• Ocean and Polar Research
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• v.35 no.1
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• pp.15-26
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• 2013

The toxic, Harmful Algal Blooms (HABs) caused by the Cochlodinium polykrikoides have a serious impact on the coastal waters of Korea. In this study, the acoustic detection system was developed for rapid HABs detection, based on the acoustic backscattering properties of the C. polykrikoides. The developed system was mainly composed of a pulser-receiver board, a signal processor board, a control board, a network board, a power board, ultrasonic sensors (3.5 and 5.0 MHz), an environmental sensor, GPS, and a land-based control unit. To evaluate the performance of the system, a trail was done at a laboratory, and two in situ trials were conducted: (1) when there was no red tide, and (2) when there was red tide. In the laboratory evaluation, the system performed well in accordance with the number of C. polykrikoides in the received level. Second, under the condition when there was no red tide in the field, there was a good correlation between the acoustic data and sampling data. Finally, under the condition when there was red tide in the field, the system successfully worked at various densities in accordance with the number of C. polykrikoides, and the results corresponded with the sampling data and monitoring result of NFRDI (National Fisheries Research & Development Institute). From the laboratory and field evaluations, the developed acoustic detection system for early detecting HABs has demonstrated that it could be a significant system to monitor the occurrence of HABs in coastal regions.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.519-524
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• 2002

In this paper we study the qualify of sequential coverage analysis under a scenario of distributed stochastic simulation known as MRIP(Multiple Replications In Parallel) in terms of the confidence intervals of coverage and the speedup. The estimator based in the F-distribution was applied to the sequential coverage analysis of steady-state means. in simulations of the $M/M/1/{\infty},\;M/D/I/{\infty}\;and\;M/H_{2}/1/{\infty}$ queueing systems on a single processor and multiple processors. By using multiple processors under the MRIP scenario, the time for collecting many replications needed in sequential coverage analysis is reduced. One can also easily collect more replications by executing it in distributed computers or clusters linked by a local area network.

• Journal of IKEEE
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• v.15 no.1
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• pp.15-22
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• 2011

In order to performing the joint operation of Korean army efficiently, informations about surveillance, reconnaissance, and situation awareness need to be possessed jointly. In the first development phase (basis type) of the Korean joint tactical data link system (JTDLS-K), essential tactical information and recognized situation are owned among platforms in common by using existing wireless terminals. In the second development phase (completion type) of the JTDLS-K, a JTDLS which can perform network centric warfare (NCW) will be developed in due consideration of technology development of the basis type and common technology maturity degree. This is a joint battlefield system that can show fighting power simultaneous and polysynthetically through providing command and control messages effectively to each platform, which is participating in the joint and combined operations. In this paper, the development of JTDLS-K with a common data processor based on common link integration processing (CLIP) is described. From the test results of the system presented in this paper, it is demonstrated that quadrature phase shift keying (QPSK) signals can be applied to the system.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.167-173
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• 2017

As the increasing use of the cloud computing, virtualization technology have been combined and applied a variety of requirements. Cloud computing has the advantage that the support computing resource by a flexible and scalable to users as they want and it utilized in a variety of distributed computing. To do this, it is especially important to ensure the stability of the cloud computing. In this paper, we analyzed a variety of component measurement using open-source tools for ensuring the performance of the system on the education system to build cloud testbed environment. And we extract the performance that may affect the virtualization environment from processor, memory, cache, network, etc on each of the host machine(Host Machine) and a virtual machine (Virtual Machine). Using this result, we can clearly grasp the state of the system and also it is possible to quickly diagnose the problem. Furthermore, the cloud computing can be guaranteed the SLA(Service Level Agreement).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1169-1174
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• 2002

In this paper, we implemented the sign board system that displays user's image, user's sentence, the information from DARC. The existing sign board is displaying only user's image and sentence. Or other existing sign board is displaying the information via CDMA network. However, our system is also able to display the user's message like other system and gain the information more cheap by DARC. This system includes the main processor, the program memory, the external memory, the DARC module and the LED display module. The external memory stores the user's message files and the order file that decides the displaying order of user's file and the DARC information The DARC module extracts the DARC information from FM signal. From the experiment, we could confirm that this system display the DARC information and the user's message by the order file.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.105-113
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• 1994

This paper introduces the Parallel Advanced Loosely coupled Multiprocessor (PALM) architecture, which is based on HCH(m,p), where m is number of links per a communication processor (CP) and p is the number of application processors (APs) connected to the CP. communication links between a pair of CPs and/or between a CP and an AP, are made of dual-Port RAMs, which provide fast and reliable word-parallel communication between processors. Among the wide spectrum of HCH networks, HCH(m,2) is also known to be a cost optimal topology, such that HCH(m,2) consists of the largest number of APs retaining the minimal number of CPs and communication links. We also implement a testbed based on HCH(2,2). The experiment result shows that the small communication/computation ratio of the PALM system would realize fine-grain parallelism on message-passing MIMD systems.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1859-1867
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• 2021

Multi LiDAR sensors are being mounted on autonomous vehicles, and a system to multi LiDAR sensors data is required. When sensors data is transmitted or processed to the main processor, a huge amount of data causes a load on the transport network or data processing. In order to minimize the number of load overhead into LiDAR sensor processors, only semantic data is transmitted through data comparison between frames in LiDAR data. When data from 4 LiDAR sensors are processed in a static environment without moving objects and a dynamic environment in which a person moves within sensor's field of view, in a static experiment environment, the transmitted data reduced by 89.5% from 232,104 to 26,110 bytes. In dynamic environment, it was possible to reduce the transmitted data by 88.1% to 29,179 bytes.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.