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 Transmission Expansion Planning (TEP) Transmission Systems are Large and Interconnected. Transmission Systems carry large quantities of electricity (from utility-scale to low voltage lines – distributed system).   Transmission Expansion Planning (TEP) is process of identifying needed investment and expansion in transmission.  Transmission Expansion Planning (TEP) is a complex decision-making process.  TEP Process include different analysis : System Cost. Reliability and Modeling. Compute Risk and burden. Number of Generators required. The number of Equipment required. How a transmission system should develop over time?  Determine the Number of Electric Power Transmission facilities required in the future power grid. Transmission Expansion Planning (TEP): A list of types of equipment can be inserted on the grid: Cables Transformers Transmission Lines Transmission Expansion Planning (TEP) Problem: Objective Functions Investment Function Operational Cost Function Power Loss Function  App

 Whale Optimization Algorithm for association rule mining. Input: Number of Maximum Iteration and Population Size, Minsupport, minconfedence.  Step 01: Initialize the population size for n search agents.[Xi(i=1,2,3,...n)] Step 02: Initialize i, A, C, L, and p. Step 03: Compute the fitness value of each search agent/whale. Step 04: X* = the best rule Step 05: While (CurrentIteration <= MaximumIteration ) Step 06: Update a, A, C, L and p. Step 07: For all whale poplation check          if (p<0.5) if(|A|<1)    For each Item in the solution Xi.    Update Items. Else if(|A|=1)     Select a random Item in Xi. Update Items. End if        For each item in the solution Xi. If the Item is odd, it belongs to the antecedent, Otherwise, it belongs to the consequence. End for Step 08: Calculate the fitness of each search agent. Step 09: Update X* if there is a better solution. Step 11: Iteration = currentIteration + 1 Step 12:    End While Step 13:    Return X* 1

Multiverse Optimization Algorithm   Q. What is Multiverse Theory? A.  Multiverse means the group of multiple universes. According to Multiverse theory, there is more than one Big Bang, and each Big Bang causes the birth of a universe. Multiple universes collide and interact with each other.  Q. What is Big Bang Theory? A.  Our universe comes into existence (13 Billion years Ago) from a single, hot and dense point. According to the Big Bang theory, our universe starts with a massive explosion. Before this explosion, nothing exists in this world. The universe contains time, energy, planets, stars, galaxies, and matter. Q. What is Multiverse Optimization Algorithm? A.  Multiverse Optimization Algorithm (MVO) is inspired by multiverse theory. Multiverse optimization algorithm is basically inspired by three main concepts of multiverse theory: White Hole, Black Hole, and Worm Hole. According to multi-verse theory, white holes / Big Bangs are created where collisions between parallel universe

 Local Binary Pattern Introduction to Local Binary Pattern (LBP) Q. What is Digital Image? A. Digital images are collections of pixels or numbers ( range from 0 to 255).  Q. What is Pixel? A. Pixel is the smallest element of any digital image. Pixel can be categorized as Dark Pixel and Bright Pixel. Dark pixels contain low pixel values and bright pixels contain high pixel values. Q. Explain Local Binary Pattern (LBP)? A. Local binary pattern is a popular technique used for image processing. We can use the local binary pattern for face detection and face recognition. Q. What is LBP Operator? A. LBP operator is an image operator. We can transform images into arrays using the LBP operator. Q. How LBP values are computed? A. LBP works in 3x3 (it contain a 9-pixel value ). Local binary pattern looks at nine pixels at a time. Using each 3x3 window in the digital image, we can extract an LBP code. Q. How to Obtain LBP operator value?  A. LBP operator values can be obtained by using the simp

