Clustering analysis using Swarm Intelligence

This thesis is concerned with the application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear partitional clustering problems. ∙ Consider partitional clustering as an optimization problem and an improved antbased algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), to automatic grouping of large unlabeled datasets. ∙ Define partitional clustering as a multiobjective optimization problem. The aim is to obtain well-separated, connected, and compact clusters and for this purpose, two objective functions have been defined based on the concepts of data connectivity and cohesion. These functions are the core of an efficient multiobjective particle swarm optimization algorithm, which has been devised for and applied to automatic grouping of large unlabeled datasets. For that purpose, this thesis is divided is five main parts: ∙ The first part, including Chapter 1, aims at introducing state of the art of swarm intelligence based clustering methods. ∙ The second part, including Chapter 2, consists in clustering analysis with combination of artificial bee colony algorithm and K-means technique. ∙ The third part, including Chapter 3, consists in a presentation of clustering analysis using opposition-based API algorithm. ∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis using particle swarm optimization. ∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses the future directions and the open issues of this research

Particle Swarm algorithm with Fuzzy decision making for a multi-objective economic and environmental optimization of design of a thermal system

Multi-Objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the thermoeconomic and Environmental aspects have been considered, simultaneously. The environmental objective function has been defined and expressed in cost terms. One of the most suitable optimization techniques developed using a particular class of search algorithms known as; Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been used here. This approach has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of fuzzy decision-making with the aid of Bellman-Zadeh approach has been presented and a final optimal solution has been introduced

Evaluating Kyphosis and Lordosis in Students by Using a Flexible Ruler and Their Relationship with Severity and Frequency of Thoracic and Lumbar Pain

Study DesignA cross-sectional, descriptive study.PurposeThis study aimed to investigate the relationship between kyphosis and lordosis measured by using a flexible ruler and musculoskeletal pain in students of Hamadan University of Medical Sciences.Overview of LiteratureThe spine supports the body during different activities by maintaining appropriate body alignment and posture. Normal alignment of the spine depends on its structural, muscular, bony, and articular performance.MethodsTwo hundred forty-one students participated in this study. A single examiner evaluated the angles of lumbar lordosis and thoracic kyphosis by using a flexible ruler. To determine the severity and frequency of pain in low-back and inter-scapular regions, a tailor-made questionnaire with visual analog scale was used. Finally, using the Kendall correlation coefficient, the data were statistically analyzed.ResultsThe mean value of lumbar lordosis was 34.46°±12.61° in female students and 22.46°±9.9° in male students. The mean value of lumbar lordosis significantly differed between female and male students (p<0.001). However, there was no difference in the level of the thoracic curve (p=0.288). Relationship between kyphosis measured by using a flexible ruler and inter-scapular pain in male and female students was not significant (p=0.946). However, the relationship between lumbar lordosis and low back pain was statistically significant (p=0.006). Also, no significant relationship was observed between abnormal kyphosis and frequency of inter-scapular pain, and between lumbar lordosis and low back pain.ConclusionsLumbar lordosis contributes to low back pain. The causes of musculoskeletal pain could be muscle imbalance and muscle and ligament strain

A systematic review of variables used to assess clinically acceptable alignment of unilateral transtibial amputees in the literature.

Prosthetic alignment is a subjective concept which lacks reliability. The outcome responsiveness to prosthetic alignment quality could help to improve subjective and instrument assisted prosthetic alignment. This study was aimed to review variables used to assess clinically acceptable alignment in the literature. The search was done in some databases including: Google Scholar, PubMed, EBSCO, EMBASE, ISI Web of Knowledge and Scopus. The first selection criterion was based on abstracts and titles to address the research questions of interest. The American Academy of Orthotics and Prosthetics checklists were used for paper risk of bias assessment. A total of 25 studies were included in this study. Twenty-four studies revealed the critics of standing position or walking to locate clinically acceptable alignment, only one study measured outcomes in both situations. A total of 253 adults with transtibial amputations and mean age of 48.71 years participated in included studies. The confidence level of included studies was low to moderate, and before-after trial was the most common study design (n = 19). The joint angle, load line location with respect to joints and center of pressure-related parameters were reported as sensitive outcomes to prosthetic alignment quality in standing posture. The amount of forces at various parts of gait cycle and time of events were sensitive to prosthetic alignment quality during walking. Standing balance and posture and temporal parameters of walking could help to locate clinically acceptable alignment.N/

