Search results for: fusion of algorithm
3247 Lateral Retroperitoneal Transpsoas Approach: A Practical Minimal Invasive Surgery Option for Treating Pyogenic Spondylitis of the Lumbar Vertebra
Authors: Sundaresan Soundararajan, Chor Ngee Tan
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Introduction: Pyogenic spondylitis, otherwise treated conservatively with long term antibiotics, would require surgical debridement and reconstruction in about 10% to 20% of cases. The classical approach adopted many surgeons have always been anterior approach in ensuring thorough and complete debridement. This, however, comes with high rates of morbidity due to the nature of its access. Direct lateral retroperitoneal approach, which has been growing in usage in degenerative lumbar diseases, has the potential in treating pyogenic spondylitis with its ease of approach and relatively low risk of complications. Aims/Objectives: The objective of this study was to evaluate the effectiveness and clinical outcome of using lateral approach surgery in the surgical management of pyogenic spondylitis of the lumbar spine. Methods: Retrospective chart analysis was done on all patients who presented with pyogenic spondylitis (lumbar discitis/vertebral osteomyelitis) and had undergone direct lateral retroperitoneal lumbar vertebral debridement and posterior instrumentation between 2014 and 2016. Data on blood loss, surgical operating time, surgical complications, clinical outcomes and fusion rates were recorded. Results: A total of 6 patients (3 male and 3 female) underwent this procedure at a single institution by a single surgeon during the defined period. One patient presented with infected implant (PLIF) and vertebral osteomyelitis while the other five presented with single level spondylodiscitis. All patients underwent lumbar debridement, iliac strut grafting and posterior instrumentation (revision of screws for infected PLIF case). The mean operating time was 308.3 mins for all 6 cases. Mean blood loss was reported at 341cc (range from 200cc to 600cc). Presenting symptom of back pain resolved in all 6 cases while 2 cases that presented with lower limb weakness had improvement of neurological deficits. One patient had dislodged strut graft while performing posterior instrumentation and needed graft revision intraoperatively. Infective markers normalized for all patients subsequently. All subjects also showed radiological evidence of fusion on 6 months follow up. Conclusions: Lateral approach in treating pyogenic spondylitis is a viable option as it allows debridement and reconstruction without the risk that comes with other anterior approaches. It allows efficient debridement, short surgical time, moderate blood loss and low risk of vascular injuries. Clinical outcomes and fusion rates by this approach also support its use as practical MIS option surgery for such infection cases.Keywords: lateral approach, minimally invasive, pyogenic spondylitis, XLIF
Procedia PDF Downloads 1693246 Design of EV Steering Unit Using AI Based on Estimate and Control Model
Authors: Seong Jun Yoon, Jasurbek Doliev, Sang Min Oh, Rodi Hartono, Kyoojae Shin
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Electric power steering (EPS), which is commonly used in electric vehicles recently, is an electric-driven steering device for vehicles. Compared to hydraulic systems, EPS offers advantages such as simple system components, easy maintenance, and improved steering performance. However, because the EPS system is a nonlinear model, difficult problems arise in controller design. To address these, various machine learning and artificial intelligence approaches, notably artificial neural networks (ANN), have been applied. ANN can effectively determine relationships between inputs and outputs in a data-driven manner. This research explores two main areas: designing an EPS identifier using an ANN-based backpropagation (BP) algorithm and enhancing the EPS system controller with an ANN-based Levenberg-Marquardt (LM) algorithm. The proposed ANN-based BP algorithm shows superior performance and accuracy compared to linear transfer function estimators, while the LM algorithm offers better input angle reference tracking and faster response times than traditional PID controllers. Overall, the proposed ANN methods demonstrate significant promise in improving EPS system performance.Keywords: ANN backpropagation modelling, electric power steering, transfer function estimator, electrical vehicle driving system
Procedia PDF Downloads 153245 Hybrid Structure Learning Approach for Assessing the Phosphate Laundries Impact
Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed
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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).Keywords: Bayesian network, classification, expert knowledge, structure learning, surface water analysis
