBN: (纸本)9798400710773

The deployment of Large Language models (LLMs) for code debugging (e.g., C and Python) is widespread, benefiting from their ability to understand and interpret intricate concepts. However, in the semiconductor industry, utilising LLMs to debug Register Transfer Level (RTL) code is still insufficient, largely due to the underrepresentation of RTL-specific data in training sets. This work introduces a novel framework, Make Each Iteration Count (MEIC), which contrasts with traditional one-shot LLM-based debugging methods that heavily rely on prompt engineering, model tuning, and model training. MEIC utilises LLMs in an iterative process to overcome the limitation of LLMs in RTL code debugging, which is suitable for identifying and correcting both syntax and function errors, while effectively managing the uncertainties inherent in LLM operations. To evaluate our framework, we provide an open-source dataset comprising 178 common RTL programming errors. The experimental results demonstrate that the proposed debugging framework achieves fix rate of 93% for syntax errors and 78% for function errors, with up to 48x speedup in debugging processes when compared with experienced engineers. The Repo. of dataset and code: https://***/SEU-ACAL/reproduce-MEIC-ICCAD. © 2024 Copyright is held by the owner/author(s).

关键词： Program debugging

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Accessibility Framework for Determining Collisions and Coverage for Radiation Scanning 25th

Accessibility Framework for Determining Collisions and Cov...

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25th Annual conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Bettles, Joshua West, Andrew Andrew, Jeremy Darby, Iain Lennox, Barry Manchester Centre for Robotics and AI The University of Manchester ManchesterM13 9PL United Kingdom Nuclear Restoration Services Ltd. Dounreay Thurso CaithnessKW14 7TZ United Kingdom National Nuclear Laboratory Chadwick House Birchwood Park Warrington CheshireWA3 6AE United Kingdom School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783031720581

Radioactive contamination monitoring is an important part of radiological protection. automation of surface monitoring poses difficulties, with a major challenge being determining the coverage of a radiation probe over an object in close proximity without collision. We propose a new accessibility framework to determine if radiation probes, modelled as convex hulls, collide with 3D point clouds representing the objects. We explain how to structure and analyze point clouds to extract properties such as the surface normal for each point. Our method for approximating radiation probes is demonstrated using the BP4 probe. This approach models both the probe and the sensor’s effective scan volume with geometric primitives, providing a computationally efficient way to detect collisions with flat surfaces. The accessibility assessment builds on common methods within computer science for determining intersections. A laptop in various positions was used to demonstrate that the framework can efficiently categorise points as accessible or inaccessible, identifying unscannable regions. The output of this framework can then be used to plan collision-free paths over objects and will be the foundation of a robotic survey assistant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Probes

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Self-Supervised Multi-View Reconstruction of Eyeglasses Frames with Thin Structures 16

Self-Supervised Multi-View Reconstruction of Eyeglasses Fram...

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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Li, Lili Zhou, Lang Zhang, Fan Deng, Pan Ji, Ziyue Su, Zhiyong College of Information Engineering Nanjing University of Finance and Economics China School of Automation Nanjing University of Science and Technology China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Using traditional depth and image-based methods for reconstructing eyeglass frames poses significant challenges due to their unique properties, such as minimal textural features and thin components. In this paper, we propose a novel self-supervised approach for reconstructing eyeglass frames based on vertex displacement prediction. This method enables the precise 3D shape recovery of full-frame eyeglass models from multi-view images. To begin, we first establish an eyeglasses frame template, leveraging prior and domain-specific knowledge. Subsequently, when provided with an input image of an eyeglass frame characterized by thin structures and few texture features, we develop an iterative self-supervised camera pose estimation module to accurately predict the camera pose. Following this, we employ a self-supervised vertex offset prediction network to extract features from both the input multi-view images and the 3D template eyeglass frame model. This network predicts the vertex displacement of the frame to be reconstructed relative to the template frame model. We iteratively optimize the network to obtain precise vertex offsets by minimizing the image loss between rendered images and input images using differentiable rendering. Experimental findings from both synthetic and real datasets demonstrate the effectiveness of our proposed method. © 2025 SPIE.

关键词： Self-supervised learning

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Sequential Optimization of Healthcare Processes using Reinforcement Learning and Sequential Function models 3

Sequential Optimization of Healthcare Processes using Reinfo...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Akkur, Malatesh Singh, B.P. Pachayappan, R. Singh, Takveer Panda, Binayak Marwaha, Pradeep Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India

ISBN: (纸本)9798331542375

The optimization of healthcare processes is so important, and it directly affects outcomes. However, traditional methods like Six Sigma and Lean cannot manage the complexities of complex and dynamic healthcare processes. Thus, alternative solutions must strive to enable the optimization of healthcare services. We intend to use reinforcement learning (RL) and sequential function model (SFM) models combined as a new Sequential optimization framework for healthcare process optimizations in this work. It is a machine learning tool that provides equivalent human actions by deciding to choose certain future moves independently. Results for the second experiment: SFM visually explains process flow and capiases dependency between different steps in a sort order. These two techniques are combined in the proposed framework to learn and optimize healthcare processes sequentially. Sequential optimization: the RL algorithms started learning from previous experiences to optimize the best decisions at different process steps. From there, the SFM provides a visual rendering of this streamlined process. The effect of the process changes can be experimented with via simulation and altered as such. This iterative process improves efficiency in processes and reduces costs to healthcare systems. The proposed framework may transform healthcare processes' optimization and, by extension, improve patient outcomes. © 2025 IEEE.

