Search results for: Clustering Validation

Authors: Eman AbuKhousa, Marwan Z. Bataineh

Abstract:

The 21^st century higher education and globalization challenge new faculty members to build effective professional networks and partnership with industry in order to accelerate their growth and success. This creates the need for community of practice (CoP)-oriented development approaches that focus on cognitive apprenticeship while considering individual predisposition and future career needs. This work adopts data mining, clustering analysis, and social networking technologies to present the CoP-Network as a virtual space that connects together similar career-aspiration individuals who are socially influenced to join and engage in a process for domain-related knowledge and practice acquisitions. The CoP-Network model can be integrated into higher education to extend traditional graduate and professional development programs.

Keywords: Clustering analysis, community of practice, data mining, higher education, new faculty challenges, social networks, social influence, professional development.

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Authors: Mohammadreza Razzazi, Maryam Bayat

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Selection of the best possible set of suppliers has a significant impact on the overall profitability and success of any business. For this reason, it is usually necessary to optimize all business processes and to make use of cost-effective alternatives for additional savings. This paper proposes a new efficient context-aware supplier selection model that takes into account possible changes of the environment while significantly reducing selection costs. The proposed model is based on data clustering techniques while inspiring certain principles of online algorithms for an optimally selection of suppliers. Unlike common selection models which re-run the selection algorithm from the scratch-line for any decision-making sub-period on the whole environment, our model considers the changes only and superimposes it to the previously defined best set of suppliers to obtain a new best set of suppliers. Therefore, any recomputation of unchanged elements of the environment is avoided and selection costs are consequently reduced significantly. A numerical evaluation confirms applicability of this model and proves that it is a more optimal solution compared with common static selection models in this field.

Keywords: Supplier Selection, Context-Awareness, OnlineAlgorithms, Data Clustering.

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Authors: Hamid R. S. Mojaveri, Seyed S. Mousavi, Mojtaba Heydar, Ahmad Aminian

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The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. Finally, forecasting methods' stability is analyzed by using raw data and even the effectiveness of clustering analysis is measured.

Keywords: Artificial Neural Networks (ANN), bullwhip effect, demand forecasting, Support Vector Machine (SVM).

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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.

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Authors: Amol Bapuso Rajmane, Pradeep M. Patil, Prakash J. Kulkarni

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The Information Retrieval community is facing the problem of effective representation of Web search results. When we organize web search results into clusters it becomes easy to the users to quickly browse through search results. The traditional search engines organize search results into clusters for ambiguous queries, representing each cluster for each meaning of the query. The clusters are obtained according to the topical similarity of the retrieved search results, but it is possible for results to be totally dissimilar and still correspond to the same meaning of the query. People search is also one of the most common tasks on the Web nowadays, but when a particular person’s name is queried the search engines return web pages which are related to different persons who have the same queried name. By placing the burden on the user of disambiguating and collecting pages relevant to a particular person, in this paper, we have developed an approach that clusters web pages based on the association of the web pages to the different people and clusters that are based on generic entity search.

Keywords: Entity resolution, information retrieval, graph based disambiguation, web people search, clustering.

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Authors: A. Rajagopal, S. Somasundaram, B. Sowmya, T. Suguna

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Wireless Sensor Networks (WSNs) enable new applications and need non-conventional paradigms for the protocol because of energy and bandwidth constraints, In WSN, sensor node’s life is a critical parameter. Research on life extension is based on Low-Energy Adaptive Clustering Hierarchy (LEACH) scheme, which rotates Cluster Head (CH) among sensor nodes to distribute energy consumption over all network nodes. CH selection in WSN affects network energy efficiency greatly. This study proposes an improved CH selection for efficient data aggregation in sensor networks. This new algorithm is based on Bacterial Foraging Optimization (BFO) incorporated in LEACH.

Keywords: Bacterial Foraging Optimization (BFO), Cluster Head (CH), Data-aggregation protocols, Low-Energy Adaptive Clustering Hierarchy (LEACH).

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Authors: A. Kowcika, E. Umamaheswari, T.V. Geetha

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In this paper we focus on event extraction from Tamil news article. This system utilizes a scoring scheme for extracting and grouping event-specific sentences. Using this scoring scheme eventspecific clustering is performed for multiple documents. Events are extracted from each document using a scoring scheme based on feature score and condition score. Similarly event specific sentences are clustered from multiple documents using this scoring scheme. The proposed system builds the Event Template based on user specified query. The templates are filled with event specific details like person, location and timeline extracted from the formed clusters. The proposed system applies these methodologies for Tamil news articles that have been enconverted into UNL graphs using a Tamil to UNL-enconverter. The main intention of this work is to generate an event based template.

Keywords: Event Extraction, Score based Clustering, Segmentation, Template Generation.

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Authors: Lei Lei, Hongbo Zhang, Donald J. Bergstrom, Bing Zhang, K. Y. Song, W. J. Zhang

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This paper presents a model for a modified T-junction device for microspheres generation. The numerical model is developed using a commercial software package: COMSOL Multiphysics. In order to test the accuracy of the numerical model, multiple variables, such as the flow rate of cross-flow, fluid properties, structure, and geometry of the microdevice are applied. The results from the model are compared with the experimental results in the diameter of the microsphere generated. The comparison shows a good agreement. Therefore the model is useful in further optimization of the device and feedback control of microsphere generation if any.

Keywords: CFD modeling, validation, microsphere generation, modified T-junction.

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Authors: Chutima Ruanguttamanun

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Despite the relatively large number of studies that have examined the use of appeals in advertisements, research on the use of appeals in green advertisements is still underdeveloped and needs to be investigated further, as it is definitely a tool for marketers to create illustrious ads. In this study, content analysis was employed to examine the nature of green advertising appeals and to match the appeals with the green advertisements. Two different types of green print advertisings, product orientation and organizational image orientation were used. Thirty highly educated participants with different backgrounds were asked individually to ascertain three appeals out of thirty-four given appeals found among forty real green advertisements. To analyze participant responses and to group them based on common appeals, two-step K-mean clustering is used. The clustering solution indicates that eye-catching graphics and imaginative appeals are highly notable in both types of green ads. Depressed, meaningful and sad appeals are found to be highly used in organizational image orientation ads, whereas, corporate image, informative and natural appeals are found to be essential for product orientation ads.

Keywords: Advertising appeals, green marketing, green advertisement, printed advertisement.

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Authors: Prakash G L, Thejaswini M, S H Manjula, K R Venugopal, L M Patnaik

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Wireless sensor network can be applied to both abominable and military environments. A primary goal in the design of wireless sensor networks is lifetime maximization, constrained by the energy capacity of batteries. One well-known method to reduce energy consumption in such networks is data aggregation. Providing efcient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present privacy-preserving data aggregation scheme for additive aggregation functions. The Cluster-based Private Data Aggregation (CPDA)leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

Keywords: Aggregation, Clustering, Query Processing.

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Authors: Ma Shidong, He Yuncheng, Wang Zhong, Yang Guoqing

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In this paper, enhanced ground proximity warning simulation and validation system is designed and implemented. First, based on square grid and sub-grid structure, the global digital terrain database is designed and constructed. Terrain data searching is implemented through querying the latitude and longitude bands and separated zones of global terrain database with the current aircraft position. A combination of dynamic scheduling and hierarchical scheduling is adopted to schedule the terrain data, and the terrain data can be read and delete dynamically in the memory. Secondly, according to the scope, distance, approach speed information etc. to the dangerous terrain in front, and using security profiles calculating method, collision threat detection is executed in real-time, and provides caution and warning alarm. According to this scheme, the implementation of the enhanced ground proximity warning simulation system is realized. Simulations are carried out to verify a good real-time in terrain display and alarm trigger, and the results show simulation system is realized correctly, reasonably and stable.

