Narrow Results

Secure and private user data are more important than ever with the explosion of online gaming platforms and the resulting deluge of user information. Intending to protect gaming ecosystems and maintain user confidence, Heuristic Predictive Modeling provides a proactive security strategy by allowing early detection and mitigation of potential risks. The ever-changing nature of the game, the wide variety of user interactions, and the always-evolving strategies of cybercriminals all contribute to the singular problems that data management and security encounter in modern gaming settings. This research proposes Heuristic Predictive Modeling for Gaming Security (HPM-GS). This system can analyze gaming data in real time and detect trends and abnormalities that could indicate security breaches. It uses advanced algorithms and machine learning approaches. With HPM-GS, gaming platforms can keep their users safe and secure by anticipating and proactively addressing security threats. Several areas of gaming security can benefit from HPM-GS, such as user authentication, detection of cheats, prevention of fraud, and incident response. Enhanced user experience and platform reliability can be achieved by incorporating HPM-GS into pre-existing security frameworks, which allows gaming platforms to strengthen their defenses and efficiently reduce risks. Extensive simulation studies assess the effectiveness of HPM-GS in gaming security. The performance metrics of HPM-GS, such as detection accuracy, false positive rates, and response time, are evaluated using real-world datasets and simulated attack scenarios. The simulation findings show that HPM-GS is a good solution for protecting gaming environments from cyber-attacks. The HPM-GS is a proactive, elastic gaming application data management and security method. The purpose of this research is to emphasize the potential of HPM-GS to improve the security posture of online gaming platforms and to ensure that players have a gaming experience that is both safer and more pleasant. This is accomplished by addressing the significance of HPM-GS, potential difficulties, proposed techniques, implementations, and simulation analysis.

Cloud computing’s simulation and modeling capabilities are crucial for big data analysis in smart grid power; they are the key to finding practical insights, making the grid resilient, and improving energy management. Due to issues with data scalability and real-time analytics, advanced methods are required to extract useful information from the massive, ever-changing datasets produced by smart grids. This research proposed a Dynamic Resource Cloud-based Processing Analytics (DRC-PA), which integrates cloud-based processing and analytics with dynamic resource allocation algorithms. Computational resources must be able to adjust the changing grid circumstances, and DRC-PA ensures that big data analysis can scale as well. The DRC-PA method has several potential uses, including power grid optimization, anomaly detection, demand response, and predictive maintenance. Hence the proposed technique enables smart grids to proactively adjust to changing conditions, boosting resilience and sustainability in the energy ecosystem. A thorough simulation analysis is carried out using realistic circumstances within smart grids to confirm the usefulness of the DRC-PA approach. The methodology is described in the intangible, showing how DRC-PA is more efficient than traditional methods because it is more accurate, scalable, and responsive in real-time. In addition to resolving existing issues, the suggested method changes the face of contemporary energy systems by paving the way for innovations in grid optimization, decision assistance, and energy management.

This paper presents the development of an autopilot system for self-driving an autonomous wheeled vehicle. A mathematical model, including the power allocation system, has been designed for a vehicle with three degrees of freedom. All model parameters have been identified through experimental trials. Heading and speed controllers were designed based on Lyapunov theory. These controllers have been further fine-tuned and tested through simulations to verify their robustness against external disturbances in the system dynamics. Moreover, this work proposes guidance approaches that allow the vehicle to track desired waypoints (line of sight (LOS)), and follow a given path (cross-track error) and a predefined trajectory with obstacle avoidance. A comparative study was also proposed in this paper, wherein we evaluate the paths followed by the vehicle using distinct yaw moment control techniques which are; differential thrust controller, solely relying on a steering controller, and a combination of both. To validate the effectiveness of the proposed autopilot system, we have conducted experimental tests, specifically focusing on waypoint tracking control (LOS method). The results underscore the system’s capabilities and its potential in real-world applications.

The Thrust vector control (TVC) is a method for controlling the angular velocity and attitude of aerial vehicles (AV) by manipulating the thrust direction of propulsion. This technology enhances maneuverability and allows for dynamic aerobatics at low speeds and near-zero airspeeds without stalling at high angles of attack (AOA). As the aerodynamic control surfaces are ineffective for vehicles operating outside the atmosphere, TVC is a suitable technique for these applications. To design a control system for AVs utilizing TVC, an accurate mathematical model is essential to simulate flight parameters and optimize the control gains. This work presents a complete six degrees-of-freedom (6-DOF) high-fidelity simulation model of a thrust vector control aerial vehicle (TVC-AV). The nonlinear model is developed by dividing the mathematical representation into five submodules, including the geometrical model, the actuation model that was experimentally identified, and an aerodynamic model that was validated through semi-empirical techniques, computational fluid dynamics (CFD), and wind-tunnel experiments; in addition, the propulsion model’s characteristics are identified through experimentation, and the atmospheric model is based on International Standard Atmosphere (ISA) values. The integrated model was implemented in MATLAB^Ⓡ (Simulink) that provides a foundation for designing effective flight controllers and guidance systems.

Genetic algorithms (GAs) have been well applied in solving scheduling problems and their performance advantages have also been recognized. However, practitioners are often troubled by parameters setting when they are tuning GAs. Population Size (PS) has been shown to greatly affect the efficiency of GAs. Although some population sizing models exist in the literature, reasonable population sizing for task scheduling is rarely observed. In this paper, based on the PS deciding model proposed by Harik, we present a model to represent the relation between the success ratio and the PS for the GA applied in time-critical task scheduling, in which the efficiency of GAs is more necessitated than in solving other kinds of problems. Our model only needs some parameters easy to know through proper simplifications and approximations. Hence, our model is applicable. Finally, our model is verified through experiments.

Using a relatively simple model we examine how variations of the extracellular potassium concentration can give rise to synchronization of two nearby pacemaker cells. With the volume of the extracellular space and the rate of potassium diffusion as control parameters, the dual nature of this resource-mediated coupling is found to be responsible for the coexistence of competing patterns of in- and anti-phase synchronization between identical cells. Cell heterogeneity produces significant modifications of the dynamical regimes in the control parameter plane. By comparison with conventional gap junctional coupling, potassium signaling gives rise to considerable changes of the cellular response to external stimuli.

Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.

This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.

Traditionally, it is considered that neuronal synchronization in epilepsy is caused by a chain reaction of synaptic excitation. However, it has been shown that synchronous epileptiform activity may also arise without synaptic transmission. In order to investigate the respective roles of synaptic interactions and nonsynaptic mechanisms in seizure transitions, we developed a computational model of hippocampal cells, involving the extracellular space, realistic dynamics of ${\text{Na}}^{+}$, ${\text{K}}^{+}$, ${\text{Ca}}^{2+}$ and ${\text{Cl}}^{-}$ ions, glial uptake and extracellular diffusion mechanisms. We show that the network behavior with fixed ionic concentrations may be quite different from the neurons’ behavior when more detailed modeling of ionic dynamics is included. In particular, we show that in the extended model strong discharge of inhibitory interneurons may result in long lasting accumulation of extracellular ${\text{K}}^{+}$, which sustains the depolarization of the principal cells and causes their pathological discharges. This effect is not present in a reduced, purely synaptic network. These results point to the importance of nonsynaptic mechanisms in the transition to seizure.

As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilistic knowledge, they are applied to increasingly larger problem domains. These large projects demand a systematic approach to handle the complexity in knowledge engineering. The needs include modularity in representation, distribution in computation, as well as coherence in inference. Multiply Sectioned Bayesian Networks (MSBNs) provide a distributed multiagent framework to address these needs.

According to the framework, a large system is partitioned into subsystems and represented as a set of related Bayesian subnets. To ensure exact inference, the partition of a large system into subsystems and the representation of subsystems must follow a set of technical constraints. How to satisfy these goals for a given system may not be obvious to a practitioner. In this paper, we address three practical modeling issues.

We propose a method of description and modeling of complex symmetrical images, which can be used for the synthesis of ornamental patterns. Our approach allows considerable memory reduction for storing symmetrical images and considerable time reduction for their synthesis. Our algorithms for ornamental patterns synthesis suggest the design of a special purpose ornamental patterns editor which can store and synthesize symmetrical images.

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful "white box" neural network model with better generalization performance. In this paper, the problem formulation, the neural network configuration, and the associated optimization software are discussed in detail. This methodology is then applied to a practical real-world system to illustrate its effectiveness.

In the past years, research in eye tracking development and applications has attracted much attention and the possibility of interacting with a computer employing just gaze information is becoming more and more feasible. Efforts in eye tracking cover a broad spectrum of fields, system mathematical modeling being an important aspect in this research. Expressions relating to several elements and variables of the gaze tracker would lead to establish geometric relations and to find out symmetrical behaviors of the human eye when looking at a screen. To this end a deep knowledge of projective geometry as well as eye physiology and kinematics are basic. This paper presents a model for a bright-pupil technique tracker fully based on realistic parameters describing the system elements. The system so modeled is superior to that obtained with generic expressions based on linear or quadratic expressions. Moreover, model symmetry knowledge leads to more effective and simpler calibration strategies, resulting in just two calibration points needed to fit the optical axis and only three points to adjust the visual axis. Reducing considerably the time spent by other systems employing more calibration points renders a more attractive model.

One classic example of a binary classifier is one which employs the mean and standard deviation of the data set as a mechanism for classification. Indeed, principle component analysis has played a major role in this effort. In this paper, we propose that one should also include skew in order to make this method of classification a little more precise. One needs a simple probability distribution function which can be easily fit to a data set and use this pdf to create a classifier with improved error rates and comparable to other classifiers.

The concealed microcracks in shield tunnel lining present the characteristics of being of small size, unknown shape, and are difficult to detect. Based on the finite-difference time domain (FDTD) approach, this study proposed a new construction method of a refined grid accommodating and combining the variable shapes of microcracks, and capable of designing cross type, mesh type, and wave type microcrack models. The proposed new method also configured steel bars in the models to simulate actual engineering conditions, and characteristic response images of the models under different working conditions were obtained using ground penetrating radar (GPR) technology, which were then compared and analyzed to identify the imaging characteristics and differences of microcracks with variable geometric shapes. The waveform, amplitude, and time span of the characteristic single channel signal were furthermore studied. The results showed that the new method could successfully simulate the GPR characteristic response images of 0.5mm microcracks of diverse geometric shapes. When the microcracks were wavy, their real shape could only be determined after signal pre-processing; the density and quantity of steel bars directly affected the appearance of microcrack characteristic signals; the greater the density and quantity of steel bars, the greater the interference on the waveform, amplitude, and time-frequency range of electromagnetic wave signals; a special correlation existed between the maximum mean root square value of the amplitude and the single channel signal of the cracks. Moreover, the finding that the extension in time and distance in the GPR time distance profile intersected with the cracks was deemed potentially to provide fresh insights into identifying the characteristic points of the cracks in the GPR images. The new method proposed in this study successfully obtained the GPR numerical simulation images and characteristic signals of microcracks with variable geometric shapes. Through the processing and analysis of the characteristic response signals of microcracks, the conclusions obtained were considered to provide an interpretation basis for the detection of microcracks in practical engineering.

Medical imaging is a powerful mean to access dynamic function of 3D deformable organs of the body. Due to the flow of incoming data, multiresolution methods on parallel computers is the only way to achieve complex processings in reasonable time. We present an active pyramid to model dynamic volumes. This pyramid is built on the first volume of a sequence and contains a binary model of the objects of interest. Previous knowledge is introduced within this binary model. The structure of the pyramid is rigid, but its main interest is that its components are deformed to fit the data using energetical constraints. A multiresolution algorithm based on self-organizing maps is then applied to deform the model through time. This algorithm matches the different levels of the pyramid in a coarse to fine approach. The output of the matching process is the field of deformation, modeling the transformations. This pyramid is applied to real data in the result section. The rigid structure of the pyramid is suitable for massively parallel architectures.

Reduction of power dissipation in digital circuits is a subject of research in industry and academia. A major component of power dissipation in modern microprocessors is due to their large interconnect networks which are responsible for the distribution of power and clocks as well as for the intra-chip communication. Communication is realized by data and address buses. In this paper we (i) discuss an analytical model for energy estimation in deep submicron buses, (ii) present statistical energy measures based on the analytical model, (iii) derive the energy limits of communication through buses, (iv) and introduce energy efficiency measures of communication.

The complexity of modern embedded systems requires a revised and systematic approach to efficient and concurrent management of hardware (HW) and software (SW) parts in a codesign process. In order to optimally meet the ever-increasing design requirements and at the same time leverage design productivity, higher level aspects need to be addressed before worrying about the HW/SW boundary. This paper deals with high-level aspects of system-level modeling and provides modeling extension, from which contemporary related methodologies can greatly benefit. High-level aspects, their influence on the entire design flow, and systematic integration into the codesign environment are presented. An approach is proposed with a higher level of abstraction that helps bridge the gap between informal and formal system specifications. Higher level design decisions are enabled, avoiding premature ad hoc design decisions. Applicability of the proposed high-level codesign concepts is illustrated with a case study.

This paper presents a simplified analysis of a generalized cycloconverter and suggests a new method for the estimation of the cycloconverter period as a function of the control mode. The paper presents generalized theoretical results supported by computer simulations and demonstrated by examples.

This paper offers a new approach to analyses of cycloconverter operation. The difference equations describing the cycloconverters' transient and steady-state operating regimes are derived. Theoretical predictions were validated by a computer program which calculated the load current of different cycloconverter topologies using the proposed methodology. The calculated and experimental results are compared and found to be in good agreement.