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The role of data frequency and method selection in electricity price estimation: Comparative evidence from Turkey in pre-pandemic and pandemic periods

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RENEWABLE ENERGY 2022年第0期186卷 217-225页

作者： Depren, Serpil Kilic Kartal, Mustafa Tevfik Ertugrul, Hasan Murat Depren, Ozer Yildiz Tech Univ Dept Stat Istanbul Turkey Borsa Istanbul Financial Reporting & Subsidiari Istanbul Turkey Minist Treasury & Finance Ankara Turkey Yapi Kredi Bank Customer Experience Res Ctr Istanbul Turkey

The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods;(ii) high-frequency data increases the performance of estimation models;(iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data;(iv) the pandemic causes an adverse effect on the performance of estimation models;(v) energy-related variables are more important than other variables although all are significant;(vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Electricity prices Data frequency machine learning algorithms Time series econometric models Turkey

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Can Software Engineering Harness the Benefits of Advanced AI?

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IEEE SOFTWARE 2022年第6期39卷 99-104页

作者： Shaw, Mary Zhu, Liming Carnegie Mellon Univ Comp Sci Pittsburgh PA 15217 USA Univ New South Wales Sydney NSW 2015 Australia

Artificial intelligence (AI) has allowed us to build systems beyond anything deemed possible earlier. Can we evolve existing techniques in software engineering to meet the needs of AI enabled systems or do we need to ... 详细信息

关键词： Artificial Intelligence Software Engineering Software Systems Codes Software Reliability machine learning algorithms Deep learning machine learning

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Margin Distribution Analysis

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第8期33卷 3948-3960页

作者： Wang, Jun Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China

Margin is an important concept in machine learning;theoretical analyses further reveal that the distribution of margin plays a more critical role than the minimum margin in generalization power. Recently, several approaches have achieved performance breakthroughs by optimizing the margin distribution, but their computational cost, which is usually higher than before, still hinders them to be widely applied. In this article, we propose margin distribution analysis (MDA), which optimizes the margin distribution more simply by maximizing the margin mean and minimizing the margin variance simultaneously. MDA is efficient and resistive to class-imbalance naturally, since its objective distinguishes the margin means of different classes and can be broken up into two linear equations. In practice, it can also cooperate with other frameworks such as reweight-minimization when facing complex circumstances with noise and outliers. Empirical studies validate the superiority of MDA in real-world data sets, and demonstrate that simple approaches can also perform competitively by optimizing margin distribution.

关键词： Support vector machines Kernel Training Computational efficiency machine learning algorithms learning systems Boosting Class-imbalance classification margin margin distribution reweight-minimization

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Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

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IEEE SIGNAL PROCESSING MAGAZINE 2023年第1期40卷 85-97页

作者： Kamilov, Ulugbek S. Bouman, Charles A. Buzzard, Gregery T. Wohlberg, Brendt Washington Univ Computat Imaging Grp St Louis MO USA Purdue Univ Elect & Comp Engn & Biomed Engn W Lafayette IN USA Purdue Univ Math Fac W Lafayette IN 47907 USA Los Alamos Natl Lab Theoret Div Los Alamos NM USA

Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly successful applications of PnP algorithms include biomicroscopy, computerized tomography (CT), magnetic resonance imaging (MRI), and joint ptychotomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. We also point the way toward further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.

关键词： Training data machine learning algorithms Computational modeling Magnetic resonance imaging Computed tomography Imaging Signal processing algorithms

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Range-Constrained and Stable Federated Charging Scheme for Electricity Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15886-15896页

作者： Zhang, Jiancong Li, Shining Wang, Changhao Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

Due to the increasing number of electric vehicles and limited charging stations, charging service providers need to consider the minimum range of electric vehicles and the congestion level of charging stations to formulate pricing strategies and guide charging vehicles. However, this service faces the challenges of privacy protection and utility coupling between CSs and EVs. Besides, the minimum driving range is also affected by the time-varying data characteristics of vehicles. Therefore, this paper proposes an FL-assisted stable charging matching strategy considering congestion factors and range constraints. Specifically, we employ a federated learning scheme to predict the congestion factor of each charging station by learning the complex hidden characteristics of vehicles under the condition of privacy protection. Then, the pricing strategy and utility function are updated based on the congestion factor. Furthermore, under the driving range constraint, we transform the impact of the congestion factor on the expected utility into a regret value and use the Gale-Shapley algorithm based on regret minimization to achieve stable matching between vehicles and charging stations. Experimental results demonstrate the scheme's effectiveness and that the charging success rate is improved by up to 11.7% compared with mainstream methods.

