Search results for: winding probability.

Authors: M. Fotuhi-Firuzabad, A. Salehi Dobakhshari

Abstract:

This paper presents a reliability-based approach to select appropriate wind turbine types for a wind farm considering site-specific wind speed patterns. An actual wind farm in the northern region of Iran with the wind speed registration of one year is studied in this paper. An analytic approach based on total probability theorem is utilized in this paper to model the probabilistic behavior of both turbines- availability and wind speed. Well-known probabilistic reliability indices such as loss of load expectation (LOLE), expected energy not supplied (EENS) and incremental peak load carrying capability (IPLCC) for wind power integration in the Roy Billinton Test System (RBTS) are examined. The most appropriate turbine type achieving the highest reliability level is chosen for the studied wind farm.

Keywords: Wind Turbine Generator, Wind Farm, Power System Reliability, Wind Turbine Type Selection

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Authors: Hui-Min Ting, Tsair-Fwu Lee, Ming-Yuan Cho, Pei-Ju Chao, Chun-Ming Chang, Long-Chang Chen, Fu-Min Fang

Abstract:

To evaluate the ability to predict xerostomia after radiotherapy, we constructed and compared neural network and logistic regression models. In this study, 61 patients who completed a questionnaire about their quality of life (QoL) before and after a full course of radiation therapy were included. Based on this questionnaire, some statistical data about the condition of the patients’ salivary glands were obtained, and these subjects were included as the inputs of the neural network and logistic regression models in order to predict the probability of xerostomia. Seven variables were then selected from the statistical data according to Cramer’s V and point-biserial correlation values and were trained by each model to obtain the respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65 for SSE, and 13.7% and 19.0% for MAPE, respectively. These parameters demonstrate that both neural network and logistic regression methods are effective for predicting conditions of parotid glands.

Keywords: NPC, ANN, logistic regression, xerostomia.

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Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

Abstract:

In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.

Keywords: Image segmentation, objects tracking, Parzen window, singular value decomposition, target recognition.

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Authors: V. Fuis, T. Navrat

Abstract:

The paper deals with calculation of the parameters of ceramic material from a set of destruction tests of ceramic heads of total hip joint endoprosthesis. The standard way of calculation of the material parameters consists in carrying out a set of 3 or 4 point bending tests of specimens cut out from parts of the ceramic material to be analysed. In case of ceramic heads, it is not possible to cut out specimens of required dimensions because the heads are too small (if the cut out specimens were smaller than the normalised ones, the material parameters derived from them would exhibit higher strength values than those which the given ceramic material really has). On that score, a special testing jig was made, in which 40 heads were destructed. From the measured values of circumferential strains of the head-s external spherical surface under destruction, the state of stress in the head under destruction was established using the final elements method (FEM). From the values obtained, the sought for parameters of the ceramic material were calculated using Weibull-s weakest-link theory.

Keywords: Hip joint endoprosthesis, ceramic head, FEM analysis, Weibull's weakest-link theory, failure probability, material parameters

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Authors: N. A. Ibrahim, A. Kudus, I. Daud, M. R. Abu Bakar

Abstract:

Competing risks survival data that comprises of more than one type of event has been used in many applications, and one of these is in clinical study (e.g. in breast cancer study). The decision tree method can be extended to competing risks survival data by modifying the split function so as to accommodate two or more risks which might be dependent on each other. Recently, researchers have constructed some decision trees for recurrent survival time data using frailty and marginal modelling. We further extended the method for the case of competing risks. In this paper, we developed the decision tree method for competing risks survival time data based on proportional hazards for subdistribution of competing risks. In particular, we grow a tree by using deviance statistic. The application of breast cancer data is presented. Finally, to investigate the performance of the proposed method, simulation studies on identification of true group of observations were executed.

Keywords: Competing risks, Decision tree, Simulation, Subdistribution Proportional Hazard.

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Authors: Danilo López, Edwin Rivas, Leyla López

Abstract:

This paper presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks. The algorithm dynamically uses a neural network construction technique using the geometric pyramid topology and trains a Multilayer Perceptron Neural Networks (MLPNN) based on the historical arrival of an SU to estimate future applications. This will allow efficiently managing the information in the BS, since it precedes the arrival of the SUs in the stage of selection of the best channel in CRN. As a result, the software application determines the probability of arrival at a future time point and calculates the performance metrics to measure the effectiveness of the predictions made.

Keywords: Cognitive radio, MLPNN, base station, prediction, best effort, real time.