Keywords: enhanced ground proximity warning system, digital terrain, look-ahead terrain alarm, terrain display, simulation and validation

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Authors: J. Bouchard, B. Prosperi, G. Bavre, M. Daudé, E. Jeandupeux

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Young patients suffering from Cerebral Palsy are facing difficult choices concerning heavy surgeries. Diagnosis settled by surgeons can be complex and on the other hand decision for patient about getting or not such a surgery involves important reflection effort. Proposed software combining prediction for surgeries and post surgery kinematic values, and from 3D model representing the patient is an innovative tool helpful for both patients and medicine professionals. Beginning with analysis and classification of kinematics values from Data Base extracted from gait analysis in 3 separated clusters, it is possible to determine close similarity between patients. Prediction surgery best adapted to improve a patient gait is then determined by operating a suitable preconditioned neural network. Finally, patient 3D modeling based on kinematic values analysis, is animated thanks to post surgery kinematic vectors characterizing the closest patient selected from patients clustering.

Keywords: Cerebral Palsy, Clustering, Crouch Gait, 3-D Modeling.

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: Clustering algorithms, coastal engineering, data mining, data summarization, statistical methods.

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Authors: Nurcan Tuncbag, Turkan Haliloglu, Ozlem Keskin

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Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.

Keywords: Pair-wise protein interactions, DIP database, functional correlations, biclustering.

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Authors: Mahbub C. Mishu, Venketesh N. Dubey, Tamas Hickish, Jonathan Cole

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Pressure ulcer is a common problem for today’s healthcare industry. It occurs due to external load applied to the skin. Also when the subject is immobile for a longer period of time and there is continuous load applied to a particular area of human body, blood flow gets reduced and as a result pressure ulcer develops. Body support surface has a significant role in preventing ulceration so it is important to know the characteristics of support surface under loading conditions. In this paper we have presented mathematical models of different types of viscoelastic materials and also we have shown the validation of our simulation results with experiments.

Keywords: Pressure ulcer, viscoelastic material, mathematical model, experimental validation.

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Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

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Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: LiDAR, real-time system, clustering, tracking, data association.

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Authors: C. Perez, O. Reinoso, N. Garcia, J. M. Sabater, L. Gracia

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For high-speed control of robots, a good knowledge of system modelling is necessary to obtain the desired bandwidth. In this paper, we present a cartesian robot with a pan/tilt unit in end-effector (5 dof). This robot is implemented with powerful direct drive AC induction machines. The dynamic model, parameter identification and model validation of the robot are studied (including actuators). This work considers the cartesian robot coupled and non linear (contrary to normal considerations for this type of robots). The mechanical and control architecture proposed in this paper is efficient for industrial and research application in which high speed, well known model and very high accuracy are required.

Keywords: Robot modelling, parameter identification and validation, AC servo-motors.

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Authors: Vahid Anari, Mina Bakhshi

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Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: Positive cell, color segmentation, HSV color space, immunohistochemistry, meningioma, thresholding, fuzzy c-means.

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Authors: Narottam Chand

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Wireless sensor networks (WSNs) consist of number of tiny, low cost and low power sensor nodes to monitor some physical phenomenon. The major limitation in these networks is the use of non-rechargeable battery having limited power supply. The main cause of energy consumption in such networks is communication subsystem. This paper presents an energy efficient Cluster Cooperative Caching at Sensor (C3S) based upon grid type clustering. Sensor nodes belonging to the same cluster/grid form a cooperative cache system for the node since the cost for communication with them is low both in terms of energy consumption and message exchanges. The proposed scheme uses cache admission control and utility based data replacement policy to ensure that more useful data is retained in the local cache of a node. Simulation results demonstrate that C3S scheme performs better in various performance metrics than NICoCa which is existing cooperative caching protocol for WSNs.

Keywords: Cooperative caching, cache replacement, admission control, WSN, clustering.

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Authors: Anurag Sharma, Christian W. Omlin

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Self-organizing map (SOM) is a well known data reduction technique used in data mining. Data visualization can reveal structure in data sets that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very tedious. There are several techniques for the automatic detection of clusters of code vectors found by SOMs, but they generally do not take into account the distribution of code vectors; this may lead to unsatisfactory clustering and poor definition of cluster boundaries, particularly where the density of data points is low. In this paper, we propose the use of a generic particle swarm optimization (PSO) algorithm for finding cluster boundaries directly from the code vectors obtained from SOMs. The application of our method to unlabeled call data for a mobile phone operator demonstrates its feasibility. PSO algorithm utilizes U-matrix of SOMs to determine cluster boundaries; the results of this novel automatic method correspond well to boundary detection through visual inspection of code vectors and k-means algorithm.

