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Design Space Exploration of Many-Core Architecture for Sound Synthesis of Guitar on Portable Device
Myeongsu Kang(강명수),Jong-Myon Kim(김종면) 한국컴퓨터정보학회 2014 한국컴퓨터정보학회 학술발표논문집 Vol.22 No.1
Although physical modeling synthesis is becoming more and more efficient in rich and natural high-quality sound synthesis, its high computational complexity limits its use in portable devices. This constraint motivated research of single-instruction multiple-data many-core architectures that support the tremendous amount of computations by exploiting massive parallelism inherent in physical modeling synthesis. Since no general consensus has been reached which grain sizes of many-core processors and memories provide the most efficient operation for sound synthesis, design space exploration is conducted for seven processing element (PE) configurations. To find an optimal PE configuration, each PE configuration is evaluated in terms of execution time, area and energy efficiencies. Experimental results show that all PE configurations are satisfied with the system requirements to be implemented in portable devices.
GA-Based Adaptive Window Length Estimation for Highly Accurate Audio Segmentation
Myeongsu Kang,Jong-Myon Kim 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.1
Accurate audio segmentation has recently received increasing attention for its applications in automatic indexing, content analysis and information retrieval. Hence, this paper proposes a highly accurate audio segmentation methodology using a genetic algorithm-based approach to adapting and optimizing segmentation window lengths. Specifically, this paper analyzes the parameter sequence of the root-mean-square values of an input audio stream with optimal sliding window (or segmentation window) lengths found and adapted by a genetic algorithm. In addition, this paper determines whether an audio-cut occurs or not by utilizing the parameter sequences as inputs of a support vector machine. Experimental results indicate that the proposed approach achieves 100.00% and 98.69% in the average precision and recall rates of segmentation performance, respectively.
Kang, Myeongsu,Kim, Jaeyoung,Jeong, In-Kyu,Kim, Jong-Myon,Pecht, Michael Institute of Electrical and Electronics Engineers 2016 IEEE transactions on industrial electronics Vol. No.
<P>The fact that rolling element bearing faults have an amplitude-modulating effect on their characteristic frequencies calls for sub-band analysis to determine an optimal sub-band signal that contains intrinsic information about bearing faults. In this regard, it is significant to accurately assess the presence of a bearing's abnormal symptoms. Hence, a bearing abnormality index (BAI) that properly quantifies how much information a sub-band signal contains about bearing faults is presented. Additionally, to facilitate real-time sub-band analysis based on the BAI, a massively parallel approach is introduced, where the approach involves the use of the multicore system. Likewise, the multicore system supports high-performance computing by exploiting 128 processing elements operating at 200 MHz in a Xilinx Virtex-7 field-programmable gate array (FPGA) device.</P>
Myeongsu Kang,Islam, Md Rashedul,Jaeyoung Kim,Jong-Myon Kim,Pecht, Michael IEEE 2016 IEEE transactions on industrial electronics Vol.63 No.5
<P>In practice, outliers, defined as data points that are distant from the other agglomerated data points in the same class, can seriously degrade diagnostic performance. To reduce diagnostic performance deterioration caused by outliers in data-driven diagnostics, an outlier-insensitive hybrid feature selection (OIHFS) methodology is developed to assess feature subset quality. In addition, a new feature evaluation metric is created as the ratio of the intraclass compactness to the interclass separability estimated by understanding the relationship between data points and outliers. The efficacy of the developed methodology is verified with a fault diagnosis application by identifying defect-free and defective rolling element bearings under various conditions.</P>
Myeongsu Kang,Jaeyoung Kim,Wills, Linda M.,Jong-Myon Kim Institute of Electrical and Electronics Engineers 2015 IEEE transactions on industrial electronics Vol. No.
<P>This paper presents a reliable fault diagnosis methodology for various single and multiple combined defects of low-speed rolling element bearings. This method temporally partitions an acoustic emission (AE) signal and selects a portion of the signal, which contains intrinsic information about the bearing failures. This paper then performs frequency analysis for the selected time-domain AE signal by using multilevel finite-impulse response filter banks to obtain the most informative subband signals involving abnormal symptoms of the bearing defects. It does this by using a 2-D visualization tool that represents the percentage of the Gaussian-mixture-model-based residual component-to-defect component ratios via time-varying and multiresolution envelope analysis (TVMREA). Then, fault signatures in the time and frequency domains are extracted in the informative subband signals. Since all the extracted fault features may not be equally useful for diagnosis, the proposed genetic algorithm (GA)-based discriminative feature analysis (GADFA) selects the most discriminative subset of fault signatures. In experiments, single and multiple combined bearing defects under various conditions are used to validate the effectiveness of this fault diagnosis scheme using TVMREA and GADFA. Experimental results indicate that this reliable fault diagnosis methodology accurately identifies bearing failure type across a variety of conditions. In addition, GADFA outperforms other state-of-the-art feature analysis techniques, yielding 7.3%-46.6% performance improvements in average classification accuracy.</P>
Myeongsu Kang,Jaeyoung Kim,Jong-Myon Kim IEEE 2015 IEEE transactions on industrial electronics Vol.62 No.4
<P>The demand for online fault diagnosis has recently increased in order to prevent severe unexpected failures in machinery. To address this issue, this paper first proposes a comprehensive bearing fault diagnosis algorithm, which consists of fault signature extraction through time-frequency analysis and one-against-all multiclass support vector machines in order to make reliable decisions. In addition, acoustic emission (AE) signals sampled at 1 MHz are used for the early identification of bearing failures. Despite the fact that the proposed fault diagnosis methodology shows satisfactory classification accuracy, its computation complexity limits its use in real-time applications. Therefore, this paper also presents a high-performance multicore architecture, including 64 processing elements operating at 50 MHz in a Xilinx Virtex-7 field-programmable gate array device to support online fault diagnosis. The experimental results indicate that the multicore approach executes 1339.3x and 1293.1x faster than the high-performance Texas Instrument (TI) TMS320C6713 and TMS320C6748 digital signal processors (DSPs), respectively, by exploiting the massive parallelism inherent in the bearing fault diagnosis algorithm. In addition, the multicore approach outperforms the equivalent sequential approach that runs on the TI DSPs by substantially reducing the energy consumption.</P>