Improved backtracking search optimization algorithm for PV/Wind/FC system

This paper uses a novel optimization method based on the improved backtracking search optimization algorithm (IBSA). The study is conducted for a hybrid stand-alone system composed of photovoltaic panel (PV), wind turbine generator and fuel cell electrolyzer (FC). To demonstrate the effectiveness of the IBSA, four benchmark functions are used. The result shows the better exploration and exploitation of the improved backtracking search optimization algorithm in terms of convergence and speed for system comprinsing PV panel wind, turbine generator and fuel cell. The proposed algorithm is used to optimize the annual total cost (ATC) of the energy produced and feed up the load demand. The economic evaluation of the Hybrid PV/Wind/FC system is done throughout hourly demand and daily wind speed and insulation. The simulation results justify the robustness of the IBSA

Efficient search and comparison algorithms for 3D protein binding site retrieval and structure alignment from large-scale databases

Finding similar 3D structures is crucial for discovering potential structural, evolutionary, and functional relationships among proteins. As the number of known protein structures has dramatically increased, traditional methods can no longer provide the life science community with the adequate informatics capability needed to conduct large-scale and complex analyses. A suite of high-throughput and accurate protein structure search and comparison methods is essential. To meet the needs of the community, we develop several bioinformatics methods for protein binding site comparison and global structure alignment. First, we developed an efficient protein binding site search that is based on extracting geometric features both locally and globally. The main idea of this work was to capture spatial relationships among landmarks of binding site surfaces and bfuild a vocabulary of visual words to represent the characteristics of the surfaces. A vector model was then used to speed up the search of similar surfaces that share similar visual words with the query interface. Second, we developed an approach for accurate protein binding site comparison. Our algorithm provides an accurate binding site alignment by applying a two-level heuristic process which progressively refines alignment results from coarse surface point level to accurate residue atom level. This setting allowed us to explore different combinations of pairs of corresponding residues, thus improving the alignment quality of the binding site surfaces. Finally, we introduced a parallel algorithm for global protein structure alignment. Specifically, to speed up the time-consuming structure alignment process of protein 3D structures, we designed a parallel protein structure alignment framework to exploit the parallelism of Graphics Processing Units (GPUs). As a general-purpose GPU platform, the framework is capable of parallelizing traditional structure alignment algorithms. Our findings can be applied in various research areas, such as prediction of protein inte

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

GPU parallelization strategies for metaheuristics: a survey

Metaheuristics have been showing interesting results in solving hard optimization problems. However, they become limited in terms of effectiveness and runtime for high dimensional problems. Thanks to the independency of metaheuristics components, parallel computing appears as an attractive choice to reduce the execution time and to improve solution quality. By exploiting the increasing performance and programability of graphics processing units (GPUs) to this aim, GPU-based parallel metaheuristics have been implemented using different designs. RecentresultsinthisareashowthatGPUstendtobeeffectiveco-processors forleveraging complex optimization problems.In thissurvey, mechanisms involvedinGPUprogrammingforimplementingparallelmetaheuristicsare presentedanddiscussedthroughastudyofrelevantresearchpapers. Metaheuristics can obtain satisfying results when solving optimization problems in a reasonable time. However, they suffer from the lack of scalability. Metaheuristics become limited ahead complex highdimensional optimization problems. To overcome this limitation, GPU based parallel computing appears as a strong alternative. Thanks to GPUs, parallelmetaheuristicsachievedbetterresultsintermsofcomputation,and evensolutionquality

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

Frontiers of Membrane Computing: Open Problems and Research Topics

This is a list of open problems and research topics collected after the Twelfth Conference on Membrane Computing, CMC 2012 (Fontainebleau, France (23 - 26 August 2011), meant initially to be a working material for Tenth Brainstorming Week on Membrane Computing, Sevilla, Spain (January 30 - February 3, 2012). The result was circulated in several versions before the brainstorming and then modified according to the discussions held in Sevilla and according to the progresses made during the meeting. In the present form, the list gives an image about key research directions currently active in membrane computing

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations