Search results for: imperialist competition algorithm
4297 A Rapid Code Acquisition Scheme in OOC-Based CDMA Systems
Authors: Keunhong Chae, Seokho Yoon
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We propose a code acquisition scheme called improved multiple-shift (IMS) for optical code division multiple access systems, where the optical orthogonal code is used instead of the pseudo noise code. Although the IMS algorithm has a similar process to that of the conventional MS algorithm, it has a better code acquisition performance than the conventional MS algorithm. We analyze the code acquisition performance of the IMS algorithm and compare the code acquisition performances of the MS and the IMS algorithms in single-user and multi-user environments.Keywords: code acquisition, optical CDMA, optical orthogonal code, serial algorithm
Procedia PDF Downloads 5324296 Efficient Heuristic Algorithm to Speed Up Graphcut in Gpu for Image Stitching
Authors: Tai Nguyen, Minh Bui, Huong Ninh, Tu Nguyen, Hai Tran
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GraphCut algorithm has been widely utilized to solve various types of computer vision problems. Its expensive computational cost encouraged many researchers to improve the speed of the algorithm. Recent works proposed schemes that work on parallel computing platforms such as CUDA. However, the problem of low convergence speed prevents the usage of GraphCut for real time applications. In this paper, we propose global suppression heuristic to boost the conver-gence process of the algorithm. A parallel implementation of GraphCut algorithm on CUDA designed for the image stitching problem is introduced. Our method achieves up to 3× time boost on the graph of size 80 × 480 compared to the best sequential GraphCut algorithm while achieving satisfactory stitched images, suitable for panorama applications. Our source code will be soon available for further research.Keywords: CUDA, graph cut, image stitching, texture synthesis, maxflow/mincut algorithm
Procedia PDF Downloads 1204295 Good Marketing is an Important Factor for the Success of the Institution
Authors: Maamar Moumena
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the Follower of the movement of international competition finds that the success of Japanese companies to break into global markets and win a competitive edge and meet the challenges of this competition, due primarily to the adoption of these companies to the modern concept of marketing, and possession of sophisticated marketing systems, with a focus on pricing policy. The institution's ability to produce goods and services be limited unless accompanied by an effective marketing effort. So the satisfaction of the consumer needs efficiently and effectiveness are unwarranted economic and social presence in the market, and ensure the continuity and achieve their goals, and this can only be achieved through marketing activity, where he activity facet which translates the output of the institution and its presence in the form of financial compensation, and that the inclusion of and marketing function within the functions of the institution and awarded each of gravity reflects the extent of their importance in the conduct of the future of the institution, and depending on excellence in performance and a good application of the basic concepts of marketing and primarily make the consumer focus of attention, so the pleasing of the consumer and earn his allegiance reflects the success of an organization.Keywords: competition, marketing, institution, consumer
Procedia PDF Downloads 2784294 Travel Planning in Public Transport Networks Applying the Algorithm A* for Metropolitan District of Quito
Authors: M. Fernanda Salgado, Alfonso Tierra, Wilbert Aguilar
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The present project consists in applying the informed search algorithm A star (A*) to solve traveler problems, applying it by urban public transportation routes. The digitization of the information allowed to identify 26% of the total of routes that are registered within the Metropolitan District of Quito. For the validation of this information, data were taken in field on the travel times and the difference with respect to the times estimated by the program, resulting in that the difference between them was not greater than 2:20 minutes. We validate A* algorithm with the Dijkstra algorithm, comparing nodes vectors based on the public transport stops, the validation was established through the student t-test hypothesis. Then we verified that the times estimated by the program using the A* algorithm are similar to those registered on field. Furthermore, we review the performance of the algorithm generating iterations in both algorithms. Finally, with these iterations, a hypothesis test was carried out again with student t-test where it was concluded that the iterations of the base algorithm Dijsktra are greater than those generated by the algorithm A*.Keywords: algorithm A*, graph, mobility, public transport, travel planning, routes
Procedia PDF Downloads 2304293 Assessment of Memetic and Genetic Algorithm for a Flexible Integrated Logistics Network
Authors: E. Behmanesh, J. Pannek
