Integrating energy and solar imagery is essential for electrical engineers in renewable energy prediction, consumption analysis, regression modeling, and fault detection applications. A significant challenge in these ...
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Integrating energy and solar imagery is essential for electrical engineers in renewable energy prediction, consumption analysis, regression modeling, and fault detection applications. A significant challenge in these areas is the limited availability of high-quality datasets, which can hinder the accuracy of the predictive models. To address this issue, this paper proposes leveraging Generative Adversarial Networks (GANs) to generate synthetic samples for training. Despite their potential, traditional GAN face challenges such as mode collapse, vanishing gradients, and pixel integrity issues. This paper introduces a novel architecture, Penca-GAN, which enhances GANs through three key modifications: (1) dual loss functions to ensure pixel integrity and promote diversity in augmented images, effectively mitigating mode collapse and improving the quality of synthetic data;(2) the integration of an identity block to stabilize training, preserving essential input features and facilitating smoother gradient flow;and (3) a pancreas-inspired metaheuristic loss function that dynamically adapts to variations in training data to maintain pixel coherence and diversity. Extensive experiments on three renewable energy datasets—SKY images, Solar images, and Wind Turbine images—demonstrate the effectiveness of the Penca-GAN architecture. Our comparative analysis revealed that Penca-GAN consistently achieved the lowest Fréchet Inception Distance (FID) scores (164.45 for SKY, 113.54 for Solar, and 109.34 for Wind Turbine), indicating superior image quality compared to other architectures. Additionally, it attains the highest Inception Score (IS) across all datasets, scoring 71.43 for SKY, 87.65 for Solar, and 90.32 for Wind Turbine. Furthermore, the application of Penca-GAN significantly enhanced the fault detection capabilities, achieving accuracy improvements from 85.92 to 90.04% for solar panels and from 86.06 to 90.43% for wind turbines. These results underscore Penca-GAN’s robust
In this paper we characterize all the lexsegment ideals which are normally torsion-free. This will provide a large class of normally torsion-free monomial ideals which are not square-free. Our characterization is give...
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In this paper we characterize all the lexsegment ideals which are normally torsion-free. This will provide a large class of normally torsion-free monomial ideals which are not square-free. Our characterization is given in terms of the ends of the lexsegment. We also prove that, for lexsegment ideals, the property being normally torsion-free is equivalent to the property of the depth function being constant.
In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linea...
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We study embeddings of spaces of Besov-Morrey type, MB p1^s1 q1^r1（R^d）→MB p2^s2 q2^r2（R^d） and obtain necessary and sufficient conditions for this. Moreover, we can also charaeterise the special weighted situat...
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We study embeddings of spaces of Besov-Morrey type, MB p1^s1 q1^r1（R^d）→MB p2^s2 q2^r2（R^d） and obtain necessary and sufficient conditions for this. Moreover, we can also charaeterise the special weighted situation Bp1^s1 （R^d ,w）→MB p2^s2 q2^r2（R^d） for a Muekenhoupt A∞ weight w, with wα（x） = ｜x｜^a, 〉 -d, as a typical example.
Pneumonia and Tuberculosis are two major illnesses that pose significant threats to global health. Effective intervention and management of these disorders require an efficient early diagnostic system. In our work, we...
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This research aims to solve Thai word boundaries problem by grouping vectors of words obtained from various tokenizers in the same sentence using Bi-LSTM and DistilBERT encoders. Generally, one sentence can achieve di...
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This paper presents a hybrid CNN-LSTM model integrated with GMAC for intrusion detection and secure communication in IoT networks. The system uses Yule-Simon Distribution-Based Lyrebird Optimization Algorithm for feat...
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ISBN:
(数字)9798331512088
ISBN:
(纸本)9798331512095
This paper presents a hybrid CNN-LSTM model integrated with GMAC for intrusion detection and secure communication in IoT networks. The system uses Yule-Simon Distribution-Based Lyrebird Optimization Algorithm for feature selection that can achieve the highest anomaly detection accuracy at minimal computational cost in real-time applications. The proposed model uses GMAC with simultaneous encryption and authentication and the CNN-LSTM hybrid for the detection of intrusion in IoT traffic data. Hence, the detection accuracy and the processing time is much better compared to the traditional methods. System detection accuracy at 99.10% was achieved with a latency of 980ms; thus, there is robust performance with minimal overhead of computation. This advanced approach improves the security of IoT and offers an effective and scalable solution for smart city and industrial IoT networks, providing high accuracy, low latency, and strong encryption.
When examining the performance of hashing algorithms, it usually is assumed that the hash function distributes the n keys randomly over the m table positions. In this exact filling model, the m to the nth degree poss...
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When examining the performance of hashing algorithms, it usually is assumed that the hash function distributes the n keys randomly over the m table positions. In this exact filling model, the m to the nth degree possible arrangements are equally probable. In some instances, the analysis with this model becomes too difficult, and a Poisson filling model is employed instead, under which the total number of keys follows a Poisson distribution. The results obtained with the Poisson model typically are interpreted as an approximation of those obtained operating under the exact model. An explicit form is found for all the terms of the asymptotic expansion of Gonnet and Munro's (1981) approximation theorem. It also is shown that these terms satisfy a recurrence relation, which may be more useful for the actual computation of these terms.
Programs that combine I/O and countable probabilistic choice, modulo either bisimilarity or trace equivalence, can be seen as describing a probabilistic strategy. For well-founded programs, we might expect to axiomati...
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