Search results for: logistic principal component analysis

Authors: Takaaki Ishibashi

Abstract:

Independent component analysis can estimate unknown source signals from their mixtures under the assumption that the source signals are statistically independent. However, in a real environment, the separation performance is often deteriorated because the number of the source signals is different from that of the sensors. In this paper, we propose an estimation method for the number of the sources based on the joint distribution of the observed signals under two-sensor configuration. From several simulation results, it is found that the number of the sources is coincident to that of peaks in the histogram of the distribution. The proposed method can estimate the number of the sources even if it is larger than that of the observed signals. The proposed methods have been verified by several experiments.

Keywords: blind source separation, independent component analysys, estimation for the number of the blind sources, voice activity detection, target extraction.

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Authors: Anil Kumar Tripathi, Ratneshwer

Abstract:

All the software engineering researches and best industry practices aim at providing software products with high degree of quality and functionality at low cost and less time. These requirements are addressed by the Component Based Software Engineering (CBSE) as well. CBSE, which deals with the software construction by components’ assembly, is a revolutionary extension of Software Engineering. CBSE must define and describe processes to assure timely completion of high quality software systems that are composed of a variety of pre built software components. Though these features provide distinct and visible benefits in software design and programming, they also raise some challenging problems. The aim of this work is to summarize the pertinent issues and considerations in CBSE to make an understanding in forms of concepts and observations that may lead to development of newer ways of dealing with the problems and challenges in CBSE.

Keywords: Software Component, Component Based Software Engineering, Software Process, Testing, Maintenance.

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Authors: Sebastian Bertsch

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In the past decades, the environment of production companies showed a permanent increase in dynamic and volatility in the form of demand fluctuations, new technologies or global crises. As a reaction to these new requirements, changeability of production systems came into attention. A changeable production system can adapt to these changes quickly and with little effort. Even though demand for changeable production exists for some time, the practical application is still insufficient.

To overcome this deficit, a three year research project at the Department of Production Systems and Logistics at the Leibniz University of Hanover/ Germany was initiated. As a result of this project, different concepts have been developed to design production changeable. An excerpt of the results will be presented in this paper. An eight step procedure will be presented to design the changeability of production logistics. This procedure has been applied at a German manufacturer of high demanding weighing machines. The developed procedure, their application in industry, as well as the major results of the application will be presented.

Keywords: Changeability, Change Drivers, Production Logistics.

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Authors: Michael Zimba

Abstract:

A duplicated image region may be subjected to a number of attacks such as noise addition, compression, reflection, rotation, and scaling with the intention of either merely mating it to its targeted neighborhood or preventing its detection. In this paper, we present an effective and robust method of detecting duplicated regions inclusive of those affected by the various attacks. In order to reduce the dimension of the image, the proposed algorithm firstly performs discrete wavelet transform, DWT, of a suspicious image. However, unlike most existing copy move image forgery (CMIF) detection algorithms operating in the DWT domain which extract only the low frequency subband of the DWT of the suspicious image thereby leaving valuable information in the other three subbands, the proposed algorithm simultaneously extracts features from all the four subbands. The extracted features are not only more accurate representation of image regions but also robust to additive noise, JPEG compression, and affine transformation. Furthermore, principal component analysis-eigenvalue decomposition, PCA-EVD, is applied to reduce the dimension of the features. The extracted features are then sorted using the more computationally efficient Radix Sort algorithm. Finally, same affine transformation selection, SATS, a duplication verification method, is applied to detect duplicated regions. The proposed algorithm is not only fast but also more robust to attacks compared to the related CMIF detection algorithms. The experimental results show high detection rates. 

Keywords: Affine Transformation, Discrete Wavelet Transform, Radix Sort, SATS.

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Authors: Nor Azuana Ramli, Mohd Tahir Ismail, Hooy Chee Wooi

Abstract:

Developing a stable early warning system (EWS) model that is capable to give an accurate prediction is a challenging task. This paper introduces k-nearest neighbour (k-NN) method which never been applied in predicting currency crisis before with the aim of increasing the prediction accuracy. The proposed k-NN performance depends on the choice of a distance that is used where in our analysis; we take the Euclidean distance and the Manhattan as a consideration. For the comparison, we employ three other methods which are logistic regression analysis (logit), back-propagation neural network (NN) and sequential minimal optimization (SMO). The analysis using datasets from 8 countries and 13 macro-economic indicators for each country shows that the proposed k-NN method with k = 4 and Manhattan distance performs better than the other methods.

