This study presents a geospatial analysis approach to derive the failure probability of GPS spoofing attacks by utilizing Poisson Point process (PPP) modeling. The research begins by establishing a robust framework fo...
This study presents a geospatial analysis approach to derive the failure probability of GPS spoofing attacks by utilizing Poisson Point process (PPP) modeling. The research begins by establishing a robust framework for characterizing GPS spoofing attacks based on available data and historical attack patterns. Leveraging the PPP model, which is commonly used in spatial point processanalysis, the study integrates real-world information such as GPS receiver locations, environmental conditions, and spoofing device parameters to simulate the spatial distribution of spoofing attack attempts across a given area. Through the application of PPP modeling, the study aims to estimate the failure probability of GPS spoofing attacks, which is defined as the likelihood of the spoofer's signal being successfully received and accepted by a targeted GPS receiver. This approach allows for the incorporation of various geospatial factors, including receiver density, signal propagation, and environmental interference, to provide a more accurate representation of the vulnerability of the GPS system to spoofing attacks in specific geographic areas. The findings from this research are expected to contribute to enhancing the understanding of GPS vulnerability and improving countermeasures against spoofing attacks. The closed form expression for GPS Spoofing attack failure probability is derived using the Poisson point process. The simulation is compared with the theoretical results and observed that it is exactly close to each other.
In nuclear inspection environments, a tether cable is used to transfer power and data between the underwater robotic system and the surface control unit. During underwater nuclear inspection, the tether cable can beco...
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ISBN:
(纸本)9781665489218
In nuclear inspection environments, a tether cable is used to transfer power and data between the underwater robotic system and the surface control unit. During underwater nuclear inspection, the tether cable can become entangled and loop with the environment such as nuclear waste boxes and objects. The risk of colliding with underwater objects is increased by the presence of more inspection robots underwater, especially if they are equipped with manipulator arms. As a result of the loops and knots around the cable, the inspection process may be affected and the ROV may not be able to perform its job. The present article is an extended development of the previous Collision Avoidance Robotic Tether (CART-I) model [1]. The CART-I system consists of micro thrusters that are attached to the base unit by a tether cable. The micro thrust unit is capable of generating a small amount of thrust that can move the tether away from obstacles in the water, particularly in restricted spaces. The use of light detection technologies such as IR or LiDAR for obstacle detection is not effective underwater due to the complex motion dynamics of the tether underwater and the size of obstacles, which makes it impossible to provide definite identification of the objects within a given time period. In order to provide the surroundings of the micro thrust units with obstacle detection capability, we have developed an autonomous force soft sensor. Additionally, the soft moulded sealed encase was developed for effective force detection underwater, and was experimentally tested in a water tank to validate our proposed design. Simulation and experimental results of the sensor is provided. The overall goal of the CART-ii is to provide a smart autonomous vision of obstacle avoidance using soft force sensing capabilities. This paper presents the full kinematic model and the simulation with finite element analysis of the CART-ii system with the hardware and physical implementation of the soft sensor
Co-incineration is one of the best ways to treat sewage sludge (SS) and municipal solid waste (MSW), but it is very important to understand its combustion and gas reaction in the grate to control the emission of pollu...
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ISBN:
(数字)9798350387780
ISBN:
(纸本)9798350387797
Co-incineration is one of the best ways to treat sewage sludge (SS) and municipal solid waste (MSW), but it is very important to understand its combustion and gas reaction in the grate to control the emission of pollutants. In this paper, the discussion of SS co-incineration based on the dioxin emission numerical simulation model for the MSW incineration (MSWI) process is proposed. Firstly, the numerical simulation model of DXN emission for the MSWI process is established in ASPEN Plus software by using collected sample data in an MSWI plant in Beijing. Then, the SS and MSW co-incineration numerical simulation model is constructed. Finally, based on SS data in reference, the discussion and analysis are made. These research results provide support for the future research on co-incineration of SS and MSW in Beijing.
Diagnosing flight route faults based on QAR data is crucial for preventing flight accidents and reducing maintenance costs. This paper presents a method for diagnosing flight route faults using a Long Short-Term Memor...
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ISBN:
(数字)9798350388855
ISBN:
(纸本)9798350388862
Diagnosing flight route faults based on QAR data is crucial for preventing flight accidents and reducing maintenance costs. This paper presents a method for diagnosing flight route faults using a Long Short-Term Memory Network and Decision tree algorithm (LSTM-DT). We used feature optimization algorithms to process the original QAR data, including rolling window operation and principal component analysis. The optimized features were then used to establish an LSTM anomaly detection model to identify abnormal points of the faulty route. We established a decision tree fault diagnosis model using these detected outliers to determine the corresponding fault types. When tested with real airline data, the fault diagnosis accuracy of this method reached 99.45%, confirming its effectiveness.
The understanding of scene representation is a deep knowledge service structure strategy arising from the increasing scale of data and the need for complex logic solving. This study proposes a modeling improvement met...
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ISBN:
(纸本)9781665494021
The understanding of scene representation is a deep knowledge service structure strategy arising from the increasing scale of data and the need for complex logic solving. This study proposes a modeling improvement method based on the fusion of complex feature data and exploration behavior trajectory extremes, which effectively utilizes the artistic feature study of the interplay between the unique colorant mixture attachment features of Chinese painting and complex handwriting features as the orientation region, realizes the classification constraint of colorant data through multispectral detection, and characterizes the handwriting as the behavior law, realizes the parametric extraction and then couples the solution encoding to complete the improvement of the algorithm. Since all scenes in Chinese painting are recorded in the bearer medium with handwriting characteristics after mixing Chinese brushes and colorants, the computational model of Chinese painting algorithm proposed in this paper starts from the processing of representation hierarchical structure and painting behavior of various scenes deposited to realize the principle of describing their material deposition goals and information exchange functions. The experimental analysis shows that i. deep knowledge understanding achieves the derivation of sparse feature validity, ii. the coverage calculation obtained by drawing on technological means can vividly describe the implicit characteristics of handwriting behavior, and iii. the improved modelingprocess has more humanized perceptual habits and enhances the accuracy and robustness of service domain requirements.
Layout generation plays a crucial role in graphic design intelligence. One important characteristic of the graphic layouts is that they usually follow certain design principles. For example, the principle of repetitio...
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ISBN:
(纸本)9781577358800
Layout generation plays a crucial role in graphic design intelligence. One important characteristic of the graphic layouts is that they usually follow certain design principles. For example, the principle of repetition emphasizes the reuse of similar visual elements throughout the design. To generate a layout, previous works mainly attempt at predicting the absolute value of bounding box for each element, where such target representation has hidden the information of higher-order design operations like repetition (e.g. copy the size of the previously generated element). In this paper, we introduce a novel action schema to encode these operations for better modeling the generation process. Instead of predicting the bounding box values, our approach autoregressively outputs the intermediate action sequence, which can then be deterministically converted to the final layout. We achieve state-of-the-art performances on three datasets. Both automatic and human evaluations show that our approach generates high-quality and diverse layouts. Furthermore, we revisit the commonly used evaluation metric FID adapted in this task, and observe that previous works use different settings to train the feature extractor for obtaining real/generated data distribution, which leads to inconsistent conclusions. We conduct an in-depth analysis on this metric and settle for a more robust and reliable evaluation setting. Code is available at this website (1).
As a hot artificial intelligence method, machine learning has penetrated into various industries. It has also been gradually introduced into the field of slope prevention and control, which has greatly promoted the de...
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In an era where data volume is growing exponentially, effective data management techniques are more crucial than ever. Traditional methods typically manage the size of large datasets by reducing or aggregating data us...
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The article describes the problems of mathematical modeling of processes using an experimental database and a knowledge base. This research relates to multidimensional dependency building. It uses regression analysis ...
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ISBN:
(纸本)9783030941413;9783030941406
The article describes the problems of mathematical modeling of processes using an experimental database and a knowledge base. This research relates to multidimensional dependency building. It uses regression analysis and machine learning techniques within the framework of probability theory and mathematical statistics. A large observation table often cannot be processed on a single computer. The analysis of such data requires parallel computations and in this article it is carried out by the method of interval mathematics, which allows performing such computations. The analysis of linear dependences on parameters is reduced to solving systems of interval linear algebraic equations. Among the approaches to systems study known in the literature, an approach was chosen that takes into account the so-called "single set of solutions". This method provides a guaranteed estimate of the required dependencies and allows the use of linear programming in some cases. Using this method, interval forecasts of the output variable of the modeled process are calculated. Interval estimates of the parameters of the studied dependence were also obtained. Two methods of sequential and parallel analysis of a large database are proposed, using methods for solving large-scale linear programming problems. The optimality of the algorithms is substantiated using the well-known technique of removing constraints in optimization problems of large dimension. The research was carried out on model processes and on real data of statistics of road traffic accidents in England.
This research utilizes Finite Element modeling (FEM) to predict cutting forces in end milling operations. Simulations were performed using Abaqus, with models developed in SolidWorks, across different cutting paramete...
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ISBN:
(数字)9798331523268
ISBN:
(纸本)9798331523275
This research utilizes Finite Element modeling (FEM) to predict cutting forces in end milling operations. Simulations were performed using Abaqus, with models developed in SolidWorks, across different cutting parameters including feed rate, spindle speed, and depth of cut. Two models—one with and one without temperature effects—were examined to evaluate cutting force dynamics in the X, Y, and Z directions. The results indicate that temperature significantly influences force predictions, and varying cutting parameters result in corresponding ranges of force oscillations, consistent with findings from existing literature. The proposed simulations demonstrate the potential of FEM to enhance machining efficiency, extend tool life, and improve surface quality, supporting its future use in end mill process optimization and adaptive CNC control.
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