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The distribution-allocation problem is known as one of the most comprehensive strategic decision. In real-world cases, it is impossible to solve a distribution-allocation problem in traditional ways with acceptable time. Hence researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near-optimal solutions particularly for large scales test problems. This paper, presents an integrated supply chain model which is flexible in the delivery path. As the solution methodology, we apply a memetic algorithm with a novelty in population presentation. To illustrate the performance of the proposed memetic algorithm, LINGO optimization software serves as a comparison basis for small size problems. In large size cases that we are dealing with in the real world, the Genetic algorithm as the second metaheuristic algorithm is considered to compare the results and show the efficiency of the memetic algorithm.Keywords: integrated logistics network, flexible path, memetic algorithm, genetic algorithm
Procedia PDF Downloads 3714292 A Speeded up Robust Scale-Invariant Feature Transform Currency Recognition Algorithm
Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim
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All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features
Procedia PDF Downloads 2264291 Features Reduction Using Bat Algorithm for Identification and Recognition of Parkinson Disease
Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta
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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.Keywords: parkinson, gait, feature selection, bat algorithm
Procedia PDF Downloads 5354290 Selecting the Best RBF Neural Network Using PSO Algorithm for ECG Signal Prediction
Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar
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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy
Procedia PDF Downloads 6184289 Application of a New Efficient Normal Parameter Reduction Algorithm of Soft Sets in Online Shopping
Authors: Xiuqin Ma, Hongwu Qin
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A new efficient normal parameter reduction algorithm of soft set in decision making was proposed. However, up to the present, few documents have focused on real-life applications of this algorithm. Accordingly, we apply a New Efficient Normal Parameter Reduction algorithm into real-life datasets of online shopping, such as Blackberry Mobile Phone Dataset. Experimental results show that this algorithm is not only suitable but feasible for dealing with the online shopping.Keywords: soft sets, parameter reduction, normal parameter reduction, online shopping
Procedia PDF Downloads 5054288 Discretization of Cuckoo Optimization Algorithm for Solving Quadratic Assignment Problems
Authors: Elham Kazemi
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Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem.Keywords: Quadratic Assignment Problem (QAP), Discrete Cuckoo Optimization Algorithm (DCOA), meta-heuristic algorithms, optimization algorithms
Procedia PDF Downloads 5084287 Cartel's Little Helpers: A Comparative Study of the Case Law Regarding the Facilitators of Collusion in Latin America Competition Law and Policy
Authors: Andres Calderon
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In order to avoid detection and punishment, cartels have recruited the help of third parties to organize, execute and disguise the anticompetitive practices cartel members have agreed upon. These third parties may take the form of consultancy firms, guilds or professional advisors that do not perform an economic activity in the market where the collusion takes place. This paper takes a look into how national competition authorities and national legislators have dealt with the emergence of the cartels’ facilitators in Latin America. Following the practice of other jurisdictions such as United States (Toys R' Us, Apple), European Union (AC Treuhand), United Kingdom (Replica Kits, Hasbro) and Spain (Urban, Snap-On), some countries (e.g. Argentina, Chile) in Latin America have started to conduct investigations and find antitrust liability in cartels’ facilitators for helping others to violate their national competition laws. Some countries (e.g. Peru and Colombia) have also amended their legislation to amplify the subjective scope of application in order to include cartels’ facilitators. The Latin American case is one of special relevance because public officials are often prone to promote or indulge agreements between competitors in sectors of political interest. A broad definition of cartels’ facilitator, consequently, could lead to the prosecution of punishment of public officials that may hinder the competitive process.Keywords: anticompetitive practices, cartel, collusion, competition, facilitator, hub and spoke
Procedia PDF Downloads 1614286 Constant Factor Approximation Algorithm for p-Median Network Design Problem with Multiple Cable Types
Authors: Chaghoub Soraya, Zhang Xiaoyan
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This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques.Keywords: approximation algorithms, buy-at-bulk, combinatorial optimization, network design, p-median