Keywords: Currency crisis, k-nearest neighbour method, logit, neural network.

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Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

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Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: Cluster analysis, construction management, earned value, schedule.

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Authors: J. H. Lv, W. Z. Wang, S.W. Liu

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The major concern in the aviation industry is the flight safety. Although great effort has been put onto the development of material and system reliability, the failure cases of fatal accidents still occur nowadays. Due to the complexity of the aviation system, and the interaction among the failure components, the failure analysis of the related equipment is a little difficult. This study focuses on surveying the failure cases in aviation, which are extracted from failure analysis journals, including Engineering Failure Analysis and Case studies in Engineering Failure Analysis, in order to obtain the failure sensitive factors or failure sensitive parts. The analytical results show that, among the failure cases, fatigue failure is the largest in number of occurrence. The most failed components are the disk, blade, landing gear, bearing, and fastener. The frequently failed materials consist of steel, aluminum alloy, superalloy, and titanium alloy. Therefore, in order to assure the safety in aviation, more attention should be paid to the fatigue failures.

Keywords: Aviation industry, failure analysis, failure component, fatigue.

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Authors: Sachin Man Bajimaya, SangChul Park, Gi-Nam Wang

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The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.

Keywords: Artificial Neural Network (ANN), Indirect Extrusion, Finite Element Analysis, MES.

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Authors: Eakong Atiptamvaree, Twittie Senivongse

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Process-oriented software development is a new software development paradigm in which software design is modeled by a business process which is in turn translated into a process execution language for execution. The building blocks of this paradigm are software units that are composed together to work according to the flow of the business process. This new paradigm still exhibits the characteristic of the applications built with the traditional software component technology. This paper discusses an approach to apply a traditional technique for software component fabrication to the design of process-oriented software units, called process components. These process components result from decomposing a business process of a particular application domain into subprocesses, and these process components can be reused to design the business processes of other application domains. The decomposition considers five managerial goals, namely cost effectiveness, ease of assembly, customization, reusability, and maintainability. The paper presents how to design or decompose process components from a business process model and measure some technical features of the design that would affect the managerial goals. A comparison between the measurement values from different designs can tell which process component design is more appropriate for the managerial goals that have been set. The proposed approach can be applied in Web Services environment which accommodates process-oriented software development.

Keywords: Business Process Model, Managerial Goals, ProcessComponent.

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Authors: Hossein Anaraki-Ardakani, Tayebe Heidari-Rakati

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An environmentally benign protocol for the one-pot, three-component synthesis of Triazolo[1,2-a]indazole-1,3,8-trione derivatives by condensation of dimedone, urazole and aromatic aldehydes catalyzed by HClO4/SiO2 NPS as an ecofriendly catalyst with high catalytic activity and reusability at 100ºC under solventfree conditions is reported. The reaction proceeds to completion within 20-30 min in 77-86% yield.

Keywords: One-pot reaction, Dimedone, Triazoloindazole, Urazole.

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Authors: Toshihiko Takemura, Makoto Osajima, Masatoshi Kawano

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This paper includes a positive analysis to quantitatively grasp the relationship among vulnerability, information security incidents, and the countermeasures by using data based on a 2007 questionnaire survey for Japanese ISPs (Internet Service Providers). To grasp the relationships, logistic regression analysis is used. The results clarify that there are relationships between information security incidents and the countermeasures. Concretely, there is a positive relationship between information security incidents and the number of information security systems introduced as well as a negative relationship between information security incidents and information security education. It is also pointed out that (especially, local) ISPs do not execute efficient information security countermeasures/ investment concerned with systems, and it is suggested that they should positively execute information security education. In addition, to further heighten the information security level of Japanese telecommunication infrastructure, the necessity and importance of the government to implement policy to support the countermeasures of ISPs is insisted.