Procedia PDF Downloads 1213244 A Supervised Goal Directed Algorithm in Economical Choice Behaviour: An Actor-Critic Approach
Authors: Keyvanl Yahya
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This paper aims to find a algorithmic structure that affords to predict and explain economic choice behaviour particularly under uncertainty (random policies) by manipulating the prevalent Actor-Critic learning method that complies with the requirements we have been entrusted ever since the field of neuroeconomics dawned on us. Whilst skimming some basics of neuroeconomics that might be relevant to our discussion, we will try to outline some of the important works which have so far been done to simulate choice making processes. Concerning neurological findings that suggest the existence of two specific functions that are executed through Basal Ganglia all the way down to sub-cortical areas, namely 'rewards' and 'beliefs', we will offer a modified version of actor/critic algorithm to shed a light on the relation between these functions and most importantly resolve what is referred to as a challenge for actor-critic algorithms, that is lack of inheritance or hierarchy which avoids the system being evolved in continuous time tasks whence the convergence might not emerge.Keywords: neuroeconomics, choice behaviour, decision making, reinforcement learning, actor-critic algorithm
Procedia PDF Downloads 3913243 Towards a Balancing Medical Database by Using the Least Mean Square Algorithm
Authors: Kamel Belammi, Houria Fatrim
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imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes
Procedia PDF Downloads 5243242 Influence of the Line Parameters in Transmission Line Fault Location
Authors: Marian Dragomir, Alin Dragomir
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In the paper, two fault location algorithms are presented for transmission lines which use the line parameters to estimate the distance to the fault. The first algorithm uses only the measurements from one end of the line and the positive and zero sequence parameters of the line, while the second one uses the measurements from both ends of the line and only the positive sequence parameters of the line. The algorithms were tested using a transmission grid transposed in MATLAB. In a first stage it was established a fault location base line, where the algorithms mentioned above estimate the fault locations using the exact line parameters. After that, the positive and zero sequence resistance and reactance of the line were calculated again for different ground resistivity values and then the fault locations were estimated again in order to compare the results with the base line results. The results show that the algorithm which uses the zero sequence impedance of the line is the most sensitive to the line parameters modifications. The other algorithm is less sensitive to the line parameters modification.Keywords: estimation algorithms, fault location, line parameters, simulation tool
Procedia PDF Downloads 3463241 Computational Aerodynamic Shape Optimisation Using a Concept of Control Nodes and Modified Cuckoo Search
Authors: D. S. Naumann, B. J. Evans, O. Hassan
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This paper outlines the development of an automated aerodynamic optimisation algorithm using a novel method of parameterising a computational mesh by employing user–defined control nodes. The shape boundary movement is coupled to the movement of the novel concept of the control nodes via a quasi-1D-linear deformation. Additionally, a second order smoothing step has been integrated to act on the boundary during the mesh movement based on the change in its second derivative. This allows for both linear and non-linear shape transformations dependent on the preference of the user. The domain mesh movement is then coupled to the shape boundary movement via a Delaunay graph mapping. A Modified Cuckoo Search (MCS) algorithm is used for optimisation within the prescribed design space defined by the allowed range of control node displacement. A finite volume compressible NavierStokes solver is used for aerodynamic modelling to predict aerodynamic design fitness. The resulting coupled algorithm is applied to a range of test cases in two dimensions including the design of a subsonic, transonic and supersonic intake and the optimisation approach is compared with more conventional optimisation strategies. Ultimately, the algorithm is tested on a three dimensional wing optimisation case.Keywords: mesh movement, aerodynamic shape optimization, cuckoo search, shape parameterisation
Procedia PDF Downloads 3293240 Experimental Study of Hydrogen and Water Vapor Extraction from Helium with Zeolite Membranes for Tritium Processes
Authors: Rodrigo Antunes, Olga Borisevich, David Demange
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The Tritium Laboratory Karlsruhe (TLK) has identified zeolite membranes as most promising for tritium processes in the future fusion reactors. Tritium diluted in purge gases or gaseous effluents, and present in both molecular and oxidized forms, can be pre-concentrated by a stage of zeolite membranes followed by a main downstream recovery stage (e.g., catalytic membrane reactor). Since 2011 several membrane zeolite samples have been tested to measure the membrane performances in the separation of hydrogen and water vapor from helium streams. These experiments were carried out in the ZIMT (Zeolite Inorganic Membranes for Tritium) facility where mass spectrometry and cold traps were used to measure the membranes’ performances. The membranes were tested at temperatures ranging from 25 °C up to 130 °C, at feed pressures between 1 and 3 bar, and typical feed flows of 2 l/min. During this experimental campaign, several zeolite-type membranes were studied: a hollow-fiber MFI nanocomposite membrane purchased from IRCELYON (France), and tubular MFI-ZSM5, NaA and H-SOD membranes purchased from Institute for Ceramic Technologies and Systems (IKTS, Germany). Among these membranes, only the MFI-based showed relevant performances for the H2/He separation, with rather high permeances (~0.5 – 0.7 μmol/sm2Pa for H2 at 25 °C for MFI-ZSM5), however with a limited ideal selectivity of around 2 for H2/He regardless of the feed concentration. Both MFI and NaA showed higher separation performances when water vapor was used instead; for example, at 30 °C, the separation factor for MFI-ZSM5 is approximately 10 and 38 for 0.2% and 10% H2O/He, respectively. The H-SOD evidenced to be considerably defective and therefore not considered for further experiments. In this contribution, a comprehensive analysis of the experimental methods and results obtained for the separation performance of different zeolite membranes during the past four years in inactive environment is given. These results are encouraging for the experimental campaign with molecular and oxidized tritium that will follow in 2017.Keywords: gas separation, nuclear fusion, tritium processes, zeolite membranes
Procedia PDF Downloads 2423239 Breast Cancer Diagnosing Based on Online Sequential Extreme Learning Machine Approach
Authors: Musatafa Abbas Abbood Albadr, Masri Ayob, Sabrina Tiun, Fahad Taha Al-Dhief, Mohammad Kamrul Hasan
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Breast Cancer (BC) is considered one of the most frequent reasons of cancer death in women between 40 to 55 ages. The BC is diagnosed by using digital images of the FNA (Fine Needle Aspirate) for both benign and malignant tumors of the breast mass. Therefore, this work proposes the Online Sequential Extreme Learning Machine (OSELM) algorithm for diagnosing BC by using the tumor features of the breast mass. The current work has used the Wisconsin Diagnosis Breast Cancer (WDBC) dataset, which contains 569 samples (i.e., 357 samples for benign class and 212 samples for malignant class). Further, numerous measurements of assessment were used in order to evaluate the proposed OSELM algorithm, such as specificity, precision, F-measure, accuracy, G-mean, MCC, and recall. According to the outcomes of the experiment, the highest performance of the proposed OSELM was accomplished with 97.66% accuracy, 98.39% recall, 95.31% precision, 97.25% specificity, 96.83% F-measure, 95.00% MCC, and 96.84% G-Mean. The proposed OSELM algorithm demonstrates promising results in diagnosing BC. Besides, the performance of the proposed OSELM algorithm was superior to all its comparatives with respect to the rate of classification.Keywords: breast cancer, machine learning, online sequential extreme learning machine, artificial intelligence
Procedia PDF Downloads 1003238 Optimal Bayesian Control of the Proportion of Defectives in a Manufacturing Process
Authors: Viliam Makis, Farnoosh Naderkhani, Leila Jafari
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In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example.Keywords: Bayesian control chart, semi-Markov decision process, quality control, partially observable process
Procedia PDF Downloads 3123237 Refactoring Object Oriented Software through Community Detection Using Evolutionary Computation
Authors: R. Nagarani
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An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.Keywords: community detection, complex network, genetic algorithm, package, refactoring
Procedia PDF Downloads 4103236 Improving Load Frequency Control of Multi-Area Power System by Considering Uncertainty by Using Optimized Type 2 Fuzzy Pid Controller with the Harmony Search Algorithm
Authors: Mehrdad Mahmudizad, Roya Ahmadi Ahangar
