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Path-Weights and Layer-Wise Relevance Propagation for Explainability of ANNs with fMRI data 9th

Path-Weights and Layer-Wise Relevance Propagation for Explai...

引用

9th Annual conference on Machine Learning, Optimization and data science (LOD)

作者： Marques dos Santos, Jose Diogo Marques dos Santos, Jose Paulo Univ Porto Fac Engn R Dr Roberto Frias P-4200465 Porto Portugal Univ Porto Abel Salazar Biomed Sci Inst R Jorge Viterbo Ferreira P-4050313 Porto Portugal Univ Maia Ave Carlos de Oliveira Campos P-4475690 Maia Portugal Univ Porto LIACC Artificial Intelligence & Comp Sci Lab R Dr Roberto Frias P-4200465 Porto Portugal Univ Porto Fac Med Unit Expt Biol Alameda Prof Hernani Monteiro P-4200319 Porto Portugal

ISBN: (纸本)9783031539657;9783031539664

The application of artificial neural networks (ANNs) to functional magnetic resonance imaging (fMRI) data has recently gained renewed attention for signal analysis, modeling the underlying processes, and knowledge extraction. Although adequately trained ANNs characterize by high predictive performance, the intrinsic models tend to be inscrutable due to their complex architectures. Still, explainable artificial intelligence (xAI) looks to find methods that can help to delve into ANNs' structures and reveal which inputs most contribute to correct predictions and how the networks unroll calculations until the final decision. Several methods have been proposed to explain the black-box ANNs' decisions, with layer-wise relevance propagation (LRP) being the current state-of-the-art. This study aims to investigate the consistency between LRP-based and path-weight-based analysis and how the network's pruning and retraining processes affect each method in the context of fMRI data analysis. The procedure is tested with fMRI data obtained in a motor paradigm. Both methods were applied to a fully connected ANN, and to pruned and retrained versions. The results show that both methods agree on the most relevant inputs for each stimulus. The pruning process did not lead to major disagreements. Retraining affected both methods similarly, exacerbating the changes initially observed in the pruning process. Notably, the inputs retained for the ultimate ANN are in accordance with the established neuroscientific literature concerning motor action in the brain, validating the procedure and explaining methods. Therefore, both methods can yield valuable insights for understanding the original fMRI data and extracting knowledge.

关键词： Artificial neural networks (ANN) Explainable artificial intelligence (XAI) Layer-wise relevance propagation (LRP) Functional magnetic resonance imaging (fMRI)

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Generative Commissioning: AI & Digital Practice in the Building Commissioning process

Generative Commissioning: AI & Digital Practice in the Build...

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2024 ASHRAE Annual conference

作者： Aluko, Theophilus Adeoye, Elijah OLA Consulting Engineers PC HawthorneNY United States Unveil TacomaWA United States

Artificial intelligence and spatial modeling tools are shaping how we design, optimize and operate our buildings and HVAC equipment. This paper highlights case studies of real-world applications of these tools in improving indoor environmental quality and addressing climate change within the commissioning process. These case studies include applications in training;commissioning documentation;benchmarking;indoor environmental quality (IEQ) performance measurement;equivalent clean airflow analysis based on ASHRAE Standard 241, "control of Infectious Aerosols";healthy humidity threshold predictive inferences;as well as AI-aided occupant thermal comfort analysis based on ASHRAE Standard 55, "Thermal Environmental Conditions for Human Occupancy", via AI-based thermodynamic simulations;and prescriptive maintenance. This paper also covers threats that these tools pose, as well as how to build resilient digital practice frameworks in view of new health-conscious standards and federal data governance requirements. © 2024 ASHRAE.

关键词： ASHRAE standards

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Development of a Model Predictive Airpath controller for a Diesel Engine on a High-Fidelity Engine Model with Transient Thermal Dynamics

Development of a Model Predictive Airpath Controller for a D...