Keywords: Information security countermeasures, information security incidents, internet service providers, positive analysis

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Authors: A. Rakhmadi, M. S. M. Rahim, A. Bade, H. Haron, I. M. Amin

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In this study, a Loop Back Algorithm for component connected labeling for detecting objects in a digital image is presented. The approach is using loop back connected component labeling algorithm that helps the system to distinguish the object detected according to their label. Deferent than whole window scanning technique, this technique reduces the searching time for locating the object by focusing on the suspected object based on certain features defined. In this study, the approach was also implemented for a face detection system. Face detection system is becoming interesting research since there are many devices or systems that require detecting the face for certain purposes. The input can be from still image or videos, therefore the sub process of this system has to be simple, efficient and accurate to give a good result.

Keywords: Image processing, connected components labeling, face detection.

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Authors: M.K. Bhuyan, Aragala Jagan.

Abstract:

Current image-based individual human recognition methods, such as fingerprints, face, or iris biometric modalities generally require a cooperative subject, views from certain aspects, and physical contact or close proximity. These methods cannot reliably recognize non-cooperating individuals at a distance in the real world under changing environmental conditions. Gait, which concerns recognizing individuals by the way they walk, is a relatively new biometric without these disadvantages. The inherent gait characteristic of an individual makes it irreplaceable and useful in visual surveillance. In this paper, an efficient gait recognition system for human identification by extracting two features namely width vector of the binary silhouette and the MPEG-7-based region-based shape descriptors is proposed. In the proposed method, foreground objects i.e., human and other moving objects are extracted by estimating background information by a Gaussian Mixture Model (GMM) and subsequently, median filtering operation is performed for removing noises in the background subtracted image. A moving target classification algorithm is used to separate human being (i.e., pedestrian) from other foreground objects (viz., vehicles). Shape and boundary information is used in the moving target classification algorithm. Subsequently, width vector of the outer contour of binary silhouette and the MPEG-7 Angular Radial Transform coefficients are taken as the feature vector. Next, the Principal Component Analysis (PCA) is applied to the selected feature vector to reduce its dimensionality. These extracted feature vectors are used to train an Hidden Markov Model (HMM) for identification of some individuals. The proposed system is evaluated using some gait sequences and the experimental results show the efficacy of the proposed algorithm.

Keywords: Gait Recognition, Gaussian Mixture Model, PrincipalComponent Analysis, MPEG-7 Angular Radial Transform.

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Authors: Liming Zhang

Abstract:

In this paper, a new adaptive Fourier decomposition (AFD) based time-frequency speech analysis approach is proposed. Given the fact that the fundamental frequency of speech signals often undergo fluctuation, the classical short-time Fourier transform (STFT) based spectrogram analysis suffers from the difficulty of window size selection. AFD is a newly developed signal decomposition theory. It is designed to deal with time-varying non-stationary signals. Its outstanding characteristic is to provide instantaneous frequency for each decomposed component, so the time-frequency analysis becomes easier. Experiments are conducted based on the sample sentence in TIMIT Acoustic-Phonetic Continuous Speech Corpus. The results show that the AFD based time-frequency distribution outperforms the STFT based one.

Keywords: Adaptive fourier decomposition, instantaneous frequency, speech analysis, time-frequency distribution.

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Authors: Faisal AlShammari, Abdulmajid Addali

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Monitoring the conditions of rotating machinery, such as bearings, is important in order to improve the stability of work. Acoustic Emission (AE) and vibration analysis are some of the most accomplished techniques used for this purpose. Acoustic emission has the ability to detect the initial phase of component degradation. Moreover, it has been observed that vibration analysis is not as successful at low rotational speeds (below 100 rpm). This because the energy generated within this speed region is not detectable using conventional vibration. From this perspective, this paper has presented a brief review of using acoustic emission techniques for monitoring bearing conditions.

Keywords: Condition monitoring, stress wave analysis, low-speed bearings, bearing defect diagnosis.

