Search results for: Intrusion Detection Information Exchange

Authors: Sana Ktata, Kaïs Ouni, Noureddine Ellouze

Abstract:

Electrocardiogram (ECG) data compression algorithm is needed that will reduce the amount of data to be transmitted, stored and analyzed, but without losing the clinical information content. A wavelet ECG data codec based on the Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm has achieved notable success in still image coding. We modified the algorithm for the one-dimensional (1-D) case and applied it to compression of ECG data. By this compression method, small percent root mean square difference (PRD) and high compression ratio with low implementation complexity are achieved. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. Compression ratios of up to 48:1 for ECG signals lead to acceptable results for visual inspection.

Keywords: Discrete Wavelet Transform, ECG compression, SPIHT.

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Authors: Joachim Kimmerle, Johannes Moskaliuk, Ulrike Cress

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The development of Internet technology in recent years has led to a more active role of users in creating Web content. This has significant effects both on individual learning and collaborative knowledge building. This paper will present an integrative framework model to describe and explain learning and knowledge building with shared digital artifacts on the basis of Luhmann-s systems theory and Piaget-s model of equilibration. In this model, knowledge progress is based on cognitive conflicts resulting from incongruities between an individual-s prior knowledge and the information which is contained in a digital artifact. Empirical support for the model will be provided by 1) applying it descriptively to texts from Wikipedia, 2) examining knowledge-building processes using a social network analysis, and 3) presenting a survey of a series of experimental laboratory studies.

Keywords: Individual learning, collaborative knowledge building, systems theory, equilibration.

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Authors: Jyotsna Singh, Parul Garg, Alok Nath De

Abstract:

In this paper, we present a non-blind technique of adding the watermark to the Fourier spectral components of audio signal in a way such that the modified amplitude does not exceed the maximum amplitude spread (MAS). This MAS is due to individual Discrete fourier transform (DFT) coefficients in that particular frame, which is derived from the Energy Spreading function given by Schroeder. Using this technique one can store double the information within a given frame length i.e. overriding the watermark on the host of equal length with least perceptual distortion. The watermark is uniformly floating on the DFT components of original signal. This helps in detecting any intentional manipulations done on the watermarked audio. Also, the scheme is found robust to various signal processing attacks like presence of multiple watermarks, Additive white gaussian noise (AWGN) and mp3 compression.

Keywords: Discrete Fourier Transform, Spreading Function, Watermark, Pseudo Noise Sequence, Spectral Masking Effect

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Authors: S. P. Kozaitis, R. L. Kriner

Abstract:

The information revealed by derivatives can help to better characterize digital near-end crosstalk signatures with the ultimate goal of identifying the specific aggressor signal. Unfortunately, derivatives tend to be very sensitive to even low levels of noise. In this work we approximated the derivatives of both quiet and noisy digital signals using a wavelet-based technique. The results are presented for Gaussian digital edges, IBIS Model digital edges, and digital edges in oscilloscope data captured from an actual printed circuit board. Tradeoffs between accuracy and noise immunity are presented. The results show that the wavelet technique can produce first derivative approximations that are accurate to within 5% or better, even under noisy conditions. The wavelet technique can be used to calculate the derivative of a digital signal edge when conventional methods fail.

Keywords: digital signals, electronics, IBIS model, printedcircuit board, wavelets

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Authors: Fei Hao, Hai Wang

Abstract:

One of research issues in social network analysis is to evaluate the position/importance of users in social networks. As the information diffusion in social network is evolving, it seems difficult to evaluate the importance of users using traditional approaches. In this paper, we propose an evaluation approach for user importance with fractal view in social networks. In this approach, the global importance (Fractal Importance) and the local importance (Topological Importance) of nodes are considered. The basic idea is that the bigger the product of fractal importance and topological importance of a node is, the more important of the node is. We devise the algorithm called TFRank corresponding to the proposed approach. Finally, we evaluate TFRank by experiments. Experimental results demonstrate our TFRank has the high correlations with PageRank algorithm and potential ranking algorithm, and it shows the effectiveness and advantages of our approach.