Dragonfly Optimization Algorithm (DOA) Dragonfly Algorithm is developed by Mirjalili in 2016. Dragonfly Algorithm is a metaheuristic algorithm inspired by the behavior of dragonflies in nature. There are about 5000 known species of dragonflies. Dragonfly is a symbol of Strength, Courage, and Happiness in Japan.  Dragonfly Algorithm Step-by-Step: - Step 01: Initialize Dragonfly Population Randomly (𝑋_𝑖, Where i = 1,2,3,4,…n).  Step 02: Initialize Step vector / Size for dragonfly (〖∆𝑋〗_𝑖). Step 03: While(CurrentIteration < MaximumIteration) Step 04: Computer Fitness Values for each dragonfly. Step 05: Update Food sources and enemy.  Step 06: Update parameters w, s, a, c, f, and e. Step 07: Calculate S, A, C, and F. Step 08: Update neighboring radius.  Step 09: If the dragonfly has at least one neighboring dragonfly. {     Update Velocity and Position; }  else { Update Position; } Elseif { Check and correct new position based on boundaries of variable; } Note: To Improve rand

 Q. What is Optimization?  A.  Optimization means Optimum Point Where conditions are best and most favorable. Optimization algorithms help to obtain the best solutions for complex problems. Different numerical methods for optimization are used to design better systems.  Q. Why we do Optimization? A. To Find the better/best among different possible solutions Q. Why Objective functions are used? A. Objective functions are used to Maximize or Minimize values that you are trying to Optimize. Using objective functions you can obtain a minimum or maximum value. Q. Define Meta-heuristic optimization.  A. Metaheuristic algorithms plays important role in solving real-life problems. Metaheuristic algorithms are Optimization methods used to solve complex engineering problems. A Metaheuristic is an advanced technique for finding good solutions to a complex problem.  Q. Define multi-objective optimization problems?     A. When designers want to optimize two or more two objective functions simulta

 Ant colony optimization algorithms Do you know: How the Ant Colony Optimization algorithm works?   We all know about ants. Researchers have estimated about 13800 known ant species. In an ant colony, there is Queen (i.e., female ant), fertilized male ant (also known as drone), female ant workers, and soldiers. Queen ant can live up to 30 years. Ants live in colonies (also known as ant nest). An average ant colony contains 1000 individual ants and an ant super colony can contain 300 million individual ants. Ants communicate with each other indirectly using pheromones, sound, and touch. Ant colony optimization is a Nature Inspired Population Based Metaheuristic Optimization Algorithm. Ant colony optimization (ACO) is inspired by the real ant food searching behavior. Ant colony optimization algorithm is basically inspired by the pheromone-based ant communication . Ant colony optimization is developed by Marco Dorigo in 1992.  Author developed this algorithm to solve Discrete Variable Comb

 Sparrow Search Algorithm Sparrow Search Algorithm is inspired by the foraging behaviors of Sparrows. 2 Types of Sparrow according to their roles in foraging:  Producers (they collect food from different sources) Scroungers ( obtain food discovered by producers ) OUTPUT: Best Position and Fitness value. Viedo Link:  https://youtu.be/Yxy0kszRzdY Sparrow Search Algorithm Steps Step 01: Initialize Sparrows population Randomly & its Parameters.  Step 02: Calculate fitness values for each agent.  Step 03: Update Sparrow Location for Producers and Scroungers in the search space.  Step 04 : Update Current New Location. Step 05 : If New Location is Better than before. [Update it] Step 06 : Increment Counter i.e., t = t + 1. [until condition satisfy (t<MaxT)]. Step 07: Return Current Best Position (𝑋_𝐵𝑒𝑠𝑡) and Fitness Value (𝑓_𝑔).  New Optimization Algorithm 2021 #SSA #Sparrowsearchalgorithm #OptimizationAlgorithm  #Metaheuristic #Algorithms Meta-heuristic Algorithms Link - Click

