Digital Library[ Search Result ]
Data-Driven Computer-Aided Diagnosis of Ventricular Fibrillation Based on Ensemble Empirical Mode Decomposition of ECG
http://doi.org/10.5626/JOK.2020.47.4.387
In this paper, we propose a novel computer-aided diagnosis method to detect VF(ventricular fibrillation), one of the hazardous cardiac symptoms of arrhythmia by applying the EEMD(Ensemble Empirical Mode Decomposition) to the ECG signals. Using the EEMD to the ECG signals, it is shown that VF in the EMD region has a higher correlation with the IMFs (intrinsic mode functions) than the NSR (normal sinus rhythm) and other types of arrhythmia. To quantify this characteristic, we calculate the angle between the ECG signal and the specific IMFs, and classify the pathology by differentiating the angles. To verify the effectiveness of the proposed algorithm, we measured the accuracy of diagnosis using arrhythmia data from the PhysioNet database and confirm capacity of the proposed method.
Parser Generators Sharing LR Automaton Generators and Accepting General Purpose Programming Language-based Specifications
Jintaeck Lim, Gayoung Kim, Seunghyun Shin, Kwanghoon Choi, Iksoon Kim
http://doi.org/10.5626/JOK.2020.47.1.52
This paper proposes two ways to develop LR parsers easily. First, one can write a parser specification in a general programming language and derive the benefits of syntax error checking, code completion, and type-error checking over the specification from the language’s development environment. Second, to make it easy to develop a parser tool for a new programming language, the automata generation for the parser specifications is in a modular form. With the idea proposed in this study, we developed a tool for writing parsers in Python, Java, C++, and Haskell. We also demonstrated the two aforementioned advantages in an experiment.
Enhancing the Performance of Multiple Parallel Applications using Heterogeneous Memory on the Intel"s Next-Generation Many-core Processor
Seungwoo Rho, Seoyoung Kim, Dukyun Nam, Geunchul Park, Jik-Soo Kim
http://doi.org/10.5626/JOK.2017.44.9.878
This paper discusses performance bottlenecks that may occur when executing high-performance computing MPI applications in the Intel’s next generation many-core processor called Knights Landing(KNL), as well as effective resource allocation techniques to solve this problem. KNL is composed of a host processor to enable self-booting in addition to an existing accelerator consisting of a many-core processor, and it was released with a new type of on-package memory with improved bandwidth on top of existing DDR4 based memory. We empirically verified an improvement of the execution performance of multiple MPI applications and the overall system utilization ratio by studying a resource allocation method optimized for such new many-core processor architectures.
A Review of Science of Databases and Analysis of Its Case Studies
Young-Kyoon Suh, Jong Wook Kim
In this paper we introduce a novel database research area called science of databases (SoDB) and carry out a comprehensive analysis of its case studies. SoDB aims to better understand interesting phenomena observed across multiple database management systems (DBMSes). While mathematical and engineering work in the database field has been dominant, less attention has been given to scientific approaches through which DBMSes can be better understood. Scientific investigations can lead to better engineered designs through deeper understanding of query optimizers and transaction processing. The SoDB research has investigated several interesting phenomena observed across different DBMSes and provided several engineering implications based on our uncovered results. In this paper we introduce a novel scientific, empirical methodology and describe the research infrastructure to enable the methodology. We then review each of a selected group of phenomena studied and present an identified structural causal model associated with each phenomenon. We also conduct a comprehensive analysis on the case studies. Finally, we suggest future directions to expand the SoDB research.
Optimizing Constant Value Generation in Just-in-time Compiler for 64-bit JavaScript Engine
JavaScript is widely used in web pages with HTML. Many JavaScript engines adopt Just-in-time compilers to accelerate the execution of JavaScript programs. Recently, many newly introduced devices are adopting 64-bit CPUs instead of 32-bit and Just-in-time compilers for 64-bit CPU are slowly being introduced in JavaScript engines. However, there are many inefficiencies in the currently available Just-in-time compilers for 64-bit devices. Especially, the size of code is significantly increased compared to 32-bit devices, mainly due to 64-bit wide addresses in 64-bit devices. In this paper, we are going to address the inefficiencies introduced by 64-bit wide addresses and values in the Just-in-time compiler for the V8 JavaScript engine and propose more efficient ways of generating constant values and addresses to reduce the size of code. We implemented the proposed optimization in the V8 JavaScript engine and measured the size of code as well as performance improvements with Octane and SunSpider benchmarks. We observed a 3.6% performance gain and 0.7% code size reduction in Octane and a 0.32% performance gain and 2.8% code size reduction in SunSpider.
Efficient Parallel CUDA Random Number Generator on NVIDIA GPUs
In this paper, we implemented a parallel random number generation program on GPU"s, which are known for high performance computing, using LCG (Linear Congruential Generator). Random numbers are important in all fields requiring the use of randomness, and LCG is one of the most widely used methods for the generation of pseudo-random numbers. We explained the parallel program using the NVIDIA CUDA model and MPI(Message Passing Interface) and showed uniform distribution and performance results. We also used a Monte Carlo algorithm to calculate pi(π) comparing the parallel random number generator with cuRAND, which is a CUDA library function, and showed that our program is much more efficient. Finally we compared performance results using multi-GPU’s with those of ideal speedups.
Motor Imagery EEG Classification Method using EMD and FFT
David Lee, Hee-Jae Lee, Sang-Goog Lee
Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr