Knowledge-based Simulation System for Reliability and Performance Analysis of Computer Networks

Modeling and analysis of performance of computer networks is essential for ensuring smooth operation of an organization’s networks and preventing major failures. Mathematical analysis and simulation modeling are the common procedures for network system performance analysis. In this paper, a knowledge-based simulation system is developed that can be used for assessment and prediction of network performance and reliability

Knowledge-based simulation model for performance analysis and reliability of computer networks

Modeling and analysis of performance of computer networks is essential for ensuring smooth operation of an organization\u27s networks and preventing major failures. Mathematical analysis and simulation modeling are the common procedures for network system performance analysis. In this paper, a knowledge-based simulation model is developed that can be used for predicting network performance and reliability

[MODELING OF PH NEUTRALIZATION PROCESS PILOT PLANT]

System Identification is an art of constructing a mathematical model for a dynamic response system. The modeling process is based on the observed input and output data for a system. To start a modeling process, a good understanding of process behavior is required as it will determine the important parameters and characteristics to be analyzed. pH neutralization is a very nonlinear process. It is not easy to get an accurate model as compared to the actual model. Modeling using conventional methods does not seem to give a reliable model for this process. Thus, for wide a range of neutralization pH values, conventional modeling methods are not sufficient. Therefore, for this project, intelligent approaches are considered. The conventional methods that are used by the Author are mathematical modeling, empirical modeling and statistical modeling. Mathematical modeling is done to see the relation of inputs and output. Empirical modeling is the common method used for plant modeling. Statistical modeling is more a to computerized modeling where it requires a good computer configuration basic in order to achieve the desired output. Neural Network is used for the intelligent method. Neural network is an intelligent approach that has the capability to predict future plant performance by training several datasets. These conventional and intelligent methods are compared between each other in term of the model accuracy, model reliability and flexibility. Modeling using mathematical modeling is tedious and requires more effort on the block diagram configuration in order to get an accurate result. Empirical modeling is basically good enough for plant identification, unfortunately for a highly nonlinear system, the method does not seem reliable. Statistical modeling has the ability to predict an acceptable higher order model. On top of that, neural network could give a more reliable and accurate result

A Piecewise Linear State Variable Technique for Real Time Propulsion System Simulation

The emphasis on increased aircraft and propulsion control system integration and piloted simulation has created a need for higher fidelity real time dynamic propulsion models. A real time propulsion system modeling technique which satisfies this need and which provides the capabilities needed to evaluate propulsion system performance and aircraft system interaction on manned flight simulators was developed and demonstrated using flight simulator facilities at NASA Ames. A piecewise linear state variable technique is used. This technique provides the system accuracy, stability and transient response required for integrated aircraft and propulsion control system studies. The real time dynamic model includes the detail and flexibility required for the evaluation of critical control parameters and propulsion component limits over a limited flight envelope. The model contains approximately 7.0 K bytes of in-line computational code and 14.7 K of block data. It has an 8.9 ms cycle time on a Xerox Sigma 9 computer. A Pegasus-Harrier propulsion system was used as a baseline for developing the mathematical modeling and simulation technique. A hydromechanical and water injection control system was also simulated. The model was programmed for interfacing with a Harrier aircraft simulation at NASA Ames. Descriptions of the real time methodology and model capabilities are presented

Using Discrete Geometric Models in an Automated Layout

The application of discrete (voxel) geometric models in computer-aided design problems is shown. In this case, the most difficult formalized task of computer-aided design is considered—computer-aided layout. The solution to this problem is most relevant when designing products with a high density of layout (primarily transport equipment). From a mathematical point of view, these are placement problems; therefore, their solution is based on the use of a geometric modeling apparatus. The basic provisions and features of discrete modeling of geometric objects, their place in the system of geometric modeling, the advantages and disadvantages of discrete geometric models, and their primary use are described. Their practical use in solving some of the practical problems of automated layout is shown. This is the definition of the embeddability of the placed objects and the task of tracing and evaluating the shading. Algorithms and features of their practical implementation are described. A numerical assessment of the accuracy and performance of the developed geometric modeling algorithms shows the possibility of their implementation even on modern computers of medium power. This allows us to hope for the integration of the developed layout algorithms into modern systems of solid-state geometric modeling in the form of plug-ins

The Design of an Innovative Automotive Suspension for Formula SAE Racing Applications