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This paper presents the method of designing the type 2 fuzzy PID controllers in order to solve the problem of Load Frequency Control (LFC). The Harmony Search (HS) algorithm is used to regulate the measurement factors and the effect of uncertainty of membership functions of Interval Type 2 Fuzzy Proportional Integral Differential (IT2FPID) controllers in order to reduce the frequency deviation resulted from the load oscillations. The simulation results implicitly show that the performance of the proposed IT2FPID LFC in terms of error, settling time and resistance against different load oscillations is more appropriate and preferred than PID and Type 1 Fuzzy Proportional Integral Differential (T1FPID) controllers.Keywords: load frequency control, fuzzy-pid controller, type 2 fuzzy system, harmony search algorithm
Procedia PDF Downloads 2613235 The Stem Cell Transcription Co-factor Znf521 Sustains Mll-af9 Fusion Protein In Acute Myeloid Leukemias By Altering The Gene Expression Landscape
Authors: Emanuela Chiarella, Annamaria Aloisio, Nisticò Clelia, Maria Mesuraca
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ZNF521 is a stem cell-associated transcription co-factor, that plays a crucial role in the homeostatic regulation of the stem cell compartment in the hematopoietic, osteo-adipogenic, and neural system. In normal hematopoiesis, primary human CD34+ hematopoietic stem cells display typically a high expression of ZNF521, while its mRNA levels rapidly decrease when these progenitors progress towards erythroid, granulocytic, or B-lymphoid differentiation. However, most acute myeloid leukemias (AMLs) and leukemia-initiating cells keep high ZNF521 expression. In particular, AMLs are often characterized by chromosomal translocations involving the Mixed Lineage Leukemia (MLL) gene, which MLL gene includes a variety of fusion oncogenes arisen from genes normally required during hematopoietic development; once they are fused, they promote epigenetic and transcription factor dysregulation. The chromosomal translocation t(9;11)(p21-22;q23), fusing the MLL gene with AF9 gene, results in a monocytic immune phenotype with an aggressive course, frequent relapses, and a short survival time. To better understand the dysfunctional transcriptional networks related to genetic aberrations, AML gene expression profile datasets were queried for ZNF521 expression and its correlations with specific gene rearrangements and mutations. The results showed that ZNF521 mRNA levels are associated with specific genetic aberrations: the highest expression levels were observed in AMLs involving t(11q23) MLL rearrangements in two distinct datasets (MILE and den Boer); elevated ZNF521 mRNA expression levels were also revealed in AMLs with t(7;12) or with internal rearrangements of chromosome 16. On the contrary, relatively low ZNF521 expression levels seemed to be associated with the t(8;21) translocation, that in turn is correlated with the AML1-ETO fusion gene or the t(15;17) translocation and in AMLs with FLT3-ITD, NPM1, or CEBPα double mutations. Invitro, we found that the enforced co-expression of ZNF521 in cord blood-derived CD34+ cells induced a significant proliferative advantage, improving MLL-AF9 effects on the induction of proliferation and the expansion of leukemic progenitor cells. Transcriptome profiling of CD34+ cells transduced with either MLL-AF9, ZNF521, or a combination of the two transgenes highlighted specific sets of up- or down-regulated genes that are involved in the leukemic phenotype, including those encoding transcription factors, epigenetic modulators, and cell cycle regulators as well as those engaged in the transport or uptake of nutrients. These data enhance the functional cooperation between ZNF521 and MA9, resulting in the development, maintenance, and clonal expansion of leukemic cells. Finally, silencing of ZNF521 in MLL-AF9-transformed primary CD34+ cells inhibited their proliferation and led to their extinction, as well as ZNF521 silencing in the MLL-AF9+ THP-1 cell line resulted in an impairment of their growth and clonogenicity. Taken together, our data highlight ZNF521 role in the control of self-renewal and in the immature compartment of malignant hematopoiesis, which, by altering the gene expression landscape, contributes to the development and/or maintenance of AML acting in concert with the MLL-AF9 fusion oncogene.Keywords: AML, human zinc finger protein 521 (hZNF521), mixed lineage leukemia gene (MLL) AF9 (MLLT3 or LTG9), cord blood-derived hematopoietic stem cells (CB-CD34+)
Procedia PDF Downloads 973234 Objects Tracking in Catadioptric Images Using Spherical Snake
Authors: Khald Anisse, Amina Radgui, Mohammed Rziza
