sparse matrix in data structure
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This document discusses sparse matrices. It defines a sparse matrix as a matrix with more zero values than non-zero values. Sparse matrices can save space by only storing the non-zero elements and their indices rather than allocating space for all elements. Two common representations for sparse matrices are the triplet representation, which stores the non-zero values and their row and column indices, and the linked representation, which connects the non-zero elements. Applications of sparse matrices include solving large systems of equations.
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Open Feed Water Heater (Direct Contact)
Steam and water come into direct contact.
Mixing occurs, and heat is transferred directly.
Common in low-pressure stages.
Closed Feed Water Heater (Surface Type)
Steam and water are separated by tubes.
Heat is transferred through tube walls.
Common in high-pressure systems.
⚙️ Advantages:
Improves thermal efficiency.
Reduces fuel consumption.
Lowers thermal stress on boiler components.
Minimizes corrosion by removing dissolved gases.
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITY
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...Journal of Soft Computing in Civil Engineering Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking a volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.
Compiler Design Unit1 PPT Phases of Compiler.pptx
Compiler Design Unit1 PPT Phases of Compiler.pptxRushaliDeshmukh2 Compiler phases
Lexical analysis
Syntax analysis
Semantic analysis
Intermediate (machine-independent) code generation
Intermediate code optimization
Target (machine-dependent) code generation
Target code optimization
Introduction to FLUID MECHANICS & KINEMATICS
Introduction to FLUID MECHANICS & KINEMATICSnarayanaswamygdas Fluid mechanics is the branch of physics concerned with the mechanics of fluids (liquids, gases, and plasmas) and the forces on them. Originally applied to water (hydromechanics), it found applications in a wide range of disciplines, including mechanical, aerospace, civil, chemical, and biomedical engineering, as well as geophysics, oceanography, meteorology, astrophysics, and biology.
It can be divided into fluid statics, the study of various fluids at rest, and fluid dynamics.
Fluid statics, also known as hydrostatics, is the study of fluids at rest, specifically when there's no relative motion between fluid particles. It focuses on the conditions under which fluids are in stable equilibrium and doesn't involve fluid motion.
Fluid kinematics is the branch of fluid mechanics that focuses on describing and analyzing the motion of fluids, such as liquids and gases, without considering the forces that cause the motion. It deals with the geometrical and temporal aspects of fluid flow, including velocity and acceleration. Fluid dynamics, on the other hand, considers the forces acting on the fluid.
Fluid dynamics is the study of the effect of forces on fluid motion. It is a branch of continuum mechanics, a subject which models matter without using the information that it is made out of atoms; that is, it models matter from a macroscopic viewpoint rather than from microscopic.
Fluid mechanics, especially fluid dynamics, is an active field of research, typically mathematically complex. Many problems are partly or wholly unsolved and are best addressed by numerical methods, typically using computers. A modern discipline, called computational fluid dynamics (CFD), is devoted to this approach. Particle image velocimetry, an experimental method for visualizing and analyzing fluid flow, also takes advantage of the highly visual nature of fluid flow.
Fundamentally, every fluid mechanical system is assumed to obey the basic laws :
Conservation of mass
Conservation of energy
Conservation of momentum
The continuum assumption
For example, the assumption that mass is conserved means that for any fixed control volume (for example, a spherical volume)—enclosed by a control surface—the rate of change of the mass contained in that volume is equal to the rate at which mass is passing through the surface from outside to inside, minus the rate at which mass is passing from inside to outside. This can be expressed as an equation in integral form over the control volume.
The continuum assumption is an idealization of continuum mechanics under which fluids can be treated as continuous, even though, on a microscopic scale, they are composed of molecules. Under the continuum assumption, macroscopic (observed/measurable) properties such as density, pressure, temperature, and bulk velocity are taken to be well-defined at "infinitesimal" volume elements—small in comparison to the characteristic length scale of the system, but large in comparison to molecular length scale
The Gaussian Process Modeling Module in UQLab
The Gaussian Process Modeling Module in UQLabJournal of Soft Computing in Civil Engineering We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a standalone surrogate modeling tool. We first briefly present the key mathematical tools on the basis of GP modeling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function; a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).
Degree_of_Automation.pdf for Instrumentation and industrial specialist
Degree_of_Automation.pdf for Instrumentation and industrial specialistshreyabhosale19 degree of Automation for industrial and Instrumentation learners.
IntroSlides-April-BuildWithAI-VertexAI.pdf
IntroSlides-April-BuildWithAI-VertexAI.pdfLuiz Carneiro ☁️ GDG Cloud Munich: Build With AI Workshop - Introduction to Vertex AI! ☁️
Join us for an exciting #BuildWithAi workshop on the 28th of April, 2025 at the Google Office in Munich!
Dive into the world of AI with our "Introduction to Vertex AI" session, presented by Google Cloud expert Randy Gupta.
theory-slides-for react for beginners.pptx
theory-slides-for react for beginners.pptxsanchezvanessa7896 Everything you need to know about react.
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...Journal of Soft Computing in Civil Engineering
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sparse matrix in data structure
- 1. தரவ கடடடமமபடப - சசதறசய அலடலத அடரடதடதசயறடற அணசகளட Data Structures- Sparse Matrix P Mahalakshmi
- 2. Data Structures- Sparse Matrix P Mahalakshmi
- 3. What is Sparse Matrix? ● A matrix can be defined with a 2-dimensional array. ● Any array with 'm' rows and 'n' columns represent a m X n matrix ● There may be a situation in which a matrix contains more number of ZERO values than NON-ZERO values. Such matrix is known as sparse matrix. ● Sparse matrix is a matrix which contains very few non-zero elements.
- 4. Example ● Consider a matrix of size 100 X 100 containing only 10 non-zero elements. ● In this matrix, only 10 spaces are filled with non-zero values and remaining spaces of the matrix are filled with zero. ● Totally we allocate 100 X 100 X 2 = 20000 bytes of space to store this integer matrix. ● To access these 10 non-zero elements we have to make scanning for 10000 times.
- 5. Sparse Matrix Representations ● Triplet Representation (Array Representation) ● Linked Representation
- 6. Triplet Representation (Array Representation) ● In this representation, we consider only non-zero values along with their row and column index values. ● the 0th row stores the total number of rows, total number of columns and the total number of non-zero values in the sparse matrix. ● For example, consider a matrix of size 5 X 6 containing 6 number of non-zero values.
- 8. Applications