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Permanent URI for this collectionhttps://hdl.handle.net/10361/7000

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Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
(© 2016 Old City Publishing, Inc., 2016) Islam, Md Rashedul; Uddin, Jia; Kim, Jongmyon; Department of Computer Science and Engineering
Reliable and efficient fault diagnosis of induction motors is an important issue in industrial environments. This paper proposes a method for reliable fault diagnosis of induction motors using signal processing of acoustic emission (AE) data, including Gabor filtering and the use of multiclass support vector machines (MCSVMs), where a ZigBee based wireless sensor network (WSN) model is used for efficiently transmitting AE signals to a diagnosis server. In the proposed fault diagnosis approach, the induction motor’s different state signals are acquired through proper placement of AE sensors. The AE data are sent to a server through the wireless sensor network and decomposed using discrete wavelet transformation (DWT). An appropriate band is then selected using the maximum energy ratio, and a one-dimensional (1D) Gabor filter with various frequencies and orientation angles is applied to reduce abnormalities and extract various statistical parameters for generating features. In addition, principal component analysis (PCA) is applied to the extracted features to select the most dominant feature dimensions. Finally, one-against-one multiclass support vector machines (OAA-MCSVMs) are used to classify multiple fault types of an induction motor, where each SVM individually trains with its own features to increase the fault classification accuracy of the induction motor. In experiments, the proposed approach achieved an average classification accuracy of 99.80%, outperforming conventional fault diagnosis models.
Tuning the TCP congestion control parameters to optimize client-server systems
(© 2012 International Journal of Multimedia and Ubiquitous Engineering, 2012) Uddin, Jia; Kim, Jong-Myon; Yi, Myeong-Jae; Kim, Tae-Gong; Department of Computer Science and Engineering
The demand of high volume data communication over the internet is mounting day by day. In general, the performance of a communication system depends on the loss of data packets. It happens as there are multiple paths exist in wireless networks. For the reliable and secure data communication over Internet Transmission Control Protocol (TCP) is playing a significant role. On the development of TCP, a number of approaches are already designed and tested in the communication system. Generally, TCP congestion parameters are set in sender site in a communication system. In this paper, we investigate the download system performance tuning the TCP congestion parameters at the Ethernet port of receiver side. Based on the experimental study, it is concluded that tuning TCP parameters at receiving side improves the download system performance by reducing packet loss, increasing download speed and maintain stable I/O and time/sequence graphs.
Performance of geographic routing protocol in mobile ad-hoc networks
(© 2014 International Information Institute Ltd., 2014-05) Uddin, Jia; Haque, Mohammad Ariful; Kim, Jongmyon; Kim, Cheolhong; Yi, Myeongjae; Kim, Taegong; Department of Computer Science and Engineering
This paper investigates the performance of the Geographic Routing Protocol (GRP) in Mobile Ad-hoc Networks (MANETs) with respect to network configuration in terms of number of nodes and network area. We employ the File Transfer Protocol (FTP) traffic in different network scenarios and evaluate the network performance in terms of throughput, network load, and delay. Experimental results indicate that the number of nodes affects the performance of GRP in MANET; however, network size does not affect the performance.
High performance computing for large graphs of internet applications using GPU
(© 2014 Science and Engineering Research Support Society, 2014) Uddin, Jia; Oyekanlu, Emmanuuel; Kim, Cheol-Hong; Kim, Jong-Myon; Department of Computer Science and Engineering
The high speed CPU based routers currently in use could not handle the massive data required for real-time multimedia communication. Graphics processing units (GPUs) offer an appreciable alternative due to high computation power which results from their parallel execution units. This paper presents the implementation of the Dijkstra's link state IP routing algorithm using GPU. Experimental results show that the proposed GPU-based approach outperforms the same sequential CPU-based implementation in terms of execution time for the same dense graph. In addition, the proposed GPU-based approach reduces about 99% energy consumption over the CPU-based implementation.
Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine
(© 2014 Hindawi Publishing Corporation, 2014) Uddin, Jia; Kang, Myeongsu; Dish, V. Nguyen; Kim, Jong-Myon; Department of Computer Science and Engineering
This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments
Geographic routing protocol in mobile ad-hoc networks for reliable FTP traffic communication
(© 2014 International Information Institute Ltd., 2014) Uddin, Jia; Kim, Jongmyon; Department of Computer Science and Engineering
This paper investigates the performance of the Geographic Routing Protocol (GRP) in Mobile Ad-hoc Networks (MANETs) with respect to network configuration in terms of number of nodes and network area. We employ the File Transfer Protocol (FTP) traffic in different network scenarios and evaluate the network performance in terms of throughput, network load, and delay. Experimental results indicate that the number of nodes affects the performance of GRP in MANET; however, network size does not affect the performance.
Accelerating IP routing algorithm using graphics processing unit for high speed multimedia communication
(© 2016 Springer New York LLC, 2016) Uddin, Jia; Jeong, In-Kyu; Kang, Myeongsu; Kim, Cheol-Hong; Kim, Jong-Myon; Department of Computer Science and Engineering
This paper presents a Graphics Processing Unit (GPU)-based implementation of a Bellman-Ford (BF) routing algorithm using NVIDIA’s Compute Unified Device Architecture (CUDA). In the proposed GPU-based approach, multiple threads run concurrently over numerous streaming processors in the GPU to dynamically update routing information. Instead of computing the individual vertex distances one-by-one, a number of threads concurrently update a larger number of vertex distances, and an individual vertex distance is represented in a single thread. This paper compares the performance of the GPU-based approach to an equivalent CPU implementation while varying the number of vertices. Experimental results show that the proposed GPU-based approach outperforms the equivalent sequential CPU implementation in terms of execution time by exploiting the massive parallelism inherent in the BF routing algorithm. In addition, the reduction in energy consumption (about 99 %) achieved by using the GPU is reflective of the overall merits of deploying GPUs across the entire landscape of IP routing for emerging multimedia communications.
Accelerating 2d fault diagnosis of an induction motor using a graphics processing unit
(© 2015 Science and Engineering Research Support Society, 2015) Uddin, Jia; Van, Dinh Nguyen; Kim, Jong-Myon; Department of Computer Science and Engineering
This paper presents a computationally efficient graphics processing unit (GPU) implementation of a reliable fault diagnosis method using two-dimensional (2D) representation of vibration signals. The fault diagnosis method first converts time-domain vibration signals into 2D gray-level images to exploit texture information from the converted images. Then, the global dominant neighborhood structure (GNS) map is utilized to extract texture features by averaging local neighborhood structure (LNS) maps of central pixels. In addition, the principle component analysis (PCA) algorithm is employed to select only the most dominant features. Finally, the selected features are used as inputs to a one-against-all multi-class support vector machine (OAA-MCSVM) to identify each fault of the induction motor. Despite the fact that the 2D fault diagnosis methodology shows satisfactory classification accuracy, its computational complexity limits its use in real-time applications. To accelerate the 2D fault diagnosis method, this paper utilizes an NVIDIA GeForce GTX 580 GPU, where all tasks are executed in parallel. The experimental results indicate that the proposed GPU-based approach achieves about 118.5 faster operation than the equivalent sequential CPU implementation while maintaining 100% classification accuracy.
A two-dimensional fault diagnosis model of induction motors using a gabor filter on segmented images
(© 2016 Science and Engineering Research Support Society, 2016) Uddin, Jia; Islam, Mr. Rashedul; Kim, Jong-Myon; Kim, Cheol-Hong; Department of Computer Science and Engineering
Image segmentation has received extensive attention due to the use of high-level descriptions of image content. This paper proposes a fault diagnosis model using a Gabor filter on segmented two-dimensional (2D) gray-level images. The proposed approach first converts time domain AE signals into 2D gray-level images to exploit texture information from the converted images. 2D discrete wavelet transform (DWT) is then applied to select appropriate (vertical) texture information and reconstructed it into an image. The reconstructed image is segmented into a number of sub-images depending on the segment size and a Gabor filter is applied on each sub-image. Finally, feature vectors are extracted from the Gabor-filtered sub-images and utilized as inputs in a one-against-all multiclass support vector (OAA-MCSVM) to identify each fault in an induction motor. In this study, multiple bearing defects under various segment sizes are utilized to validate the effectiveness of the proposed method. Experimental results indicate that the proposed model outperforms conventional Gabor-filter-based 2D fault diagnosis algorithms in classification accuracy, exhibiting a 97 % average classification accuracy for 64×64 segmented images.

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