Search results for: Data driven nand gate

Authors: Arun Kumar Datta, Manisha Dubey, Shailendra Jain

Abstract:

Electric machines are driven nowadays by static system popularly known as soft starter. This paper describes a thyristor based static frequency converter (SFC) to run a large synchronous machine installed at a short circuit test laboratory. Normally a synchronous machine requires prime mover or some other driving mechanism to run. This machine doesn’t need a prime mover as it operates in dual mode. In the beginning SFC starts this machine as a motor to achieve the full speed. Thereafter whenever required it can be converted to generator mode. This paper begins with the various starting methodology of synchronous machine. Detailed of SFC with different operational modes have been analyzed. Shaft voltage is a very common phenomenon for the machines with static drives. Various causes of shaft voltages in perspective with this machine are the main attraction of this paper.

Keywords: Capacitive coupling, electric discharge machining, inductive coupling, Shaft voltage, static frequency converter.

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Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: Human machine interface, industrial internet of things, internet of things, optical character recognition, video analytic.

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Authors: J. Menčík, B. Culek, Jr., L. Beran, J. Mareš

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Large metal and concrete structures suffer by various kinds of deterioration, and accurate prediction of the remaining life is important. This paper informs about two methods for its assessment. One method, suitable for steel bridges and other constructions exposed to fatigue, monitors the loads and damage accumulation using information systems for the operation and the finite element model of the construction. In addition to the operation load, the dead weight of the construction and thermal stresses can be included into the model. The second method is suitable for concrete bridges and other structures, which suffer by carbonatation and other degradation processes, driven by diffusion. The diffusion constant, important for the prediction of future development, can be determined from the depth-profile of pH, obtained by pH measurement at various depths. Comparison with measurements on real objects illustrates the suitability of both methods.

Keywords: Bridges, carbonatation, concrete, diagnostics, fatigue, life prediction, monitoring, railway, simulation, structures.

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Authors: Saeed Hosseini, Ali Reza Tahavvor

Abstract:

Sound processing is one the subjects that newly attracts a lot of researchers. It is efficient and usually less expensive than other methods. In this paper the flow generated sound is used to estimate the flow speed of free flows. Many sound samples are gathered. After analyzing the data, a parameter named wave power is chosen. For all samples the wave power is calculated and averaged for each flow speed. A curve is fitted to the averaged data and a correlation between the wave power and flow speed is found. Test data are used to validate the method and errors for all test data were under 10 percent. The speed of the flow can be estimated by calculating the wave power of the flow generated sound and using the proposed correlation.

Keywords: Flow generated sound, sound processing, speed, wave power.

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Authors: Neveen I. Ghali, Nahed El-Dessouki, Mervat A. N., Lamiaa Bakrawi

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In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we compare between (EPSO) clustering algorithm which depends on exponential variation for the inertia weight and particle swarm optimization (PSO) clustering algorithm which depends on linear inertia weight. This comparison is evaluated on five data sets. The experimental results show that EPSO clustering algorithm increases the possibility to find the optimal positions as it decrease the number of failure. Also show that (EPSO) clustering algorithm has a smaller quantization error than (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm more accurate than (PSO) clustering algorithm.

Keywords: Particle swarm optimization, data clustering, exponential PSO.

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Authors: Yong-Gyu Lee, Jin-Hee Park, Gilwon Yoon

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In this study, we introduced a communication system where human body was used as medium through which data were transferred. Multiple biosignal sensing units were attached to a subject and wireless personal area network was formed. Data of the sensing units were shared among them. We used wideband pulse communication that was simple, low-power consuming and high data rated. Each unit functioned as independent communication device or node. A method of channel search and communication among the modes was developed. A protocol of carrier sense multiple access/collision detect was implemented in order to avoid data collision or interferences. Biosignal sensing units should be located at different locations due to the nature of biosignal origin. Our research provided a flexibility of collecting data without using electrical wires. More non-constrained measurement was accomplished which was more suitable for u-Health monitoring.

Keywords: Human body communication, wideband pulse communication, personal area network, biosignal.

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Authors: Jaheon Gu, Wooyong Chung, Mijung Koo, Seonbok Lee, Gyoutae Park, Sangguk Ahn, Hiesik Kim, Jungil Park

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In this paper, we present a portable ammonia gas detector for performing the gas safety management efficiently. The display of the detector is separated from its body. The display module is received the data measured from the detector using ZigBee. The detector has a rechargeable li-ion battery which can be use for 11~12 hours, and a Bluetooth module for sending the data to the PC or the smart devices. The data are sent to the server and can access using the web browser or mobile application. The range of the detection concentration is 0~100ppm.