Procedia PDF Downloads 1934285 A Minimum Spanning Tree-Based Method for Initializing the K-Means Clustering Algorithm
Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang
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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.Keywords: degree, initial cluster center, k-means, minimum spanning tree
Procedia PDF Downloads 4034284 A Fuzzy Multiobjective Model for Bed Allocation Optimized by Artificial Bee Colony Algorithm
Authors: Jalal Abdulkareem Sultan, Abdulhakeem Luqman Hasan
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With the development of health care systems competition, hospitals face more and more pressures. Meanwhile, resource allocation has a vital effect on achieving competitive advantages in hospitals. Selecting the appropriate number of beds is one of the most important sections in hospital management. However, in real situation, bed allocation selection is a multiple objective problem about different items with vagueness and randomness of the data. It is very complex. Hence, research about bed allocation problem is relatively scarce under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we develop a fuzzy multiobjective bed allocation model for overcoming uncertainty and multiple departments. Fuzzy objectives and weights are simultaneously applied to help the managers to select the suitable beds about different departments. The proposed model is solved by using Artificial Bee Colony (ABC), which is a very effective algorithm. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that fuzzy multi-objective model was presented suitable framework for bed allocation and optimum use.Keywords: bed allocation problem, fuzzy logic, artificial bee colony, multi-objective optimization
Procedia PDF Downloads 3214283 An Optimized Association Rule Mining Algorithm
Authors: Archana Singh, Jyoti Agarwal, Ajay Rana
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Data Mining is an efficient technology to discover patterns in large databases. Association Rule Mining techniques are used to find the correlation between the various item sets in a database, and this co-relation between various item sets are used in decision making and pattern analysis. In recent years, the problem of finding association rules from large datasets has been proposed by many researchers. Various research papers on association rule mining (ARM) are studied and analyzed first to understand the existing algorithms. Apriori algorithm is the basic ARM algorithm, but it requires so many database scans. In DIC algorithm, less amount of database scan is needed but complex data structure lattice is used. The main focus of this paper is to propose a new optimized algorithm (Friendly Algorithm) and compare its performance with the existing algorithms A data set is used to find out frequent itemsets and association rules with the help of existing and proposed (Friendly Algorithm) and it has been observed that the proposed algorithm also finds all the frequent itemsets and essential association rules from databases as compared to existing algorithms in less amount of database scan. In the proposed algorithm, an optimized data structure is used i.e. Graph and Adjacency Matrix.Keywords: association rules, data mining, dynamic item set counting, FP-growth, friendly algorithm, graph
Procedia PDF Downloads 4134282 Improved K-Means Clustering Algorithm Using RHadoop with Combiner
Authors: Ji Eun Shin, Dong Hoon Lim
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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.Keywords: big data, combiner, K-means clustering, RHadoop
Procedia PDF Downloads 4264281 Analyzing Competition in Public Construction Projects
Authors: Khaled Hesham Hyari, Amjad Almani
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Construction projects in the public sector are commonly awarded through competitive bidding. In the last decade, the Construction projects environment in the Middle East went through many changes. These changes have been caused by different factors including the economic crisis, delays in monthly payments, international competition and reduced number of projects. These factors had a great impact on the bidding behaviors of contractors and their pricing strategies. This paper examines the competition characteristics in public construction projects through an analysis of bidding results of contractors in public construction projects over a period of 6 years (2006-2011) in Jordan. The analyzed projects include all categories of projects such as infrastructure, buildings, transportation and engineering services (design and supervision contracts). Data for the projects were obtained from the General Tender’s Directorate in Jordan and includes 462 projects. The analysis performed in this projects includes, studying the bid spread in all projects as it is an indication of the level of competition in the analyzed bids. The analysis studied the factors that affect bid spread such as number of bidders, Value of the project, Project category and years. It also studying the “Signal to Noise Ratio” in all projects as it is an indication of the accuracy of cost estimating performed by competing bidders and bidder´s evaluation of project risks. The analysis performed includes the relationship between