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American control conference (ACC)

作者： Zhang, Jiadi Amini, Mohammad Reza Kolmanovsky, Ilya Tsutsumi, Munechika Nakada, Hayato Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA Hino Motors Ltd Tokyo 1918660 Japan

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

This paper presents the results of a model predictive controller (MPC) development for diesel engine air-path regulation. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by manipulating the EGR valve and variable geometry turbine (VGT) while satisfying state and control constraints. The MPC controller is designed and verified using a high-fidelity engine model in GT-Power. The controller exploits a low-order rate-based linear parameter-varying (LPV) model for prediction which is identified from transient response data generated by the GT-Power model. It is shown that transient engine thermal dynamics influence the airpath dynamics, specifically the intake manifold pressure response, however, MPC demonstrates robustness against inaccuracies in modeling these thermal dynamics. In particular, we show that MPC can be successfully implemented using a rate-based prediction model with two inputs (EGR and VGT positions) identified from data with steady-state wall temperature dynamics, however, closed-loop performance can be improved if a prediction model (i) is identified from data with transient thermal dynamics, and (ii) has the fuel injection rate as extra model input. Further, the MPC calibration process across the engine operating range to achieve improved performance is addressed. As the MPC calibration is shown to be sensitive to the operating conditions, a fast calibration process is proposed.

关键词： Manifolds Atmospheric modeling Diesel engines Predictive models Valves data models Calibration

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Gas Turbine Fault Detection by Non-linear Principle Component analysis 5th

Gas Turbine Fault Detection by Non-linear Principle Componen...

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5th International conference on Electrical Engineering and control Applications, ICEECA 2022

作者： Nadir, Fenghour Messaoud, Bouakkaz Elias, Hadjadj Department of Electromechanical Badji Mokhtar University Annaba Algeria

ISBN: (纸本)9789819700448

In this paper, the application technique for the detection of faults in a gas turbine by using the non-linear principal component analysis (NLPCA) approach is examined as, the neural network-based nonlinear principal component analysis (NLPCA) has experienced a considerable revival of interest in recent years and has been widely used in the field of diagnosis. For this purpose, the five-layer neural network NLPCA approach is used for modeling the gas turbine process in normal fault-free operating condition, with the filtered squared prediction error index (SPE) for fault detection. Consequently, we determined the optimal number of principal components retained in the PCA model, and we validated the PCA model by checking the evolution of the measured and estimated variables. Furthermore, this study for this approach is validated by a practical gas turbine example, where we used real data of the operating of the gas turbine over a period of four years. The results obtained highlight that the NLPCA approach with the five-layer neural network from the chosen detection index is more efficient. Thus, the NLPCA method appears as an efficient tool for monitoring and diagnosing faults in a gas turbine. Finally, our contribution is to validate the performance of this method using data from a dynamic and nonlinear process such as the gas turbine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Principal component analysis

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Challenges at the Stage of Simulation Experiment Planning 7

Challenges at the Stage of Simulation Experiment Planning

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7th International conference on Information, control, and Communication Technologies, ICCT 2023

作者： Demidenko, Oleg M. Aksionova, Natallia A. Varuyeu, Andrei V. F. Skorina Gomel State University Dept. Asip Gomel Belarus

ISBN: (纸本)9798350340945

The article describes an approach to optimizing the process of modeling the computational process. The main emphasis is placed on the organization of simulation experiments and their reorganization depending on the objectives of the study. An algorithm for choosing the number of sample experiments to satisfy the attainability criterion for all components of the accuracy vector is formulated. An approach to organizing the search for the culprit of the simulation model error and the place of its occurrence in the model algorithm is outlined. It is concluded that there are no restrictions on the type of dependencies between the parameters of the simulation model and the state of the external environment of a complex system. © 2023 IEEE.

关键词： changing model parameters data analysis experiment plan search for error sources

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Deep Food Insight: A Transfer Learning Approach for Food Detection and Nutrient Estimation through Image analysis

Deep Food Insight: A Transfer Learning Approach for Food Det...

