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Anomaly detection in all-sky images: An approach using robust ensemble modeling of cloud cover fraction and prediction bounds

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： da Rocha, Vinicius Roggerio Fisch, Gilberto Costa, Rodrigo Santos Ruano, Antonio Natl Inst Space Res Ave Astronautas 1758 BR-12227010 Sao Jose Dos Campos SP Brazil Univ Algarve Fac Sci & Technol P-8005139 Faro Portugal Univ Lisbon Inst Super Tecn IDMEC P-1049001 Lisbon Portugal

All-sky images (ASI) are widely used for sky monitoring, particularly in solar energy generation applications. Alignment issues and interferences demand a detection process of problematic images. With high sampling frequencies (1-2 images per minute), automating this process is crucial for managing large datasets and enabling integration into automatic systems, which is the objective of this work. For this purpose, a robust ensemble model, using the ApproxHull and Radial Basis Function (RBF) neural networks combined with multi objective genetic algorithms (MOGA) tools, was developed to compute the cloud cover fraction of each image. By computing the deviation between this result and the one obtained by the equipment, and by assessing if it lies within prediction bounds obtained in the design phase, an automatic method for detecting anomalies in All-sky images was obtained. ASI data collected during the Green Ocean (GoAmazon) Experiment 2014/5 was employed. The proposed approach obtained a Probability of Interval Coverage (PICP) similar to the user- specified level of confidence for several sets within what was classified as a "good"dataset, while being able to detect anomalies found within a "bad"dataset.

关键词： multi-objective genetic algorithms Radial Basis Function Neural Networks Convex hull All-sky images Cloud cover fraction Prediction bounds

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A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第1期40卷 323-336页

作者： Fogue, Manuel Garrido, Piedad Martinez, Francisco J. Cano, Juan-Carlos Calafate, Carlos T. Manzoni, Pietro Univ Zaragoza Comp Sci & Syst Engn Dept DIIS Teruel 44003 Spain Univ Politecn Valencia Comp Engn Dept DISCA Valencia 46022 Spain

The development of communication technologies integrated in vehicles allows creating new protocols and applications to improve assistance in traffic accidents. Combining this technology with intelligent systems will permit to automate most of the decisions needed to generate the appropriate sanitary resource sets, thereby reducing the time from the occurrence of the accident to the stabilization and hospitalization of the injured passengers. However, generating the optimal allocation of sanitary resources is not an easy task, since there are several objectives that are mutually exclusive, such as assistance improvement, cost reduction, and balanced resource usage. In this paper, we propose a novel approach for the sanitary resources allocation in traffic accidents. Our approach is based on the use of multi-objective genetic algorithms, and it is able to generate a list of optimal solutions accounting for the most representative factors. The inputs to our model are: (i) the accident notification, which is obtained through vehicular communication systems, and (ii) the severity estimation for the accident, achieved through data mining. We evaluate our approach under a set of vehicular scenarios, and the results show that a memetic version of the NSGA-II algorithm was the most effective method at locating the optimal resource set, while maintaining enough variability in the solutions to allow applying different resource allocation policies. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Resource allocation Traffic accidents assistance multi-objective genetic algorithms

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A MOGA and extended RPN based approach for heuristic key reliability characteristics analysis in manufacturing

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ISA TRANSACTIONS 2024年 150卷 134-147页

作者： Zhang, Jishan He, Yihai Zheng, Xin Feng, Tianyu Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

The manufacturing process is the last opportunity to build an ideal design reliability index into a product. With the advancement of intelligent manufacturing technology, the concept of quality evolves from conformance to fitness for use, which emphasizes that reliability should be built into product with quality control. To effectively implement reliability assurance in the manufacturing process, it is necessary to accurately identify the vital few characteristics that are critical to reliability. Thus, a heuristic key reliability characteristic (KRC) analysis in manufacturing model fusing big quality data is proposed. First, on the basis of the fusion big quality data in manufacturing-by-manufacturing system Reliability-operational process Quality- output product Reliability (RQR) chain, a data driven KRC analysis model is proposed, and a reliability proactive control framework in manufacturing driven by KRC is expounded. Second, considering mass quality and reliability data, an effective KRC identification method based on data mining using multi-objectives genetic algorithm (MOGA) is established. Third, considering manufacturing data and product failure risk, an extended risk priority number (RPN) for KRC ranking is proposed. Finally, an example of an insulating base of subway locomotive is provided to verify the proposed approach.

