• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.

• Smart Media Journal
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• v.4 no.2
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• pp.68-76
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• 2015

Recently, research on indoor locating techniques using smart device sensors has been conducted actively, Owing to the exponential increase in the use of various smart devices. However, in order to develop indoor location techniques, there are limitations due to the requirement that the tracking system has to function without GPS. In this paper, we propose an accurate indoor locating system that does not require additional infrastructure. The proposed scheme is developed based on the idea that the advantages and disadvantages of "Wi-Fi Fingerprinting" and "Step Detection" techniques are complementary. In the proposed scheme, we track users with "Step Detection," and correct errors with "Wi-Fi Fingerprinting." In this paper, we demonstrate the effectiveness and feasibility of our proposed scheme through experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.8
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• pp.641-653
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• 2013

Recent cyber attacks targeting control systems are getting sophisticated and intelligent notoriously. As the existing signature based detection techniques faced with their limitations, a whitelist model with security techniques is getting attention again. However, techniques that are being developed in a whitelist model used at the application level narrowly and cannot provide specific information about anomalism of various cases. In this paper, we classify abnormal cases that can occur in control systems of enterprises and propose a new whitelist model for detecting abnormal cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.153-157
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• 2005

Frequency selective fading occurs due to the Doppler Effect in mobile communication systems. The performances of the systems are rapidly reduced due to effect of multiuser interference under frequency selective channels at DS-CDMA systems. To overcome these problems, we adopted the multi-carrier modulation techniques, and it is able to solve the frequency selective channel effects by means of these modulation techniques, and interference problems due to multiuser access are solved by means of multiuser detection techniques. In this paper, we proposed the blind MOE/PIC multiuser detection method which is composed of both the blind multiuser detection technique and parallel interference canceller. Thus, simulation results show that the proposed method performs better than conventional methods.

• ETRI Journal
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• v.37 no.3
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• pp.491-501
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• 2015

Spectrum sensing is an elementary function in cognitive radio designed to monitor the existence of a primary user (PU). To achieve a high rate of detection, most techniques rely on knowledge of prior spectrum patterns, with a trade-off between high computational complexity and long sensing time. On the other hand, blind techniques ignore pattern matching processes to reduce processing time, but their accuracy degrades greatly at low signal-to-noise ratios. To achieve both a high rate of detection and short sensing time, we propose fast spectrum sensing with coordinate system (FSC) - a novel technique that decomposes a spectrum with high complexity into a new coordinate system of salient features and that uses these features in its PU detection process. Not only is the space of a buffer that is used to store information about a PU reduced, but also the sensing process is fast. The performance of FSC is evaluated according to its accuracy and sensing time against six other well-known conventional techniques through a wireless microphone signal based on the IEEE 802.22 standard. FSC gives the best performance overall.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.645-655
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• 2020

Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.

• Information and Communications Magazine
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• v.24 no.11
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• pp.25-36
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• 2007

Deep packet inspection is widely recognized as a powerful way which is used for intrusion detection systems for inspecting, deterring and deflecting malicious attacks over the network. Fundamentally, almost intrusion detection systems have the ability to search through packets and identify contents that match with known attach. In this paper we survey the deep packet inspection implementations techniques, research challenges and algorithm. Finally, we provide a comparison between the different applied system.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1163-1169
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• 2014

As a preliminary study for detection techniques of $CO_2$ gas bubble plumes, we have conducted a comparative experiment on artificially generated $CO_2$ gas bubbles plume by using multibeam echosounder (MBES), single beam echosounder (SBES), and sub-bottom profiler (SBP). The rising speed of artificial gas bubbles is higher than references because of compulsory release of compressed gas in the tank. Compared to single beam acoustic equipments, the MBES detects wide swath coverage. It provides exact determination of the source position and 3D information on the gas bubble plumes in the water column. Therefore, it is shown that MBES can distinctly detect gas bubble plumes compared to single beam acoustic equipments. We can establish more effective complementary detection technique by simultaneous operation of MBES and SBES. Consequently, it contributes to improve qualitative and quantitative detection techniques by understanding the acoustic characteristics of the specific gas bubbles.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.401-407
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• 2017

This paper describes wavenumber filtering for damage detection using single-frequency standing wave excitation and laser scanning sensing. An embedded piezoelectric sensor generates ultrasonic standing waves, and the responses are measured using a laser Doppler vibrometer and mirror tilting device. After scanning, newly developed damage detection techniques based on wavenumber filtering are applied to the full standing wave field. To demonstrate the performance of the proposed techniques, several experiments were performed on composite plates with delamination and aluminum plates with corrosion damage. The results demonstrated that the developed techniques could be applied to various structures to localize the damage, with the potential to improve the damage detection capability at a high interrogation speed.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.