Keywords: Ammonia, detector, gas safety, portable.

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Authors: Omid F. Valilai, Mahmoud Houshmand

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Nowadays companies strive to survive in a competitive global environment. To speed up product development/modifications, it is suggested to adopt a collaborative product development approach. However, despite the advantages of new IT improvements still many CAx systems work separately and locally. Collaborative design and manufacture requires a product information model that supports related CAx product data models. To solve this problem many solutions are proposed, which the most successful one is adopting the STEP standard as a product data model to develop a collaborative CAx platform. However, the improvement of the STEP-s Application Protocols (APs) over the time, huge number of STEP AP-s and cc-s, the high costs of implementation, costly process for conversion of older CAx software files to the STEP neutral file format; and lack of STEP knowledge, that usually slows down the implementation of the STEP standard in collaborative data exchange, management and integration should be considered. In this paper the requirements for a successful collaborative CAx system is discussed. The STEP standard capability for product data integration and its shortcomings as well as the dominant platforms for supporting CAx collaboration management and product data integration are reviewed. Finally a platform named LAYMOD to fulfil the requirements of CAx collaborative environment and integrating the product data is proposed. The platform is a layered platform to enable global collaboration among different CAx software packages/developers. It also adopts the STEP modular architecture and the XML data structures to enable collaboration between CAx software packages as well as overcoming the STEP standard limitations. The architecture and procedures of LAYMOD platform to manage collaboration and avoid contradicts in product data integration are introduced.

Keywords: CAx, Collaboration management, STEP applicationmodules, STEP standard, XML data structures

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Authors: In-Chul Jung, Young S. Kwon

Abstract:

Knowing about the customer behavior in a grocery has been a long-standing issue in the retailing industry. The advent of RFID has made it easier to collect moving data for an individual shopper's behavior. Most of the previous studies used the traditional statistical clustering technique to find the major characteristics of customer behavior, especially shopping path. However, in using the clustering technique, due to various spatial constraints in the store, standard clustering methods are not feasible because moving data such as the shopping path should be adjusted in advance of the analysis, which is time-consuming and causes data distortion. To alleviate this problem, we propose a new approach to spatial pattern clustering based on the longest common subsequence. Experimental results using real data obtained from a grocery confirm the good performance of the proposed method in finding the hot spot, dead spot and major path patterns of customer movements.

Keywords: customer path, shopping behavior, exploratoryanalysis, LCS, RFID

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Authors: Hamed.Vezvaei, Sepideh.Ordibeheshti, Mehdi.Ardjmand

Abstract:

Gasoline Octane Number is the standard measure of the anti-knock properties of a motor in platforming processes, that is one of the important unit operations for oil refineries and can be determined with online measurement or use CFR (Cooperative Fuel Research) engines. Online measurements of the Octane number can be done using direct octane number analyzers, that it is too expensive, so we have to find feasible analyzer, like ANFIS estimators. ANFIS is the systems that neural network incorporated in fuzzy systems, using data automatically by learning algorithms of NNs. ANFIS constructs an input-output mapping based both on human knowledge and on generated input-output data pairs. In this research, 31 industrial data sets are used (21 data for training and the rest of the data used for generalization). Results show that, according to this simulation, hybrid method training algorithm in ANFIS has good agreements between industrial data and simulated results.

Keywords: Adaptive Neuro-Fuzzy Inference Systems, GasolineOctane Number, Soft-sensor, Catalytic Naphtha Reforming

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Authors: Payal Abichandani, Rishi Prakash, Paras Nath Barwal, B. K. Murthy

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Information generated from various computerization processes is a potential rich source of knowledge for its designated community. To pass this information from generation to generation without modifying the meaning is a challenging activity. To preserve and archive the data for future generations it’s very essential to prove the authenticity of the data. It can be achieved by extracting the metadata from the data which can prove the authenticity and create trust on the archived data. Subsequent challenge is the technology obsolescence. Metadata extraction and standardization can be effectively used to resolve and tackle this problem. Metadata can be categorized at two levels i.e. Technical and Domain level broadly. Technical metadata will provide the information that can be used to understand and interpret the data record, but only this level of metadata isn’t sufficient to create trustworthiness. We have developed a tool which will extract and standardize the technical as well as domain level metadata. This paper is about the different features of the tool and how we have developed this.  