关键词： Pricing Protection Optimization Couplings machine learning algorithms Prediction algorithms Data privacy Charging congestion intelligent scheduling range anxiety federated learning

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A Generic Approach for Wheat Disease Classification and Verification Using Expert Opinion for Knowledge-Based Decisions

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IEEE ACCESS 2021年 9卷 31104-31129页

作者： Haider, Waleej Rehman, Aqeel-Ur Durrani, Nouman M. Rehman, Sadiq Ur Hamdard Univ Fac Engn Sci & Technol Karachi 74600 Pakistan Sir Syed Univ Engn & Technol Dept Software Engn Karachi 75300 Pakistan NUCES FAST FAST Sch Comp Karachi 75030 Pakistan Hamdard Univ Dept Elect Engn Karachi 74600 Pakistan

Crop diseases have mainly affected crop production due to the lack of modern approaches for disease identification. For many years, farmers have identified various crop diseases and have local knowledge about disease management. However, the local knowledge of one agricultural region is not utilized in other regions due to the unavailability of knowledge sharing platforms. Agricultural research also suggests that crop production has mainly decreased due to diseases, methods of cultivation, irrigation, and lack of local agricultural knowledge. In this research, the experience of agricultural experts, farmers, and cultivators is gathered through a crowd-sourced platform. The data is then processed for various disease identification. Hence, timely identification of various crop diseases can benefit farmers to apply relevant management methods. In literature, researchers have proposed various methods for disease management, mostly based on the classification of crop diseases using machine learning (ML) algorithms. However, these algorithms are unable to give trustful results due to static data provisioning and the dynamic nature of various diseases in different agricultural regions. Further, the agricultural expert's experience is also not considered in verifying the classification results. To identify the dynamic nature of wheat diseases, we acquired high-quality images and symptoms-based text data from farmers, domain experts, and users using a crowd-sourced platform. Different augmentation techniques were also used to enhance the size of training data. In this paper, a modern generic approach has been proposed for the identification and classification of wheat diseases using Decision Trees (DT) and different deep learning models. Also, results of both algorithms were then verified by domain experts that improved the decision trees accuracy by 28.5%, CNN accuracy by 4.3% (leading to 97.2%), and resulted in decision rules for wheat diseases in a knowledge-based system.

关键词： Diseases Agriculture Insects Production machine learning algorithms Knowledge based systems Genetics Agricultural DSS artificial intelligence agricultural knowledge management classification of crop diseases machine learning wheat crop diseases

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Scalable Approximate FRNN-OWA Classification

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第5期28卷 929-938页

作者： Lenz, Oliver Urs Peralta, Daniel Cornelis, Chris Univ Ghent Dept Appl Math Comp Sci & Stat B-9000 Ghent Belgium Univ Ghent Data Min & Modelling Biomed Grp VIB Ctr Inflammat Res B-9000 Ghent Belgium

Fuzzy rough nearest neighbor classification with ordered weighted averaging operators (FRNN-OWA) is an algorithm that classifies unseen instances according to their membership in the fuzzy upper and lower approximations of the decision classes. Previous research has shown that the use of OWA operators increases the robustness of this model. However, calculating membership in an approximation requires a nearest neighbor search. In practice, the query time complexity of exact nearest neighbor search algorithms in more than a handful of dimensions is near linear, which limits the scalability of FRNN-OWA. Therefore, we propose approximate FRNN-OWA, a modified model that calculates upper and lower approximations of decision classes using the approximate nearest neighbors returned by hierarchical navigable small worlds (HNSW), a recent approximative nearest neighbor search algorithm with logarithmic query time complexity at constant near-100% accuracy. We demonstrate that approximate FRNN-OWA is sufficiently robust to match the classification accuracy of exact FRNN-OWA while scaling much more efficiently. We test four parameter configurations of HNSW and evaluate their performance by measuring classification accuracy and construction and query times for samples of various sizes from three large datasets. We find that with two of the parameter configurations, approximate FRNN-OWA achieves near-identical accuracy to exact FRNN-OWA for most sample sizes within query times that are up to several orders of magnitude faster.