What is the Crow search algorithm? Crow search algorithm (CSA) is a population-based algorithm. Crow search algorithm is similar to Particle Swarm Optimization (PSO) algorithm. Crow search algorithm mimics the crow's intellegent behavior. CSA is based on crow's intelligent behavior.  Key Point About Crow's Crows live in large families and care for younger ones. They eat insects, worms, nuts, fruits, food, birds, non-insects, etc. They can hide excess food in hiding places and retrieve it when needed. Age: 14-17 years. Crow can memorize the hiding place positions. They follow each other to steal their food. Crows protect their hiding places from attackers. Two main parameters used in the CSA algorithm : Flights Length, Awareness Probability.  Crow Search Optimization Algorithm Main Concepts  Crow store excess food in hiding places and retrieve it when needed.  Crow cheat each other (i.e., they steal each other food). Crow Search Algorithm Step-by-Step with Example ~xRay Pix

 Cuckoo Search Algorithm - Metaheuristic Optimization Algorithm What is Cuckoo Search Algorithm? Cuckoo Search Algorithm is a Meta-Heuristic Algorithm. Cuckoo Search Algorithm is inspired by some Cuckoo species laying their eggs in the nest of other species of birds. In this algorithm, we have 2 bird Species.  1.) Cuckoo birds   2.) Host Birds (Other Species) What if Host Bird discovered cuckoo eggs? Cuckoo eggs can be found by Host Bird.  Host bird discovers cuckoos egg with Probability of discovery of alien eggs.  If Host Bird Discovered Cuckoo Bird Eggs. The host bird can throw the egg away. Abandon the nest and build a completely new nest. Mathematically, Each egg represent a solution and it is stored in the host bird nest. In this algorithm Artificial Cuckoo Birds are used. Artificial Cuckoo can lay one egg at a time. We will replace New and better solutions with less fit solutions. It means eggs that are more similar to host bird has opportunity to develop in the new generation a

Particle Swarm Optimization (PSO) is a p opulation-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm.  In Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. After a number of iterations agents / particles will find out optimal solution in the search space. Q. What is PSO? A. PSO is a computational method that Optimizes a problem. Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to upda

Information Security means Data Protection. Information Security Laws deal with Creating, Storing, and Acquiring Information. Q. What is the Indian IT Act? A. It is an Act of the Indian Parliament. This is Law in India that deal with Cyber Crimes and Electronic Commerce.  Cyber Crime: [IT Act 200] Crime that involves computers and Networks. Cyber Crime involves crimes related to Computers. For Example-  Theft and Hacking.   Privacy Concerns Involved in Cyber Crime:  - Data/Information Privacy  - Financial Theft/Frauds Related Crimes  - Online Harassment - Online transaction Frauds    Q. What is a Patent Law? A.  It is the branch of  intellectual property law. Patent law deals with new scientific inventions. Patent Law in India starts in 1911.  Q. What is a Copyright Law? A . Law for copyright protection. Copy right law protects the original work of the creator. This law provides protection to sound recording, musical work, artwork, dramatic and film work.  Q. What is Privacy on the I

 Consider the effect of a translation in the x,y,z direction by -2, -4, and -6 respectively on the homogenous coordinate position vectors [1, 6, 4].  Solution. 3D Translation Matrix is given as: Here, Tx = -2, Ty = -4, and Tz = -6. As We know, 3D translation is performed as: Therefore, we got  New co-ordinates are [-1  2  -2 ]

 Three Dimensional Transformations  Q. What is 3D Translation? A. Translation means shifting a point or moving the whole object. In 3D we need 3 Translatio factors. Translation Matrix for 3D:  Q. How to perform 3D Transformation? A.  To perform 3D Transformation. Multiply point with translation Matrix. Here, Q. What is 3D Scaling? A. Scaling changes the size of the object. Scaling in 3D is similar to scaling in 2D. The Matrix for 3D scaling transformation given as:

 Learn, Explore and Create ~ Ritika xRay Pixy https://www.youtube.com/channel/UC5kiAu42ZR8GD1KxNtLEO7w Computer Science & Applications  NTA UGC NET 2020  Paper-2