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Tracking objects on video sequences is a very challenging task in many works in computer vision applications. However, there is no article that treats this topic in catadioptric vision. This paper is an attempt that tries to describe a new approach of omnidirectional images processing based on inverse stereographic projection in the half-sphere. We used the spherical model proposed by Gayer and al. For object tracking, our work is based on snake method, with optimization using the Greedy algorithm, by adapting its different operators. The algorithm will respect the deformed geometries of omnidirectional images such as spherical neighborhood, spherical gradient and reformulation of optimization algorithm on the spherical domain. This tracking method that we call "spherical snake" permitted to know the change of the shape and the size of object in different replacements in the spherical image.Keywords: computer vision, spherical snake, omnidirectional image, object tracking, inverse stereographic projection
Procedia PDF Downloads 3873233 Analyze and Visualize Eye-Tracking Data
Authors: Aymen Sekhri, Emmanuel Kwabena Frimpong, Bolaji Mubarak Ayeyemi, Aleksi Hirvonen, Matias Hirvonen, Tedros Tesfay Andemichael
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Fixation identification, which involves isolating and identifying fixations and saccades in eye-tracking protocols, is an important aspect of eye-movement data processing that can have a big impact on higher-level analyses. However, fixation identification techniques are frequently discussed informally and rarely compared in any meaningful way. With two state-of-the-art algorithms, we will implement fixation detection and analysis in this work. The velocity threshold fixation algorithm is the first algorithm, and it identifies fixation based on a threshold value. For eye movement detection, the second approach is U'n' Eye, a deep neural network algorithm. The goal of this project is to analyze and visualize eye-tracking data from an eye gaze dataset that has been provided. The data was collected in a scenario in which individuals were shown photos and asked whether or not they recognized them. The results of the two-fixation detection approach are contrasted and visualized in this paper.Keywords: human-computer interaction, eye-tracking, CNN, fixations, saccades
Procedia PDF Downloads 1253232 A Hybrid Based Algorithm to Solve the Multi-objective Minimum Spanning Tree Problem
Authors: Boumesbah Asma, Chergui Mohamed El-amine
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Since it has been shown that the multi-objective minimum spanning tree problem (MOST) is NP-hard even with two criteria, we propose in this study a hybrid NSGA-II algorithm with an exact mutation operator, which is only used with low probability, to find an approximation to the Pareto front of the problem. In a connected graph G, a spanning tree T of G being a connected and cycle-free graph, if k edges of G\T are added to T, we obtain a partial graph H of G inducing a reduced size multi-objective spanning tree problem compared to the initial one. With a weak probability for the mutation operator, an exact method for solving the reduced MOST problem considering the graph H is then used to give birth to several mutated solutions from a spanning tree T. Then, the selection operator of NSGA-II is activated to obtain the Pareto front approximation. Finally, an adaptation of the VNS metaheuristic is called for further improvements on this front. It allows finding good individuals to counterbalance the diversification and the intensification during the optimization search process. Experimental comparison studies with an exact method show promising results and indicate that the proposed algorithm is efficient.Keywords: minimum spanning tree, multiple objective linear optimization, combinatorial optimization, non-sorting genetic algorithm, variable neighborhood search
Procedia PDF Downloads 843231 Ensuring Uniform Energy Consumption in Non-Deterministic Wireless Sensor Network to Protract Networks Lifetime
Authors: Vrince Vimal, Madhav J. Nigam
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Wireless sensor networks have enticed much of the spotlight from researchers all around the world, owing to its extensive applicability in agricultural, industrial and military fields. Energy conservation node deployment stratagems play a notable role for active implementation of Wireless Sensor Networks. Clustering is the approach in wireless sensor networks which improves energy efficiency in the network. The clustering algorithm needs to have an optimum size and number of clusters, as clustering, if not implemented properly, cannot effectively increase the life of the network. In this paper, an algorithm has been proposed to address connectivity issues with the aim of ensuring the uniform energy consumption of nodes in every part of the network. The results obtained after simulation showed that the proposed algorithm has an edge over existing algorithms in terms of throughput and networks lifetime.Keywords: Wireless Sensor network (WSN), Random Deployment, Clustering, Isolated Nodes, Networks Lifetime