signal to noise ratio and different parameters such as project category, number of bidders and changes over years. Moreover, the analysis includes determining the bidder´s aggressiveness in bidding as it is an indication of competition level in such projects. This was performed by determining the pack price which can be considered as the true value of the project and comparing it with the lowest bid submitted for each project to determine the level of aggressiveness in submitted bids. The analysis performed in this project should prove to be useful to owners in understanding bidding behaviors of contractors and pointing out areas that needs improvement in preparing bidding documents. Also the project should be useful to contractors in understanding the competitive bidding environment and should help them to improve their bidding strategies to maximize the success rate in obtaining contracts.Keywords: construction projects, competitive bidding, public construction, competition
Procedia PDF Downloads 3284280 A Genetic Algorithm to Schedule the Flow Shop Problem under Preventive Maintenance Activities
Authors: J. Kaabi, Y. Harrath
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This paper studied the flow shop scheduling problem under machine availability constraints. The machines are subject to flexible preventive maintenance activities. The nonresumable scenario for the jobs was considered. That is, when a job is interrupted by an unavailability period of a machine it should be restarted from the beginning. The objective is to minimize the total tardiness time for the jobs and the advance/tardiness for the maintenance activities. To solve the problem, a genetic algorithm was developed and successfully tested and validated on many problem instances. The computational results showed that the new genetic algorithm outperforms another earlier proposed algorithm.Keywords: flow shop scheduling, genetic algorithm, maintenance, priority rules
Procedia PDF Downloads 4674279 Memetic Algorithm for Solving the One-To-One Shortest Path Problem
Authors: Omar Dib, Alexandre Caminada, Marie-Ange Manier
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The purpose of this study is to introduce a novel approach to solve the one-to-one shortest path problem. A directed connected graph is assumed in which all edges’ weights are positive. Our method is based on a memetic algorithm in which we combine a genetic algorithm (GA) and a variable neighborhood search method (VNS). We compare our approximate method with two exact algorithms Dijkstra and Integer Programming (IP). We made experimentations using random generated, complete and real graph instances. In most case studies, numerical results show that our method outperforms exact methods with 5% average gap to the optimality. Our algorithm’s average speed is 20-times faster than Dijkstra and more than 1000-times compared to IP. The details of the experimental results are also discussed and presented in the paper.Keywords: shortest path problem, Dijkstra’s algorithm, integer programming, memetic algorithm
Procedia PDF Downloads 4614278 Constructing the Density of States from the Parallel Wang Landau Algorithm Overlapping Data
Authors: Arman S. Kussainov, Altynbek K. Beisekov
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This work focuses on building an efficient universal procedure to construct a single density of states from the multiple pieces of data provided by the parallel implementation of the Wang Landau Monte Carlo based algorithm. The Ising and Pott models were used as the examples of the two-dimensional spin lattices to construct their densities of states. Sampled energy space was distributed between the individual walkers with certain overlaps. This was made to include the latest development of the algorithm as the density of states replica exchange technique. Several factors of immediate importance for the seamless stitching process have being considered. These include but not limited to the speed and universality of the initial parallel algorithm implementation as well as the data post-processing to produce the expected smooth density of states.Keywords: density of states, Monte Carlo, parallel algorithm, Wang Landau algorithm
Procedia PDF Downloads 4024277 Beyond Learning Classrooms: An Undergraduate Experience at Instituto Politecnico Nacional Mexico
Authors: Jorge Sandoval Lezama, Arturo Ivan Sandoval Rodriguez, Jose Arturo Correa Arredondo
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This work aims to share innovative educational experiences at IPN Mexico, that involve collaborative learning at institutional and global level through course competition and global collaboration projects. Students from universities in China, USA, South Korea, Canada and Mexico collaborate to design electric vehicles to solve global urban mobility problems. The participation of IPN students in the 2015-2016 global competition (São Paolo, Brazil and Cincinnati, USA) Reconfigurable Shared-Use Mobility Systems allowed to apply pedagogical strategies of groups of collaboration and of learning based on projects where they shared activities, commitments and goals, demonstrating that students were motivated to develop / self-generate their knowledge with greater meaning and understanding. One of the most evident achievements is that the students are self-managed, so the most advanced students train the students who join the project with CAD, CAE, CAM tools. Likewise, the motivation achieved is evident since in 2014 there were 12 students involved in the project, and there are currently more than 70 students.Keywords: collaboration projects, global competency, course competition, active learning