Keywords: TFRank, Fractal Importance, Topological Importance, Social Network

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Authors: Chantana Insra

Abstract:

The research objective focuses on creating a prototype media of the tactile texture of muscles for educational institutes to help visually impaired students learn massage extra learning materials further than the ordinary curriculum. This media is designed as an extra learning material. The population in this study was 30 blinded students between 4th - 6th grades who were able to read Braille language. The research was conducted during the second semester in 2012 at The Bangkok School for the Blind. The method in choosing the population in the study was purposive sampling. The methodology of the research includes collecting data related to visually impaired people, the production of the tactile texture media, human anatomy and Thai traditional massage from literature reviews and field studies. This information was used for analyzing and designing 14 tactile texture pictures presented to experts to evaluate and test the media.

Keywords: Blind, Tactile Texture, Muscle.

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Authors: Che Z. Zulkifli, Nursyahida M. Noor, Siti N. Semunab, Shafawati A. Malek

Abstract:

Wireless sensors, also known as wireless sensor nodes, have been making a significant impact on human daily life. The Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two complementary technologies; hence, an integrated implementation of these technologies expands the overall functionality in obtaining long-range and real-time information on the location and properties of objects and people. An approach for integrating ZigBee and RFID networks is proposed in this paper, to create an energy-efficient network improved by the benefits of combining ZigBee and RFID architecture. Furthermore, the compatibility and requirements of the ZigBee device and communication links in the typical RFID system which is presented with the real world experiment on the capabilities of the proposed RFID system.

Keywords: Mesh network, RFID, wireless sensor network, zigbee.

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Authors: Philip Doganis, Eleni Aggelogiannaki, Haralambos Sarimveis

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Model Predictive Control has been previously applied to supply chain problems with promising results; however hitherto proposed systems possessed no information on future demand. A forecasting methodology will surely promote the efficiency of control actions by providing insight on the future. A complete supply chain management framework that is based on Model Predictive Control (MPC) and Time Series Forecasting will be presented in this paper. The proposed framework will be tested on industrial data in order to assess the efficiency of the method and the impact of forecast accuracy on overall control performance of the supply chain. To this end, forecasting methodologies with different characteristics will be implemented on test data to generate forecasts that will serve as input to the Model Predictive Control module.

Keywords: Forecasting, Model predictive control, production planning.

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Authors: Ahmad R. Naghsh-Nilchi, Latifeh Pourmohammadbagher

Abstract:

Both image steganography and image encryption have advantages and disadvantages. Steganograhy allows us to hide a desired image containing confidential information in a covered or host image while image encryption is decomposing the desired image to a non-readable, non-comprehended manner. The encryption methods are usually much more robust than the steganographic ones. However, they have a high visibility and would provoke the attackers easily since it usually is obvious from an encrypted image that something is hidden! The combination of steganography and encryption will cover both of their weaknesses and therefore, it increases the security. In this paper an image encryption method based on sinc-convolution along with using an encryption key of 128 bit length is introduced. Then, the encrypted image is covered by a host image using a modified version of JSteg steganography algorithm. This method could be applied to almost all image formats including TIF, BMP, GIF and JPEG. The experiment results show that our method is able to hide a desired image with high security and low visibility.

Keywords: Sinc Approximation, Image Encryption, Sincconvolution, Image Steganography, JSTEG.

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Authors: Karandeep Singh, Baibing Li

Abstract:

Traffic density, an indicator of traffic conditions, is one of the most critical characteristics to Intelligent Transport Systems (ITS). This paper investigates recursive traffic density estimation using the information provided from inductive loop detectors. On the basis of the phenomenological relationship between speed and density, the existing studies incorporate a state space model and update the density estimate using vehicular speed observations via the extended Kalman filter, where an approximation is made because of the linearization of the nonlinear observation equation. In practice, this may lead to substantial estimation errors. This paper incorporates a suitable transformation to deal with the nonlinear observation equation so that the approximation is avoided when using Kalman filter to estimate the traffic density. A numerical study is conducted. It is shown that the developed method outperforms the existing methods for traffic density estimation.

Keywords: Density estimation, Kalman filter, speed-densityrelationship, Traffic surveillance.

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Authors: L. B. Ni, N. S. Bt Rohadi, H. Bt Alfana, A. S. Bin Ali Hassan, J. Bin Karim, C. Bt Rasin

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This paper discusses the shared effort of teaching history in K-12 schools, community colleges, four-year colleges and universities to develop students' understanding of the history and habits of thought history. This study presents and discusses the problems of K-12 schools in colleges and universities, and the establishment of secondary school principals. This study also shows that the changing nature of practice can define new trends and affect the history professional in the classroom. There are many problems that historians and teachers of college faculty share in the history of high school teachers. History teachers can and should do better to get students in the classroom. History provides valuable insights into the information and embedded solid-state analysis models that are conflicting on the planet and are quickly changing exceptionally valuable. The survey results can reflect the history teaching in Malaysia.