Procedia PDF Downloads 3283230 In-Situ Formation of Particle Reinforced Aluminium Matrix Composites by Laser Powder Bed Fusion of Fe₂O₃/AlSi12 Powder Mixture Using Consecutive Laser Melting+Remelting Strategy
Authors: Qimin Shi, Yi Sun, Constantinus Politis, Shoufeng Yang
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In-situ preparation of particle-reinforced aluminium matrix composites (PRAMCs) by laser powder bed fusion (LPBF) additive manufacturing is a promising strategy to strengthen traditional Al-based alloys. The laser-driven thermite reaction can be a practical mechanism to in-situ synthesize PRAMCs. However, introducing oxygen elements through adding Fe₂O₃ makes the powder mixture highly sensitive to form porosity and Al₂O₃ film during LPBF, bringing challenges to producing dense Al-based materials. Therefore, this work develops a processing strategy combined with consecutive high-energy laser melting scanning and low-energy laser remelting scanning to prepare PRAMCs from a Fe₂O₃/AlSi12 powder mixture. The powder mixture consists of 5 wt% Fe₂O₃ and the remainder AlSi12 powder. The addition of 5 wt% Fe₂O₃ aims to achieve balanced strength and ductility. A high relative density (98.2 ± 0.55 %) was successfully obtained by optimizing laser melting (Emelting) and laser remelting surface energy density (Eremelting) to Emelting = 35 J/mm² and Eremelting = 5 J/mm². Results further reveal the necessity of increasing Emelting, to improve metal liquid’s spreading/wetting by breaking up the Al₂O₃ films surrounding the molten pools; however, the high-energy laser melting produced much porosity, including H₂₋, O₂₋ and keyhole-induced pores. The subsequent low-energy laser remelting could close the resulting internal pores, backfill open gaps and smoothen solidified surfaces. As a result, the material was densified by repeating laser melting and laser remelting layer by layer. Although with two-times laser scanning, the microstructure still shows fine cellular Si networks with Al grains inside (grain size of about 370 nm) and in-situ nano-precipitates (Al₂O₃, Si, and Al-Fe(-Si) intermetallics). Finally, the fine microstructure, nano-structured dispersion strengthening, and high-level densification strengthened the in-situ PRAMCs, reaching yield strength of 426 ± 4 MPa and tensile strength of 473 ± 6 MPa. Furthermore, the results can expect to provide valuable information to process other powder mixtures with severe porosity/oxide-film formation potential, considering the evidenced contribution of laser melting/remelting strategy to densify material and obtain good mechanical properties during LPBF.Keywords: densification, laser powder bed fusion, metal matrix composites, microstructures, mechanical properties
Procedia PDF Downloads 1473229 Diffusion Adaptation Strategies for Distributed Estimation Based on the Family of Affine Projection Algorithms
Authors: Mohammad Shams Esfand Abadi, Mohammad Ranjbar, Reza Ebrahimpour
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This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results.Keywords: selective partial update, affine projection, dynamic selection, diffusion, adaptive distributed networks
Procedia PDF Downloads 6943228 Spatio-Temporal Data Mining with Association Rules for Lake Van
Authors: Tolga Aydin, M. Fatih Alaeddinoğlu
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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatio-temporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newly-formed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.Keywords: apriori algorithm, association rules, data mining, spatio-temporal data
Procedia PDF Downloads 3633227 Performance Evaluation of GPS/INS Main Integration Approach
Authors: Othman Maklouf, Ahmed Adwaib
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This paper introduces a comparative study between the main GPS/INS coupling schemes, this will include the loosely coupled and tightly coupled configurations, several types of situations and operational conditions, in which the data fusion process is done using Kalman filtering. This will include the importance of sensors calibration as well as the alignment of the strap down inertial navigation system. The limitations of the inertial navigation systems are investigated.Keywords: GPS, INS, Kalman filter, sensor calibration, navigation system
Procedia PDF Downloads 5803226 Effect of Variable Fluxes on Optimal Flux Distribution in a Metabolic Network
Authors: Ehsan Motamedian