Procedia PDF Downloads 2704276 A Preliminary Study for Design of Automatic Block Reallocation Algorithm with Genetic Algorithm Method in the Land Consolidation Projects
Authors: Tayfun Çay, Yasar İnceyol, Abdurrahman Özbeyaz
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Land reallocation is one of the most important steps in land consolidation projects. Many different models were proposed for land reallocation in the literature such as Fuzzy Logic, block priority based land reallocation and Spatial Decision Support Systems. A model including four parts is considered for automatic block reallocation with genetic algorithm method in land consolidation projects. These stages are preparing data tables for a project land, determining conditions and constraints of land reallocation, designing command steps and logical flow chart of reallocation algorithm and finally writing program codes of Genetic Algorithm respectively. In this study, we designed the first three steps of the considered model comprising four steps.Keywords: land consolidation, landholding, land reallocation, optimization, genetic algorithm
Procedia PDF Downloads 4234275 Upon One Smoothing Problem in Project Management
Authors: Dimitri Golenko-Ginzburg
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A CPM network project with deterministic activity durations, in which activities require homogenous resources with fixed capacities, is considered. The problem is to determine the optimal schedule of starting times for all network activities within their maximal allowable limits (in order not to exceed the network's critical time) to minimize the maximum required resources for the project at any point in time. In case when a non-critical activity may start only at discrete moments with the pregiven time span, the problem becomes NP-complete and an optimal solution may be obtained via a look-over algorithm. For the case when a look-over requires much computational time an approximate algorithm is suggested. The algorithm's performance ratio, i.e., the relative accuracy error, is determined. Experimentation has been undertaken to verify the suggested algorithm.Keywords: resource smoothing problem, CPM network, lookover algorithm, lexicographical order, approximate algorithm, accuracy estimate
Procedia PDF Downloads 2974274 Implementation of CNV-CH Algorithm Using Map-Reduce Approach
Authors: Aishik Deb, Rituparna Sinha
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We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing
Procedia PDF Downloads 1274273 To What Extent Does Physical Activity and Standard of Competition Affect Quantitative Ultrasound (QUS) Measurements of Bone in Accordance with Muscular Strength and Anthropometrics in British Young Males?
Authors: Joseph Shanks, Matthew Taylor, Foong Kiew Ooi, Chee Keong Chen
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Introduction: Evidences of relationship between bone, muscle and standard of competition among young British population is limited in literature. The current literature recognises the independent and synergistic effects of fat free and fat mass as the stimulus for osteogenesis. This study assessed the extent to which physical activity (PA) and standard of competition (CS) influences quantitative ultrasound (QUS) measurements of bone on a cross-sectional basis accounting for muscular strength and anthropometrics in British young males. Methods: Pre-screening grouped 66 males aged 18-25 years into controls (n=33) and district level athletes (DLAs) (n=33) as well as low (n=21), moderate (n=23) and high (n=22) physical activity categories (PACs). All participants underwent QUS measurements of bone (4 sites, i.e. dominant distal radius (DR), dominant mid-shaft tibia (DT), non-dominant distal radius (NR) and non-dominant mid-shaft tibia (NT)), isokinetic strength tests (dominant and non-dominant knee flexion and extension) and anthropometric measurements. Results: There were no significant differences between any of the groups with respect to QUS measurements of bone at all sites with regards to PACs or CS. Significant higher isokinetic strength values were observed in DLAs than controls (p < 0.05), and higher than low PACs (p < 0.05) at 60o.s-1 of concentric and eccentric measurements. No differences in subcutaneous fat thickness were found between all the groups (CS or PACs). Percentages of body fat were significantly higher (p < .05) in low than high PACs and CS groups. There were significant positive relationships between non dominant radial speed of sound and fat free mass at both DR (r=0.383, p=0.001) and NR (r=0.319, p=0.009) sites in all participants. Conclusion: The present study findings indicated that muscular strength and body fat are closely related to physical activity level and standard of competition. However, bone health status reflected by quantitative ultrasound (QUS) measurements of bone is not related to physical activity level and standard of competition in British young males.Keywords: bone, muscular strength, physical activity, standard of competition