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Authors: N. Arulanand, K. Premalatha

Abstract:

Bloom filter is a probabilistic and memory efficient data structure designed to answer rapidly whether an element is present in a set. It tells that the element is definitely not in the set but its presence is with certain probability. The trade-off to use Bloom filter is a certain configurable risk of false positives. The odds of a false positive can be made very low if the number of hash function is sufficiently large. For spam detection, weight is attached to each set of elements. The spam weight for a word is a measure used to rate the e-mail. Each word is assigned to a Bloom filter based on its weight. The proposed work introduces an enhanced concept in Bloom filter called Bin Bloom Filter (BBF). The performance of BBF over conventional Bloom filter is evaluated under various optimization techniques. Real time data set and synthetic data sets are used for experimental analysis and the results are demonstrated for bin sizes 4, 5, 6 and 7. Finally analyzing the results, it is found that the BBF which uses heuristic techniques performs better than the traditional Bloom filter in spam detection.

Keywords: Cuckoo search algorithm, levy’s flight, metaheuristic, optimal weight.

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Authors: C. Ramsauer, J. Karandikar, D. Leitner, T. Schmitz, F. Bleicher

Abstract:

Machining instability, or chatter, can impose an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the TU Wien, Vienna, Austria. The recorded data from a milling test cut were used to classify the cut as stable or unstable based on a frequency analysis. The test cut result was used in a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculated the probability of stability as a function of axial depth of cut and spindle speed based on the test result and recommended parameters for the next test cut. The iterative process between two transatlantic locations was repeated until convergence to a stable optimal process parameter set was achieved.

Keywords: Bayesian learning, instrumented tool holder, machining stability, optimization strategy.

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Authors: Julio Molleda, Daniel F. García, Juan C. Granda, Francisco J. Suárez

Abstract:

One of the main trouble in a steel strip manufacturing line is the breakage of whatever weld carried out between steel coils, that are used to produce the continuous strip to be processed. A weld breakage results in a several hours stop of the manufacturing line. In this process the damages caused by the breakage must be repaired. After the reparation and in order to go on with the production it will be necessary a restarting process of the line. For minimizing this problem, a human operator must inspect visually and manually each weld in order to avoid its breakage during the manufacturing process. The work presented in this paper is based on the Bayesian decision theory and it presents an approach to detect, on real-time, steel strip defective welds. This approach is based on quantifying the tradeoffs between various classification decisions using probability and the costs that accompany such decisions.

Keywords: Classification, Pattern Recognition, ProbabilisticReasoning, Statistical Data Analysis.

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Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

Abstract:

This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: Landslide, limit analysis, ANN, soil properties.

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Authors: Jihad S. Daba, J. P. Dubois

Abstract:

Understanding the statistics of non-isotropic scattering multipath channels that fade randomly with respect to time, frequency, and space in a mobile environment is very crucial for the accurate detection of received signals in wireless and cellular communication systems. In this paper, we derive stochastic models for the probability density function (PDF) of the shift in the carrier frequency caused by the Doppler Effect on the received illuminating signal in the presence of a dominant line of sight. Our derivation is based on a generalized Clarke’s and a two-wave partially developed scattering models, where the statistical distribution of the frequency shift is shown to be consistent with the power spectral density of the Doppler shifted signal.

Keywords: Doppler shift, filtered Poisson process, generalized Clark’s model, non-isotropic scattering, partially developed scattering, Rician distribution.

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Authors: Ouadoudi Zytoune, Youssef Fakhri, Driss Aboutajdine

Abstract:

This paper focuses on reducing the power consumption of wireless sensor networks. Therefore, a communication protocol named LEACH (Low-Energy Adaptive Clustering Hierarchy) is modified. We extend LEACHs stochastic cluster-head selection algorithm by a modifying the probability of each node to become cluster-head based on its required energy to transmit to the sink. We present an efficient energy aware routing algorithm for the wireless sensor networks. Our contribution consists in rotation selection of clusterheads considering the remoteness of the nodes to the sink, and then, the network nodes residual energy. This choice allows a best distribution of the transmission energy in the network. The cluster-heads selection algorithm is completely decentralized. Simulation results show that the energy is significantly reduced compared with the previous clustering based routing algorithm for the sensor networks.

Keywords: Wireless Sensor Networks, Energy efficiency, WirelessCommunications, Clustering-based algorithm.