This thesis details an analytical approach to an innovative suspension system design for implementation to the Formula SAE collegiate competition. It focuses specifically on design relating to geometry, mathematical modeling, energy element relationships, and computer analysis and simulation to visualize system behavior. The bond graph approach is utilized for a quarter car model to facilitate understanding of the analytical process, then applied to a comparative analysis between two transverse half car models. The second half car model contains an additional transverse linkage with a third damper, and is compared against the baseline of the first half car model without the additional linkage. The transverse third damper is an innovative design said to improve straight-line tire contact during single-sided disturbance, help mitigate the adverse effects of squat and dive, while not inhibiting the function of the anti-roll bar in cornering capability. Additional work is done investigating an optimization of suspension geometry through mathematical modeling in MATLAB of a four-bar linkage system. This code helps visualize the complex motion of the upright and calculates the wheel camber rate and variation to compare against tire data analysis to match maximum tire performance characteristics with camber angle

Simulating Operation of a Planetary Rover

Simulating Operation of a Planetary Rover Rover Analysis, Modeling, and Simulations (ROAMS) is a computer program that simulates the operation of a robotic vehicle (rover) engaged in exploration of a remote planet. ROAMS is a roverspecific extension of the DARTS and Dshell programs, described in prior NASA Tech Briefs articles, which afford capabilities for mathematical modeling of the dynamics of a spacecraft as a whole and of its instruments, actuators, and other subsystems. ROAMS incorporates mathematical models of kinematics and dynamics of rover mechanical subsystems, sensors, interactions with terrain, solar panels and batteries, and onboard navigation and locomotion-control software. ROAMS provides a modular simulation framework that can be used for analysis, design, development, testing, and operation of rovers. ROAMS can be used alone for system performance and trade studies. Alternatively, ROAMS can be used in an operator-in-the-loop or flight-software closed-loop environment. ROAMS can also be embedded within other software for use in analysis and development of algorithms, or for Monte Carlo studies, using a variety of terrain models, to generate performance statistics. Moreover, taking advantage of realtime features of the underlying DARTS/Dshell simulation software, ROAMS can also be used for real-time simulations

Petri Net Based Reliable Work Flow Framework for Nephrology Unit in Hospital Environment

The 21st century has witnessed a revolution in Biology and Medicine that has radically changed the way health, diagnosis, prognosis, etc., of a disease is monitored nowadays. Accordingly, hospital redesign, workforce planning and scheduling, patient flow, performance management, disease monitoring, and health care technology assessment need to be modeled efficiently. Mathematical modeling and computer simulation techniques have been shown to be increasingly valuable in providing useful information to aid planning and management. Petri Net (PN) is considered as a powerful model since it combines well-defined mathematical theory with a graphical representation which reflects the dynamic behavior of systems of interest. Due to dynamic characteristics, it is found to be more suitable for modeling Hospital Management System (HMS). In this paper, a Petri net model-based reliable workflow framework for Nephrology unit in hospital environment is proposed to track the movement of patients in the unit. The key objective of the proposed reliable workflow framework is to provide a well-organized health care unit to reduce the waiting time of the resource/ patient. The performance of the proposed Petri net model-based reliable workflow framework is simulated and validated through reachability graph using HPSim tool. The proposed Petri net workflow framework for the Nephrology unit can be used to deliver highly efficient and reliable healthcare services

[MODELING OF PH NEUTRALIZATION PROCESS PILOT PLANT]

System Identification is an art of constructing a mathematical model for a dynamic response system. The modeling process is based on the observed input and output data for a system. To start a modeling process, a good understanding of process behavior is required as it will determine the important parameters and characteristics to be analyzed. pH neutralization is a very nonlinear process. It is not easy to get an accurate model as compared to the actual model. Modeling using conventional methods does not seem to give a reliable model for this process. Thus, for wide a range of neutralization pH values, conventional modeling methods are not sufficient. Therefore, for this project, intelligent approaches are considered. The conventional methods that are used by the Author are mathematical modeling, empirical modeling and statistical modeling. Mathematical modeling is done to see the relation of inputs and output. Empirical modeling is the common method used for plant modeling. Statistical modeling is more a to computerized modeling where it requires a good computer configuration basic in order to achieve the desired output. Neural Network is used for the intelligent method. Neural network is an intelligent approach that has the capability to predict future plant performance by training several datasets. These conventional and intelligent methods are compared between each other in term of the model accuracy, model reliability and flexibility. Modeling using mathematical modeling is tedious and requires more effort on the block diagram configuration in order to get an accurate result. Empirical modeling is basically good enough for plant identification, unfortunately for a highly nonlinear system, the method does not seem reliable. Statistical modeling has the ability to predict an acceptable higher order model. On top of that, neural network could give a more reliable and accurate result