关键词： Automatic manufacturing process Key reliability characteristics Reliability proactive control multi-objective genetic algorithms Extended risk priority number

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A multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting

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ENERGIES 2024年第3期17卷 696页

作者： Ruano, Maria da Graca Ruano, Antonio Univ Algarve Fac Sci & Technol P-8005139 Faro Portugal Univ Coimbra CISUC P-3030290 Coimbra Portugal Univ Lisbon Inst Super Tecn IDMEC P-1049001 Lisbon Portugal

The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last few years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained from a small energy community of four houses located in the southern region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

关键词： multi-objective genetic algorithms neural networks forecasting models ensemble models prediction intervals probabilistic forecasting day-ahead energy markets

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Evolutionary feature selection for machine learning based malware classification

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2024年 56卷

作者： Kale, Gulsade Bostanci, Gazi Erkan Celebi, Fatih Vehbi Kilis 7 Aralik Univ High Vocat Sch Tech Sci Dept Comp Technol TR-79000 Kilis Turkiye Ankara Univ Fac Engn Dept Comp Engn TR-06830 Ankara Turkiye Ankara Yildirim Beyazit Univ Fac Engn & Nat Sci Dept Comp Engn TR-06830 Ankara Turkiye

Conducting thorough research, analysis, and detection of cyber-threatening malware with the right parameters is crucial for safeguarding a country's security and economy. Increasingly sophisticated cyber-attacks directly affect individual welfare, social dynamics, and political stability. So, due to the evolving nature of malware, which continuously improves itself to evade detection, it is even more essential to select effective and decisive parameters, considering interactions among various malware features. As malware evolves with new technologies and techniques, signature-based detection systems are becoming inadequate. Instead of relying on these still widely used but insufficient systems, in this study a new system was established focusing on malware behavior and the relationships between malware features resulting from these behaviors. In this system, rather than using a uniform approach, multi-objective genetic algorithms (MOGAs) are employed to select critical and decisive features for malware detection. These selected features are then utilized by machine learning (ML) algorithms within the implemented hybrid framework to accurately detect and classify malware. The aim of this paper is to identify the optimal feature selection and classification methods yielding the highest accuracy within the Cuckoo Sandbox environment. Specifically, the J48 Decision Tree (J48), Reduced Error Pruning Tree (REP Tree), Adaptive Boosting Model 1 (AdaboostM1), multilayer Perceptron (MLP), and Naive Bayes (NB) classifiers were assessed. Through our analysis, the feature set was refined from 335 to 200, considering inter-feature relationships, resulting in a peak accuracy of 93.33% and a corresponding 40% performance enhancement due to the reduction in the number of features. The obtained metrics were meticulously compared and evaluated with respect to the employed algorithms and methodologies. Additionally, Mc Nemar's test was utilized to evaluate the performance of different

关键词： Malware analysis Malware classification Feature selection Machine learning multi-objective genetic algorithms

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Optimizing space cooling systems in single-family houses: A neural network-based model predictive control approach for energy efficiency and comfort enhancement 9

Optimizing space cooling systems in single-family houses: A ...

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9th International Conference on Smart and Sustainable Technologies (SpliTech)

作者： Aruta, Giuseppe Ascione, Fabrizio Bianco, Nicola Iovane, Teresa Mauro, Gerardo Maria Tariello, Francesco Univ Napoli Federico II Dept Ind Engn Naples Italy Univ Sannio Dept Engn DING Benevento Italy Univ Molise Dept Agricoltural Environm & Food Sci DiAAA Campobasso Italy

ISBN: (纸本)9798350390797;9789532901351

This study utilizes artificial neural networks within a simulation and optimization framework to implement model predictive control (MPC) for cooling residential spaces. Based on weather forecasts, the framework is intended to give the best setpoint scheduling on a day-ahead planning horizon, to reduce energy consumption, costs, and thermal discomfort. Employing a multi-objective Pareto approach, the aim is to optimize a comfort penalty function. To tackle the optimization task a genetic algorithm is employed, using a non-linear autoregressive network with exogenous inputs (NARX) as simulation tool. Several NARX networks are trained to predict internal temperature and cooling loads, demonstrating promising accuracy when compared to the building model outputs. Comparisons between the proposed optimal solutions and a fixed setpoint control strategy (specifically, 26 degrees C) reveal reductions in energy consumption and comfort penalty. Results underline that the prediction of the cooling need through metamodels is much more complicated, due to the relevant effects of the envelope inertia (besides the heat transfer coefficients and thermal transmittance) that makes the transient estimation of heat transfer much more complicated during the summer compared to the winter. The goal is to exploit artificial neural networks to minimize computational time and develop a framework suitable for integration into real-time automation systems for MPC.

关键词： HVAC systems model predictive control machine learning thermal comfort multi-objective genetic algorithms

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Evolved explainable classifications for lymph node metastases

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NEURAL NETWORKS 2022年第0期148卷 1-12页

作者： Sousa, Iam Palatnik de Vellasco, Marley M. B. R. Silva, Eduardo Costa da Pontif Catholic Univ Rio Janeiro Marques Silo Vicente St 225 225-Gavea BR-22541041 Rio De Janeiro Brazil

A novel evolutionary approach for Explainable Artificial Intelligence is presented: the "Evolved Expla-nations "model (EvEx). This methodology combines Local Interpretable Model Agnostic Explanations (LIME) with multi-objective genetic algorithms to allow for automated segmentation parameter tuning in image classification tasks. In this case, the dataset studied is Patch-Camelyon, comprised of patches from pathology whole slide images. A publicly available Convolutional Neural Network (CNN) was trained on this dataset to provide a binary classification for presence/absence of lymph node metastatic tissue. In turn, the classifications are explained by means of evolving segmentations, seeking to optimize three evaluation goals simultaneously. The final explanation is computed as the mean of all explanations generated by Pareto front individuals, evolved by the developed genetic algorithm. To enhance reproducibility and traceability of the explanations, each of them was generated from several different seeds, randomly chosen. The observed results show remarkable agreement between different seeds. Despite the stochastic nature of LIME explanations, regions of high explanation weights proved to have good agreement in the heat maps, as computed by pixel-wise relative standard deviations. The found heat maps coincide with expert medical segmentations, which demonstrates that this methodology can find high quality explanations (according to the evaluation metrics), with the novel advantage of automated parameter fine tuning. These results give additional insight into the inner workings of neural network black box decision making for medical data.(c) 2021 Elsevier Ltd. All rights reserved.

关键词： Artificial intelligence Convolutional Neural Networks Explainable AI multi-objective genetic algorithms

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The weighted intruder path covering problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期297卷 347-358页

作者： Haywood, Adam B. Lunday, Brian J. Robbins, Matthew J. Pachter, Meir N. Air Force Inst Technol Dept Operat Sci Wright Patterson AFB OH 45433 USA Air Force Inst Technol Dept Elect & Comp Engn Wright Patterson AFB OH 45433 USA