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Authors: HoYoung Son, DongHoon Shin, WooYoung Jung

Abstract:

This study presented the seismic fragility framework of concrete weir structure subjected to strong seismic ground motions and in particular, concrete aging condition of the weir structure was taken into account in this study. In order to understand the influence of concrete aging on the weir structure, by using probabilistic risk assessment, the analytical seismic fragility of the weir structure was derived for pre- and post-deterioration of concrete. The performance of concrete weir structure after five years was assumed for the concrete aging or deterioration, and according to after five years’ condition, the elastic modulus was simply reduced about one–tenth compared with initial condition of weir structures. A 2D nonlinear finite element analysis was performed considering the deterioration of concrete in weir structures using ABAQUS platform, a commercial structural analysis program. Simplified concrete degradation was resulted in the increase of almost 45% of the probability of failure at Limit State 3, in comparison to initial construction stage, by analyzing the seismic fragility.

Keywords: Weir, FEM, concrete, fragility, aging

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Authors: M. Azadbakht, M. E. Shayan, H. Jafari, E. Ghajarjazi, A. Kiapei

Abstract:

Transmission shafts are affected by various forces, for example, during acceleration or sudden breaks, bending during transportation, vertical forces that lead to cuts. One of the main failures in combines is breaking shaft which repairmen refer it. Structural resistance of canal against torque is very important in the beginning of the movement. For analyzing stress, a typical sample from a type of combine was selected, called JD955 combine. Long shaft in this combine was analyzed with finite element method by Ansys13 generic package under static load. Conducted analysis showed that there is a maximum stress in contact surfaces of indentations and also in place of changing diameter. Safety factor value is low in parts of the shaft and this increases the probability of failure at these points. To improve the conditions with the least cost and an approach of product improvement, using alternative alloy is important.

Keywords: John Deere, Ansys, Shaft, Stress, Grain Combine harvester, Finite element, Failure.

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Authors: H. Iranmanesh, M. Madadi

Abstract:

Work Breakdown Structure (WBS) is one of the most vital planning processes of the project management since it is considered to be the fundamental of other processes like scheduling, controlling, assigning responsibilities, etc. In fact WBS or activity list is the heart of a project and omission of a simple task can lead to an irrecoverable result. There are some tools in order to generate a project WBS. One of the most powerful tools is mind mapping which is the basis of this article. Mind map is a method for thinking together and helps a project manager to stimulate the mind of project team members to generate project WBS. Here we try to generate a WBS of a sample project involving with the building construction using the aid of mind map and the artificial intelligence (AI) programming language. Since mind map structure can not represent data in a computerized way, we convert it to a semantic network which can be used by the computer and then extract the final WBS from the semantic network by the prolog programming language. This method will result a comprehensive WBS and decrease the probability of omitting project tasks.

Keywords: Expert System, Mind map, Semantic network, Work breakdown structure,

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Authors: Kailash C. Madan

Abstract:

We study four models of a three server queueing system with Bernoulli schedule optional server vacations. Customers arriving at the system one by one in a Poisson process are provided identical exponential service by three parallel servers according to a first-come, first served queue discipline. In model A, all three servers may be allowed a vacation at one time, in Model B at the most two of the three servers may be allowed a vacation at one time, in model C at the most one server is allowed a vacation, and in model D no server is allowed a vacation. We study steady the state behavior of the four models and obtain steady state probability generating functions for the queue size at a random point of time for all states of the system. In model D, a known result for a three server queueing system without server vacations is derived.

Keywords: A three server queue, Bernoulli schedule server vacations, queue size distribution at a random epoch, steady state.

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Authors: Hatem A. Khater, A. Baith Mohamed, Sara M. Kamel

Abstract:

Software Development Risks Identification (SDRI), using Fault Tree Analysis (FTA), is a proposed technique to identify not only the risk factors but also the causes of the appearance of the risk factors in software development life cycle. The method is based on analyzing the probable causes of software development failures before they become problems and adversely affect a project. It uses Fault tree analysis (FTA) to determine the probability of a particular system level failures that are defined by A Taxonomy for Sources of Software Development Risk to deduce failure analysis in which an undesired state of a system by using Boolean logic to combine a series of lower-level events. The major purpose of this paper is to use the probabilistic calculations of Fault Tree Analysis approach to determine all possible causes that lead to software development risk occurrence

Keywords: Software Development Risks Identification (SDRI), Fault Tree Analysis (FTA), Taxonomy for Software Development Risks (TSDR), Probabilistic Risk Assessment (PRA).

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Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

Abstract:

Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: Data quality, feature selection, probability distribution, string classification, string length.