Keywords: Digital Preservation, Metadata, OAIS, PDI, XML.

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Authors: T. Niknam, M. Nayeripour, B.Bahmani Firouzi

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Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.

Keywords: Ant Colony Optimization (ACO), Data clustering, Hybrid evolutionary optimization algorithm, K-means clustering, Particle Swarm Optimization (PSO).

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Authors: Syamsul Syafiq, Maslina Daud, Hafizah Hasan, Ahmad Zairi, Shazil Imri, Ezaini Akmar, Norbazilah Rahim

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Over recent years, web development has changed significantly. Driven largely by the rise of trends like mobiles, the world of development is rapidly evolving. The rise of the Internet makes web applications crucial nowadays. The web application has been an interface for a company and one of the ways they present their portfolio to the client. On the other hand, the web has become part of the file management system which takes over the role of paper. Due to high demand in web applications, developers are required to develop a web application that are cost-effective, secure and well coded. A framework has been proposed to develop an application rather than using library style development. The framework is helping the developer in creating the structure of a web automatically. This paper will compare the advantages and disadvantages of web development using framework against library-style development. This comparison is based on a previous research paper focusing on two main indicators, which are the impact to management and impact to the developer.

Keywords: Framework, library Style development, web application development, traditional web, static web, dynamic web.

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Authors: Madiha Tariq, Hena Rabbani

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Recently, cavity-optomechanics becomes an extensive research field that has manipulated the mechanical effects of light for coupling of the optical field with other physical objects specifically with regards to dynamical localization. We investigate the dynamical localization (both in momentum and position space) for a vibrational mirror in a Fabry-Pérot cavity driven by a single mode optical field and a transverse probe field. The weak probe field phenomenon results in classical chaos in phase space and spatio temporal dynamics in position |ψ(x)²| and momentum space |ψ(p)²| versus time show quantum localization in both momentum and position space. Also, we discuss the parametric dependencies of dynamical localization for a designated set of parameters to be experimentally feasible. Our work opens an avenue to manipulate the other optical phenomena and applicability of proposed work can be prolonged to turn-able laser sources in the future.

Keywords: Dynamical localization, cavity optomechanics, hamiltonian chaos, probe field.

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Authors: M. Anandhavalli Gauthaman

Abstract:

DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data.

Keywords: DNA microarray, gene expression, association rule mining.

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Authors: Sema Kurtuluş, Kemal Kurtuluş

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The objective of this paper is to review and assess the methodological issues and problems in marketing research, data and knowledge mining in Turkey. As a summary, academic marketing research publications in Turkey have significant problems. The most vital problem seems to be related with modeling. Most of the publications had major weaknesses in modeling. There were also, serious problems regarding measurement and scaling, sampling and analyses. Analyses myopia seems to be the most important problem for young academia in Turkey. Another very important finding is the lack of publications on data and knowledge mining in the academic world.

Keywords: Marketing research, data mining, knowledge mining, research modeling, analyses.

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Authors: Swapnil Singhal, Santosh Kumar Singh

Abstract:

The emerging Cognitive Radio is combo of both the technologies i.e. Radio dynamics and software technology. It involve wireless system with efficient coding, designing, and making them artificial intelligent to take the decision according to the surrounding environment and adopt themselves accordingly, so as to deliver the best QoS. This is the breakthrough from fixed hardware and fixed utilization of the spectrum. This software-defined approach of research is centralized at user-definition and application driven model, various software method are used for the optimization of the wireless communication. This paper focused on the Spectrum allocation technique using genetic algorithm GA to evolve radio, represented by chromosomes. The chromosomes gene represents the adjustable parameters in given radio and by using GA, evolving over the generations, the optimized set of parameters are evolved, as per the requirement of user and availability of the spectrum, in our prototype the gene consist of 6 different parameters, and the best set of parameters are evolved according to the application need and availability of the spectrum holes and thus maintaining best QoS for user, simultaneously maintaining licensed user rights. The analyzing tool Matlab is used for the performance of the prototype.

Keywords: ASDR, Cognitive Radio, QoS, Spectrum.

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Authors: İsmet Öztürk, İbrahim Soğukpınar

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With the fast progression of data exchange in electronic way, information security is becoming more important in data storage and transmission. Because of widely using images in industrial process, it is important to protect the confidential image data from unauthorized access. In this paper, we analyzed current image encryption algorithms and compression is added for two of them (Mirror-like image encryption and Visual Cryptography). Implementations of these two algorithms have been realized for experimental purposes. The results of analysis are given in this paper.

