Search results for: image search

Authors: Abu Bakar Sambah, Fusanori Miura

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Remote sensing image processing, spatial data analysis through GIS approach, and analytical hierarchy process were introduced in this study for assessing the vulnerability area and inundation area due to tsunami hazard in the area of Rikuzentakata, Iwate Prefecture, Japan. Appropriate input parameters were derived from GSI DEM data, ALOS AVNIR-2, and field data. We used the parameters of elevation, slope, shoreline distance, and vegetation density. Five classes of vulnerability were defined and weighted via pairwise comparison matrix. The assessment results described that 14.35km² of the study area was under tsunami vulnerability zone. Inundation areas are those of high and slightly high vulnerability. The farthest area reached by a tsunami was about 7.50km from the shoreline and shows that rivers act as flooding strips that transport tsunami waves into the hinterland. This study can be used for determining a priority for land-use planning in the scope of tsunami hazard risk management.

Keywords: AHP, GIS, remote sensing, tsunami vulnerability.

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Authors: H. Shayeghi, H. A. Shayanfar, G. Azimi

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Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural networks for load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. We find good performance for the large neural networks. The proposed methodology gives lower percent errors all the time. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

Keywords: Feed-forward Large Neural Network, Short-TermLoad Forecasting, Continuous Genetic Algorithm.

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Authors: Habibollah Najafi, Amir Abbas Yazdani, Hosseinali Nahavandi

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The review performed on the condition of energy consumption & rate in Iran, shows that unfortunately the subject of optimization and conservation of energy in active industries of country lacks a practical & effective method and in most factories, the energy consumption and rate is more than in similar industries of industrial countries. The increasing demand of electrical energy and the overheads which it imposes on the organization, forces companies to search for suitable approaches to optimize energy consumption and demand management. Application of value engineering techniques is among these approaches. Value engineering is considered a powerful tool for improving profitability. These tools are used for reduction of expenses, increasing profits, quality improvement, increasing market share, performing works in shorter durations, more efficient utilization of sources & etc. In this article, we shall review the subject of value engineering and its capabilities for creating effective transformations in industrial organizations, in order to reduce energy costs & the results have been investigated and described during a case study in Mazandaran wood and paper industries, the biggest consumer of energy in north of Iran, for the purpose of presenting the effects of performed tasks in optimization of energy consumption by utilizing value engineering techniques in one case study.

Keywords: Value Engineering (VE), Expense, Energy, Industrial

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Authors: Luis Gracia, Carlos Perez-Vidal, Carlos Gracia

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This paper presents the theoretical background and the real implementation of an automated computer system to introduce machine vision in flower, fruit and vegetable processing for recollection, cutting, packaging, classification, or fumigation tasks. The considerations and implementation issues presented in this work can be applied to a wide range of varieties of flowers, fruits and vegetables, although some of them are especially relevant due to the great amount of units that are manipulated and processed each year over the world. The computer vision algorithms developed in this work are shown in detail, and can be easily extended to other applications. A special attention is given to the electromagnetic compatibility in order to avoid noisy images. Furthermore, real experimentation has been carried out in order to validate the developed application. In particular, the tests show that the method has good robustness and high success percentage in the object characterization.

Keywords: Image processing, Vision system, Automation

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Authors: A. Nachour, L. Ouzizi, Y. Aoura

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Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.

Keywords: Edge detection, medical MR images, multi-agent systems, vector field convolution.

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Authors: Ferreira F. C. L., Souza D. N., Rodrigues T. M. A., Cunha C. J., Dullius M. A., Andrade J. E., Sousa A. H., Vieira J. P. C., Carvalho Júnior A. B., Santos L. P. B., Passos R. O.

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The objective this study was to characterize and develop anthropomorphic liver phantoms in tomography hepatic procedures for quality control and improvement professionals in nuclear medicine. For the conformation of the anthropomorphic phantom was used in plaster and acrylic. We constructed three phantoms representing processes with liver cirrhosis. The phantoms were filled with 99mTc diluted with water to obtain the scintigraphic images. Tomography images were analyzed anterior and posterior phantom representing a body with a greater degree cirrhotic. It was noted that the phantoms allow the acquisition of images similar to real liver with cirrhosis. Simulations of hemangiomas may contribute to continued professional education of nuclear medicine, on the question of image acquisition, allowing of the study parameters such of the matrix, energy window and count statistics.

Keywords: Nuclear medicine, liver phantom, control quality

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Authors: J. L. Liu, L. Wang, B. Zhu, J. Zhou, W. D. Gao

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Fabric textures are very common in our daily life. However, the representation of fabric textures has never been explored from neuroscience view. Theoretical studies suggest that primary visual cortex (V1) uses a sparse code to efficiently represent natural images. However, how the simple cells in V1 encode the artificial textures is still a mystery. So, here we will take fabric texture as stimulus to study the response of independent component analysis that is established to model the receptive field of simple cells in V1. We choose 140 types of fabrics to get the classical fabric textures as materials. Experiment results indicate that the receptive fields of simple cells have obvious selectivity in orientation, frequency and phase when drifting gratings are used to determine their tuning properties. Additionally, the distribution of optimal orientation and frequency shows that the patch size selected from each original fabric image has a significant effect on the frequency selectivity.

Keywords: Fabric Texture, Receptive Filed, Simple Cell, Spare Coding.

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Authors: S. N. Deepa, G. Sugumaran

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This paper proposes a new version of the Particle Swarm Optimization (PSO) namely, Modified PSO (MPSO) for model order formulation of Single Input Single Output (SISO) linear time invariant continuous systems. In the General PSO, the movement of a particle is governed by three behaviors namely inertia, cognitive and social. The cognitive behavior helps the particle to remember its previous visited best position. In Modified PSO technique split the cognitive behavior into two sections like previous visited best position and also previous visited worst position. This modification helps the particle to search the target very effectively. MPSO approach is proposed to formulate the higher order model. The method based on the minimization of error between the transient responses of original higher order model and the reduced order model pertaining to the unit step input. The results obtained are compared with the earlier techniques utilized, to validate its ease of computation. The proposed method is illustrated through numerical example from literature.

Keywords: Continuous System, Model Order Formulation, Modified Particle Swarm Optimization, Single Input Single Output, Transfer Function Approach

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Authors: E. B. Jo, J. H. Lee, J. Y. Park, S. M. Kim

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In this study, we present an advanced detection technique for mass type breast cancer based on texture information of organs. The proposed method detects the cancer areas in three stages. In the first stage, the midpoints of mass area are determined based on AHE (Adaptive Histogram Equalization). In the second stage, we set the threshold coefficient of homogeneity by using MLE (Maximum Likelihood Estimation) to compute the uniformity of texture. Finally, mass type cancer tissues are extracted from the original image. As a result, it was observed that the proposed method shows an improved detection performance on dense breast tissues of Korean women compared with the existing methods. It is expected that the proposed method may provide additional diagnostic information for detection of mass-type breast cancer.

Keywords: Mass Type Breast Cancer, Mammography, Maximum Likelihood Estimation (MLE), Ranklets, SVM

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Authors: Diego Saqui, José H. Saito, José R. Campos, Lúcio A. de C. Jorge

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Hyperspectral images and remote sensing are important for many applications. A problem in the use of these images is the high volume of data to be processed, stored and transferred. Dimensionality reduction techniques can be used to reduce the volume of data. In this paper, an approach to band selection based on clustering algorithms is presented. This approach allows to reduce the volume of data. The proposed structure is based on Fuzzy C-Means (or K-Means) and NWHFC algorithms. New attributes in relation to other studies in the literature, such as kurtosis and low correlation, are also considered. A comparison of the results of the approach using the Fuzzy C-Means and K-Means with different attributes is performed. The use of both algorithms show similar good results but, particularly when used attributes variance and kurtosis in the clustering process, however applicable in hyperspectral images.

Keywords: Band selection, fuzzy C-means, K-means, hyperspectral image.

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Authors: Mina Alibeigi, Sattar Hashemi, Ali Hamzeh

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Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. In this paper, we propose a new unsupervised feature selection method which will remove redundant features from the original feature space by the use of probability density functions of various features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several datasets derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both classification accuracy and the number of selected features.

