Master control unit based power exchange strategy for interconnected microgrids
- Authors: Batool, Munira , Islam, Syed , Shahnia, Farhad
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 2017 Australasian Universities Power Engineering Conference, AUPEC 2017; Melbourne, Australia; 19th-22nd November 2017 Vol. 2017, p. 1-6
- Full Text:
- Reviewed:
- Description: Large remote area networks normally have self-suffi-cient electricity systems. These systems also rely on non-dispatchable DGs (N-DGs) for overall reduction in cost of electricity production. It is a fact that uncertainties included in the nature of N-DGs as well as load demand can cause cost burden on islanded microgrids (MGs). This paper proposes development of power exchange strategy for an interconnected MGs (IMG) system as part of large remote area network with optimized controls of dispatchable (D-DGs) which are members of master control unit (MCU). MCU analysis includes equal cost increment principle to give idea about the amount of power exchange which could take place with neighbor MGs in case of overloading situation. Sudden changes in N-DGs and load are defined as interruptions and are part of analysis too. Optimization problem is formulated on the basis of MCU adjustment for overloading or under loading situation and suitability of support MG (S-MG) in IMG system for power exchange along with key features of low cost and minimum technical impacts. Mixed integer linear programming (MILP) technique is applied to solve the formulated problem. The impact of proposed strategy is assessed by numerical analysis in MATLAB programming under stochastic environment.
Power transaction management amongst coupled microgrids in remote areas
- Authors: Batool, Munira , Islam, Syed , Shahnia, Farhad
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 7th IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2017;Auckland, New Zealand; 4th-7th December 2017 p. 1-6
- Full Text:
- Reviewed:
- Description: Large remote areas normally have isolated and self-sufficient electricity supply systems, often referred to as microgrids. These systems also rely on a mix of dispatchable and non-dispatcha- ble distributed energy resources to reduce the overall cost of electricity production. Emergencies such as shortfalls, overloading, and faults can cause problems in the operation of these remote area microgrids. This paper presents a power transaction management scheme amongst a few such microgrids when they are coupled provisionally during emergencies. By definition, power transaction is an instance of buying and selling of electricity amongst problem and healthy microgrids. The developed technique aims to define the suitable power generation from all dispatchable sources and regulate the power transaction amongst the coupled microgrids. To this end, an optimization problem is formulated that aims to define the above parameters while minimizing the costs and technical impacts. A mixed- integer linear programming technique is used to solve the formulated problem. The performance of the proposed management strategy is evaluated by numerical analysis in MATLAB.
On unified modeling, theory, and method for solving multi-scale global optimization problems
- Authors: Gao, David
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2016; Pizzo Calabro; Italy; 19th-25th June 2016; published in AIP Conference Proceedings Vol. 1776, p. 1-8
- Full Text:
- Reviewed:
- Description: A unified model is proposed for general optimization problems in multi-scale complex systems. Based on this model and necessary assumptions in physics, the canonical duality theory is presented in a precise way to include traditional duality theories and popular methods as special applications. Two conjectures on NP-hardness are proposed, which should play important roles for correctly understanding and efficiently solving challenging real-world problems. Applications are illustrated for both nonconvex continuous optimization and mixed integer nonlinear programming.
Decoupled modeling of gene regulatory networks using Michaelis-Menten kinetics
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: A set of genes and their regulatory interactions are represented in a gene regulatory network (GRN). Since GRNs play a major role in maintaining the cellular activities, inferring these networks is significant for understanding biological processes. Among the models available for GRN reconstruction, our recently developed nonlinear model [1] using Michaelis-Menten kinetics is considered to be more biologically relevant. However, the model remains coupled in the current form making the process computationally expensive, especially for large GRNs. In this paper, we enhance the existing model leading to a decoupled form which not only speeds up the computation, but also makes the model more realistic by representing the strength of each regulatory arc by a distinct Michaelis-Menten constant. The parameter estimation is carried out using differential evolution algorithm. The model is validated by inferring two synthetic networks. Results show that while the accuracy of reconstruction is similar to the coupled model, they are achieved at a faster speed. © Springer International Publishing Switzerland 2015.
An optimization model for multi-state weighted kout-of-n system reliability value
- Authors: Korshidi, Hadi , Gunawan, Indra , Ibrahim, Yousef
- Date: 2013
- Type: Text , Conference proceedings
- Relation: IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, Austria Nov. 2013, p.4357-4361
- Full Text: false
- Reviewed:
- Description: A reliability optimization model is proposed in this paper for multi-state weighted k-out-of-n systems. In this model, income generated by components through each functioning period is used as a reliability index. Therefore, time value of money is used in the presented optimization model to estimate both system's reliability and cost. The system reliability is evaluated by Universal Generating Function (UGF). The model's objective function is to maximize the net present value (NPV) of the system. Therefore, it would maximize the system reliability and minimize the system cost simultaneously. A numerical example is presented in this paper to illustrate the model by finding the optimal design of the system, and the best time for maintenance plan. Also, a discussion is provided based on the result.
Range-free passive localization using static and mobile sensors
- Authors: Iqbal, Anindya , Murshed, Manzur
- Date: 2012
- Type: Text , Conference proceedings
- Relation: 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), San Francisco, CA, 25th-28th June, 2012 p. 1-6
- Full Text: false
- Reviewed:
- Description: In passive localization, sensors try to locate an event without any knowledge of event's emitted power. So, this is a more challenging problem compared to active localization. Existing passive localization schemes use expensive and noise-vulnerable range-based techniques. In this paper, we propose, to the best of our knowledge for the first time, a cost-effective range-free passive localization scheme exploiting hybrid sensor network model where mobile sensors are deployed on demand once an event is sensed by a static sensor. Efficient use of mobile sensors leads to two concomitant optimization problems: (1) positioning the mobile sensors so that the expected possible event location area is minimized; and (2) minimizing their overall traversed distance. To solve the first problem, we have developed a novel arc-coding based range-free localization technique that can accurately define the area of possible event location from the feedback of arbitrarily placed sensors without relying on expensive hardware to estimate range of signals. We have achieved significantly high localization accuracy with a low number of mobile sensors even after considering significant environmental noise. To solve the second problem, three alternative deployment strategies for the mobile sensors were simulated to recommend the best.
Parameter optimization for Support Vector Machine Classifier with IO-GA
- Authors: Zhou, Jing , Maruatona, Omaru , Wang, Wei
- Date: 2011
- Type: Text , Conference proceedings
- Full Text: false
- Description: The Support Vector Machine method has a good learning and generalization ability. Unfortunately, there are no comprehensive theories to guide the parameter selection of the SVM, which largely limits its application. In order to get the optimal parameters automatically, researchers have tried a variety of methods. Using genetic algorithms to optimize parameters of an SVM Classifier has become one of the favorite methods in recent years. In this paper, we explain how the Standard Genetic Algorithm (SGA) causes the problem of premature convergence and limits the accuracy of the SVM. We also put forward a new genetic algorithm with improved genetic operators (IO-GA) to optimize the SVM classifier's parameters. Experimental results show that the parameters obtained by this method can greatly improve the classification performance of SVM. We therefore conclude that this method is effective. © 2011 IEEE.
Zero-day malware detection based on supervised learning algorithms of API call signatures
- Authors: Alazab, Mamoun , Venkatraman, Sitalakshmi , Watters, Paul , Alazab, Moutaz
- Date: 2011
- Type: Text , Conference proceedings
- Full Text:
- Description: Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k-Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO - PolyKernel, SMO - Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today. © 2011, Australian Computer Society, Inc.
- Description: 2003009506
GOM: New Genetic Optimizing Model for broadcasting tree in MANET
- Authors: Elaiwat, Said , Alazab, Ammar , Venkatraman, Sitalakshmi , Alazab, Mamoun
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Data broadcasting in a mobile ad-hoc network (MANET) is the main method of information dissemination in many applications, in particular for sending critical information to all hosts. Finding an optimal broadcast tree in such networks is a challenging task due to the broadcast storm problem. The aim of this work is to propose a new genetic model using a fitness function with the primary goal of finding an optimal broadcast tree. Our new method, called Genetic Optimisation Model (GOM) alleviates the broadcast storm problem to a great extent as the experimental simulations result in efficient broadcast tree with minimal flood and minimal hops. The result of this model also shows that it has the ability to give different optimal solutions according to the nature of the network. © 2010 IEEE.
Literature on image segmentation based on split - and - Merge techniques
- Authors: Faruquzzaman, A. B. M. , Paiker, Nafize , Arafat, Jahidul , Ali, Mortuza , Sorwar, Golam
- Date: 2008
- Type: Text , Conference proceedings , Conference paper
- Relation: ICITA 2008, Cairns, Qld., 23-26 June, ICITA, published in Proceedings of 5th International Conference on Information Technology and Application pp. 120-125.
- Full Text: false
- Reviewed:
- Description: Image segmentation is a feverish issue due to drastically increasing the use of computer and the Internet. Various algorithms have been invented on this aspect. Among them, split-and-merge (SM) algorithm is highly lucrative now-a-days due to its simplicity and effectiveness in the sector of image processing. Numerous researchers have performed their research work on this algorithm to triumph over its drawbacks for its sustainable and competent implementation. This paper has consolidated the useful consideration and proposal of various researchers to formulate a strong base of knowledge for the future researcher. It has also tinted few unsettled drawbacks of SM algorithm which will open the casement of brainstorming as well as persuade them for future research on SM algorithm, thereby allow SM algorithm to attain a globally optimal algorithm for image segmentation.
- Description: 5th International Conference on Information Technology and Applications, ICITA 2008