• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.1-9
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• 2015

As attacks in cyber space become advanced and complex, monotonous defense approach of one-one matching manner between attack and defense may be limited to defend them. More efficient defense method is required. This paper proposes multi layers security scheme that can support to defend assets against diverse cyber attacks in systematical and adaptive. We model multi layers security scheme based on Defense Zone including several defense layers and also discuss essential technical elements necessary to realize multi layers security scheme such as cyber threats analysis and automated assignment of defense techniques. Also effects of multi layers security scheme and its applicability are explained. In future, for embodiment of multi layers security scheme, researches about detailed architecture design for Defense Zone, automated method to select the best defense technique against attack and modeling normal state of asset for attack detection are needed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.4
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• pp.121-128
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• 2003

In recent years, various artificial neural network algorithms are used in the damage assessment of civil infrastructures. So far, many researchers have used the artificial neural network as a pattern classifier for the structural damage assessment but, in this paper, the neural network is used as a structural reanalysis tool not as a pattern classifier. For the model updating using the optimization algorithm, the summation of the absolute differences in the structural vibration modes between undamaged structures and damaged ones is considered as an objective function. The stiffness of structural components are treated as unknown parameters to be determined. The structural damage detection is achieved using model updating based on the optimization techniques which determine the estimated stiffness of components minimizing the objective function. For the verification of the proposed damage identification algorithm, it is numerically applied to a simply supported bridge model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1141-1149
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• 2007

This paper describes two major techniques; image processing and gas vent insert and rejection control, for efficient gas vent exchange and holes detecting on the shoes mold. The key idea is to detect holes on the mold to select which holes to insert and to reject automatically guide center of hole's position. This allows us to save labor time while minimizing defective rate of PU shoes mold forming and production costs for gas vent exchange such as insertion and rejection.. Our experimental results have demonstrated that the hole's detection and gasvent exchange mechanism are more efficient and provide accurate mechanism to mitigate risks of vent injection/rejection failures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1696-1702
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• 2008

A MANET(Mobile Ad-hoc Network) is a multi-hop routing protocol formed by a collection without the intervention of infrastructure. So the MANET also depended on the property as like variable energy, high degree of mobility, location environments of nodes etc. Generally the various clustering technique and routing algorithm would have proposed for improving the energy efficiency. One of the popular approach methods is a cluster-based routing algorithm using in MANET. In this paper, we propose an algorithm techniques which is TICC (Time Interval Clustering Control) based on energy value in property of each node for solving cluster problem. It provides improving cluster energy efficiency how can being node manage to order each node's energy level. TICC could be able to manage the clustering, re-configuration, maintenance and detection of Node in MANET. Furthermore, the results of modeling shown that Node's energy efficiency and lifetime are improved in MANET.

• Journal of the Korea Society of Computer and Information
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• v.7 no.3
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• pp.30-35
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• 2002

This study is for developing an algorithm to detect defects of manufactured textile fiber. We used CCD Video Input Equipments in order to capture fiber textile images. Though most of the fiber manufacturing procedures consist of automatic systems, present textile detecting systems are depending on manual inspection system. However this method is not very economical. Therefore we can expect high production rate in the fiber manufacturing area if we could develop and utilize an image processing algorithm to inspect defects of textile fiber. The study was aimed at and achieved development of an detecting algorithm using image processing methods and related mechanical system which enable to detect missing of threads, mixing of unnecessary materials, polluted areas, scars, and colour differences in the textile. Through this study we could devise a manless system for detection of fiber textile and Provide possibilities which apply the image Processing techniques to the other manufacturing inspection systems.

• Current Optics and Photonics
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• v.2 no.5
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• pp.422-430
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• 2018

Rolling shutter operation of CMOS cameras can be utilized in optical camera communications in order to transmit data from an LED to mobile devices such as smart-phones. From temporally modulated light, a spatial flicker pattern is obtained in the captured image, and this is used for signal recovery. Due to the degradation of rolling shutter images caused by light smear, motion blur, and focus blur, the conventional decoding schemes for rolling shutter cameras based on the pattern width for 'OFF' and 'ON' cannot guarantee robust communications performance for practical uses. Aside from conventional techniques, such as polynomial fitting, histogram equalization can be used for blurry light mitigation, but it requires additional computation abilities resulting in burdens on mobile devices. This paper proposes a transition-based decoding scheme for rolling shutter cameras in order to offer simple and robust data decoding in the presence of image degradation. Based on the designed synchronization pulse and modulated data symbols according to the LED dimming level, the decoding process is conducted by observing the transition patterns of two sequential symbol pulses. For this, the extended symbol pulse caused by consecutive symbol pulses with the same level determines whether the second pulse should be included for the next bit decoding or not. The proposed method simply identifies the transition patterns of sequential symbol pulses other than the pattern width of 'OFF' and 'ON' for data decoding, and thus, it is simpler and more accurate. Experimental results ensured that the transition-based decoding scheme is robust even in the presence of blurry lights in the captured image at various dimming levels

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.327-335
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• 2021

Microcracks can rapidly grow and develop in high-strength steels used in offshore structures. It is important to render these microcracks harmless to ensure the safety and reliability of offshore structures. Here, the dependence of the aspect ratio (As) of the maximum depth of harmless crack (a_hlm) was evaluated under three different conditions considering the threshold stress intensity factor (Δk_th) and residual stress of offshore structural steel F690. The threshold stress intensity factor and fatigue limit of fatigue crack propagation, dependent on crack dimensions, were evaluated using Ando's equation, which considers the plastic behavior of fatigue and the stress ratio. a_hlm by peening was analyzed using the relationship between Δk_th obtained by Ando's equation and Δk_th obtained by the sum of applied stress and residual stress. The plate specimen had a width 2W = 12 mm and thickness t = 20 mm, and four value of As were considered: 1.0, 0.6, 0.3, and 0.1. The a_hlm was larger as the compressive residual stress distribution increased. Additionally, an increase in the values of As and Δk_th(l) led to a larger a_hlm. With a safety factor (N) of 2.0, the long-term safety and reliability of structures constructed using F690 can be secured with needle peening. It is necessary to apply a more sensitive non-destructive inspection technique as a non-destructive inspection method for crack detection could not be used to observe fatigue cracks that reduced the fatigue limit of smooth specimens by 50% in the three types of residual stresses considered. The usefulness of non-destructive inspection and non-damaging techniques was reviewed based on the relationship between a_hlm, a_NDI (minimum crack depth detectable in non-destructive inspection), a_{cr N} (crack depth that reduces the fatigue limit to 1/N), and As.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.979-993
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• 2021

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). In this study, we considered the problem that ASC faces in real-world applications that the model used should have low-complexity. We compared several models that apply light-weight techniques. First, a base CNN model was proposed using log mel-spectrogram, deltas, and delta-deltas features. Second, depthwise separable convolution, linear bottleneck inverted residual block was applied to the convolutional layer, and Quantization was applied to the models to develop a low-complexity model. The model considering low-complexity was similar or slightly inferior to the performance of the base model, but the model size was significantly reduced from 503 KB to 42.76 KB.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.