Keywords: Feature, Feature Selection, Filter, Probability Density Function

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Authors: García López

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The Government Departments of Health have the task of informing and educating citizens about public health issues. For this, they use channels like Twitter, key in the search for health information and the propagation of content. The tweets, important in the virality of the content, may contain emotions that influence the contagion and exchange of knowledge. The goal of this study is to perform an analysis of the emotional projection of health information shared on Twitter by official American accounts: the disease control account CDCgov, National Institutes of Health, NIH, the government agency HHSGov, and the professional organization PublicHealth. For this, we used Tone Analyzer, an International Business Machines Corporation (IBM) tool specialized in emotion detection in text, corresponding to the categorical model of emotion representation. For 15 days, all tweets from these accounts were analyzed with the emotional analysis tool in text. The results showed that their tweets contain an important emotional load, a determining factor in the success of their communications. This exposes that official accounts also use subjective language and contain emotions. The predominance of emotion joy over sadness and the strong presence of emotions in their tweets stimulate the virality of content, a key in the work of informing that government health departments have.

Keywords: Emotions in tweets emotion detection in text, health information on Twitter, American health official accounts, emotions on Twitter, emotions and content.

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Authors: Hossein Pazhoumand-Dar, Amir Mohsen Toliyat Abolhassani, Ehsan Saeedi

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Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.

Keywords: Blur Analysis, Fuzzy sets, Speed estimation.

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Authors: A. Biran, P. Sobhe Bidari, A. Almazroe V. Lakshminarayanan, K. Raahemifar

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Diabetic Retinopathy (DR) is a severe retinal disease which is caused by diabetes mellitus. It leads to blindness when it progress to proliferative level. Early indications of DR are the appearance of microaneurysms, hemorrhages and hard exudates. In this paper, an automatic algorithm for detection of DR has been proposed. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Also, Support Vector Machine (SVM) Classifier is used to classify retinal images to normal or abnormal cases including non-proliferative or proliferative DR. The proposed method has been tested on images selected from Structured Analysis of the Retinal (STARE) database using MATLAB code. The method is perfectly able to detect DR. The sensitivity specificity and accuracy of this approach are 90%, 87.5%, and 91.4% respectively.

Keywords: Diabetic retinopathy, fundus images, STARE, Gabor filter, SVM.

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Authors: Yue Zhao, Chao Li

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Camera calibration is an important step in 3D reconstruction. Camera calibration may be classified into two major types: traditional calibration and self-calibration. However, a calibration method in using a checkerboard is intermediate between traditional calibration and self-calibration. A self is proposed based on a square in this paper. Only a square in the planar template, the camera self-calibration can be completed through the single view. The proposed algorithm is that the virtual circle and straight line are established by a square on planar template, and circular points, vanishing points in straight lines and the relation between them are be used, in order to obtain the image of the absolute conic (IAC) and establish the camera intrinsic parameters. To make the calibration template is simpler, as compared with the Zhang Zhengyou-s method. Through real experiments and experiments, the experimental results show that this algorithm is feasible and available, and has a certain precision and robustness.

Keywords: Absolute conic, camera calibration, circle point, vanishing point.

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Authors: Sangram Keshori Mohapatra, Sidhartha Panda, Prasant Kumar Satpathy

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Power-system stability improvement by simultaneous tuning of power system stabilizer (PSS) and a Static Var Compensator (SVC) based damping controller is thoroughly investigated in this paper. Both local and remote signals with associated time delays are considered in the present study. The design problem of the proposed controller is formulated as an optimization problem, and differential evolution (DE) algorithm is employed to search for the optimal controller parameters. The performances of the proposed controllers are evaluated under different disturbances for both single-machine infinite bus power system and multi-machine power system. The performance of the proposed controllers with variations in the signal transmission delays has also been investigated. The proposed stabilizers are tested on a weakly connected power system subjected to different disturbances. Nonlinear simulation results are presented to show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and disturbances. Further, the proposed design approach is found to be robust and improves stability effectively even under small disturbance conditions.

Keywords: Differential Evolution Algorithm, Power System Stability, Power System Stabilizer, Static Var Compensator

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Authors: Mounir Sayadi, Farhat Fnaiech

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In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.

Keywords: Texture analysis, statistical features, adaptive filters, Chandrasekhar algorithm.