关键词： Patient treatment

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Enhancing Sensitivity and Specificity in Diabetic Retinopathy Detection Using CNN-LSTM models 3

Enhancing Sensitivity and Specificity in Diabetic Retinopath...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Mehta, Shiva Bhardwaj, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Center for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331542375

Early and accurate detection of diabetic retinopathy (DR), as the leading cause of blindness worldwide, is important to prevent blindness. We propose a hybrid Convolutional Neural Network and Long Short Term Memory (CNN LSTM) model to increase DR classification through a combination of spatial feature extractions and sequential learning. We illustrate the training and evaluation of the proposed model on the APTOS 2019 and EyePACS datasets of retinal fundus images, each annotated into five severity stages of DR. Experimental results show that the proposed CNN-LSTM model achieves 93.1% accuracy, which is better than ResNet-50 (89.2%), and DenseNet-121 (90.3%) baseline models. Overall, the model had sensitivity of 92.0% vs. 87.5% and 88.9% for ResNet-50 and DenseNet-121 respectively, which proves (that the model) was better at identifying DR cases. Furthermore, the CNN-LSTM model achieved specificity of 92.8%, which leads to a drop in the false positive rate, fairly superior to ResNet-50 (88.6%), DenseNet-121 (89.5%). Using the proposed model displayed superior discriminative power (Area Under the Curve (AUC) score 0.96) compared to ResNet-50 (0.91) and DenseNet-121 (0.93). Finally, we analyzed the performance of the model though training-validations curves, confusion matrix analysis and achieved final training accuracy of 94.5 % and validation accuracy of 93.1% with stable learning but very minimal overfitting. The results presented show the potential of the proposed CNN-LSTM framework in improving DR detection and thus, as a viable tool for use in automated screening. Towards future work, we will study methods to improve generalization with attention mechanisms as well as with larger scale datasets. © 2025 IEEE.

关键词： Convolutional neural networks

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Research on Energy Efficiency Improvement Strategy of Air Conditioning in Comprehensive Energy System Based on Neural Network Algorithm

Research on Energy Efficiency Improvement Strategy of Air Co...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Miao Shan Jie Liang Chao Chai Shaokun Jia Beijing Fibrlink Communications Co. LTD. Beijing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

To enhance the precision of the integrated energy system's operational approach, the study suggests utilizing a BP neural network for forecasting thermal and cooling demands, thereby minimizing forecast inaccuracies. This paper develops models for ice storage, gas boilers, and ground source heat pump cooling and heating equipment outputs. To enhance system efficiency, an energy efficiency target function is established. Simulation analysis shows that the BP neural network reduces prediction errors more effectively than exponential smoothing and gray prediction methods. The optimized operation model for the integrated energy system boosts the air conditioning system's comprehensive energy efficiency from 3.2 to 3.7.

关键词： Air conditioning Smoothing methods Heat pumps Optimization models Prediction methods Prediction algorithms Energy efficiency Ice Forecasting Load modeling

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SiQA: A Large Multi-Modal Question Answering Model for Structured Images Based on RAG

SiQA: A Large Multi-Modal Question Answering Model for Struc...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Jiawang Tao, Ye Wang, Fei Li, Hui Qin, Xiugong School of Information Science and Technology Qingdao University of Science and Technology Qingdao China Beijing Research Institute of Automation for Machinery Industry Co. Ltd. Beijing China

ISBN: (纸本)9798350368741

Existing Large Multimodal models (LMMs) demonstrate excellent performance in handling visual tasks in everyday scenarios. However, they still face challenges in understanding structured images, such as flowcharts and organizational charts, which are characterized by text-rich and complex hierarchical components. In this paper, we propose SiQA, a knowledge construction and Retrieval-Augmented Generation(RAG)-based multimodal Question-Answering model designed for Structured Images. SiQA operates in three stages: Knowledge Graph (KG) generation, retrieval-augmented, and answer generation. First, a KG representing the semantics of the structured images is generated through component analysis. We then performed similarity retrieval between the KG and queries, using a node-first algorithm to construct the most relevant subgraph. Finally, after performing an encoding alignment on the multimodal information, it is fed into the LLM to generate the answer. Additionally, we introduce a new dataset, OCQA1, which includes 5, 112 questions derived from 1, 000 Organizational Charts. We evaluated SiQA's structured image detection and question-answering capabilities on the FD-DETR (a flowchart dataset) and SCQA, and verified its effectiveness and strong generalization ability through comparisons with existing state-of-the-art (SOTA) methods. © 2025 IEEE.

关键词： multimodal QA RAG structured images

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