Keywords: Particle swarm optimization, self-organizing maps, clustering, data mining.

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Authors: M. Govindarajan, R. M.Chandrasekaran

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Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high.

Keywords: Text Data Mining, Comparative Cross-validation, Radial Basis Function, runtime, accuracy.

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Authors: D. Satish Kumar, S. Karthik

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A dual tiered network model is designed to overcome the problem of energy alert and fault tolerance. This model minimizes the delay time and overcome failure of links. Performance analysis of the dual tiered network model is studied in this paper where the CA and LS schemes are compared with DEO optimal. We then evaluate  the Integrated Network Topological Control and Key Management (INTK) Schemes, which was proposed to add security features of the wireless sensor networks. Clustering efficiency, level of protections, the time complexity is some of the parameters of INTK scheme that were analyzed. We then evaluate the Cluster based Energy Competent n-coverage scheme (CEC n-coverage scheme) to ensure area coverage for wireless sensor networks.

Keywords: CEC n-coverage scheme, Clustering efficiency, Dual tired network, Wireless sensor networks.

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Authors: Tarek M. Nazih El-Derini, Ahmed K. El-Shenawy

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This paper presents the hardware implemented and validation for a special system to assist the unprofessional users of car with back trailers. The system consists of two platforms; the front car platform (C) and the trailer platform (T). The main objective is to control the Trailer platform using the actuators found in the front platform (c). The mobility of the platform (C) is investigated and inverse and forward kinematics model is obtained for both platforms (C) and (T).The system is simulated using Matlab M-file and the simulation examples results illustrated the system performance. The system is constructed with a hardware setup for the front and trailer platform. The hardware experimental results and the simulated examples outputs showed the validation of the hardware setup.

Keywords: Kinematics, Modeling, Wheeled Mobile Robot.

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Authors: Marley Rosa Luciano, Rodney Rezende Saldanha

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The race for electrification and the need for innovation to attract customers has led the automotive industry to do something different with vehicles. New emissions control challenges and efficient technological availability are the pillars of creation. The growing demand to upgrade industrial manufacturing systems creates actions that directly impact vehicle production. With this comes the search for new prototyping methods and virtual tools for component testing and validation, and vehicle systems have established themselves. The demand for Electronic Control Units (ECU) is increasing due to the availability of intelligence and safety in today's vehicles, directly affecting their development, performance, and functional testing. In order to keep up with global changes, the automotive industry uses different virtual environments to produce, verify and validate their vehicles and test prototypes used during development. Therefore, in this paper, integration and validation were performed using the Hardware in the Loop (HIL) test platform, focusing on the ECU Body Control Module (BCM). Then, a brief commentary reviews other test medium platforms, such as the Plywood Buck (PWB), and examines the reliability, flexibility, installation time, and cost of the three test platforms, software in the loop (SIL), Model in the loop (MIL), and HIL, to review their benefits, challenges, and issues in use and information to optimize the use of each platform and test medium.

Keywords: Automotive, Electronic Central Unit, xIL, Hardware in the loop.

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Authors: Summiya Moheuddin, Afzel Noore, Muhammad Choudhry

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This paper proposes a novel solution for optimizing the size and communication overhead of a distributed multiagent system without compromising the performance. The proposed approach addresses the challenges of scalability especially when the multiagent system is large. A modified spectral clustering technique is used to partition a large network into logically related clusters. Agents are assigned to monitor dedicated clusters rather than monitor each device or node. The proposed scalable multiagent system is implemented using JADE (Java Agent Development Environment) for a large power system. The performance of the proposed topologyindependent decentralized multiagent system and the scalable multiagent system is compared by comprehensively simulating different fault scenarios. The time taken for reconfiguration, the overall computational complexity, and the communication overhead incurred are computed. The results of these simulations show that the proposed scalable multiagent system uses fewer agents efficiently, makes faster decisions to reconfigure when a fault occurs, and incurs significantly less communication overhead.