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Authors: Cuong Nguyen Khac, Ju H. Park, Ho-Youl Jung

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This paper proposes a new approach to perform the problem of real-time face detection. The proposed method combines primitive Haar-Like feature and variance value to construct a new feature, so-called Variance based Haar-Like feature. Face in image can be represented with a small quantity of features using this new feature. We used SVM instead of AdaBoost for training and classification. We made a database containing 5,000 face samples and 10,000 non-face samples extracted from real images for learning purposed. The 5,000 face samples contain many images which have many differences of light conditions. And experiments showed that face detection system using Variance based Haar-Like feature and SVM can be much more efficient than face detection system using primitive Haar-Like feature and AdaBoost. We tested our method on two Face databases and one Non-Face database. We have obtained 96.17% of correct detection rate on YaleB face database, which is higher 4.21% than that of using primitive Haar-Like feature and AdaBoost.

Keywords: AdaBoost, Haar-Like feature, SVM, variance, Variance based Haar-Like feature.

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Authors: Ali Ihtiyar, Serdar Durdyev, Syuhaida Ismail

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Marketing is an essential issue to the survival of any real estate company in Turkey. There are some factors which are constraining the achievements of the marketing and sales strategies in the Turkey real estate industry. This study aims to identify and prioritise the most significant constraints to marketing in real estate sector and new strategies based on those constraints. This study is based on survey method, where the respondents such as credit counsellors, real estate investors, consultants, academicians and marketing representatives in Turkey were asked to rank forty seven sub-factors according to their levels of impact. The results of Multiattribute analytical technique indicated that the main subcomponents having impact on marketing in real estate sector are interest rates, real estate credit availability, accessibility, company image and consumer real income, respectively. The identified constraints are expected to guide the marketing team in a sales-effective way.

Keywords: Marketing, marketing constraints, Real estate marketing, Turkey real estate sector

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Authors: Anastasia Rita Widiarti

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This paper compares Hilditch, Rosenfeld, Zhang- Suen, dan Nagendraprasad Wang Gupta (NWG) thinning algorithms for Javanese character image recognition. Thinning is an effective process when the focus in not on the size of the pattern, but rather on the relative position of the strokes in the pattern. The research analyzes the thinning of 60 Javanese characters. Time-wise, Zhang-Suen algorithm gives the best results with the average process time being 0.00455188 seconds. But if we look at the percentage of pixels that meet one-pixel thickness, Rosenfelt algorithm gives the best results, with a 99.98% success rate. From the number of pixels that are erased, NWG algorithm gives the best results with the average number of pixels erased being 84.12%. It can be concluded that the Hilditch algorithm performs least successfully compared to the other three algorithms.

Keywords: Hilditch algorithm, Nagendraprasad-Wang-Guptaalgorithm, Rosenfeld algorithm, Thinning, Zhang-suen algorithm

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Authors: Nikolaos Vassilas, Theocharis Tsenoglou, Djamchid Ghazanfarpour

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In this work, we explore the capability of the mean shift algorithm as a powerful preprocessing tool for improving the quality of spatial data, acquired from airborne scanners, from densely built urban areas. On one hand, high resolution image data corrupted by noise caused by lossy compression techniques are appropriately smoothed while at the same time preserving the optical edges and, on the other, low resolution LiDAR data in the form of normalized Digital Surface Map (nDSM) is upsampled through the joint mean shift algorithm. Experiments on both the edge-preserving smoothing and upsampling capabilities using synthetic RGB-z data show that the mean shift algorithm is superior to bilateral filtering as well as to other classical smoothing and upsampling algorithms. Application of the proposed methodology for 3D reconstruction of buildings of a pilot region of Athens, Greece results in a significant visual improvement of the 3D building block model.

Keywords: 3D buildings reconstruction, data fusion, data upsampling, mean shift.

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Authors: Giovanni Luca Masala

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A typical definition of the Computer Aided Diagnosis (CAD), found in literature, can be: A diagnosis made by a radiologist using the output of a computerized scheme for automated image analysis as a diagnostic aid. Often it is possible to find the expression Computer Aided Detection (CAD or CADe): this definition emphasizes the intent of CAD to support rather than substitute the human observer in the analysis of radiographic images. In this article we will illustrate the application of CAD systems and the aim of these definitions. Commercially available CAD systems use computerized algorithms for identifying suspicious regions of interest. In this paper are described the general CAD systems as an expert system constituted of the following components: segmentation / detection, feature extraction, and classification / decision making. As example, in this work is shown the realization of a Computer- Aided Detection system that is able to assist the radiologist in identifying types of mammary tumor lesions. Furthermore this prototype of station uses a GRID configuration to work on a large distributed database of digitized mammographic images.