Multiobjective clustering analysis using particle swarm optimization

Clustering is a significant data mining task which partitions datasets based on similarities among data. This technique plays a very important role in the rapidly growing field known as exploratory data analysis. A key difficulty of effective clustering is to define proper grouping criteria that reflect fundamentally different aspects of a good clustering solution such as compactness and separation of clusters. Moreover, in the conventional clustering algorithms only a single criterion is considered that may not conform to the diverse and complex shapes of the underlying clusters. In this study, partitional clustering is defined as a multiobjective optimization problem. The aim is to obtain well-separated, connected, and compact clusters and for this purpose, two objective functions have been defined based on the concepts of data connectivity and cohesion. These functions are the core of an efficient multiobjective particle swarm optimization algorithm, which has been devised for and applied to automatic grouping of large unlabeled datasets. A comprehensive experimental study is conducted and the obtained results are compared with the results of four other state-of-the-art clustering techniques. It is shown that the proposed algorithm can achieve the optimal number of clusters, is robust and outperforms, in most cases, the other methods on the selected benchmark datasets

Clustering Analysis using Opposition-based API Algorithm

Clustering is a significant data mining task which partitions datasets based on similarities among data. In this study, partitional clustering is considered as an optimization problem and an improved ant-based algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), is applied to automatic grouping of large unlabeled datasets. The proposed algorithm employs Opposition-Based Learning (OBL) for ants' hunting sites generation phase in API. Experimental results are compared with the classical API clustering algorithm and three other recently evolutionary-based clustering techniques. It is shown that the proposed algorithm can achieve the optimal number of clusters and, in most cases, outperforms the other methods on several benchmark datasets in terms of accuracy and convergence speed

Multiobjective optimization for force and moment balance of a four-bar linkage using evolutionary algorithms

In this study, force and moment balance of a planar four-bar linkage is implemented using evolutionary algorithms. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects, independent of input angular velocity. A proposed multiobjective particle swarm optimization, and non-dominated sorting genetic algorithm II are applied to minimize two objective functions subject to some design constraints. The applied algorithms produced a set of feasible solutions called pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is utilized to select the best solution among the obtained pareto solutions. The results show that optimal solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously

Particle Swarm algorithm with Fuzzy decision making for a multi-objective economic and environmental optimization of design of a thermal system

Multi-Objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the thermoeconomic and Environmental aspects have been considered, simultaneously. The environmental objective function has been defined and expressed in cost terms. One of the most suitable optimization techniques developed using a particular class of search algorithms known as; Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been used here. This approach has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of fuzzy decision-making with the aid of Bellman-Zadeh approach has been presented and a final optimal solution has been introduced

The Effect of Rocker Bar Ankle Foot Orthosis on Functional Mobility in Post-Stroke Hemiplegic Patients

Objectives: Ankle Foot Orthoses (AFOs) are widely utilized to improve walking ability in hemiplegic patients. The present study aimed to evaluate the effect of Rocker bar Ankle Foot Orthosis (RAFO) on functional mobility in post-stroke hemiplegic patients. Methods: Fifteen hemiplegic patients (men and women) who were at least 6-months post-stroke and able to walk without assistive device for at least 10 meters voluntarily participated in this study. The patients were examined with and without RAFO. Their functional mobility was evaluated through 10-meter walk test and Timed Up and Go (TUG) test. Also, paired t-test was used to analyze obtained data. Results: When patients used RAFO, their gait speed significantly increased (P<0.05). Also, the time of performing TUG test experienced a significant decrease using RAFO compared with utilizing shoe only (P<0.05). Discussion: RAFO led to a significant improvement in functional mobility in hemiplegic patient&rsquo;s secondary to stroke. It seems that, it has been due to the positive effect of rocker modification on improving push off and transferring weight during stance phase of gait

Fully Integrated, 80 GHz Bandwidth, 1.3μ m InAs/InGaAs CW-PW Quantum Dot Passively Colliding-Pulse Mode-Locked (CPM) Lasers for IR Sensing Application

Integrated quantum dot passively mode-locked lasers: IQDMLLs open opportunities for IR sensing. Therefore, here, we investigated structure of quantum dot passively mode-locked lasers (QDMLLs) with colliding-pulse mode-locked (CPM) method in order to reinforce the performance of QDMLLs for ultrahigh-bit-rate compared to self-colliding pulse mode-locking (SCPM) method. The proposed structure is numerically based on finite difference traveling wave (FDTW) method. By considering bias current of 80 mA, which is the bias applied to the gain section, the laser is at its maximum output power. Finally, by comparing the QDMLLs with self-colliding pulse mode-locking (SCPM) method, it is shown that due to the grating caused by the collision of the two counter-propagating pulses in the saturable absorber, the laser bit rate in the structure of CPM is twice the bit rate in structure of SCPM somehow bit rate from 40 GHz in SCPM method increased to 80 GHz in CPM method. Also, grating coupling factor (GCF) changes due to the proposed structure has been shown, so that in the range of $1cm^{ - 1} \le \kappa \le 5cm^{ - 1}$ the laser output power is pulsed wave (PW), while after this range the laser output power changes from a pulsed regime to a continuous wave (CW) regime