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Authors: S. Umarani, D. Sharmila

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A Distributed Denial of Service (DDoS) attack is a major threat to cyber security. It originates from the network layer or the application layer of compromised/attacker systems which are connected to the network. The impact of this attack ranges from the simple inconvenience to use a particular service to causing major failures at the targeted server. When there is heavy traffic flow to a target server, it is necessary to classify the legitimate access and attacks. In this paper, a novel method is proposed to detect DDoS attacks from the traces of traffic flow. An access matrix is created from the traces. As the access matrix is multi dimensional, Principle Component Analysis (PCA) is used to reduce the attributes used for detection. Two classifiers Naive Bayes and K-Nearest neighborhood are used to classify the traffic as normal or abnormal. The performance of the classifier with PCA selected attributes and actual attributes of access matrix is compared by the detection rate and False Positive Rate (FPR).

Keywords: Distributed Denial of Service (DDoS) attack, Application layer DDoS, DDoS Detection, K- Nearest neighborhood classifier, Naive Bayes Classifier, Principle Component Analysis.

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Authors: Humberto N. Teixeira, Isabel S. Lopes, Sérgio D. Sousa

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This article proposes a new methodology to be used by SMEs (Small and Medium enterprises) to characterize their performance in quality, highlighting weaknesses and area for improvement. The methodology aims to identify the principal causes of quality problems and help to prioritize improvement initiatives. This is a self-assessment methodology that intends to be easy to implement by companies with low maturity level in quality. The methodology is organized in six different steps which includes gathering information about predetermined processes and subprocesses of quality management, defined based on the well-known Juran-s trilogy for quality management (Quality planning, quality control and quality improvement) and, predetermined results categories, defined based on quality concept. A set of tools for data collecting and analysis, such as interviews, flowcharts, process analysis diagrams and Failure Mode and effects Analysis (FMEA) are used. The article also presents the conclusions obtained in the application of the methodology in two cases studies.

Keywords: Continuous improvement, Diagnosis, Quality Management, Self-assessment, SMEs

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Authors: Mikel Alonso López, María Francisca Blasco López, Víctor Molero Ayala

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The present study explains the somatic marker theory of Antonio Damasio, which indicates that when making a decision, the stored or possible future scenarios (future memory) images allow people to feel for a moment what would happen when they make a choice, and how this is emotionally marked. This process can be conscious or unconscious. The development of new Neuromarketing techniques such as functional magnetic resonance imaging (fMRI), carries a greater understanding of how the brain functions and consumer behavior. In the results observed in different studies using fMRI, the evidence suggests that the somatic marker and future memories influence the decision-making process, adding a positive or negative emotional component to the options. This would mean that all decisions would involve a present emotional component, with a rational cost-benefit analysis that can be performed later.

Keywords: Emotions, decision making, somatic marker, consumer´s brain.

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Authors: S. Sarumathi, N. Shanthi, P. Ranjetha

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Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand.

Keywords: Cluster Ensemble, Consensus Function, CSPA, Diversity, HGPA, MCLA.

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Authors: G.H. Farrahi, S.A. Faghidian, D.J. Smith

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A thin layer on the component surface can be found with high tensile residual stresses, due to turning operations, which can dangerously affect the fatigue performance of the component. In this paper an analytical approach is presented to reconstruct the residual stress field from a limited incomplete set of measurements. Airy stress function is used as the primary unknown to directly solve the equilibrium equations and satisfying the boundary conditions. In this new method there exists the flexibility to impose the physical conditions that govern the behavior of residual stress to achieve a meaningful complete stress field. The analysis is also coupled to a least squares approximation and a regularization method to provide stability of the inverse problem. The power of this new method is then demonstrated by analyzing some experimental measurements and achieving a good agreement between the model prediction and the results obtained from residual stress measurement.

Keywords: Residual stress, Limited measurements, Inverse problems, Turning process.

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Authors: Xinjian Kou, Linlin Li, Yongju Zhou, Jimian Song

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We introduce the redundancy matrix that expresses clearly the geometrical/topological configuration of the structure. With the matrix, the redundancy of the structure is resolved into redundant components and assigned to each member or rigid joint. The values of the diagonal elements in the matrix indicates the importance of the corresponding members or rigid joints, and the geometrically correlations can be shown with the non-diagonal elements. If a member or rigid joint failures, reassignment of the redundant components can be calculated with the recursive method given in the paper. By combining the indexes of reliability and redundancy components, we define an index concerning the structural robustness. To further explain the properties of the redundancy matrix, we cited several examples of statically indeterminate structures, including two trusses and a rigid frame. With the examples, some simple results and the properties of the matrix are discussed. The examples also illustrate that the redundancy matrix and the relevant concepts are valuable in structural safety analysis.