Keywords: History issue, history teaching, school-university collaboration, history profession.

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Authors: Suwit Sadsunk

Abstract:

The study is about the designed and decorative fabric printing that derived from the Five-color porcelain (Benjarong). The researcher examined the pattern and creativity of the decorative design of the Five-color porcelain (Benjarong) by the artists in order to apply for contemporary arts so that young generation will acknowledge the importance of the Five-color porcelain (Benjarong). The research methodology is both quantitative and qualitative. The researcher conducted an in-depth interview with the operator of five-color porcelain (Benjarong) at Ampawa, Samutsongkram. The information from the interview can be useful and implemented for designing the fabric patterns. The researcher found that there were many formats and designs of the Five-color porcelain (Benjarong) from the past to the present. Its unique design can be applied for the fabric patterns and ready-to-wear clothes properly. After advertising and showing the work of the Five-color porcelain (Benjarong) publicly, there were more young people interested in the Five-color porcelain (Benjarong) than expected which exceeded the objective with positive attitudes towards the Five-color porcelain (Benjarong).

Keywords: Decorative fabric printing, Five-color porcelain (Benjarong).

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Authors: Supaporn Wimonchailerk

Abstract:

This research aims to critical analyze the feminine violent within Thai daily newspaper. This study was qualitative base; content analysis from two popular newspapers (Thairath and Dailynews) two qualitative newspapers (Thaipost and Mathichon). Purposive sampling was used to select eleven specialize news reporters to do in-depth interview. The result found that, popular newspapers, Thairath and dailynews have presented feminine violent news in their paper more than Thaipost and Mathichon the qualitative newspaper. Beside, majority of sample present the feminine violent within news under the code of ethic, The National Press Council of Thailand. Interesting, the age of feminine violent victim was the information that has been focused most. The popular newspaper have illustrated crime scene photo on their first-page while qualitative newspaper used only headline to present the same news.

Keywords: Ethic, Feminine, Journalism, Newspaper, Violent Victim.

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Authors: Madina Kenzhegaranova, Aibol Mukhsiynov, Houman Sanandaji

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The article is about government programs and projects and their description which are aimed at improving the socioeconomic situation in the Republic of Kazakhstan. A brief historical overview, as well as information about current socio-economic, political and transitional contexts of the country are provided. Two theories were described in the article to inform this descriptive study. According to the United Nation's Development Reports for 2005 and 2011, the country's human development index (HDI) rose by several points despite the socio-economic and political imbalances taking place in the republic since it gained its independence in 1991. It is stated in the article that government support programs are one of the crucial factors that increase the population welfare which in its turn may lead to reduction of social crisis processes in the country.

Keywords: human capital, social crisis, state programs, unemployment

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Authors: H. Ghanbarpour Asl, S. H. Pourtakdoust

Abstract:

Extended Kalman Filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, not only it has difficulties arising from linearization but also many times it becomes numerically unstable because of computer round off errors that occur in the process of its implementation. To overcome linearization limitations, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. Kalman filter that uses UT for calculation of the first two statistical moments is called Unscented Kalman Filter (UKF). Square-root form of UKF (SRUKF) developed by Rudolph van der Merwe and Eric Wan to achieve numerical stability and guarantee positive semi-definiteness of the Kalman filter covariances. This paper develops another implementation of SR-UKF for sequential update measurement equation, and also derives a new UD covariance factorization filter for the implementation of UKF. This filter is equivalent to UKF but is computationally more efficient.

Keywords: Unscented Kalman filter, Square-root unscentedKalman filter, UD covariance factorization, Target tracking.

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Authors: Ahmad Luthfi

Abstract:

One of the main goals of a computer forensic analyst is to determine the cause and effect of the acquisition of a digital evidence in order to obtain relevant information on the case is being handled. In order to get fast and accurate results, this paper will discuss the approach known as Ontology Framework. This model uses a structured hierarchy of layers that create connectivity between the variant and searching investigation of activity that a computer forensic analysis activities can be carried out automatically. There are two main layers are used, namely Analysis Tools and Operating System. By using the concept of Ontology, the second layer is automatically designed to help investigator to perform the acquisition of digital evidence. The methodology of automation approach of this research is by utilizing Forward Chaining where the system will perform a search against investigative steps and atomically structured in accordance with the rules of the Ontology.