Effectively detecting and interdicting intruders within a defender's territory is a common security problem. Often, the defender's territory is decomposed into spatially distinct stages for organizational convenience. Given an intruder attempting to traverse a spatially-decomposed region via multiple possible paths, this research aims to effectively and cost-efficiently identify a defensive strategy that locates sets of detection resources and interdiction resources, each of which has different types of resources that vary by cost and capability. We formulate and validate a mixed-integer nonlinear programming model to solve the underlying problem first using a leading commercial solver (BARON) and then via two genetic algorithms (RWGA and NSGA-II). Computational testing first identifies instance size limitations for identifying a global optimal solution via BARON, motivating the use of metaheuristics. Subsequent testing demonstrates the superior performance of RWGA and NSGA-II on 10 randomly generated instances for each of 20 various instance sizes. For each 20 of these instance sizes, both RWGA and NSGA-II produce higher-quality and more non-dominated solutions than BARON while using much less computational effort. Subsequent testing of only RWGA and NSGA-II over a designed set of test instances identifies NSGA-II as the recommended technique to solve larger-sized instances of the underlying problem. (c) 2021 Elsevier B.V. All rights reserved.

关键词： OR in defense Intrusion defense Global optimization multi-objective optimization multi-objective genetic algorithms

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Addressing Evolutionary-Based Dynamic Problems: A New Methodology for Evaluating Immigrants Strategies in MOGAs

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IEEE ACCESS 2022年 10卷 27611-27629页

作者： Panizo-Lledot, Angel Pedemonte, Martin Bello-Orgaz, Gema Camacho, David Tech Univ Madrid Comp Syst Engn Dept Madrid 28040 Spain Univ Republica Inst Comp Montevideo 11200 Uruguay

multi-objective genetic algorithms (MOGAs) have been successfully used to address dynamic problems in a wide variety of domains. In these domains, data changes over time, so a non-static analysis is required to obtain feasible solutions. In this type of environments, MOGAs are often time-consuming and require special adaptation to work properly. A number of different techniques have been proposed to adapt MOGAs to dynamic environments for tackling the previous problems such as hypermutation, memory and immigrant schemes or multi-population methods, among others. In particular, immigrant strategies are one of the most commonly used methods, for that reason, this work proposes a new methodology that allows to make a detailed evaluation of their performance when these strategies are used. The proposed methodology works on two levels, a coarse-grain one and a fine-grain one. In the former, an overall evaluation of the different immigrant strategies is made based on three different dimensions: Quality, Stability and Speed. In the latter, a detailed study of the status of the immigrant individuals during the evolution of the algorithm is carried out. This is a very relevant aspect to take into account in order to evaluate whether an immigrant strategy is working properly or not. To deploy this methodology, a new visualization technique for population mixing analysis is proposed in this work. In order to validate the proposed methodology, a test case in the context of the Dynamic Community Detection problem (DCD) has been selected using a MOGA that applies several different immigrant schemes, showing both how the methodology works and how it could be employed in a particular dynamic problem.

关键词： Statistics Sociology Heuristic algorithms genetic algorithms Optimization Europe Convergence Dynamic problems immigrant strategies multi-objective genetic algorithms dynamic community detection social network analysis

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multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第9期39卷 7822-7833页

作者： Cha, Young-Jin Agrawal, Anil K. Kim, Yeesock Raich, Anne M. Worcester Polytech Inst Dept Civil & Environm Engn Worcester MA 01609 USA CUNY City Coll New York NY 10021 USA Lafayette Coll Easton PA 18042 USA

This paper proposes a multi-objective genetic algorithm (MOGA) for optimal placements of control devices and sensors in seismically excited civil structures through the integration of an implicit redundant representation genetic algorithm with a strength Pareto evolutionary algorithm 2. Not only are the total number and locations of control devices and sensors optimized, but dynamic responses of structures are also minimized as objective functions in the multi-objective formulation, i.e., both cost and seismic response control performance are simultaneously considered in structural control system design. The linear quadratic Gaussian control algorithm, hydraulic actuators and accelerometers are used for synthesis of active structural control systems on large civil structures. Three and twenty-story benchmark building structures are considered to demonstrate the performance of the proposed MOGA. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement of actuators and sensors in seismically excited large buildings such that the performance on dynamic responses is also satisfied. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithms Optimal placement Structural control ASCE control benchmark Actuator location Sensor location

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