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Authors: N. Aldea, V. Rednic, F. Matei, Tiandou Hu, M. Neumann

Abstract:

The nickel and gold nanoclusters as supported catalysts were analyzed by XAS, XRD and XPS in order to determine their local, global and electronic structure. The present study has pointed out a strong deformation of the local structure of the metal, due to its interaction with oxide supports. The average particle size, the mean squares of the microstrain, the particle size distribution and microstrain functions of the supported Ni and Au catalysts were determined by XRD method using Generalized Fermi Function for the X-ray line profiles approximation. Based on EXAFS analysis we consider that the local structure of the investigated systems is strongly distorted concerning the atomic number pairs. Metal-support interaction is confirmed by the shape changes of the probability densities of electron transitions: Ni K edge (1s → continuum and 2p), Au LIII-edge (2p3/2 → continuum, 6s, 6d5/2 and 6d3/2). XPS investigations confirm the metal-support interaction at their interface.

Keywords: local and global structure, metal-support interaction, supported metal catalysts, synchrotron radiation, X-ray absorptionspectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy.

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Authors: Isa Nakhai Kamal Abadi, Esmaeel Saberi, Ehsan Mirjafari

Abstract:

Every organization is continually subject to new damages and threats which can be resulted from their operations or their goal accomplishment. Methods of providing the security of space and applied tools have been widely changed with increasing application and development of information technology (IT). From this viewpoint, information security management systems were evolved to construct and prevent reiterating the experienced methods. In general, the correct response in information security management systems requires correct decision making, which in turn requires the comprehensive effort of managers and everyone involved in each plan or decision making. Obviously, all aspects of work or decision are not defined in all decision making conditions; therefore, the possible or certain risks should be considered when making decisions. This is the subject of risk management and it can influence the decisions. Investigation of different approaches in the field of risk management demonstrates their progress from quantitative to qualitative methods with a process approach.

Keywords: Risk Management, Information Security, Methodology, Probability.

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Authors: Jeerapa Sappakitkamjorn, Sa-aat Niwitpong

Abstract:

In many practical applications in various areas, such as engineering, science and social science, it is known that there exist bounds on the values of unknown parameters. For example, values of some measurements for controlling machines in an industrial process, weight or height of subjects, blood pressures of patients and retirement ages of public servants. When interval estimation is considered in a situation where the parameter to be estimated is bounded, it has been argued that the classical Neyman procedure for setting confidence intervals is unsatisfactory. This is due to the fact that the information regarding the restriction is simply ignored. It is, therefore, of significant interest to construct confidence intervals for the parameters that include the additional information on parameter values being bounded to enhance the accuracy of the interval estimation. Therefore in this paper, we propose a new confidence interval for the coefficient of variance where the population mean and standard deviation are bounded. The proposed interval is evaluated in terms of coverage probability and expected length via Monte Carlo simulation.  

Keywords: Bounded parameters, coefficient of variation, confidence interval, Monte Carlo simulation.

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Authors: E. Golden Julie, S. Tamil Selvi, Y. Harold Robinson

Abstract:

Many Wireless Sensor Network (WSN) applications necessitate secure multicast services for the purpose of broadcasting delay sensitive data like video files and live telecast at fixed time-slot. This work provides a novel method to deal with end-to-end delay and drop rate of packets. Opportunistic Routing chooses a link based on the maximum probability of packet delivery ratio. Null Key Generation helps in authenticating packets to the receiver. Markov Decision Process based Adaptive Scheduling algorithm determines the time slot for packet transmission. Both theoretical analysis and simulation results show that the proposed protocol ensures better performance in terms of packet delivery ratio, average end-to-end delay and normalized routing overhead.

Keywords: Delay-sensitive data, Markovian Decision Process based Adaptive Scheduling, Opportunistic Routing, Digital Signature authentication.

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Authors: Xiaohua Liu, Juan F. Beltran, Nishant Mohanchandra, Godfried T. Toussaint

Abstract:

Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here three methods used to speed up the computation of the SVM classifiers are compared experimentally using a musical genre classification problem. The simplest method pre-selects a random sample of the data before the application of the SVM algorithm. Two additional methods use proximity graphs to pre-select data that are near the decision boundary. One uses k-Nearest Neighbor graphs and the other Relative Neighborhood Graphs to accomplish the task.

Keywords: Machine learning, data mining, support vector machines, proximity graphs, relative-neighborhood graphs, k-nearestneighbor graphs, random sampling, training data condensation.

