Yadwinder Singh Brar

Multi Criteria Decision Making Approach for Selecting Effort Estimation Model

Effort Estimation has always been a challenging task for the Project managers. Many researchers have tried to help them by creating different types of models. This has been already proved that none is successful for all types of projects and every type of environment. Analytic Hierarchy Process (AHP) has been identified as the tool that would help in Multi Criteria Decision Making. Researchers have identified that AHP can be used for the comparison of effort estimation of different models and techniques. But the problem with traditional AHP is its inability to deal with the imprecision and subjectivity in the pairwise comparison process. The motive of this paper is to propose Fuzzy Analytic Hierarchy Process, which can be used to rectify the subjectivity and imprecision of AHP and can be used for selecting the type of Model best suited for estimating the effort for a given problem type or environment. Instead of single crisp value, Fuzzy AHP uses a range of values to incorporate decision maker‟s uncertainty. From this range, decision maker can select the value that reflects his confidence and also he can specify his attitude like optimistic, pessimistic or moderate. In this work, the comparison of AHP and Fuzzy AHP is concluded using a case study of selection of effort estimation model.

Multiobjective load dispatch using Particle Swarm Optimization

Major objective for the Thermal power generation is to minimize fuel consumption by allocating optimal power generation from each unit and to maintain emissions within the environmental license limit subject to equality and inequality constraints. The objectives considered in this paper for minimization i.e. cost and emission are of conflicting nature, therefore weighting method has been applied to convert multiobjective optimization problem to a scalar optimization. The weighting method assigns different weights to each objective function based upon their importance. The Particle Swarm Optimization (PSO) technique has been applied to find the best comprised solution. The equality constraints have been handled by exterior penalty method. Fuzzy cardinal approach has been used for the evaluation of the best solution. In order to show the effectiveness, the proposed approach has been tested on three test systems. Numerical results obtained from the proposed approach are compared with different techniques.

Artificial neural network approaches for fault classification: comparison and performance

This manuscript focuses the implementation of artificial neural network-based algorithms to classify different types of faults in a power transformer, meant particularly for NonDestructive Test for transformer fault classification. The performance analysis of Probabilistic Neural Network (PNN) and Backpropagation Network classifiers has been carried out using the database of dissolved gases collected from Punjab State Electricity Board, Patiala, India. Features from the preprocessed data have been extracted using dimensionality reduction technique, i.e., principal component analysis. The selected features were used as inputs to the Backpropagation Network and PNN classifiers. A comparative study of the two intelligent classifiers has been carried out, which reveals that PNN classifier outperforms the Backpropagation Network classifier.

Combined Active and Reactive Power Dispatch Using Particle Swarm Optimization

Major objective for the Thermal power generation is to minimize fuel consumption by allocating optimal power generation from each unit subject to equality and inequality constraints. In most of cases fuel cost consist of active power cost only however reactive power is very essential for secure and reliable operation of power systems, so the reactive power cost has to be included in the cost calculation function. However, reactive power production by a generator will reduce its capability to produce active power. Hence, provision of reactive power by generator will result in reduction of its active power production, so the reactive power pricing is equally important with real power pricing, therefore a fair price calculation method seems to be essential. The objectives considered in this paper are minimization of active power cost and reactive power cost subject to equality and inequality constraints. In this paper Particle Swarm Optimization (PSO) technique has been applied to minimize both active and reactive power cost. The equality constraints have been handled by exterior penalty method. In order to show the effectiveness, the proposed approach has been tested on IEEE 9-bus standard network. Numerical results obtained from the proposed approach are compared with another technique confirms its validity and effectiveness.

Incentive based demand response program: An effective way to tackle peaking electricity crisis

This paper presents an investigation on incentive based demand response program (DRP) in deregulated power system environment. The major issues which are addressed in this paper, under demand response scenario are (i) demand curve flattening (ii) locational marginal pricing (LMP), and (iii) nodal voltage profile. The various issues mentioned above are compared under the power system modeling without and with incentive based DRP. The simulation work is carried out on IEEE 9-bus system by selecting practical load curve of Punjab State Transmission Corporation Limited from India. The results reveal that incentive based DRP is an effective way of tackling peaking electricity crisis under restructured power sector scenario.