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International conference on Computer Vision, Robotics, and Automation Engineering, CRAE 2024

作者： Yaqoob, Jahanzaib Li, Ruirui Yuandong, Hu Javed, Umair Yang, Yukun Meng, Kexin Wang, Naihao Liu, Yiran Dept. of Information Science and Technology Beijing University of Chemical technology Beijing China Provincial Center for Disease Control and Prevention Guizhou Provincial Center for Disease Control and Prevention Guizhou China

ISBN: (纸本)9781510682283

Dietary choices have a substantial impact on the health of an individual. This AI-driven research aims to recognize, classify, and estimate the origin and nutrition of food. The proposed system is trained using a diverse dataset containing images of food (101 Classes) in different lights and environmental conditions. In this research a transfer learning approach applied with ResNet and InceptionV3 architectures using their pre-trained weights with finetuning of hyperparameters (Learning rate, Batch size and Optimizer). As a result of this approach, the ability to learn intricate features relevant to food recognition was retained while training rapidly. The system achieves impressive accuracy: 96.6% and 96.1% respectively for food identification, nutrient, and origin estimation. The system accurately recognizes popular foods like pizza, sushi, and salads, even in low light. Furthermore, to provide reliable food information to end users, we have developed a user-friendly web application. The app allows users to upload pictures of their meals to receive nutritional and origin information, empowering them to make healthier choices. This simplifies the process of making informed dietary choices for individuals. © 2024 SPIE.

关键词： Education and training data modeling Deep learning Image analysis Image classification Image processing Performance modeling databases Visual process modeling Tunable filters

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

International Conference of Electrical, Electronic and Netwo...

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

ISBN: (纸本)9789819618637

The proceedings contain 384 papers. The special focus in this conference is on Electrical, Electronic and Networked Energy Systems. The topics include: Novel control Strategy for Single-Phase Single-Stage Isolated AC/DC Converters;Dynamic Spatial-Temporal Graph Neural Network for Ultra Short-Term PV Power Generation Forecasting;selective Miniature Circuit Breaker Operation Mechanism Motion process Mechanical Properties Optimization analysis;a Compact All-Solid-State Bipolar Pulse Adder;a Novel Reluctance Machine with Skewed Magnetized Stator Permanent Magnet for Low-Speed High Torque Density Applications;numerical Simulation Study of the Film-Forming Characteristics of Water Mist in Battery Packs;research on Influence of Asynchronous Condenser on the Frequency Characteristics of New Energy Power System;Research on Electromagnetic Interference Suppression Method for Parallel-Connected Inverter Module Based on Si IGBT/SiC MOSFET;Research on the Operation Optimization Strategy of Electricity Sales Companies Based on GridLAB-D;loss analysis of Converter Transformer Under Non-Sinusoidal Operating Conditions;a Study of data Quality Optimisation Methods for Intelligent Power Marketing;a Dynamic Cluster-Aware modeling Approach for Distribution Networks Based on New Energy Intelligent Individuals;research on Distributed Wind and Solar Power modeling Method Based on Swarm Intelligence;synthesis Load Research and Load Model;design on Unipolar Symmetrical Trapezoidal Pulsed Current Transmission Circuit for Grounding Grid Fault Detection in Substations;A Modified VSG control Scheme with Transient Damping to Improve Transient and Frequency Stability;fault Detection Method of Substation Ground Grid Based on Transient Electromagnetic Method;planning of Renewable Energy Bases in Desert Areas Considering the Response Characteristics of Compressed Carbon Dioxide Energy Storage.

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

International Conference of Electrical, Electronic and Netwo...

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

ISBN: (纸本)9789819620418

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

International Conference of Electrical, Electronic and Netwo...

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

ISBN: (纸本)9789819620791

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Dynamic modeling of Strip Rolling process Using Probabilistic Neural Network

Dynamic Modeling of Strip Rolling Process Using Probabilisti...

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18th IEEE International conference on Advanced Motion control, AMC 2024

作者： Deng, Jifei Sierla, Seppo Sun, Jie Vyatkin, Valeriy Northeastern University State Key Laboratory of Rolling and Automation Shenyang China Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350342819

This paper proposes an intelligent dynamic modeling method for strip rolling process. Actuators of a cold rolling mill perform actions, including work roll bending, intermediate roll bending, and roll gap tilting, to regulate the product quality. Conventional first-principles modeling necessitates intricate mathematical equations derived from process analysis and action evaluation. However, the complexity of the model increases the difficulty of upgrading the model. To address this limitation, a novel approach involving a deep neural network with a Gaussian distribution layer is proposed to effectively capture the process dynamics. A negative log likelihood is established as the loss function for the model using real-world industrial data. Experiment outcomes demonstrate the efficacy of the proposed method in achieving an accurate dynamic model. © 2024 IEEE.

关键词： Deep neural networks

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