Social Spider Optimization (SSO) and Particle Swarm Optimization (PSO) algorithms applied to Intrusion Detection Systems (IDS) across small and large datasets. The study aims to evaluate and compare both PSO and SSO a...
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A method based on data fusion is proposed for detection of fire in dense bus ducts in buildings. The environmental temperature data, infrared image data and gas concentration data are used in the fire warning system. ...
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A method based on data fusion is proposed for detection of fire in dense bus ducts in buildings. The environmental temperature data, infrared image data and gas concentration data are used in the fire warning system. The features of temperature data and gas concentration data are extracted by statistical method, the features of infrared image are extracted by Faster RCNN (Region Convolutional Neural Networks), then three types of features are fused and inputted into the SVM (Support Vector Machine) model to detect fire early. The experimental results show that this method has high recognition rate and good robustness. & COPY;2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 3rdinternationalconference on Power and Electrical engineering, ICPEE, 2022.
Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operational data corresponding to a specific...
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ISBN:
(纸本)9798400705915
Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operational data corresponding to a specific period of time and that they are continuously evaluated on newly emerging data. Operational data is constantly changing over time, which affects the performance of deployed anomaly detection models. Therefore, continuous model maintenance is required to preserve the performance of anomaly detectors over time. In this work, we analyze two different anomaly detection model maintenance techniques in terms of the model update frequency, namely blind model retraining and informed model retraining. We further investigate the effects of updating the model by retraining it on all the available data (full-history approach) and only the newest data (sliding window approach). Moreover, we investigate whether a data change monitoring tool is capable of determining when the anomaly detection model needs to be updated through retraining.
In this paper, the entropy weight method was first used to calculate the weights of different indicators based on PRES dataset, and then the influence factors were analyzed using principal component regression to veri...
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Large language model (LLM) based chatbots, such as ChatGPT, have attracted huge interest in foundation models. It is widely believed that foundation models will serve as the fundamental building blocks for future AI s...
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ISBN:
(纸本)9798400705915
Large language model (LLM) based chatbots, such as ChatGPT, have attracted huge interest in foundation models. It is widely believed that foundation models will serve as the fundamental building blocks for future AI systems. However, the architecture design of foundation model based systems has not yet been systematically explored. There is limited understanding about the impact of introducing foundation models in software architecture. Therefore, in this paper, we propose a taxonomy of foundation model based systems, which classifies and compares the characteristics of foundation models and system design options. Our taxonomy comprises three categories: the pretraining and adaptation of foundation models, the architecture design of foundation model based systems, and responsible-AI-by-design. This taxonomy can serve as concrete guidance for designing foundation model based systems.
The proceedings contain 48 papers. The topics discussed include: a taxonomy of foundation model-based systems through the lens of software architecture;investigating the impact of SOLID design principles on machine le...
ISBN:
(纸本)9798400705915
The proceedings contain 48 papers. The topics discussed include: a taxonomy of foundation model-based systems through the lens of software architecture;investigating the impact of SOLID design principles on machine learning code understanding;identifying architectural design decisions for achieving green ML serving;modeling resilience of collaborative ai systems;an exploratory study of V-model in building ML-enabled software: a systems engineering perspective;engineering challenges in industrial AI;what about the data? a mapping study on dataengineering for AI systems;unmasking data secrets: an empirical investigation into data smells and their impact on data quality;an exploratory study of dataset and model management in open source machine learning applications;developer experiences with a contextualized AI coding assistant: usability, expectations, and outcomes;innovating translation: lessons learned from BWX generative language engine;and towards a responsible AI metrics catalogue: a collection of metrics for AI accountability.
With the increasing complexity of the power system, higher requirements have been put forward for the stable and efficient operation of equipment. Predictive maintenance, as a strategy aimed at identifying and correct...
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ISBN:
(纸本)9798350366105;9798350366099
With the increasing complexity of the power system, higher requirements have been put forward for the stable and efficient operation of equipment. Predictive maintenance, as a strategy aimed at identifying and correcting potential faults in advance, provides an effective means to ensure the stable operation of power equipment. This study delves into the application and effectiveness of DL (Deep Learning) technology in predictive maintenance of equipment in power enterprises. The article first outlines the advantages and potential of DL in predictive maintenance, especially its unique advantages in handling large-scale, high-dimensional, and nonlinear data. Subsequently, we discussed in detail the key steps of data collection, preprocessing, and feature extraction, emphasizing the DL model's ability to automatically extract features. In the process of model design and validation, this article explores various DL algorithms and their practical application effects in predictive maintenance of power equipment. Finally, through simulation verification, the DL model shows higher prediction accuracy compared to traditional methods.
In this paper, based on the GBET dataset, 12 factors are used as secondary indicators, and then the 12 indicators are extracted into 3 primary indicators, and then the fuzzy comprehensive evaluation model is used for ...
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This paper presents an algorithm for extracting tabular data. Among them, the text structure recognition uses ResNet as the backbone network, and the FPN module and TS-CB module are added to increase the detection per...
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ISBN:
(纸本)9798400717048
This paper presents an algorithm for extracting tabular data. Among them, the text structure recognition uses ResNet as the backbone network, and the FPN module and TS-CB module are added to increase the detection performance of the model for edge distribution. Method of text prediction network to optimize the structure of CRNN, the CRCRNet model was proposed, and the row reconstruction module and column reconstruction module were used in the network to improve the recognition and prediction ability of the model. The process of text recognition prediction and table reconstruction are integrated into a whole, and a complete table recognition algorithm is proposed. In Chapter 5, the experimental results on ICDAR2013data set are given and compared with other form recognition methods. The experimental results show that the proposed recognition algorithm has better performance.
This study focuses on the problem of vehicle dynamics modeling within the framework of intelligent vehicles cyber-physical systems. Initially, a mechanistic analysis of vehicle dynamics is conducted, and, leveraging i...
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ISBN:
(纸本)9798350366105;9798350366099
This study focuses on the problem of vehicle dynamics modeling within the framework of intelligent vehicles cyber-physical systems. Initially, a mechanistic analysis of vehicle dynamics is conducted, and, leveraging its characteristics, we design a composite neural network that integrates Gated Recurrent Unit (GRU) and Feedforward Neural Network (FNN), employing a data-driven modeling methodology. Subsequently, a novel neural network-based digital mapping proxy model for vehicle dynamics is formulated. Comparative experiments among various methods demonstrate that our proposed approach yields higher precision in both lateral and longitudinal dynamic models. The application of our model to the vehicle longitudinal speed tracking control system validates its suitability and real-time performance in control system simulation.
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