Keywords: Ontology, Framework, Automation, Forensics.

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Authors: Mahdieh Allahviranloo

Abstract:

This paper introduces a mixed integer programming model to find the optimum development plan for port Anzali. The model minimizes total system costs taking into account both port infrastructure costs and shipping costs. Due to the multipurpose function of the port, the model consists of 1020 decision variables and 2490 constraints. Results of the model determine the optimum number of berths that should be constructed in each period and for each type of cargo. In addition to, the results of sensitivity analysis on port operation quantity provide useful information for managers to choose the best scenario for port planning with the lowest investment risks. Despite all limitations-due to data availability-the model offers a straightforward decision tools to port planners aspiring to achieve optimum port planning steps.

Keywords: MILP, Multipurpose Terminal, Port Operation Optimization, Port Anzali.

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Authors: Jaeyoung Lee

Abstract:

Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: Edge network, embedded network, MMA, matrix multiplication accelerator and semantic segmentation network.

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Authors: Hichem Sassi, Tawfik Najeh, Noureddine Liouane

Abstract:

Information of nodes’ locations is an important criterion for lots of applications in Wireless Sensor Networks. In the hop-based range-free localization methods, anchors transmit the localization messages counting a hop count value to the whole network. Each node receives this message and calculates its own distance with anchor in hops and then approximates its own position. However the estimative distances can provoke large error, and affect the localization precision. To solve the problem, this paper proposes an algorithm, which makes the unknown nodes fix the nearest anchor as a reference and select two other anchors which are the most accurate to achieve the estimated location. Compared to the DV-Hop algorithm, experiment results illustrate that proposed algorithm has less average localization error and is more effective.

Keywords: Wireless Sensors Networks, Localization problem, localization average error, DV–Hop Algorithm, MATLAB.

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Authors: Y. Harold Robinson, M. Rajaram, Honey Raju

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Image segmentation and color identification is an important process used in various emerging fields like intelligent robotics. A method is proposed for the manipulator to grasp and place the color object into correct location. The existing methods such as PSO, has problems like accelerating the convergence speed and converging to a local minimum leading to sub optimal performance. To improve the performance, we are using watershed algorithm and for color identification, we are using EPSO. EPSO method is used to reduce the probability of being stuck in the local minimum. The proposed method offers the particles a more powerful global exploration capability. EPSO methods can determine the particles stuck in the local minimum and can also enhance learning speed as the particle movement will be faster.

Keywords: Color information, EPSO, hue, saturation, value (HSV), image segmentation, particle swarm optimization (PSO). Active Contour, GMM.

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Authors: Kawecki J., Kowalska-Koczwara A., Stypula K.

Abstract:

The increasing influence of traffic on building objects and people residing in them should be taken into account in diagnosis and design. Users of buildings expect that vibrations occurring in their environment, will not only lead to damage to the building or its accelerated wear, but neither would affect the required comfort in rooms designed to accommodate people. This article describes the methods and principles useful in designing and building diagnostics located near transportation routes, with particular emphasis on the impact of traffic vibration on people in buildings. It also describes the procedures used in obtaining information about the parameters of vibrations in different cases of diagnostics and design. A universal algorithm of procedure in diagnostics and design of buildings taking into account assurance of human vibration comfort of people residing in the these buildings was presented.

Keywords: diagnostics, influence of public transport, influence of vibrations on humans, transport vibrations

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Authors: Tami Alghamdi, Terence Soule

Abstract:

To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed that combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population.

Keywords: Transfer Learning, Multiple Sources, Knowledge Transfer, Domain Adaptation, Source, Target.

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Authors: Shankar Kambhampaty, Harish Rohan Kambhampaty

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Data in IT systems in enterprises have been growing at phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several Artificial Intelligence/Machine Learning (AI/ML) and Business Intelligence (BI) tools and technologies available in marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information and intelligence components and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations.

Keywords: Architecture, model, intelligence, artificial intelligence, business intelligence, AI, BI, ML, analytics, enterprise.