Keywords: image encryption, image cryptosystem, security, transmission

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Authors: Nermin Ozgulbas, Ali Serhan Koyuncugil

Abstract:

One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.

Keywords: Early Warning Systems, Data Mining, Financial Risk, SMEs.

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Authors: Naeem Muhammad, Yves Vandewoude, Yolande Berbers, Robert Feldt

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The Requirements Abstraction Model (RAM) helps in managing abstraction in requirements by organizing them at four levels (product, feature, function and component). The RAM is adaptable and can be tailored to meet the needs of the various organizations. Because software requirements are an important source of information for developing high-level tests, organizations willing to adopt the RAM model need to know the suitability of the RAM requirements for developing high-level tests. To investigate this suitability, test cases from twenty randomly selected requirements were developed, analyzed and graded. Requirements were selected from the requirements document of a Course Management System, a web based software system that supports teachers and students in performing course related tasks. This paper describes the results of the requirements document analysis. The results show that requirements at lower levels in the RAM are suitable for developing executable tests whereas it is hard to develop from requirements at higher levels.

Keywords: Market-driven requirements engineering, requirements abstraction model, requirements abstraction, system testing.

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Authors: Taras Agryzkov, Almudena Nolasco-Cirugeda, Jos´e L. Oliver, Leticia Serrano-Estrada, Leandro Tortosa, Jos´e F. Vicent

Abstract:

This paper aims to represent the commercial activity of a city taking as source data the social network Foursquare. The city of Murcia is selected as case study, and the location-based social network Foursquare is the main source of information. After carrying out a reorganisation of the user-generated data extracted from Foursquare, it is possible to graphically display on a map the various city spaces and venues especially those related to commercial, food and entertainment sector businesses. The obtained visualisation provides information about activity patterns in the city of Murcia according to the people‘s interests and preferences and, moreover, interesting facts about certain characteristics of the town itself.

Keywords: Social networks, Foursquare, spatial analysis, data visualization, geocomputation.

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Authors: Zainab Almukhtar, Adel Merabet

Abstract:

In this paper, a method for maximum power point tracking of a photovoltaic energy conversion system is presented. This method is based on using the difference between the power from the solar panel and an estimated power value to control the DC-DC converter of the photovoltaic system. The difference is continuously compared with a preset error permitted value. If the power difference is more than the error, the estimated power is multiplied by a factor and the operation is repeated until the difference is less or equal to the threshold error. The difference in power will be used to trigger a DC-DC boost converter in order to raise the voltage to where the maximum power point is achieved. The proposed method was experimentally verified through a PV energy conversion system driven by the OPAL-RT real time controller. The method was tested on varying radiation conditions and load requirements, and the Photovoltaic Panel was operated at its maximum power in different conditions of irradiation.

Keywords: Control system, power error, solar panel, MPPT.

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Authors: Chatchai Pesee

Abstract:

This paper examines long-range dependence or longmemory of financial time series on the exchange rate data by the fractional Brownian motion (fBm). The principle of spectral density function in Section 2 is used to find the range of Hurst parameter (H) of the fBm. If 0< H <1/2, then it has a short-range dependence (SRD). It simulates long-memory or long-range dependence (LRD) if 1/2< H <1. The curve of exchange rate data is fBm because of the specific appearance of the Hurst parameter (H). Furthermore, some of the definitions of the fBm, long-range dependence and selfsimilarity are reviewed in Section II as well. Our results indicate that there exists a long-memory or a long-range dependence (LRD) for the exchange rate data in section III. Long-range dependence of the exchange rate data and estimation of the Hurst parameter (H) are discussed in Section IV, while a conclusion is discussed in Section V.

Keywords: Fractional Brownian motion, long-rangedependence, memory, short-range dependence.

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

Abstract:

Leo Breimans Random Forests (RF) is a recent development in tree based classifiers and quickly proven to be one of the most important algorithms in the machine learning literature. It has shown robust and improved results of classifications on standard data sets. Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques to the random forests. We experiment the working of the ensembles of random forests on the standard data sets available in UCI data sets. We compare the original random forest algorithm with their ensemble counterparts and discuss the results.

Keywords: Random Forests [RF], ensembles, UCI.