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Finding all optimal flux distributions of a metabolic model is an important challenge in systems biology. In this paper, a new algorithm is introduced to identify all alternate optimal solutions of a large scale metabolic network. The algorithm reduces the model to decrease computations for finding optimal solutions. The algorithm was implemented on the Escherichia coli metabolic model to find all optimal solutions for lactate and acetate production. There were more optimal flux distributions when acetate production was optimized. The model was reduced from 1076 to 80 variable fluxes for lactate while it was reduced to 91 variable fluxes for acetate. These 11 more variable fluxes resulted in about three times more optimal flux distributions. Variable fluxes were from 12 various metabolic pathways and most of them belonged to nucleotide salvage and extra cellular transport pathways.Keywords: flux variability, metabolic network, mixed-integer linear programming, multiple optimal solutions
Procedia PDF Downloads 4263225 A Quinary Coding and Matrix Structure Based Channel Hopping Algorithm for Blind Rendezvous in Cognitive Radio Networks
Authors: Qinglin Liu, Zhiyong Lin, Zongheng Wei, Jianfeng Wen, Congming Yi, Hai Liu
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The multi-channel blind rendezvous problem in distributed cognitive radio networks (DCRNs) refers to how users in the network can hop to the same channel at the same time slot without any prior knowledge (i.e., each user is unaware of other users' information). The channel hopping (CH) technique is a typical solution to this blind rendezvous problem. In this paper, we propose a quinary coding and matrix structure-based CH algorithm called QCMS-CH. The QCMS-CH algorithm can guarantee the rendezvous of users using only one cognitive radio in the scenario of the asynchronous clock (i.e., arbitrary time drift between the users), heterogeneous channels (i.e., the available channel sets of users are distinct), and symmetric role (i.e., all users play a same role). The QCMS-CH algorithm first represents a randomly selected channel (denoted by R) as a fixed-length quaternary number. Then it encodes the quaternary number into a quinary bootstrapping sequence according to a carefully designed quaternary-quinary coding table with the prefix "R00". Finally, it builds a CH matrix column by column according to the bootstrapping sequence and six different types of elaborately generated subsequences. The user can access the CH matrix row by row and accordingly perform its channel, hoping to attempt rendezvous with other users. We prove the correctness of QCMS-CH and derive an upper bound on its Maximum Time-to-Rendezvous (MTTR). Simulation results show that the QCMS-CH algorithm outperforms the state-of-the-art in terms of the MTTR and the Expected Time-to-Rendezvous (ETTR).Keywords: channel hopping, blind rendezvous, cognitive radio networks, quaternary-quinary coding
Procedia PDF Downloads 803224 iCCS: Development of a Mobile Web-Based Student Integrated Information System using Hill Climbing Algorithm
Authors: Maria Cecilia G. Cantos, Lorena W. Rabago, Bartolome T. Tanguilig III
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This paper describes a conducive and structured information exchange environment for the students of the College of Computer Studies in Manuel S. Enverga University Foundation in. The system was developed to help the students to check their academic result, manage profile, make self-enlistment and assist the students to manage their academic status that can be viewed also in mobile phones. Developing class schedules in a traditional way is a long process that involves making many numbers of choices. With Hill Climbing Algorithm, however, the process of class scheduling, particularly with regards to courses to be taken by the student aligned with the curriculum, can perform these processes and end up with an optimum solution. The proponent used Rapid Application Development (RAD) for the system development method. The proponent also used the PHP as the programming language and MySQL as the database.Keywords: hill climbing algorithm, integrated system, mobile web-based, student information system
Procedia PDF Downloads 3773223 Optimizing Boiler Combustion System in a Petrochemical Plant Using Neuro-Fuzzy Inference System and Genetic Algorithm
Authors: Yul Y. Nazaruddin, Anas Y. Widiaribowo, Satriyo Nugroho
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Boiler is one of the critical unit in a petrochemical plant. Steam produced by the boiler is used for various processes in the plant such as urea and ammonia plant. An alternative method to optimize the boiler combustion system is presented in this paper. Adaptive Neuro-Fuzzy Inference System (ANFIS) approach is applied to model the boiler using real-time operational data collected from a boiler unit of the petrochemical plant. Nonlinear equation obtained is then used to optimize the air to fuel ratio using Genetic Algorithm, resulting an optimal ratio of 15.85. This optimal ratio is then maintained constant by ratio controller designed using inverse dynamics based on ANFIS. As a result, constant value of oxygen content in the flue gas is obtained which indicates more efficient combustion process.Keywords: ANFIS, boiler, combustion process, genetic algorithm, optimization.
