Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
Enhancing the flexibility of hydropower units is essential for adapting to future power systems dominated by intermittent renewable energy sources such as wind and solar, which introduce significant frequency stability challenges due to their inherent variability. To improve the primary frequency regulation capability of the hydropower unit, this study incorporates a flywheel energy storage system—known for its fast response and high short-term power output. Using fuzzy control theory, a frequency regulation command decomposition method with a variable filtering time constant is proposed. In this fuzzy control design, the frequency change rate and the state of charge of the flywheel energy storage are used as inputs to dynamically adjust the filtering time constant, which serves as the output. Additionally, a logistic function is introduced to constrain the output power of the flywheel energy storage under different states of charge, ensuring operational safety and durability. Based on these techniques, a fuzzy frequency division control strategy is designed for flywheel-assisted hydropower primary frequency regulation. Simulation results show that the integration of flywheel energy storage significantly improves the primary frequency regulation performance of the hydropower unit. Compared to the system without energy storage, the proposed strategy reduces the maximum frequency deviation by 53.49% and the steady-state frequency deviation by 39.06%, while also markedly decreasing fluctuations in hydropower output. This study offers both a theoretical basis and practical guidance for enhancing the operational flexibility of hydropower systems....
This research utilizes a modified cellulose nanocrystal composite as an adsorbent to remove cadmium (II) through a column study. A fixed-bed column was used to remove cadmium (II) at room temperature using varying process factors, such as pH (4–8), bed height (3–9 cm), flow rate (3–7 mL/min), and concentration (10–20 mg/L). According to these findings, cadmium (II) breakthrough occurred more quickly at lower bed heights, higher flow rates, and higher cadmium (II) concentrations. The Thomas model is the most appropriate kinetic model. Deep learning models, such as the adaptive neuro-fuzzy inference model with two algorithms (backpropagation and least squares estimation), were effectively used to model the effectiveness of cadmium (II) removal in aqueous solutions via modified cellulose nanocrystals. To compare the model’s predicted results with experimental data, statistical approaches were employed, including calculating the coefficient of determination (R2) and mean square error (MSE). The ANFIS model used to predict cadmium (II) adsorption via modified cellulose nanocrystals had a strong correlation value of 0.997 for least squares estimation (LSE) and 0.999 for the gradient descent (backpropagation) method, indicating the effectiveness of the trained model in predicting the cadmium (II) adsorption process....
Network protocol fuzzing is a critical method for detecting vulnerabilities in network protocol programs. However, traditional selection algorithms used in network protocol fuzzing often fail to accurately select effective states and seeds. To address this limitation, this paper proposes a fuzzing framework called Contextual AFLNET (CAFLNET), which employs a selection algorithm that utilizes enhanced contextual information. This framework introduces key metrics, such as state in-degree, state out-degree, and trace-adjacent call count, to enhance contextual information. The selection algorithm is divided into two parts: (1) a state selection algorithm based on the linear upper confidence bound, which optimizes the balance between exploration and exploitation by utilizing enhanced contextual information, and (2) a tri-factor seed selection algorithm, designed to utilize contextual information such as seed labels, execution information, and session information to thoroughly and effectively evaluate seed value in the selection process. We evaluated our framework and AFLNET using eleven benchmark programs from ProFuzzBench and the real-world. The results demonstrate that our framework outperformed AFLNET by an average of 6.86% in terms of branch coverage, with a notable increase of 18.79% on PureFTPD. In addition, our framework slightly outperformed AFLNET in state discovery and exhibited superior performance in vulnerability detection, triggering known vulnerabilities earlier and more frequently and successfully exposing a previously unknown vulnerability....
In this study we are examining expert assessment (EA) non-linear scaled based approach to map original physical scale into a linguistical one. We introduce fuzzification of linguistic clustering mechanism for both input and output of a model under investigation, followed by appropriate decision-making apparatus. We also offered defuzzification approach for presented fuzzy model output. All results are thoroughly illustrated by proper experimental results....
Insurance fraud, characterized by false or exaggerated claims, is a major economic crime worldwide, undermining trust between insurance companies and their customers. Detecting these cases is a priority issue nowadays. This paper presents a fuzzy inference system for the early identification of suspicious claims in the compulsory motor liability insurance market. The study focuses exclusively on cases involving two privately owned passenger cars where no personal injury, but only property damage, occurred. A Mamdani-type inference system was created, using simple independent input parameters: the value (in EUR) and the age of the vehicle (in years) and the payment period of the insurance contract. The last parameter was introduced as a qualitative factor. These were linked to the risk level resulting from the characteristics of the vehicles involved in the incident. For this purpose, real insurance data were used....
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