Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario.Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.     

An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch

Economic dispatch is carried out at the energy control center to find out the optimal output of thermal generating units such that power balance criterion is met, unit operating limits are satisfied and the fuel cost is minimized. With growing environmental awareness and strict government regulations throughout the world, it has become essential to optimize not only the total fuel cost but also the harmful emissions, both, under static as well as dynamic conditions. The static environment economic dispatch finds the optimal output of generating units for a fixed load demand at a given time, while the dynamic environmental economic dispatch schedules the output of online generators with changing power demands over a certain time period (normally one day) so as to minimize these two conflicting objectives, simultaneously. In this paper, the price penalty factor approach is employed for simultaneous minimization of cost and emission. The generator ramp rate constraints, non-convex and discontinuous nature of cost function and the large number of generators in practical power plants, make this problem very difficult to solve. Here, a fuzzy ranking approach is employed to identify the solution which offers the best compromise between cost and emission objectives.     

Adaptive dehazing control factor based fast single image dehazing

The single image dehazing is performed using atmospheric scattering model (ASM). The ASM is based on transmission and atmospheric light. Thus, accurate estimation of transmission is essential for quality single image dehazing. Single image dehazing is of prime focus in research nowadays. The proposed work presents a fast and accurate method for single image dehazing. The proposed method works in two folds; (i) An adaptive dehazing control factor is proposed to estimate accurate transmission, which is based on difference of maximum and minimum color channel of hazy image, and (ii) a mathematical model to compute probability of a pixel to be at short distance is presented, which is utilized to locate haziest region of the image to compute the value of atmospheric light. The proposed method obtains visually compelling results, and recovers the information content (such as structural similarity, color, and visibility) accurately. The computation speed and accuracy of the proposed method is proved using quantitative and qualitative comparison of results with state of the art dehazing methods.

     

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user??s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.     

Bounding function for fast computation of transmission in single image dehazing

Multimedia Tools and Applications - There exist multiple dehazed images corresponding to a single hazy image due to ill-posed nature of single image dehazing (SID), making it a challenging problem....     

A review on rendezvous based data acquisition methods in wireless sensor networks with mobile sink

Wireless Networks - Solutions for energy hole problem in wireless sensor networks (WSNs) have been excessively explored using mobile sink (MS). Although, MS provides a considerable amount of energy...     

A Survey on Path Planning Techniques for Mobile Sink in IoT-Enabled Wireless Sensor Networks

Wireless Personal Communications - Recently, the Internet of Things (IoT) had emerged very rapidly and became the most important technology in today’s era. In an IoT-based environment, every...     

Ranking serials in oceanography: An analysis based on the Indian contributions and their citations

An analysis of serials preferred and cited in various communications by the Indian oceanographers during 1963 to 1992 is presented. A shift in preference of serials from general sciences to oceanography (interdisciplinary) and to core subject in oceanography is noticed. The contributions to Indian serials showed a decrease. The implications of this trend are discussed. The rank list of serials cited by Indian oceanographers was correlated with the rank list of serials cited at international level. A negative correlation with a marginal difference of−0.214 is observed between these two rank lists. This difference is attributed to studies from different geographical areas in these two rank sets. Bradford graphs for all datasets, but one, showed typical Bradford-Leimkuhler curves with or without clear Groos droops. Further research is required to explain this exceptional curve. A country-wise analysis of the serials preferred as well as cited in communications give a clear picture on the shift during the period of study. English is considered as thelingua franca by Indian ocanographers.     

Mobility prediction in mobile ad hoc networks using a lightweight genetic algorithm

A mobile ad hoc network is a collection of wireless mobile nodes creating a network without using any existing infrastructure. Much research has been carried out to find out an optimal routing protocol for the successful transmission of data in this network. The main hindrance is the mobility of the network. If the mobility pattern of the network can be predicted, it will help in improving the QoS of the network. This paper discusses a novel approach to mobility prediction using movement history and existing concepts of genetic algorithms, to improve the MANET routing algorithms. The proposed lightweight genetic algorithm performs outlier removal on the basis of heuristics and parent selection using the weighted roulette wheel algorithm. After performing the genetic operations a node to node adjacency matrix is obtained from which the predicted direction of each node is calculated using force directed graphs and vector calculations. The technique proposes a new approach to mobility prediction which does not depend on probabilistic methods and which is completely based on genetic algorithms.     

A proactive defense method for the stealthy EDoS attacks in a cloud environment

Cloud computing technology provides flexibility to Cloud Service Provider (CSP) for providing the cloud resources based on the users' requirements. In on‐demand pricing model, the attackers exploit this feature and cause unwanted scaling‐up of the cloud resources without any intent to pay for them. The associated cost for the unpaid malicious usage burdens the CSP, and over a long period, economic losses occur at the CSP end. Thus, the resources and services offered by the CSP become unsustainable, and the attack is termed as Economic Denial‐of‐Sustainability (EDoS) attack. The existing defense approaches for EDoS attacks are reactive. Thus, the associated attack detection/mitigation cost is high; consequently, the approaches are not suitable for the Small and Medium Enterprises (SMEs). The aim of this paper is to detect and mitigate, internal and external, stealthy EDoS attacks proactively. The attack is detected using average CPU utilization threshold and utility function (in terms of cost for the utilized cloud computing resources) and mitigated using virtual firewalls. Amazon Elastic Compute Cloud (Amazon EC2) is used to evaluate the performance of the proposed approach. The proposed approach accurately detects the EDoS attack and mitigates its effect as well with reduced cost. It is observed that the approach provides competitive response time, victim service downtime, and attack reporting time. Thus, the overall performance is improved.