Keywords: Computer Aided Detection, Computer Aided Diagnosis, mammography, GRID.

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Authors: Farzaneh Ahmadzadeh

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Quality control charts are very effective in detecting out of control signals but when a control chart signals an out of control condition of the process mean, searching for a special cause in the vicinity of the signal time would not always lead to prompt identification of the source(s) of the out of control condition as the change point in the process parameter(s) is usually different from the signal time. It is very important to manufacturer to determine at what point and which parameters in the past caused the signal. Early warning of process change would expedite the search for the special causes and enhance quality at lower cost. In this paper the quality variables under investigation are assumed to follow a multivariate normal distribution with known means and variance-covariance matrix and the process means after one step change remain at the new level until the special cause is being identified and removed, also it is supposed that only one variable could be changed at the same time. This research applies artificial neural network (ANN) to identify the time the change occurred and the parameter which caused the change or shift. The performance of the approach was assessed through a computer simulation experiment. The results show that neural network performs effectively and equally well for the whole shift magnitude which has been considered.

Keywords: Artificial neural network, change point estimation, monte carlo simulation, multivariate exponentially weighted movingaverage

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Authors: Sidhartha Panda, N. P. Padhy

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This paper investigates the application of Particle Swarm Optimization (PSO) technique for coordinated design of a Power System Stabilizer (PSS) and a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the power system stability. The design problem of PSS and TCSC-based controllers is formulated as a time domain based optimization problem. PSO algorithm is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. To compare the capability of PSS and TCSC-based controller, both are designed independently first and then in a coordinated manner for individual and coordinated application. The proposed controllers are tested on a weakly connected power system. The eigenvalue analysis and non-linear simulation results are presented to show the effectiveness of the coordinated design approach over individual design. The simulation results show that the proposed controllers are effective in damping low frequency oscillations resulting from various small disturbances like change in mechanical power input and reference voltage setting.

Keywords: Particle swarm optimization, Phillips-Heffron model, power system stability, PSS, TCSC.

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Authors: Ayman El Ansary, Mohamed Shalaby

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The current study describes a multi-objective optimization technique for positioning of houses in a residential neighborhood. The main task is the placement of residential houses in a favorable configuration satisfying a number of objectives. Solving the house layout problem is a challenging task. It requires an iterative approach to satisfy design requirements (e.g. energy efficiency, skyview, daylight, roads network, visual privacy, and clear access to favorite views). These design requirements vary from one project to another based on location and client preferences. In the Gulf region, the most important socio-cultural factor is the visual privacy in indoor space. Hence, most of the residential houses in this region are surrounded by high fences to provide privacy, which has a direct impact on other requirements (e.g. daylight and direction to favorite views). This investigation introduces a novel technique to optimally locate and orient residential buildings to satisfy a set of design requirements. The developed technique explores the search space for possible solutions. This study considers two dimensional house planning problems. However, it can be extended to solve three dimensional cases.

Keywords: Evolutionary optimization, Houses planning, Urban modeling, Daylight, Visual Privacy, Residential compounds.

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Authors: Leena N. Fukey, Surya S. Issac

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This review paper aims at understanding the importance of implementing sustainable green practices in the current hotel industry and the perception of the same from the point of view of the customers as well as the industry experts. Many hotels have benefited from green management such as enhanced reputation of the firm and more worth customers. For the business standing, it reduces business’s cost for posting advertisements and the clear hotel’s orientation shows hotels’ positive image which might increase employees’ recognition toward the business. Sustainability in business is the growth in lively processes which enable people to understand the potential to protect the Earth’s existent support systems. Well, looking to the future today’s green concerns will definitely become facet of more synchronized business environment, perhaps the concerns discussed in this study, may exchange a few words which hotels may consider in near future to widen awareness and improve business model.

Keywords: Environmental Protection, Green Hotel Concept, Hotel Industry, Sustainability.

