Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison

The need to identify a few important variables that affect a certain outcome of interest commonly arises in various industrial engineering applications. The genetic algorithm (GA) appears to be a natural tool for solving such a problem. In this article we first demonstrate that the GA is actually not a particularly effective variable selection tool, and then propose a very simple modification. Our idea is to run a number of GAs in parallel without allowing each GA to fully converge, and to consolidate the information from all the individual GAs in the end. We call the resulting algorithm the parallel genetic algorithm (PGA). Using a number of both simulated and real examples, we show that the PGA is an interesting as well as highly competitive and easy-to-use variable selection tool.

Darwinian evolution in parallel universes: A parallel genetic algorithm for variable selection

https://pure.hw.ac.uk/ws/files/25790703/manuscript.pdf

Stochastic model reduction for polynomial chaos expansion of acoustic waves using proper orthogonal decomposition

An approach on lifetime estimation of distribution transformers based on degree of polymerization

Transformers are playing an increasingly significant part in energy conversion, transmission, and distribution, which link various resources, including conventional, renewable, and sustainable energy, from generation to consumption. Power transformers and their components are vulnerable to various operational factors during their entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate transformer condition assessment to grasp the operating state accurately to reduce the failures and operating costs and enhance the reliability performance. In this context, comprehensive data mining and analysis based on intelligent algorithms are of great significance for promoting the comprehensiveness, efficiency, and accuracy of condition assessment. In this article, in an attempt to provide and reveal the current status and evolution of intelligent algorithms for transformer condition assessment and provide a better understanding of research perspectives, a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field are presented. First, the failure statistics analysis is outlined, and the developing mechanism of the transformer internal latent fault is investigated. Then, in combination with intelligent demands of the tasks in each stage of transformer condition assessment under big data, we analyze the data source in-depth and redefine the concept and architecture of transformer condition assessment. Furthermore, the typical methods widely used in transformer condition assessment are mainly divided into rule, information fusion, and artificial intelligence. The new findings for intelligent algorithms are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, future research directions are discussed.

/pdf/review-on-evolution-of-intelligent-algorithms-for-1p3b2dtp.pdf

Review on Evolution of Intelligent Algorithms for Transformer Condition Assessment

Traditional methods of malware detection have difficulty in detecting massive malware variants. Malware detection based on malware visualization has been proved an effective method for identifying unknown malware variants. In order to improve the accuracy and reduce the detection time of above methods, a novel method for malware classification in a light-weight CNN architecture named MalshuffleNet is proposed. The model is customized based on ShuffleNet V2 by adjusting the numbers of the fully connected layer for adopting to malware classification. Empirical results on Malimg dataset indicate that our model achieves 99.03% in accuracy, and identify an unknown malware only taking 5.3 milliseconds on average.

Malware Classification based on a Light-weight Architecture of CNN: MalShuffleNet

As an important implementation subject of the national energy strategy, the power grid undertakes the important responsibility of optimizing the allocation of energy resources and serving economic and social development. With the rapid development of China's national economy, the power demand of the whole society is gradually increasing. Under the background of building a new power system, the investment demand of China's power grid is increasing. Accurate and effective prediction of power grid investment demand can not only help to coordinate the funds, reasonably arrange the investment of power grid construction funds and reduce economic risks, but also effectively improve the operation status of power grid enterprises and improve the investment income. Based on the internal and external environment and development status of power grid investment, this paper puts forward five influencing factors affecting investment demand, and uses SSA-LSSVM algorithm to predict the investment trend of h power grid from 2021 to 2023. The results show that the investment in h power grid will increase steadily year by year after 2020.

Research on investment demand forecasting model of provincial power grid

In the field of high-speed data transmission, wireless optical communications provide a paradigm shift from the conventional tethered connections, offering promising bandwidth and minimal latency. The cornerstone of such systems lies in their ability to precisely control the propagation of Gaussian beams, which are favored due to their inherent properties of minimal divergence and high spatial coherence over long distances. Efficient transmission hinges on the proper manipulation of these beams' spatial characteristics, particularly the waist radius and the associated Rayleigh length, which together delineate the beam's diffraction and spread. This manuscript methodically explores the theoretical and practical aspects of Gaussian beam focusing through lens systems, aiming to elucidate the pivotal relationship between the optimally adjusted focal parameters and the resultant augmentation of the Rayleigh length. Through rigorous diffraction integral simulations and a keen analysis of constraints posed by finite apertures, the study articulates strategies to considerably enhance the Gaussian beam's propagation characteristics, thereby bolstering the reliability and efficacy of wireless optical communication systems.

Rayleigh length extension in long-distance free-space optical communications based on lens group optimization

PAFE: A lightweight visualization-based fast malware classification method

A massive proliferation of malware variants has posed serious and evolving threats to cybersecurity. Developing intelligent methods to cope with the situation is highly necessary due to the inefficiency of traditional methods. In this paper, a highly efficient, intelligent vision-based malware variants detection method was proposed. Firstly, a bilinear interpolation algorithm was utilized for malware image normalization, and data augmentation was used to resolve the issue of imbalanced malware data sets. Moreover, the paper improved the convolutional neural network (CNN) model by combining multi-scale feature fusion (MFF) and channel attention mechanism for more discriminative and robust feature extraction. Finally, we proposed a hyperparameter optimization algorithm based on the bat algorithm, referred to as HDBA, in order to overcome the disadvantage of the traditional hyperparameter optimization method based on manual adjustment. Experimental results indicated that our model can effectively and efficiently identify malware variants from real and daily networks, with better performance than state-of-the-art solutions.

/pdf/malware-variants-detection-model-based-on-mff-hdba-2ckdgowh.pdf

Malware Variants Detection Model Based on MFF–HDBA

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