Procedia PDF Downloads 5114272 Multimodal Optimization of Density-Based Clustering Using Collective Animal Behavior Algorithm
Authors: Kristian Bautista, Ruben A. Idoy
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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization
Procedia PDF Downloads 2234271 Hardware for Genetic Algorithm
Authors: Fariborz Ahmadi, Reza Tati
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Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only do not this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones.Keywords: hardware, genetic algorithm, computer science, engineering
Procedia PDF Downloads 4984270 A Kruskal Based Heuxistic for the Application of Spanning Tree
Authors: Anjan Naidu
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In this paper we first discuss the minimum spanning tree, then we use the Kruskal algorithm to obtain minimum spanning tree. Based on Kruskal algorithm we propose Kruskal algorithm to apply an application to find minimum cost applying the concept of spanning tree.Keywords: Minimum Spanning tree, algorithm, Heuxistic, application, classification of Sub 97K90
Procedia PDF Downloads 4364269 Particle Filter State Estimation Algorithm Based on Improved Artificial Bee Colony Algorithm
Authors: Guangyuan Zhao, Nan Huang, Xuesong Han, Xu Huang
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In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution.Keywords: particle filter, impoverishment, state estimation, artificial bee colony algorithm
Procedia PDF Downloads 1384268 European Hinterland and Foreland: Impact of Accessibility, Connectivity, Inter-Port Competition on Containerization
Authors: Dial Tassadit Rania, Figueiredo De Oliveira Gabriel
Abstract:
In this paper, we investigate the relationship between ports and their hinterland and foreland environments and the competitive relationship between the ports themselves. These two environments are changing, evolving and introducing new challenges for commercial and economic development at the regional, national and international levels. Because of the rise of the containerization phenomenon, shipping costs and port handling costs have considerably decreased due to economies of scale. The volume of maritime trade has increased substantially and the markets served by the ports have expanded. On these bases, overlapping hinterlands can give rise to the phenomenon of competition between ports. Our main contribution comparing to the existing literature on this issue, is to build a set of hinterland, foreland and competition indicators. Using these indicators? we investigate the effect of hinterland accessibility, foreland connectivity and inter-ports competition on containerized traffic of Europeans ports. For this, we have a 10-year panel database from 2004 to 2014. Our hinterland indicators are given by two indicators of accessibility; they describe the market potential of a port and are calculated using information on population and wealth (GDP). We then calculate population and wealth for different neighborhoods within a distance from a port ranging from 100 to 1000km. For the foreland, we produce two indicators: port connectivity and number of partners for each port. Finally, we compute the two indicators of inter-port competition and a market concentration indicator (Hirshmann-Herfindhal) for different neighborhood-distances around the port. We then apply a fixed-effect model to test the relationship above. Again, with a fixed effects model, we do a sensitivity analysis for each of these indicators to support the results obtained. The econometric results of the general model given by the regression of the accessibility indicators, the LSCI for port i, and the inter-port competition indicator on the containerized traffic of European ports show a positive and significant effect for accessibility to wealth and not to the population. The results are positive and significant for the two indicators of connectivity and competition as well. One of the main results of this research is that the port development given here by the increase of its containerized traffic is strongly related to the development of its hinterland and foreland environment. In addition, it is the market potential, given by the wealth of the hinterland that has an impact on the containerized traffic of a port. However, accessibility to a large population pool is not important for understanding the dynamics of containerized port traffic. Furthermore, in order to continue to develop, a port must penetrate its hinterland at a deep level exceeding 100 km around the port and seek markets beyond this perimeter. The port authorities could focus their marketing efforts on the immediate hinterland, which can, as the results shows, not be captive and thus engage new approaches of port governance to make it more attractive.Keywords: accessibility, connectivity, European containerization, European hinterland and foreland, inter-port competition
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