• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.1
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• pp.47-56
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• 2008

This paper presents a detecting technique for the improvement in quality by appling the various vibrational characteristics theory. The object of this study is to objectively point out faulty gear by developing the program which can be used to analyze and predict the vibrational characteristics caused by gear wear, deformation and nick of auto-transmission. The fault detection methods by vibrational signal analysis of gear have been progressed in the various fields of industry. These methods have the advantage of being easy to attach the accelerometer without discontinuance of the structure. But not all the methods are efficient for finding early faults. So in the thesis, we completed development of the inspection system of vibration by appling the most efficient detecting methods and verified the system's reliability through experiments.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.84-88
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• 2008

The roller bearings play an important role not only sustain radial or axial load of system, but carry out a rotatory movement as a various operating conditions. They happen that incipient faults which were caused by excessive load, manufacturing or assembling process's errors and many other reasons are created. The bearing faults make noise and vibration by a continuous collision of rotatory components, which can lower the quality and stability of auto-transmission. Therefore, it is important to detect the early fault as soon as possible. This paper presents a detecting method for the improvement in quality by developing the program which can be used to analyze and predict the vibrational characteristics caused by roller bearing faults. We completed development of the inspection system of vibration by appling the most efficient detecting methods and verified the system's reliability through experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.268-273
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• 2009

The roller bearings play an important role not only sustain radial or axial load of system, but carry out a rotatory movement as a various operating conditions. They happen that incipient faults which were caused by excessive load, manufacturing or assembling process's errors and many other reasons are created. The bearing faults make noise and vibration by a continuous collision of rotatory components, which can lower the quality and stability of auto-transmission. Therefore, it is important to detect the early fault as soon as possible. This paper presents a detecting method for the improvement in quality by developing the program which can be used to analyze and predict the vibrational characteristics caused by roller bearing faults. We completed development of the inspection system of vibration by applying the most efficient detecting methods and verified the system's reliability through experiments.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.1
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• pp.27-39
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• 2008

In this paper we present a simple, efficient method for detecting the blurry photographs. Recently many digital cameras are equipped with various auto-focusing functions to help users take well-focused pictures as easily as possible. In addition, motion compensation devices are able to compensate motion causing blurriness in the images. However, digital pictures can be degraded by limited contrast, inappropriate exposure, imperfection of auto-focusing or motion compensating devices, unskillfulness of the photographers, and so on. In order to decide whether to process the images or not, or whether to delete them or not, reliable measure of image degradation to detect blurry images from sharp ones is needed. This paper presents a blurriness/sharpness measure, and demonstrates its feasibility by using extensive experiments. This method is fast, easy to implement and accurate. Regardless of the detection accuracy, the proposed measure in this paper is not demanding in computation time. Needless to say, this measure can be used for various imaging applications including auto-focusing and astigmatism correction.

• 전자공학회논문지 IE
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• v.48 no.1
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• pp.16-24
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• 2011

In this paper, we developed a auto sorting system for T type welding nut using a vision inspector. We used edge and thread detection with histogram of image which is captured by machine vision camera. We also used a binary morphology operation for a detection of spot. As a result we performed numeric inspection of 0.1mm accuracy. This is impossible in old sorting system and inspector with naked eye. Also, we reduced the manufacturing unit cost to 25% and improved a production efficiency to 330%.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.151-160
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• 2024

Artificial Neural Networks that enabled Artificial Intelligence are being used in many fields. However, the application to mechanical structures has several problems and research is incomplete. One of the problems is that it is difficult to secure a large amount of data necessary for learning Artificial Neural Networks. In particular, it is important to detect and recognize external forces and forces for safety working and accident prevention of mechanical structures. This study examined the possibility by applying the Current Neural Network of Artificial Neural Networks to detect and recognize the load on the machine. Tens of thousands of data are required for general learning of Recurrent Neural Networks, and to secure large amounts of data, this paper derives load data from ANSYS structural analysis results and applies a stacked auto-encoder technique to secure the amount of data that can be learned. The usefulness of Stacked Auto-Encoder data was examined by comparing Stacked Auto-Encoder data and ANSYS data. In addition, in order to improve the accuracy of detection and recognition of load data with a Recurrent Neural Network, the optimal conditions are proposed by investigating the effects of related functions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.29-32
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• 2000

We proposed a neural network based “left shoulder”detector. The auto-associative neural network was trained with the “left shoulder”patterns obtained from the Korea Composite Stock Price Index, and then tested out-of-sample with a reasonably good result. A hypothetical investment strategy based on the detector achieved a return of 132% in comparison with 39% return from a buy and hold strategy

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1066-1070
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• 2002

We propose a neural network based up-trend detector. An auto-associative neural network was trained with 'up-trend' data obtained from the KOSPI 200 future price. It was then used to predict an up-trend Simple investment strategies based on the detector achieved a two year return of $19.8\%$ with no leverage.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.35.3-35
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• 2001

Developed is an optical auto-inspection system to detect some microscopic defects on the Inside surface of the hydraulic automobile brakes at the production line. A small cylindrical detection module with a solid laser source at its center has two rings of optical fibers to separately collect light reflected and scattered from the defects on the surface. The cylindrical brake part rotates with respect to the detection module that will move parallel to the rotational axis of the cylinder. Thus, the optical module can scan the whole inside surface area. The automatic detection of the defects is to compare the intensity distributions ...