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Authors: Jieh-Haur Chen, Pei-Fen Huang

Abstract:

This paper presents an integrated model that automatically measures the change of rivers, damage area of bridge surroundings, and change of vegetation. The proposed model is on the basis of a neurofuzzy mechanism enhanced by SOM optimization algorithm, and also includes three functions to deal with river imagery. High resolution imagery from FORMOSAT-2 satellite taken before and after the invasion period is adopted. By randomly selecting a bridge out of 129 destroyed bridges, the recognition results show that the average width has increased 66%. The ruined segment of the bridge is located exactly at the most scour region. The vegetation coverage has also reduced to nearly 90% of the original. The results yielded from the proposed model demonstrate a pinpoint accuracy rate at 99.94%. This study brings up a successful tool not only for large-scale damage assessment but for precise measurement to disasters.

Keywords: remote sensing image, damage assessment, typhoon disaster, bridge, ANN, fuzzy, SOM, optimization.

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Authors: Tianqiao Zhang, Ye Tian, Yanliang Yin, Yichao Tian, Suzhai Tian, Weige Sun, Ruoliang Tang

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Heavy biomechanical loads at workplaces may lead to high risks of work-related musculoskeletal disorders (WMSDs). However, there is a lack of investigations on the efficacy of the ergonomic interventions with theoretical frameworks. This study aimed to formulate an Omaha System based remote intervention program on the WMSDs among nurses by systematic literature review, interviews, expert consultation. After screening title and abstract, 11 articles out of the initial search results (i.e., n=1,418) were included, 12 nurses were interviewed, and 10 experts were consulted to review the initial intervention program. Modification to the draft included (1) supplementing traditional Chinese medicine practices, (2) adding the use of assistive patient handling equipment, (3) revising the on-line training method, (4) editing and proofreading the main text of the initial program, (5) adding quizzes and exercise scales, (6) it was determined that the associated coursework should be announced promptly with multiple follow-up reminders, and (7) removing bodyweight superman exercise, and peaceful/calm meditation. In the end, the final intervention program was developed.

Keywords: Omaha System, nurses, remote intervention, musculoskeletal disease.

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Authors: Juliana Panova

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Severe symptoms, such as dissociation, depersonalization, self-mutilation, suicidal ideations and gestures, are the main reasons for a person to be diagnosed with Borderline Personality Disorder (BPD) and admitted to an inpatient Psychiatric Hospital. However, these symptoms are also indicators of a severe traumatic history as indicated by the extensive research on the topic. Unfortunately patients with such clinical presentation often are treated repeatedly only for their symptomatic behavior, while the main cause for their suffering, the trauma itself, is usually left unaddressed therapeutically. All of the highly structured, replicable, and manualized treatments lack the recognition of the uniqueness of the person and fail to respect his/her rights to experience and react in an idiosyncratic manner. Thus the communicative and adaptive meaning of such symptomatic behavior is missed. Only its pathological side is recognized and subjected to correction and stigmatization, and the message that the person is damaged goods that needs fixing is conveyed once again. However, this time the message would be even more convincing for the victim, because it is sent by mental health providers, who have the credibility to make such a judgment. The result is a revolving door of very expensive hospitalizations for only a temporary and patchy fix. In this way the patients, once victims of abuse and hardship are left invalidated and thus their re-victimization is perpetuated in their search for understanding and help. Keywordsborderline personality disorder (BPD), complex PTSD, integrative treatment of trauma, re-victimization of trauma victims.

Keywords: borderline personality disorder (BPD), complex PTSD, integrative treatment of trauma, re-victimization of trauma victims.

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Authors: C.Kia, M.R.Arshad, A.H.Adom, P.A.Wilson

Abstract:

This paper presents recent work on the improvement of the robotics vision based control strategy for underwater pipeline tracking system. The study focuses on developing image processing algorithms and a fuzzy inference system for the analysis of the terrain. The main goal is to implement the supervisory fuzzy learning control technique to reduce the errors on navigation decision due to the pipeline occlusion problem. The system developed is capable of interpreting underwater images containing occluded pipeline, seabed and other unwanted noise. The algorithm proposed in previous work does not explore the cooperation between fuzzy controllers, knowledge and learnt data to improve the outputs for underwater pipeline tracking. Computer simulations and prototype simulations demonstrate the effectiveness of this approach. The system accuracy level has also been discussed.

Keywords: Fuzzy logic, Underwater target tracking, Autonomous underwater vehicles, Artificial intelligence, Simulations, Robot navigation, Vision system.

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