Procedia PDF Downloads 2433222 A Parallel Algorithm for Solving the PFSP on the Grid
Authors: Samia Kouki
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Solving NP-hard combinatorial optimization problems by exact search methods, such as Branch-and-Bound, may degenerate to complete enumeration. For that reason, exact approaches limit us to solve only small or moderate size problem instances, due to the exponential increase in CPU time when problem size increases. One of the most promising ways to reduce significantly the computational burden of sequential versions of Branch-and-Bound is to design parallel versions of these algorithms which employ several processors. This paper describes a parallel Branch-and-Bound algorithm called GALB for solving the classical permutation flowshop scheduling problem as well as its implementation on a Grid computing infrastructure. The experimental study of our distributed parallel algorithm gives promising results and shows clearly the benefit of the parallel paradigm to solve large-scale instances in moderate CPU time.Keywords: grid computing, permutation flow shop problem, branch and bound, load balancing
Procedia PDF Downloads 2773221 An Improved OCR Algorithm on Appearance Recognition of Electronic Components Based on Self-adaptation of Multifont Template
Authors: Zhu-Qing Jia, Tao Lin, Tong Zhou
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The recognition method of Optical Character Recognition has been expensively utilized, while it is rare to be employed specifically in recognition of electronic components. This paper suggests a high-effective algorithm on appearance identification of integrated circuit components based on the existing methods of character recognition, and analyze the pros and cons.Keywords: optical character recognition, fuzzy page identification, mutual correlation matrix, confidence self-adaptation
Procedia PDF Downloads 5313220 A Proposed Algorithm for Obtaining the Map of Subscribers’ Density Distribution for a Mobile Wireless Communication Network
Authors: C. Temaneh-Nyah, F. A. Phiri, D. Karegeya
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This paper presents an algorithm for obtaining the map of subscriber’s density distribution for a mobile wireless communication network based on the actual subscriber's traffic data obtained from the base station. This is useful in statistical characterization of the mobile wireless network.Keywords: electromagnetic compatibility, statistical analysis, simulation of communication network, subscriber density
Procedia PDF Downloads 3053219 An Efficient Algorithm of Time Step Control for Error Correction Method
Authors: Youngji Lee, Yonghyeon Jeon, Sunyoung Bu, Philsu Kim
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The aim of this paper is to construct an algorithm of time step control for the error correction method most recently developed by one of the authors for solving stiff initial value problems. It is achieved with the generalized Chebyshev polynomial and the corresponding error correction method. The main idea of the proposed scheme is in the usage of the duplicated node points in the generalized Chebyshev polynomials of two different degrees by adding necessary sample points instead of re-sampling all points. At each integration step, the proposed method is comprised of two equations for the solution and the error, respectively. The constructed algorithm controls both the error and the time step size simultaneously and possesses a good performance in the computational cost compared to the original method. Two stiff problems are numerically solved to assess the effectiveness of the proposed scheme.Keywords: stiff initial value problem, error correction method, generalized Chebyshev polynomial, node points
Procedia PDF Downloads 5603218 A Context-Sensitive Algorithm for Media Similarity Search
Authors: Guang-Ho Cha
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This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results.Keywords: context-sensitive search, image search, similarity ranking, similarity search
Procedia PDF Downloads 351