This study presents an innovative approach to mitigate mobbing as a cyberattack by integrating artificial intelligence (AI) techniques such as Latent Dirichlet Allocation (LDA) and Robustly Optimized BERT Pretraining ...
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ISBN:
(纸本)9798331534110;9798331534103
This study presents an innovative approach to mitigate mobbing as a cyberattack by integrating artificial intelligence (AI) techniques such as Latent Dirichlet Allocation (LDA) and Robustly Optimized BERT Pretraining Approach (RoBERTa) according with the Cross-Industry Standard process for data Mining methodology (CRISP-DM). Mobbing in digital environments constitutes an emerging form of cyber attack that affects both individuals and organizations. Preventing mobbing requires a comprehensive approach involving legal frameworks, employee awareness, and technological solutions. This work uses AI to identify patterns of mobbing and proposes a structured process based on CRISP-DM to address this phenomenon. It focuses on analyzing mobbing through digital data to develop a content-filtering solution for platforms like email and messaging systems. The study identified eight general phases that describe the overall context of mobbing, as well as four specific phases that outline the detailed process of harassment within it, using fine-tuning techniques for detection. The results show how AI can automate the detection and mitigation of mobbing, minimizing its impact on victims and improving cybersecurity.
CFRD have been widely used worldwide because of their superior structural stability and economy. However, with the increasing of dam height, the issue of deformation control is becoming increasingly prominent, especia...
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CFRD have been widely used worldwide because of their superior structural stability and economy. However, with the increasing of dam height, the issue of deformation control is becoming increasingly prominent, especially the panel deformation control which closely related to the internal rockfill deformation. Comparing with the monitoring data of a single monitoring point, the slope plane deformation can reflect the deformation characteristics of the dam rockfill area more effective. To ensure the long-term stability and safe operation of the CFRD, construction and analysis the slope plane deformation of the CFRD is particularly important. In order to improve the accuracy of prediction, a method of modeling and analysis of dam slope plane deformation based on multi-points monitoring data is proposed in this paper. Based on the actual monitoring data of the Hongjiadu CFRD, this paper comprehensively analyzes the influence of the filling process, water level change, and time effect on the settlement of the CFRD during the construction and impoundment periods, and establishes the settlement statistical model of all monitoring points. By studying the relationship between the parameters of the settlement statistical model of each monitoring point and its own position coordinates, a spatial-temporal distribution model of the dam slope plane is established by using the thin plate spline interpolation method, and it is used to predict the settlement of the dam slope plane. This method has great advantages in the prediction accuracy. By considering the temporal and spatial distribution characteristics, this method can effectively capture the whole and local deformation of the dam body and improve the prediction accuracy of settlement. Compared with the traditional single point monitoring method, the proposed method provides more accurate prediction results on the basis of multi-point data collaborative analysis, provides a new idea for dam displacement monitoring and settlem
The integration of semisupervised modeling and discriminative information has been sporadically discussed in the research literature of traditional classification modeling, while the former one would make full use of ...
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The integration of semisupervised modeling and discriminative information has been sporadically discussed in the research literature of traditional classification modeling, while the former one would make full use of the collected data and the latter one would further improve the classification performance. In this article, the Hessian semisupervised scatter regularized classification model is proposed as a coherent framework for the nonlinear process classification upon both labeled and unlabeled data. It is innovatively designed with a loss function to evaluate the classification accuracy and three regularization terms, respectively, corresponding to the geometry information, discriminative information, and model complexity. Both cases of the coherent framework, respectively, casted to the reproducing kernel Hilbert space and linear space, enjoy a theoretically guaranteed analytical solution. Experiments on process classification tasks on a benchmark dataset and a real industrial polyethylene process illustrate the merits of the proposed method in a sense that the class information of novel collected data is accurately predicted.
With increasing complexity of industrial processes, a number of variables are becoming increasingly large in modeling and monitoring steps, which is particularly prominent in dynamic processes. To address the issue of...
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With increasing complexity of industrial processes, a number of variables are becoming increasingly large in modeling and monitoring steps, which is particularly prominent in dynamic processes. To address the issue of information redundancy in dynamic processes, this study proposes a sparse dynamic matrix estimation method (SDMEM) based on joint sparse constraints, which can effectively remove the irrelevant process variables and implement a more flexible structure for a dynamic process. Accordingly, the problem that dynamic features are difficult to extract owing to the high sampling rate is effectively solved by introducing differential information. Furthermore, a fast iterative optimization algorithm is designed for the proposed SDMEM with differential information (SDMEM-DI). A theoretical analysis shows the superiority of the proposed optimization algorithm in reducing computational complexity. Finally, experiments are conducted on a numerical example, a continuous stirred tank reactor (CSTR), and a catalytic cracking unit data of a refining and chemical plant, and the results show the effectiveness of the proposed SDMEM-DI.
In order to solve the problem that it is difficult for traditional methods to effectively analyze the generation and propagation process of large-scale cascading failures in power grid macroscopically, a power network...
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This paper investigates the generation of realistic processdata by utilizing adversarial networks to emulate actual steel process information. The objective is to create data resembling processdata without discernib...
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In this article, a finite-horizon optimal trajectory control strategy is developed for near space hypersonic vehicle (NSHV) longitudinal model with multi-constraints including external disturbance, system modeling err...
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In this article, a finite-horizon optimal trajectory control strategy is developed for near space hypersonic vehicle (NSHV) longitudinal model with multi-constraints including external disturbance, system modeling error, and input saturation. The whole controlprocess has two parts: inner-loop attitude control and outer-loop trajectory control. First, the feedback linearization method is applied to design a tracking controller for outer-loop system, and reference signals for inner-loop attitude control can be obtained using Newton iteration method. Second, for the inner-loop attitude system with multi-constraints, a finite-horizon optimal tracking control scheme consists of feedforward control input and adaptive dynamic programming based optimal feedback controller is designed. In this way, not only the adverse effects of above multi-constraints are eliminated, but also the optimally tracking performances are guaranteed. Finally, the Lyapunov analysis method is utilized to ensure the stability of the entire closed-loop control system, and simulation tests with respect to NSHV longitudinal trajectory tracking are supplied to verify the availability of the proposed strategy. In this paper, a finite horizon optimal trajectory control strategy is developed for near space hypersonic vehicle. Not only the optimal tracking performance are guaranteed, but also the multi-constraints including external disturbance, system modeling error and input saturation are ***
In the present study, dynamic modeling, optimal operation and control of a reactive batch distillation process is illustrated based on experimental and simulation studies using methyl acetate production case study. An...
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In the present study, dynamic modeling, optimal operation and control of a reactive batch distillation process is illustrated based on experimental and simulation studies using methyl acetate production case study. An equilibrium stage model, incorporating non ideal VLE and start-up region of heating, is developed based on data generated on an experimental unit by identifying five input parameters representing uncertainties. The optimal control problem is solved using genetic algorithm, considering the objective function identified on the basis of trend analysis using the developed dynamic model, to find the optimal reflux ratio, heat input to the reboiler, and mole ratio of methanol to acetic acid in the initial reaction mixture. Open-loop and closed-loop implementations clearly illustrate the improved performance with respect to the quantity of methyl acetate in the distillate product with a reasonably high conversion and product purity within reasonably short batch duration, illustrating the successful optimal control implementation experimentally.& COPY;2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.
Electrical conduction within the context of transformer oil stands as a dynamic and effective method widely employed in the research area of electrical engineering, specifically power systems. In this paper, we presen...
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ISBN:
(纸本)9798350349740;9798350349757
Electrical conduction within the context of transformer oil stands as a dynamic and effective method widely employed in the research area of electrical engineering, specifically power systems. In this paper, we present and develop an innovative numerical model based on the finite element method to study and analyze electrical conduction in transformer oil. Our model simulates the oil's volumetric conduction process under high voltages, which facilitates the calculation of electrical field and potential distributions. To ensure the robustness and reliability of our numerical model, we undertake a systematic exploration by investigating the impact of various process parameters on shaping the conduction process in transformer oil, along with the analysis of temperature distribution and the effect of the electro-hydrodynamic (EHD) instability of the transformer oil on its electrical quantities.
The additive-manufacturing (AM) field necessitates a robust process-monitoring system for quality assurance and control. To meet this industrial requirement, quality-evaluation models have emerged as powerful tools fo...
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The additive-manufacturing (AM) field necessitates a robust process-monitoring system for quality assurance and control. To meet this industrial requirement, quality-evaluation models have emerged as powerful tools for providing quality feedback. Recently, convolutional-neural-network- (CNN)-based classification models have gained popularity in quality evaluation using image data. However, such models require sufficient image data for training, a requirement that is challenging to fulfill in the context of metallic AM due to the complexity of decomposition and analysis. This challenge is particularly pronounced in start-up or medium-sized metallic-AM enterprises. Moreover, many countries around the world have faced a decline in population and a shortage of labor in the engineering field. This growing shortage of workers to collect datasets exacerbates this challenge. In this study, experiments of directed-energy-deposition (DED) processes for single-line and single-track metallic deposition using AISI 316 L stainless-steel powders were conducted with two experimenters. After the process, a minimal amount of cross-sectional surface image data of the metallic deposition was binary-processed and analyzed across three quality states: normal state, surface burrs, and internal defects. To compensate for the lack of training data, multiple strategies are proposed, including image preprocessing and ResNet transfer learning. The selection of an optimization solver and layer depth for maximizing classification performance was discussed. The potential performance of ResNet dealing with binary images and performance standards with few training data was also identified by comparing with other higher-level architectures (Inception and Xcepition).
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