关键词： Open wireless architecture Rough sets Training Time complexity Approximation algorithms machine learning algorithms Fuzzy systems Big data applications classification algorithms fuzzy rough sets nearest neighbor searches scalability

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Near-Optimal Coresets for Least-Squares Regression

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IEEE TRANSACTIONS ON INFORMATION THEORY 2013年第10期59卷 6880-6892页

作者： Boutsidis, Christos Drineas, Petros Magdon-Ismail, Malik IBM Corp Thomas J Watson Res Ctr Dept Math Sci Yorktown Hts NY 10598 USA Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA

We study the (constrained) least-squares regression as well as multiple response least-squares regression and ask the question of whether a subset of the data, a coreset, suffices to compute a good approximate solution to the regression. We give deterministic, low-order polynomial-time algorithms to construct such core-sets with approximation guarantees, together with lower bounds indicating that there is not much room for improvement upon our results.

关键词： Least mean square algorithms machine learning algorithms regression analysis

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Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第3期9卷 1020-1032页

作者： Yang, Yixuan Hao, Fei Pang, Beibei Min, Geyong Wu, Yulei Minist Educ Key Lab Modern Teaching Technol Xian 710062 Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Shaanxi Peoples R China Univ Exeter Coll Engn Math & Phys Sci Dept Comp Sci Exeter EX4 4QF Devon England

The booming of Social Internet of Things (SIoT) has witnessed the significance of graph mining and analysis for social network management. Online Social Networks (OSNs) can be efficiently managed by monitoring users' behaviors within a cohesive social group represented by a maximal clique. They can further provide valued social intelligence for their users. Maximal Cliques Problem (MCP) as a fundamental problem in graph mining and analysis is to identify the maximal cliques in a graph. Existing studies on MCP mainly focus on static graphs, with less attention on the detection and dynamic evolution of maximal cliques in OSNs. To fill this gap, we adopt the Formal Concept Analysis (FCA) theory to represent and analyze social networks. We then develop two novel formal concepts generation algorithms, termed Add-FCA and Dec-FCA, that can be applicable to OSNs for detecting the maximal cliques and characterizing the dynamic evolution process of maximal cliques in OSNs. Extensive experimental results are conducted to investigate and demonstrate the correctness and effectiveness of the proposed algorithms. The results reveal that our algorithms can efficiently capture and manage the evolutionary patterns of maximal cliques, including unchanged, changed, added, and vanished maximal cliques in OSNs, and a quantitative relation among them is presented. In addition, an illustrative example is presented to verify the usefulness of the proposed approach.

关键词： Social networking (online) Lattices Heuristic algorithms Blogs Internet of Things machine learning algorithms Formal concept analysis Formal concept analysis maximal cliques detection maximal cliques evolution online social networks

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Multi-Parameter Maximum Corrosion Depth Prediction Model for Buried Pipelines Based on GSCV-XGBoost

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IEEE ACCESS 2023年 11卷 116452-116471页

作者： Wang, Niannian Song, Liuyang Fang, Hongyuan Li, Bin Wang, Fuming Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China Natl Local Joint Engn Lab Major Infrastructure Tes Zhengzhou 450001 Peoples R China Collaborat Innovat Ctr Water Conservancy & Transpo Zhengzhou 450001 Peoples R China

Corrosion is one of the most common types of damage inburied oil and gas pipelines. Corrosion leaks can cause serious accidents and can be harmful to pipelinesduring service. The maximum corrosion depth of an oil and gas pipeline is an important indicator for assessing the remaining strength of the pipeline. An accurate prediction of the maximum corrosion depth is important for the safe operation of pipelines. machine learning has been shown to perform well in predictive assessment efforts. However, previous studies have rarely considered the effects of corrosion characterization and parameter optimization simultaneously. In this study, a multi-parameter maximum corrosion depth prediction model for pipelines based on GSCV-XGBoost is proposed, which can be applied to real projects. The model performs feature extraction on the pipeline dataset through Pearson correlation analysis, identifies the parameters that contribute more to the maximum corrosion depth, and predicts the maximum corrosion depth of the pipeline using an optimized machine learning model. The machine learning model used in this study was obtained by optimizing the XGBoost model usingthe GirdSearchCV method. That is, the optimal hyperparameter combination of the model was obtained by 10-fold cross-validation and grid searching. The prediction results werecompared with those of five common machine learning models. The conclusions show that the GSCV-XGBoost model performs the best in predicting the maximum corrosion depth of the pipeline with the smallest error. The R-2 and Root Mean Square Error(RMSE)scores forthe test set were 0.9886 and 0.2057, respectively. The prediction accuracy was improved by 34.59% over that of the conventional XGBoost model.

关键词： Predictive models Pipelines Corrosion machine learning Prediction algorithms machine learning algorithms Analytical models Oil and gas pipelines maximum corrosion depth prediction machine learning correlation analysis XGBoost

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