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Authors: Sun H. Hwang, Young E. Lee, Yunhan Ga, Gilwon Yoon

Abstract:

EEG is a very complex signal with noises and other bio-potential interferences. EOG is the most distinct interfering signal when EEG signals are measured and analyzed. It is very important how to process raw EEG signals in order to obtain useful information. In this study, the EEG signal processing techniques such as EOG filtering and outlier removal were examined to minimize unwanted EOG signals and other noises. The two different mental states of resting and focusing were examined through EEG analysis. A focused state was induced by letting subjects to watch a red dot on the white screen. EEG data for 32 healthy subjects were measured. EEG data after 60-Hz notch filtering were processed by a commercially available EOG filtering and our presented algorithm based on the removal of outliers. The ratio of beta wave to theta wave was used as a parameter for determining the degree of focusing. The results show that our algorithm was more appropriate than the existing EOG filtering.

Keywords: EEG, focus, mental state, outlier, signal processing.

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Authors: Arijit Roy

Abstract:

Occurrence of popcorn in IC packages while assembling them onto the PCB is a well known moisture sensitive reliability issues, especially for surface mount packages. Commonly reflow soldering simulation process is conducted to assess the impact of assembling IC package onto PCB. A strain gauge-based instrumentation is developed to investigate the popcorn effect in surface mount packages during reflow soldering process. The instrument is capable of providing real-time quantitative information of the occurrence popcorn phenomenon in IC packages. It is found that the popcorn occur temperatures between 218 to 241°C depending on moisture soak condition, but not at the peak temperature of the reflow process. The presence of popcorn and delamination are further confirmed by scanning acoustic tomography as a failure analysis.

Keywords: Instrumentation, Popcorn, Real-time, Solder Reflow, Strain Gauge.

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Authors: Danesh Nourzadeh, Majid Ebad-Sichani, Shiro Takada

Abstract:

Widespread use of response spectra in seismic design and evaluation of different types of structures makes them one of the most important seismic inputs. This importance urges the local design codes to adapt precise data based on updated information about the recent major earthquakes happened and also localized geotechnical data. In this regard, this paper derives the response spectra with a geotechnical approach for various scenarios coming from the recent major earthquakes happened in Iran for different types of hard soils, and compares the results to the corresponding spectra from the current seismic code. This comparison implies the need for adapting new design spectra for seismic design, because of major differences in the frequency domains and amplifications.

Keywords: Earthquake engineering, response spectra, seismic design, site response.

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Authors: Christian Mayr, Holger Eisenreich, Stephan Henker, René Schüffny

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Image convolution similar to the receptive fields found in mammalian visual pathways has long been used in conventional image processing in the form of Gabor masks. However, no VLSI implementation of parallel, multi-layered pulsed processing has been brought forward which would emulate this property. We present a technical realization of such a pulsed image processing scheme. The discussed IC also serves as a general testbed for VLSI-based pulsed information processing, which is of interest especially with regard to the robustness of representing an analog signal in the phase or duration of a pulsed, quasi-digital signal, as well as the possibility of direct digital manipulation of such an analog signal. The network connectivity and processing properties are reconfigurable so as to allow adaptation to various processing tasks.

Keywords: Neural image processing, pulse computation application, pulsed Gabor convolution, VLSI pulse routing.

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Authors: Kyoung-Jae Kim, Hyunchul Ahn

Abstract:

This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on the clustering results. This study tries to use the cores in subgroups as an initial seed for a conventional clustering algorithm. This model chooses five cores which have the highest value of degree centrality from SNA, and then performs clustering analysis by using the cores as initial centroids (cluster centers). Then, the model amplifies the impact of friends in social network in the process of cluster-indexing CF.

Keywords: Social network analysis, Recommender systems, Collaborative filtering, Customer relationship management

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Authors: Jianwei Wang, Timo Korhonen, Yuping Zhao

Abstract:

Cross layer optimization based on utility functions has been recently studied extensively, meanwhile, numerous types of utility functions have been examined in the corresponding literature. However, a major drawback is that most utility functions take a fixed mathematical form or are based on simple combining, which can not fully exploit available information. In this paper, we formulate a framework of cross layer optimization based on Adaptively Weighted Utility Functions (AWUF) for fairness balancing in OFDMA networks. Under this framework, a two-step allocation algorithm is provided as a sub-optimal solution, whose control parameters can be updated in real-time to accommodate instantaneous QoS constrains. The simulation results show that the proposed algorithm achieves high throughput while balancing the fairness among multiple users.

Keywords: OFDMA, Fairness, AWUF, QoS.

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Authors: Yi Yu, Lin Ma, Yong Sun, Yuantong Gu

Abstract:

This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.

Keywords: Elliptical Basis Function Network, Markov Chain, Missing Covariates, Remaining Useful Life

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