Analysis of Data Mining Techniques for Software Effort Estimation

Software effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, datamining is used to improve an organizations software process quality, e.g. the accuracy of effort estimations. There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. In this study data preprocessing is implemented and effort is calculated using COCOMO Model. Then data mining techniques OLS Regression and K Means Clustering are implemented on preprocessed data and results obtained are compared and data mining techniques when implemented on preprocessed data proves to be more accurate then OLS Regression Technique.

Neural network based transformer incipient fault detection

The most common diagnosis method for power transformer faults is the dissolved gas analysis (DGA) of transformer oil. Various methods have been developed to interpret DGA results such as key gas method, and rogers ratio method. The present approach utilizes IEC 60599 ratio method to discriminate fault in transformers, which is having the advantage of usage of three gas ratios instead of four gas ratios used in other ratio methods. In some cases, the DGA results cannot be matched by the existing codes, making the diagnosis unsuccessful in multiple faults. To overcome this, the authors have proposed the use of neural networks to highlight their ability to detect the incipient faults in transformer.

Joint watermarking and fingerprinting approach for colored digital images in double DCT domain

Digital watermarking has developed immensely for multimedia uses in the current decade. The requirement for protected digital content grows vital with the enhancement in exchange of digital images on internet. Watermarking is a technique to safeguard digital media by retaining the owners possession. Fingerprinting extends the watermarking concept by embedding unique purchasers information. This paper presents a joint digital watermarking and fingerprinting approach for colored digital images applied in double DCT domain. The approach aims at copyright protection and traitor identification of digital images. Altered mid-frequency coefficients are analyzed by applying forward DCT transform. Watermark and fingerprint are embedded in a non-overlying manner. Second DCT allows for the precise determination of the block, for embedding data. The double domain can enhance the embedding capacity of the host image by choosing more than one coefficients in a given block. Simulation outcomes reveal that the watermark is immune to JPEG compression, additive noise and median filtering. Quality of the image is retained, as the results reveal a Peak Signal to Noise Ratio (PSNR) in range of 58-73 dB.

Active and reactive power dispatch using predator prey optimization approach

The economic load dispatch in thermal power plant is done for minimizing the fuel generation cost by assigning optimum power generation of each unit while maintaining the equality constraints and inequality constraints. Mostly the fuel cost is calculated only based on real power cost, but it is also very important to consider reactive power for secure procedure of power systems, therefore the cost considering reactive power has to be comprised in main cost function. Furthermore, the production of reactive power will decrease the ability of generator to produce active power. Hence, reactive power production by generator will results in decrease in its production of active power. Therefore the reactive power pricing is equally important as the pricing of real power, so a true cost function considering combined cost considering both active and reactive power has to be formulated. The main objective considered in this paper is to minimize combined cost considering both active and reactive power subject to constraints. Predator prey Optimization (PPO) algorithm has been applied to minimize combined cost considering active and reactive power. The proposed approach has tested on Standard IEEE 9-bus network in order to show the effectiveness. Numerical results obtained are compared with research works carried out by other researcher which confirms its validation and efficiency.

A Novel Elliptic Curve Cryptography Based Approach of Re-watermarking for Cheque Truncation System

Abstract Cheque Truncation System (CTS) is a technique of preventing tangible movement of cheque by substituting it with a digital image, with an aim for secure and faster clearance. For the first time re-watermarking is applied for authenticating transactions in CTS. Methodology for the proposed approach splits the host image (Ih) into two layers (L). In L1 cheque details are fused with two image color channels to form a composite message (Cm). Secure hash algorithm (SHA-512) is applied to Cm to generate a unique message digest (Md). Elliptic Curve Digital Signature Algorithm (ECDSA) is applied to Md to create a digital signature. The signature is embedded by exploiting Human Visual System (HVS) color channel sensitivity in equally parted mode. In L2, joint secret is established to form key using Elliptic Curve Diffie Hellman Protocol (ECDHP). Advanced Encryption Standard (AES) is applied to user details intricate in the transaction. Encrypted contents are then inserted in L2 by applying Discrete Cosine Transform (DCT). Experimental results state solution to integrity concerns by using ECDSA with SHA-512, reaction to security issues by AES and the response to key distribution problem by ECDHP. An effective semi-blind CTS is perceived with PSNR between 59-60 dB.