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Authors: Tom Snir, Eitan Rubin

Abstract:

Mendelian Disease Genes represent a collection of single points of failure for the various systems they constitute. Such genes have been shown, on average, to encode longer proteins than 'non-disease' proteins. Existing models suggest that this results from the increased likeli-hood of longer genes undergoing mutations. Here, we show that in saturated mutagenesis experiments performed on model organisms, where the likelihood of each gene mutating is one, a similar relationship between length and the probability of a gene being lethal was observed. We thus suggest an extended model demonstrating that the likelihood of a mutated gene to produce a severe phenotype is length-dependent. Using the occurrence of conserved domains, we bring evidence that this dependency results from a correlation between protein length and the number of functions it performs. We propose that protein length thus serves as a proxy for protein cardinality in different networks required for the organism's survival and well-being. We use this example to argue that the collection of Mendelian Disease Genes can, and should, be used to study the rules governing systems vulnerability in living organisms.

Keywords: Systems Biology, Protein Length

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Authors: Antoni Ivanov, Nikolay Dandanov, Nicole Christoff, Vladimir Poulkov

Abstract:

Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Keywords: Cognitive radio, dynamic spectrum access, GNU Radio, spectrum sensing.

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Authors: Mohamed Adnan Landolsi, Ali F. Almutairi

Abstract:

The paper addresses the problem of line-of-sight (LOS) vs. non-line-of-sight (NLOS) propagation link identification in ultra-wideband (UWB) wireless networks, which is necessary for improving the accuracy of radiolocation and positioning applications. A LOS/NLOS likelihood hypothesis testing approach is applied based on exploiting distinctive statistical features of the channel impulse response (CIR) using parameters related to the “skewness” of the CIR and its root mean square (RMS) delay spread. A log-normal fit is presented for the probability densities of the CIR parameters. Simulation results show that different environments (residential, office, outdoor, etc.) have measurable differences in their CIR parameters’ statistics, which is then exploited in determining the nature of the propagation channels. Correct LOS/NLOS channel identification rates exceeding 90% are shown to be achievable for most types of environments. Additional improvement is also obtained by combining both CIR skewness and RMS delay statistics.

Keywords: Ultra-wideband, propagation, line-of-sight, non-line-of-sight, identification.

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Authors: G. Mattia, A. Di Leo, L. Principato

Abstract:

The purchase impulsiveness is preceded by a lack of self-control: consequently, it is legitimate to believe that a consumer with a low level of self-control can result in a higher probability of cognitive dissonance. Moreover, the process of purchase is influenced by the pre-existing affective state in a considerable way. With reference to on-line purchases, digital behavior cannot be merely ascribed to the rational sphere, given the speed and ease of transactions and the hedonistic dimension of purchases. To our knowledge, this research is among the first cases of verification of the effect of moderation exerted by the positive affective state in the on-line impulse purchase of products with a high expressive value such as a smartphone on the occurrence of cognitive dissonance. To this aim, a moderation analysis was conducted on a sample of 212 impulsive millennials buyers. Three scales were adopted to measure the constructs of interest: IBTS for impulsivity, PANAS for the affective state, Sweeney for cognitive dissonance. The analysis revealed that positive affective state does not affect the onset of cognitive dissonance.

Keywords: Cognitive dissonance, impulsive buying, online shopping, online consumer behavior.

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Authors: Parvinder S. Sandhu, Satish Kumar Dhiman, Anmol Goyal

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Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. This paper introduces Genetic Algorithm based software fault prediction models with Object-Oriented metrics. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the classification of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results shows that Genetic algorithm approach can be used for finding the fault proneness in object oriented software components.

Keywords: Genetic Algorithms, Software Fault, Classification, Object Oriented Metrics.

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Authors: Wook-Won Kim, Sung-Yul Kim, Jin-O Kim

Abstract:

The fault current levels through the electric devices have a significant impact on failure probability. New fault current results in exceeding the rated capacity of circuit breaker and switching equipments and changes operation characteristic of overcurrent relay. In order to solve these problems, SFCL (Superconducting Fault Current Limiter) has rising as one of new alternatives so as to improve these problems. A fault current reduction differs depending on installed location. Therefore, a location of SFCL is very important. Also, SFCL decreases the fault current, and it prevents surrounding protective devices to be exposed to fault current, it then will bring a change of reliability. In this paper, we propose method which determines the optimal location when SFCL is installed in power system. In addition, the reliability about the power system which SFCL was installed is evaluated. The efficiency and effectiveness of this method are also shown by numerical examples and the reliability indices are evaluated in this study at each load points. These results show a reliability change of a system when SFCL was installed.

Keywords: Superconducting Fault Current Limiter, OptimalLocation, Reliability

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Authors: Leonardo Juan Ramírez López, Javier Oswaldo Rodriguez Velasquez

Abstract:

We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results.

Keywords: Entropy, mathematical, prediction, cardiac, holter, attractor.

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