Keywords: Structural robustness, structural reliability, redundancy component, redundancy matrix.

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Authors: Waing Waing Kay Khine Oo, Shwe Wut Hmon Aye, Kay Thi Lwin

Abstract:

Chromite is one of the principal ore of chromium in which the metal exists as a complex oxide (FeO.Cr2O3).The prepared chromite can be widely used as refractory in high temperature applications. This study describes the use of local chromite ore as refractory material. To study the feasibility of local chromite, chemical analysis and refractoriness are firstly measured. To produce chromite refractory brick, it is pressed under a press of 400 tons, dried and fired at 1580°C for fifty two hours. Then, the standard properties such as cold crushing strength, apparent porosity, apparent specific gravity, bulk density and water absorption that the chromite brick should possess were measured. According to the results obtained, the brick made by local chromite ore was suitable for use as refractory brick.

Keywords: chemical analysis, chromite ore, chromite refractory brick, refractoriness.

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Authors: Haiping Ding, Rongchu Zhu, Zhenxia Song

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Local irregular topography has a great impact on earthquake ground motion. For scarp topography, using numerical simulation method, the influence extent and scope of the scarp terrain on scarp's upside and downside ground motion are discussed in case of different vertical incident SV waves. The results show that: (1) The amplification factor of scarp's upside region is greater than that of the free surface, while the amplification factor of scarp's downside part is less than that of the free surface; (2) When the slope angle increases, for x component, amplification factors of the scarp upside also increase, while the downside part decrease with it. For z component, both of the upside and downside amplification factors will increase; (3) When the slope angle changes, the influence scope of scarp's downside part is almost unchanged, but for the upside part, it slightly becomes greater with the increase of slope angle; (4) Due to the existence of the scarp, the z component ground motion appears at the surface. Its amplification factor increases for larger slope angle, and the peaks of the surface responses are related with incident waves. However, the input wave has little effects on the x component amplification factors.

Keywords: Scarp topography, ground motion, amplification factor, vertical incident wave.

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Authors: Sachin Kumar, Vasilis Sotiris, Michael Pecht

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With increasing complexity in electronic systems there is a need for system level anomaly detection and fault isolation. Anomaly detection based on vector similarity to a training set is used in this paper through two approaches, one the preserves the original information, Mahalanobis Distance (MD), and the other that compresses the data into its principal components, Projection Pursuit Analysis. These methods have been used to detect deviations in system performance from normal operation and for critical parameter isolation in multivariate environments. The study evaluates the detection capability of each approach on a set of test data with known faults against a baseline set of data representative of such “healthy" systems.

Keywords: Mahalanobis distance, Principle components, Projection pursuit, Health assessment, Anomaly.

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial neural network, competitive dynamics, logistic regression, text classification, text mining.

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Authors: A. Borji, M. Hamidi

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In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefaces

Keywords: Persian font recognition, support vector machine, gabor filter.

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Authors: Rahmi Kafadar, Levent Genc

