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BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

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computer Modeling in engineering & Sciences 2022年第5期131卷 851-863页

作者： Yankun Cao Lina Xu Zhi Liu Xiaoyan Xiao Mingyu Wang Qin Li Hongji Xu Geng Yang School of Software Shandong UniversityJinan250101China School of Information Science and Engineering Shandong UniversityQingdao266237China School of Computer Science and Technology Shandong Jianzhu UniversityJinan250012China Department of Nephrology Qilu Hospital of Shandong UniversityJinan250012China State Key Laboratory of ASIC&Systems The School of MicroelectronicsFudan UniversityShanghai200433China Shenzhen Institute of Information Technology Shenzhen518172China

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary ***:In recent years,deep learning has been widely used in academia,industry,*** increasing number of companies are starting to recruit a large number of professionals in the field of deep *** numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis *** now,however,there is no practical training platform that can help radiologists learn automated medical image analysis ***:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this *** platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

关键词： BMR deep learning three dimensional medical decision support system deep learning engineer standard

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Triple Point Masking

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. © 2025 IEEE. All rights reserved.

关键词： 3D modeling

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Tri-Level Linear Programming Model for Automatic Load Shedding Using Spectral Clustering

Tri-Level Linear Programming Model for Automatic Load Sheddi...

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Innovative Smart Grid Technologies (ISGT)

作者： Mario D. Baquedano-Aguilar Arturo Bretas Sean Meyn Nader Aljohani Department of Electrical & Computer Engineering University of Florida Gainesville FL USA Distributed Systems Group Pacific Northwest National Laboratory Richland WA USA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France Department of Electrical Engineering College of Engineering Taibah University Medina Saudi Arabia

Traditional load shedding schemes can be inadequate in grids with high renewable penetration, leading to unstable events and unnecessary grid islanding. Although for both manual and automatic operating modes load shedding areas have been predefined by grid operators, they have remained fixed, and may be sub-optimal due to dynamic operating conditions. In this work, a distributed tri-level linear programming model for automatic load shedding to avoid system islanding is presented. Preventing islanding is preferred because it reduces the need for additional load shedding besides the disconnection of transmission lines between islands. This is crucial as maintaining the local generation-demand balance is necessary to preserve frequency stability. Furthermore, uneven distribution of generation resources among islands can lead to increased load shedding, causing economic and reliability challenges. This issue is further compounded in modern power systems heavily dependent on non-dispatchable resources like wind and solar. The upper-level model uses complex power flow measurements to determine the system areas to shed load depending on actual operating conditions using a spectral clustering approach. The mid-level model estimates the area system state, while the lower-level model determines the locations and load values to be shed. The solution is practical and promising for real-world applications.

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Robust Adaptive Control Allocation Schemes for Overactuated Underwater Vehicles under Actuator Faults

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IFAC-PapersOnLine 2022年第6期55卷 67-72页

作者： Waseem Akram Alessandro Casavola Nikola Miškovic Department of Computer Engineering Modeling Electronics and Systems (DIMES) University of Calabria Rende Italy Faculty of Electrical Engineering and Computing LABUST - Laboratory for Underwater Systems and Technologies University of Zagreb Unska 3 Zagreb Croatia

In this paper, we investigate the fault-tolerant path following control problem for an overactuated underwater vehicle subject to input saturation and rate of change constraints, actuator faults and parametric (mass and inertia) uncertainty. A robust adaptive control allocation strategy is proposed capable to reconfigure the distribution of the control effort among the remaining healthy actuators in the case some of them failed. The online estimation of actuators' effectiveness and bias fault parameters is integrated with the control allocation and reconfiguration unit. The robustness and performance of the scheme are shown by evaluating the results of simulations and pool experiments of different path-following control problems using overactuated underwater vehicles.

关键词： Adaptive control Underwater vehicles Actuator faults trajectory tracking

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AI/ML Based Detection and Categorization of Covert Communication in IPv6 Network

arXiv

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arXiv 2025年

作者： Rahman, Mohammad Wali Ur Lin, Yu-Zheng Weeks, Carter Ruddell, David Gabriellini, Jeff Hayes, Bill Hariri, Salim Ziegler, Edward V. Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States Department of Management Information Systems University of Arizona TucsonAZ85721 United States Department of Systems and Industrial Engineering University of Arizona TucsonAZ85721 United States Laboratory for Advanced Cybersecurity Research National Security Agency United States

The flexibility and complexity of IPv6 extension headers allow attackers to create covert channels or bypass security mechanisms, leading to potential data breaches or system compromises. The mature development of machine learning has become the primary detection technology option used to mitigate covert communication threats. However, the complexity of detecting covert communication, evolving injection techniques, and scarcity of data make building machine-learning models challenging. In previous related research, machine learning has shown good performance in detecting covert communications, but oversimplified attack scenario assumptions cannot represent the complexity of modern covert technologies and make it easier for machine learning models to detect covert communications. To bridge this gap, in this study, we analyzed the packet structure and network traffic behavior of IPv6, used encryption algorithms, and performed covert communication injection without changing network packet behavior to get closer to real attack scenarios. In addition to analyzing and injecting methods for covert communications, this study also uses comprehensive machine learning techniques to train the model proposed in this study to detect threats, including traditional decision trees such as random forests and gradient boosting, as well as complex neural network architectures such as CNNs and LSTMs, to achieve detection accuracy of over 90%. This study details the methods used for dataset augmentation and the comparative performance of the applied models, reinforcing insights into the adaptability and resilience of the machine learning application in IPv6 covert communication. In addition, we also proposed a Generative AI-assisted interpretation concept based on prompt engineering as a preliminary study of the role of Generative AI agents in covert communication. © 2025, CC BY-NC-ND.

关键词： Decision trees

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Evaluating Non-Expert Stakeholder Interaction with Artificial Intelligence on Energy Urban Domain Using VIRTSI: The Case of ChatGPT

Evaluating Non-Expert Stakeholder Interaction with Artificia...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： George A. Tsihrintzis Elissaios Sarmas Vangelis Marinakis Dimitrios Panagoulias Evangelia-Aikaterini Tsihrintzi Maria Virvou Department of Informatics University of Piraeus Piraeus Greece Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece Department of Informatics and Telecommunicatios National and Kapodistrian University of Athens Athens Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

This paper investigates the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT. VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), is a rigorous computational model for human-AI Interaction that simulates human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The research employed an 16-question survey, evaluating the accuracy and usefulness of ChatGPT's responses regarding energy consumption, cost-effective solutions, and renewable energy production for residential buildings in Greece. Each answer was assessed by human stakeholders who were non-expert in the energy Urban Domain (e.g house owners, building managers, etc.), who either accepted or rejected the responses based on validation processes. The analysis highlighted key aspects such as repetition, specification, and objections in the interaction with ChatGPT, offering insights into the effectiveness of AI in supporting energy-related decisions. The findings reveal that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation. This highlights both the potential and limitations of integrating AI tools like ChatGPT in enhancing non-expert stakeholder engagement and decision-making in urban energy management, emphasizing the ongoing need for human expert involvement.

关键词： Surveys Renewable energy sources Accuracy Computational modeling Decision making Buildings Chatbots Stakeholders Artificial intelligence Energy management

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Inspection of a helicopter blade using drone based active thermography 45

Inspection of a helicopter blade using drone based active th...

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Thermosense: Thermal Infrared Applications XLV 2023

作者： Pant, Shashank Genest, Marc Ibarra-Castanedo, Clemente Avdelidis, Nicolas P. Valdes, Julio Deane, Shakeb Zolotas, Argyrios Maldague, Xavier P. National Research Council Canada OttawaONK1A 0R6 Canada Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University Quebec CityQCG1V 0A6 Canada School of Aerospace Transport and Manufacturing Cranfield University CranfieldMK43 0AL United Kingdom

ISBN: (纸本)9781510661868

A drone-based inspection system that can move "freely" around an aircraft to perform the inspection of all the areas of interest in a fast and effective manner can have significant impact in reducing inspection time and cost. However, active thermography inspection using drone is challenging because the drone carrying the optical and thermal cameras is subjected to vibration and undesired motion. Since active thermography relies on the pixel temperature evolution over time, an unstable thermal video from a flying drone can cause error in the output results as any movement between the acquired images will affect the pixel position in the successive frames and thus disrupt the monitoring of the temperature evolution. This paper presents the outcome of experimental runs, where a commercially available drone equipped with both thermal and optical cameras was used to inspect a helicopter Main Rotor Blade (MRB) in a laboratory environment. © 2023 SPIE.

关键词： Inspection

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Enhancing Four-Class Motor Imagery Detection Through Advanced Feature Extraction Techniques

Enhancing Four-Class Motor Imagery Detection Through Advance...

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ACS/IEEE International Conference on computer systems and Applications

作者： Jihen Souissi Sourour Karmani Kais Belwafi Ridha Djemal ATMS Laboratory ENIS National Engineering School of Sfax Sfax University Tunisia Tunisia Laboratory of Electronics and Microelectronics FSM Higher Institute of Applied Sciences and Technology of Sousse University of Sousse University of Monastir Tunisia Computer Engineering Department CCI University of Sharjah Sharjah UAE Electrical Engineering Department National Engineering School of Sfax ATMS Lab. Sfax University Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Brain-computer interface (BCI) technology has great potential in control, communication, and neurological diagnostics by interpreting EEG signals. This paper introduces a novel method for extracting distinguishing features from EEG signals using a Common Spatial Patterns (CSP) model, enhanced with a Phase Space Reconstruction model and followed by an autoregressive (AR) model. Initially, EEG signals are denoised using an IIR Butterworth filter before feature extraction. Two classifiers, Linear Discriminant Analysis (LDA) and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery (MI) tasks. For a four-class MI task, combining spatial and temporal-frequency domain features, LDA achieves an accuracy of 67.40%, while MLP reaches 64.78%. LDA yields precision and recall scores of 67.40% and 67.20%, respectively, for the F1-score, whereas MLP records precision, recall, and F1-scores of 65.36%, 64.78%, and 64.28%, respectively. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.

关键词： Accuracy Pattern classification computer architecture Signal processing Feature extraction Brain modeling Motors Electroencephalography Brain-computer interfaces Linear discriminant analysis

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An FPGA-based 7-ENOB 600 msample/s adc without any external components 21

An FPGA-based 7-ENOB 600 msample/s adc without any external ...

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2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA 2021

作者： Leuenberger, Lukas Amiet, Dorian Wei, Tao Zbinden, Paul OST-Eastern Switzerland University of Applied Sciences Imes Institute for Microelectronics and Embedded Systems Rapperswil Switzerland University of Rhode Island Department of Electrical Computer and Biomedical Engineering Kingston United States

ISBN: (纸本)9781450382182

Analog to digital converters (ADCs) are indispensable nowadays. Analog signals are digitized earlier and earlier in the processing chain to reduce the need for complex analog signal processing. For this reason, ADCs are often integrated directly into field-programmable gate arrays (FPGA) or microprocessors. However, such ADCs are designed for a specific set of requirements with limited flexibility. In this paper, a new structure of an FPGA-based ADC is proposed. The ADC is based on the slope ADC, where a time-to-digital converter (TDC) measures the time from the beginning of a reference slope until the slope reaches the voltage-to-be-measured. Only FPGA-internal elements are used to build the ADC. It is fully reconfigurable and does not require any external components. This innovation offers the flexibility to convert almost any digital input/output (I/O) into an ADC. Considering the very high number of digital I/O ports available in today's FPGA systems, this enables the construction of a massive and powerful ADC array directly on a standard FPGA. The proposed ADC has a resolution of 9.3 bit and achieves an effective number of bits (ENOB) of 7 at a sample rate of 600 MSample/s. The differential nonlinearity (DNL) ranges from-0.9 to 0.9 bit, and the integral nonlinearity (INL) is in the range between-1.1 and 0.9 bit. An alternative version of the ADC operates at 1.2 GSample/s and achieves an ENOB of 5.3. © 2021 ACM.

关键词： Signal processing

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Fuzzy Object Ambiguity Determination and Human Attention Assessment for Domestic Service Robots

Fuzzy Object Ambiguity Determination and Human Attention Ass...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Kevin Fan Melanie Jouaiti Kerstin Dautenhahn Chrystopher L. Nehaniv Department of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada Departments of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada

Domestic service robots have the promising potential of bringing significant services to the general population, and more importantly, successful applications of universal domestic service robots can potentially help mitigate critical societal issues such as senior care. In order to do so, domestic service robots need to integrate seamlessly into home environments. However, home environments are dynamic, complex and filled with personal items. Therefore, ambiguity can quickly arise for robots operating in such rich environments. In this paper, we propose an object ambiguity determination system that can determine the level of ambiguity in robot object selection tasks with fuzzy logic data integration. Additionally, we propose a functional human attention assessment system with fuzzy logic that enables the robot to determine user attention before committing to general disambiguation processes. Our preliminary results show that the proposed fuzzy logic inference systems can reliably estimate the robot object selection task ambiguity from object confidence level and the number of potential target objects that satisfy the user's command. Furthermore, fuzzy inference is applied to decide human eye gaze direction robustly. These subsystems can be utilized in the context of human-robot interaction to guide the robot when to seek feedback from a human partner in order to disambiguate reference in domestic service tasks. The source code of all proposed systems is available publicly on GitHub. 1

关键词： Fuzzy logic Service robots Source coding Sociology Human-robot interaction Data integration Reliability

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