Keywords: Multiagent system, scalable design, spectral clustering, reconfiguration.

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: Classification, singing, spectral analysis, vocal emission, vocal register.

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Authors: Francisca Grimón, Marylin Giugni, Josep Monguet F., Joaquín Fernández, Luis León G.

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In blended learning environments, the Internet can be combined with other technologies. The aim of this research was to design, introduce and validate a model to support synchronous and asynchronous activities by managing content domains in an Adaptive Hypermedia System (AHS). The application is based on information recovery techniques, clustering algorithms and adaptation rules to adjust the user's model to contents and objects of study. This system was applied to blended learning in higher education. The research strategy used was the case study method. Empirical studies were carried out on courses at two universities to validate the model. The results of this research show that the model had a positive effect on the learning process. The students indicated that the synchronous and asynchronous scenario is a good option, as it involves a combination of work with the lecturer and the AHS. In addition, they gave positive ratings to the system and stated that the contents were adapted to each user profile.

Keywords: Blended Learning, System Adaptive, Model, Clustering Algorithms.

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Authors: Sotirios Raptis

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Bayesian reasoning (BR) or Linear (Auto) Regression (AR/LR) can predict different sources of data using priors or other data, and can link social service demands in cohorts, while their consideration in isolation (self-prediction) may lead to service misuse ignoring the context. The paper advocates that BR with Binomial (BD), or Normal (ND) models or raw data (.D) as probabilistic updates can be compared to AR/LR to link services in Scotland and reduce cost by sharing healthcare (HC) resources. Clustering, cross-correlation, along with BR, LR, AR can better predict demand. Insurance companies and policymakers can link such services, and examples include those offered to the elderly, and low-income people, smoking-related services linked to mental health services, or epidemiological weight in children. 22 service packs are used that are published by Public Health Services (PHS) Scotland and Scottish Government (SG) from 1981 to 2019, broken into 110 year series (factors), joined using LR, AR, BR. The Primary component analysis found 11 significant factors, while C-Means (CM) clustering gave five major clusters.

Keywords: Bayesian probability, cohorts, data frames, regression, services, prediction.

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Authors: F. Tafnout, E. Belut, B. Oesterlé, J.R. Fontaine

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As a part of the development of a numerical method of close capture exhausts systems for machining devices, a test rig recreating a situation similar to a grinding operation, but in a perfectly controlled environment, is used. The properties of the obtained spray of solid particles are initially characterized using particle tracking velocimetry (PTV), in order to obtain input and validation parameters for numerical simulations. The dispersion of a tracer gas (SF6) emitted simultaneously with the particle jet is then studied experimentally, as the dispersion of such a gas is representative of that of finer particles, whose aerodynamic response time is negligible. Finally, complete modeling of the test rig is achieved to allow comparison with experimental results and thus to progress towards validation of the models used to describe a twophase flow generated by machining operation.

Keywords: Pollutants, capture, tracer gas, SF6, PTV, numericalmodeling.

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Authors: Thair Sh. Mahmoud, Tang Sai Hong, Mohammed H. Marhaban

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In this paper, Neuro-Fuzzy based Fuzzy Subtractive Clustering Method (FSCM) and Self Tuning Fuzzy PD-like Controller (STFPDC) were used to solve non-linearity and trajectory problems of pitch AND yaw angles of Twin Rotor MIMO system (TRMS). The control objective is to make the beams of TRMS reach a desired position quickly and accurately. The proposed method could achieve control objectives with simpler controller. To simplify the complexity of STFPDC, ANFIS based FSCM was used to simplify the controller and improve the response. The proposed controllers could achieve satisfactory objectives under different input signals. Simulation results under MATLAB/Simulink® proved the improvement of response and superiority of simplified STFPDC on Fuzzy Logic Controller (FLC).

Keywords: Fuzzy Subtractive Clustering Method, Neuro Fuzzy, Self Tuning Fuzzy Controller, and Twin Rotor MIMO System.

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