• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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• pp.1618-1631
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• 1994

In this paper, in-process chatter detection methodology which utilizes nondimensional characteristic variables is introduced. To obtain nondimensional chatter detection indexes which are constant regardless of the cutting conditions during machining with the same tool and workpiece material, both the cutting forces and accelerations are measured and processed in time and frequency domain. The indexes are calculated from the present and past value of the acceleration and cutting force signals in time and frequency domain. The chatter is identified when these chatter detection indexes are bigger than the threshold which is decided by preliminary experiments. The experiment shows that these indexes works very well in-process chatter detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.135-141
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• 2016

Indoor zone-based services have continuously become popular by increased prevalence of smartphones. Bluetooth and ultrasound can be used for zone detection. However, bluetooth does not guarantee precise zone detection if the signal degrades due to the obstacles. Ultrasound can be easily forged by recording sound on the smartphone. For that reason, zone detection based on ultrasound has a security hole. To remedy each limitation, we propose an advanced zone detection method, that combines bluetooth and ultrasound. An authentication server issues a one-time password to the user over bluetooth. The user generates an ultrasound signal that encodes the password. In this manner, the proposed method ensures secure and accurate zone detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.243-249
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• 2020

Event detection in wireless sensor networks is a key requirement in many applications. Acoustic sensors are one of the most frequently used sensors for event detection in sensor networks, but they are sensitive and difficult to handle because they vary greatly depending on the environment and target characteristics of the sensor field. In this paper, we propose a learning-based improvement of CFAR algorithm for increasing node-level event detection performance in acoustic sensor networks, and verify the effectiveness of the designed algorithm by comparing and evaluating the event detection performance with other algorithms. Our experimental results demonstrate the superiority of the proposed algorithm by increasing the detection accuracy by more than 45.16% by significantly reducing false positives by 7.97 times while slightly increasing the false negative compared to the existing algorithm.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.69-79
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• 2017

In this paper, we propose our four-phase life cycle of P2P botnet with corresponding detection methods and the future direction for more effective P2P botnet detection. Our proposals are based on the intensive analysis that compares existing P2P botnet detection schemes in different points of view such as life cycle of P2P botnet, machine learning methods for data mining based detection, composition of data sets, and performance matrix. Our proposed life cycle model composed of linear sequence stages suggests to utilize features in the vulnerable phase rather than the entire life cycle. In addition, we suggest the hybrid detection scheme with data mining based method and our proposed life cycle, and present the improved composition of experimental data sets through analysing the limitations of previous works.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.410-413
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• 2008

Existing Outlier detections have been widely studied in geosensor networks. Recently, machine learning and data mining have been applied the outlier detection method to build a model that distinguishes outliers based on anchored criterion. However, it is difficult for the existing methods to detect outliers against incoming time-variant data, because outlier detection needs to monitor incoming data and classify irregular attacks. Therefore, in order to solve the problem, we propose a time-variant outlier detection using 2-dimensional grid method based on unanchored criterion. In the paper, outliers using geosensor data was performed to classify efficiently. The proposed method can be utilized applications such as network intrusion detection, stock market analysis, and error data detection in bank account.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.4
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• pp.39-46
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• 1994

In this paper, the idea of using a multiple symbol obervation interval to improve error probability performance is applied to differential detection of MTCM(multiple trellis code modulation) with ${\Pi}$/4 shift QPSK, 8DPSK and 16DPSK. We propose two types of sliding multiple symbol differential detection algorithm, type 1 and type 2. The two types of sliding detection scheme are examined and compared with conventional(symbol-by-symbol) detection and bolck detection with these modulation formats in an additive white Gaussian noise(AWGN) using the Monte Carlo simulation. We show that the amount of improvement over conventional and block detection depends on the number of phases and the number of additional symbol intervals added to the observation. Computer simulagtion results are presented for 2,4,8 states in AWGN channel.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.287-297
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• 2015

A railway security detection system is very important. There are many safety factors that directly affect the safe operation of trains. