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Ensemble ranking: An aggregation of multiple multicriteria methods and scenarios and its application to power generation planning

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Decision Analytics Journal 2024年 10卷

作者： Alves, Marcos Antonio Oliveira, Bruno Alberto Soares Guimarães, Frederico Gadelha Graduate Program in Electrical Engineering Av. Antônio Carlos 6627 MG Belo Horizonte 31270-901 Brazil FITec - Technological Innovations Av. Cristóvão Colombo 485 Funcionários MG Belo Horizonte 30140-150 Brazil Department of Computer Science Rua Reitor Píres Albuquerque ICEx MG Belo Horizonte 31270-901 Brazil

Evaluating maintenance plans for power generation is a critical task managed by the National System Operator, as it is directly related to criteria such as operational cost, rationing, and availability of natural resources. This study identifies the most suitable maintenance schedule between uncertainties related to solution ranking methods and future scenarios. We investigated the performance of various multicriteria decision-making methods, including the Analytic Hierarchy Process (AHP), TODIM ((an acronym in Portuguese for Interactive Multicriteria decision-making), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), and Weighted Aggregated Sum Product ASsessment (WASPAS), in prioritizing maintenance plans across three scenarios. These included a scenario derived from an energy generation problem and two others based on variations in hydrology and energy demand parameters. The results analyze and discuss the similarities and differences in the rankings. Due to the lack of consensus among the methods regarding the optimal maintenance plan, we propose an ensemble ranking that considers an important factor for both the method and the scenario. The solution from the ensemble ranking, compared to the lowest-cost solution obtained in the optimization process, showed a slight cost increase of 0.32% and a 9.34% deviation from the base maintenance plan, yet it can deal with different uncertainties. The findings of this study can provide useful and robust information to energy stakeholders and serve as a reference for energy policy. © 2024 The Authors

关键词： Generation maintenance scheduling problems Hydrothermal dispatch Multicriteria decision-making Power generation planning Robust decision-making Uncertainty scenarios

Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification

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arXiv 2022年

作者： Chatterjee, Soumick Yassin, Hadya Dubost, Florian Nürnberger, Andreas Speck, Oliver Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Faculty of Nature Sciences Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Institute for Medical Engineering Faculty of Electrical Engineering and Information Technology Otto von Guericke University Magdeburg Germany Department of Biomedical Data Science Stanford University StanfordCA United States Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany

Deep learning models have shown their potential in recent years for several applications. However, most of the models are opaque in nature and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some fields, such as medicine, require a high degree of transparency to accept and adopt such technologies. Consequently, there is a movement towards creating explainable/interpretable models or applying post-hoc explainability/interpretability methods on classifiers to build trust in deep learning models. Moreover, deep learning methods can be used for segmentation tasks, which typically require hard-to-obtain, time-consuming manually-annotated segmentation labels for training. This paper introduces three inherently-explainable classifiers to tackle both of these problems as one. The localisation heatmaps provided by the networks - representing the models' focus areas and being used in classification decision-making - can be directly interpreted, without requiring any post-hoc methods to derive information for model explanation. The models are trained by using the input image and only the classification labels as groundtruth in a supervised fashion - without using any information about the location of the region of interest (i.e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels. The final segmentation of the regions of interest is obtained by thresholding these heatmaps. The models were employed for the task of multi-class brain tumour classification using two different datasets, resulting in the best F1-score of 0.93 for the supervised classification task while securing a median Dice score of 0.67 ± 0.08 for the weakly-supervised segmentation task. Furthermore, the obtained accuracy on a subset of tumour-only images outperformed the state-of-the-art glioma tumour grading binary classifiers with the best model achieving 98.7% accuracy. Copyright © 2022, The Autho

关键词： Magnetic resonance imaging

Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension

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arXiv 2023年

作者： Zhang, Yuhong Li, Qin Nahata, Sujal Jamal, Tasnia Cheng, Shih-Kuen Cauwenberghs, Gert Jung, Tzyy-Ping The School of Engineering Brown University ProvidenceRI02912 United States Institute for Neural Computation University of California San Diego La Jolla CA92093 United States Department of Radiology and Biomedical Imaging Yale University New HavenCT06520 United States The Department of Bioengineering University of California Los Angeles Los AngelesCA90095 United States The Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States The Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States The Institute of Cognitive Neuroscience National Central University Taoyuan32001 Taiwan The Department of Bioengineering Institute for Neural Computation University of California San Diego La Jolla CA92093 United States The Institute for Neural Computation University of California San Diego La Jolla CA92093 United States

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals’ neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use a feature engineering approach to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60% across 12 subjects. Words of high relevance to the inference keyword had significantly more eye fixations per word: 1.0584 compared to 0.6576 when excluding no-fixation words, and 1.5126 compared to 1.4026 when including them. This study represents the first attempt to classify brain states at a word level using LLM knowledge. It provides valuable insights into human cognitive abilities and the realm of Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

Knee deep in Error: Unraveling the accuracy limitations of consumer-grade wearable motion sensors with limited degrees of freedom computing knee joint angles

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computers in Biology and Medicine 2025年 193卷 110469页

作者： Hollingworth, Abby L. Hutchinson, Jayson B. Bechtel, Jocelynn R. Chapman, Ryan M. University of Rhode Island Department of Electrical Computer and Biomedical Engineering Fascitelli Center for Advanced Engineering 2 East Alumni Avenue KingstonRI02881 United States University of Rhode Island Department of Kinesiology 25 W. Independence Way KingstonRI02881 United States

Consumer-grade wearable inertial measurement units (IMUs) with limited degrees of freedom (DOF) are common computing knee motion before/after clinical intervention. Appropriate use requires attaching sensors per manufacturer's instructions. However, IMU misplacement error remains problematic. Thus, we assessed error computing sagittal knee angles across sensor misplacement orientations of one consumer-grade IMU system with 6DOF. We hypothesized error would increase during frontal plane misplacement. Ten young, healthy individuals (20.9 ± 0.7 years) were enrolled. IMUs were adhered/calibrated per manufacturer instructions and IMU-computed knee angles were compared to optical motion capture (MOCAP) during self-selected speed treadmill walking. Shin/thigh IMUs were then moved across sagittal, frontal, and transverse planes (±10°, ±20°). At each orientation, IMU calibration and treadmill walking was re-completed. Error was computed between MOCAP/IMU by calculating the difference and computing the absolute value (i.e. absolute value error). This was averaged across participants at each sensor position (i.e. average absolute value error). Bland Altman analyses assessed MOCAP/IMU agreement. ANOVAs assessed how plane, segment, and falsified IMU angle impacted error. MOCAP/IMUs were highly correlated with IMUs achieving clinically acceptable error ( © 2025 Elsevier Ltd

关键词： Mastication

Analytical Estimation of Out-of-plane Strain in Ultrasound Elastography to Improve Axial and Lateral Displacement Fields*

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Analytical Estimation of Out-of-plane Strain in Ultrasound E...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Niusha Kheirkhah Ali Sadeghi-Naini Abbas Samani School of Biomedical Engineering Western University London Canada Department of Electrical Engineering and Computer Science York University Toronto Canada Departments of Medical Biophysics and Electrical and Computer Engineering Western University London Canada

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Many types of cancers are associated with changes in tissue mechanical properties. This has led to the development of elastography as a clinically viable method where tissue mechanical properties are mapped and visualized for cancer detection and staging. In quasi-static ultrasound elastography, a mechanical stimulation is applied to the tissue using ultrasound probe. Using ultrasound radiofrequency (RF) data acquired before and after the stimulation, the tissue displacement field can be estimated. Elasticity image reconstruction algorithms use this displacement data to generate images of the tissue elasticity properties. The accuracy of the generated elasticity images depends highly on the accuracy of the tissue displacement estimation. Tissue incompressibility can be used as a constraint to improve the estimation of axial and, more importantly, the lateral displacements in 2D ultrasound elastography. Especially in clinical applications, this requires accurate estimation of the out-of-plane strain. Here, we propose a method for providing an accurate estimate of the out-of-plane strain which is incorporated in the incompressibility equation to improve the axial and lateral displacements estimation before elastography image reconstruction. The method was validated using in silico and tissue mimicking phantom studies, leading to significant improvement in the estimated displacement.

关键词： Mathematical model Strain Breast Estimation Elastography Ultrasonic imaging Probes

Alternate inter-digit finger tapping pattern resembles rudimentary finger grasping pattern

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biomedical engineering Advances 2025年 9卷

作者： Stefan Manoharan June Seung Lee Hangue Park Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA MetaBioHealth Sungkyunkwan University Suwon South Korea Department of Biomedical Engineering Sungkyunkwan University Suwon South Korea Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University Suwon South Korea

Neurotraumas often result in loss of finger grasping ability, thereby deteriorating quality of life. To develop effective rehabilitation method for finger grasping, objective evaluation metrics of grasping capability is a basic requirement. However, current evaluation methods for grasping confront challenges with their non-rhythmic nature and limited sample size, resulting in low statistical power and high variability. Finger tapping may shed light on this issue, based on its rhythmic nature and a common underlying mechanism between rhythmic finger tapping and rudimentary finger grasping. In this study, we investigated “phase difference” at inter-digit finger tapping, which indicates a deviation from the temporal symmetry of inter-digit tapping and potentially reflects a rudimentary finger grasping pattern. This was accomplished by analysing the metacarpophalangeal (MCP) angle of subjects’ fingers, measured by an infrared hand-motion sensor, during finger-tapping tasks. Subjects performed alternate or simultaneous rhythmic tapping tasks between two adjacent fingers among the thumb, index, and middle fingers while the MCP angle for the fingers was measured. Using the oscillating nature of the MCP angles for each finger, we established phase difference as a metric to describe relative interdigital motion. We identified congruency in phase difference between the alternate inter-digit tapping pattern and the rudimentary grasping pattern. Experimental results with phase analysis suggest that alternate inter-digit tapping, which is a rhythmic and intuitive task, is strongly correlated with the rudimentary grasping in kinematics, while the simultaneous tapping is not. Accordingly, we predict potential applications of alternate inter-digit finger tapping and its metrics in grasping diagnostics and rehabilitation monitoring.

关键词： Finger tapping Grasping pattern Grasping diagnostics Rehabilitation

Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data

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arXiv 2021年

作者： Che, Hui Radbel, Jared Sunderram, Jag Nosher, John L. Patel, Vishal M. Hacihaliloglu, Ilker The Department of Biomedical Engineering Rutgers University NJ United States The Department of Medicine Rutgers Robert Wood Johnson Medical School NJ United States The Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States The Department of Electrical and Computer Engineering Johns Hopkins University MD United States The Department of Biomedical Engineering Rutgers University The Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States

The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools. However, difficulty in eliminating the risk of disease transmission, radiation exposure and not being cost-effective are some of the challenges for CT and CXR imaging. This fact induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due to its practical advantages of noninvasiveness, repeatability, and sensitive bedside property. In this paper, we utilize a deep learning model to perform the classification of COVID-19 from LUS data, which could produce objective diagnostic information for clinicians. Specifically, all LUS images are processed to obtain their corresponding local phase filtered images and radial symmetry transformed images before fed into the multi-scale residual convolutional neural network (CNN). Secondly, image combination as the input of the network is used to explore rich and reliable features. Feature fusion strategy at different levels is adopted to investigate the relationship between the depth of feature aggregation and the classification accuracy. Our proposed method is evaluated on the point-of-care US (POCUS) dataset together with the Italian COVID-19 Lung US database (ICLUS-DB) and shows promising performance for COVID-19 prediction. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

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arXiv 2025年

作者： Nielsen, Aleksandra Weronika Manoochehri, Hafez Eslami Zhong, Hua Panwar, Vandana Jarmale, Vipul Jasti, Jay Nourani, Mehrdad Rakheja, Dinesh Brugarolas, James Kapur, Payal Rajaram, Satwik Lyda Hill Department of Bioinformatics University of Texas Southwestern Medical Center DallasTX United States Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX United States Department of Pathology University of Texas Southwestern Medical Center DallasTX United States Kidney Cancer Program Simmons Comprehensive Cancer Center University of Texas Southwestern Medical Center DallasTX United States University of Texas Southwestern Medical Center DallasTX United States Department of Urology University of Texas Southwestern Medical Center at Dallas DallasTX United States

The ability of tumors to evolve and adapt by developing subclones in different genetic and epigenetic states is a major challenge in oncology. Traditional tools like multi-regional sequencing used to study tumor evolution and the resultant intra-tumor heterogeneity (ITH) are often impractical because of their resource-intensiveness and limited scalability. Here, we present MorphoITH, a novel framework that leverages histopathology slides to deconvolve molecular ITH through tissue morphology. MorphoITH integrates a self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) with rigorous methods to eliminate spurious sources of variation. Using a prototype of ITH, clear cell renal cell carcinoma (ccRCC), we show that MorphoITH captures clinically-significant biological features, such as vascular architecture and nuclear grades. Furthermore, we find that MorphoITH recognizes differential biological states corresponding to subclonal changes in key driver genes (BAP1/PBRM1/SETD2). Finally, by applying MorphoITH to a multi-regional sequencing experiment, we postulate evolutionary trajectories that largely recapitulate genetic evolution. In summary, MorphoITH provides a scalable phenotypic lens that bridges the gap between histopathology and genomics, advancing precision oncology. © 2025, CC BY-NC-ND.

关键词： Tumors

Implementation of MPPT technique of solar module with supervised machine learning

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arXiv 2021年

作者： Sharmin, Ruhi Chowdhury, Sayeed Shafayet Abedin, Farihal Rahman, Kazi Mujibur Department of Biomedical Engineering Purdue University 206 S. Martin Jischke Drive West LafayetteIN47907-2032 United States Department of Electrical & Computer Engineering Purdue University 501 Northwestern Ave. West LafayetteIN47907-2035 United States Dhaka Bangladesh

Automated calibration of a maximum power point tracking (MPPT) algorithm and its efficacious implementation for the photo-voltaic (PV) system is pivotal for harnessing maximum possible energy from solar power. However, most existing calibration methods of such an MPPT system are cumbersome and vary greatly with the environmental condition. Hence, an automated pipeline capable of performing suitable adjustments and accurate analysis for solar PV systems is highly desirable. To counter this issue, numerous algorithms have been proposed so far, such as perturb and observe (P&O) method, incremental conductance (IC) method, fractional open circuit voltage (FOCV) method, short circuit current method, fuzzy logic based algorithm etc. While these approaches perform pretty well and produce overall acceptable results, recent surge of data-driven machine learning (ML) approaches hold great promise in this research domain. Since, supervised ML methods are trained directly based on the data, no human-designed heuristics are involved, which makes these techniques highly accurate and robust. As a result, in this paper, we proposed a method using supervised ML in solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulated to create a dataset in MATLAB/ Simulink. Thus, by analyzing the output characteristics of a solar cell, an improved MPPT algorithm on the basis of neural network (NN) method is put forward to track the maximum power point (MPP) of solar cell modules. To perform the task, Bayesian Regularization method was chosen as the training algorithm as it works best even for smaller data supporting the wide range of the train data set. The theoretical results show that the improved NN MPPT algorithm has higher efficiency compared with the Perturb and Observe method in the same environment, and the PV system can keep working at MPP without oscillation and probability of any kind of misjudgment. So it can not only r

关键词： Maximum power point trackers