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In present study, it was aimed to determine potential agricultural lands (PALs) in Gokceada (Imroz) Island of Canakkale province, Turkey. Seven-band Landsat 8 OLI images acquired on July 12 and August 13, 2013, and their 14-band combination image were used to identify current Land Use Land Cover (LULC) status. Principal Component Analysis (PCA) was applied to three Landsat datasets in order to reduce the correlation between the bands. A total of six Original and PCA images were classified using supervised classification method to obtain the LULC maps including 6 main classes (“Forest”, “Agriculture”, “Water Surface”, “Residential Area- Bare Soil”, “Reforestation” and “Other”). Accuracy assessment was performed by checking the accuracy of 120 randomized points for each LULC maps. The best overall accuracy and Kappa statistic values (90.83%, 0.8791% respectively) were found for PCA images which were generated from 14-bands combined images called 3- B/JA. Digital Elevation Model (DEM) with 15 m spatial resolution (ASTER) was used to consider topographical characteristics. Soil properties were obtained by digitizing 1:25000 scaled soil maps of Rural Services Directorate General. Potential Agricultural Lands (PALs) were determined using Geographic information Systems (GIS). Procedure was applied considering that “Other” class of LULC map may be used for agricultural purposes in the future properties. Overlaying analysis was conducted using Slope (S), Land Use Capability Class (LUCC), Other Soil Properties (OSP) and Land Use Capability Sub-Class (SUBC) properties. A total of 901.62 ha areas within “Other” class (15798.2 ha) of LULC map were determined as PALs. These lands were ranked as “Very Suitable”, “Suitable”, “Moderate Suitable” and “Low Suitable”. It was determined that the 8.03 ha were classified as “Very Suitable” while 18.59 ha as suitable and 11.44 ha as “Moderate Suitable” for PALs. In addition, 756.56 ha were found to be “Low Suitable”. The results obtained from this preliminary study can serve as basis for further studies.

Keywords: Digital Elevation Model (DEM), Geographic Information Systems (GIS), LANDSAT 8 OLI-TIRS, Land Use Land Cover (LULC).

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Authors: Ponco Iswanto, Mochammad Chasani, Muhammad Hanafi, Iqmal Tahir, Eva Vaulina YD, Harjono, Lestari Solikhati, Winkanda S. Putra, Yayuk Yuliantini

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Quantitative Structure-Activity Relationship (QSAR) approach for discovering novel more active Calanone derivative as anti-leukemia compound has been conducted. There are 6 experimental activities of Calanone compounds against leukemia cell L1210 that are used as material of the research. Calculation of theoretical predictors (independent variables) was performed by AM1 semiempirical method. The QSAR equation is determined by Principle Component Regression (PCR) analysis, with Log IC50 as dependent variable and the independent variables are atomic net charges, dipole moment (μ), and coefficient partition of noctanol/ water (Log P). Three novel Calanone derivatives that obtained by this research have higher activity against leukemia cell L1210 than pure Calanone.

Keywords: AM1 semiempirical calculation, Calanone, Principle Component Regression, QSAR approach.

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Authors: T. Kritsana, P. Sawitri, P. Teeratas

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This article aims to study the effect of pressure on rocket motor case by Finite Element Method simulation to select optimal material in rocket motor manufacturing process. In this study, cylindrical tubes with outside diameter of 122 mm and thickness of 3 mm are used for simulation. Defined rocket motor case materials are AISI4130, AISI1026, AISI1045, AL2024 and AL7075. Internal pressure used for the simulation is 22 MPa.

The result from Finite Element Method shows that at a pressure of 22 MPa rocket motor case produced by AISI4130, AISI1045 and AL7075 can be used. A comparison of the result between AISI4130, AISI1045 and AL7075 shows that AISI4130 has minimum principal stress and confirm the results of Finite Element Method by the used of calculation method found that, the results from Finite Element Method has good reliability.

Keywords: Rocket motor case, Finite Element Method, principal Stress.

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Authors: Krasimir Ivanov, Dimitar Dimitrov, Boyan Boyanov

Abstract:

The paper relates to a catalyst, comprising copperchromium spinel, coated on carrier γ-Al2O3. The effect of preparation conditions on the active component composition and activity behavior of the catalysts is discussed. It was found that the activity of carbon monoxide, DME, formaldehyde and methanol oxidation reaches a maximum at an active component content of 20 – 30 wt. %. Temperature calcination at 500oC seems to be optimal for the γ– alumina supported CuO-Cr2O3 catalysts for CO, DME, formaldehyde and methanol oxidation. A three months industrial experiment was carried out to elucidate the changes in the catalyst composition during industrial exploitation of the catalyst and the main reasons for catalyst deactivation. It was concluded that the CuO–Cr2O3/γ–alumina supported catalysts have enhanced activity toward CO, DME, formaldehyde and methanol oxidation and that these catalysts are suitable for industrial application. The main reason for catalyst deactivation seems to be the deposition of iron and molybdenum, coming from the main reactor, on the active component surface.

Keywords: catalyst deactivation, CuO-Cr2O3 catalysts, deep oxidation.

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