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Authors: Monika Chuchro

Abstract:

This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain subgroups of time series data with normal distribution from the inflow into wastewater treatment plant data, composed of several groups differing by mean value. Two simple algorithms, K-mean and EM, were chosen as a clustering method. The Rand index was used to measure the similarity. After simple meta-clustering, a regression model was performed for each subgroups. The final model was a sum of the subgroups models. The quality of the obtained model was compared with the regression model made using the same explanatory variables, but with no clustering of data. Results were compared using determination coefficient (R2), measure of prediction accuracy- mean absolute percentage error (MAPE) and comparison on a linear chart. Preliminary results allow us to foresee the potential of the presented technique.

Keywords: Clustering, Data analysis, Data mining, Predictive models.

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Authors: S. Noor, M. W. Siddiqi

Abstract:

This study examines causal link between energy use and economic growth for five South Asian countries over period 1971-2006. Panel cointegration, ECM and FMOLS are applied for short and long run estimates. In short run unidirectional causality from per capita GDP to per capita energy consumption is found, but not vice versa. In long run one percent increase in per capita energy consumption tend to decrease 0.13 percent per capita GDP. i.e. Energy use discourage economic growth. This short and long run relationship indicate energy shortage crisis in South Asia due to increased energy use coupled with insufficient energy supply. Beside this long run estimated coefficient of error term suggest that short term adjustment to equilibrium are driven by adjustment back to long run equilibrium. Moreover, per capita energy consumption is responsive to adjustment back to equilibrium and it takes 59 years approximately. It specifies long run feedback between both variables.

Keywords: Energy consumption, Income, Panel co-integration, Causality.

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Authors: N C Santhosh Kumar, N K Kishore

Abstract:

Using mini modules of Tmotes, it is possible to automate a small personal area network. This idea can be extended to large networks too by implementing multi-hop routing. Linking the various Tmotes using Programming languages like Nesc, Java and having transmitter and receiver sections, a network can be monitored. It is foreseen that, depending on the application, a long range at a low data transfer rate or average throughput may be an acceptable trade-off. To reduce the overall costs involved, an optimum number of Tmotes to be used under various conditions (Indoor/Outdoor) is to be deduced. By analyzing the data rates or throughputs at various locations of Tmotes, it is possible to deduce an optimal number of Tmotes for a specific network. This paper deals with the determination of optimum distances to reduce the cost and increase the reliability of the entire sensor network with Wireless Local Loop (WLL) capability.

Keywords: Average throughput, data rate, multi-hop routing, optimum data transfer, throughput, Tmotes, wireless local loop.

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Authors: Supunmali Ahangama, Danny Chiang Choon Poo

Abstract:

Health analytics (HA) is used in healthcare systems for effective decision making, management and planning of healthcare and related activities. However, user resistances, unique position of medical data content and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. Success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose a HA process model with features from rational unified process (RUP) model and agile methodology.

Keywords: Agile methodology, health analytics, unified process model, UML.

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Authors: Mohamed T. Sorour, Mohamed A. Abdellatif, Ahmed A. Ramadan, Ahmed A. Abo-Ismail

Abstract:

This paper describes the development of an autonomous robot for painting the interior walls of buildings. The robot consists of a painting arm with an end effector roller that scans the walls vertically and a mobile platform to give horizontal feed to paint the whole area of the wall. The painting arm has a planar twolink mechanism with two joints. Joints are driven from a stepping motor through a ball screw-nut mechanism. Four ultrasonic sensors are attached to the mobile platform and used to maintain a certain distance from the facing wall and to avoid collision with side walls. When settled on adjusted distance from the wall, the controller starts the painting process autonomously. Simplicity, relatively low weight and short painting time were considered in our design. Different modules constituting the robot have been separately tested then integrated. Experiments have shown successfulness of the robot in its intended tasks.

Keywords: Automated roller painting, Construction robots, Mobile robots, service robots, two link planar manipulator

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Authors: S. Pattanapairoj, D. Chetchotsak

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This paper proposed classification models that would be used as a proxy for hard disk drive (HDD) functional test equitant which required approximately more than two weeks to perform the HDD status classification in either “Pass" or “Fail". These models were constructed by using committee network which consisted of a number of single neural networks. This paper also included the method to solve the problem of sparseness data in failed part, which was called “enforce learning method". Our results reveal that the constructed classification models with the proposed method could perform well in the sparse data conditions and thus the models, which used a few seconds for HDD classification, could be used to substitute the HDD functional tests.

Keywords: Sparse data, Classifications, Committee network

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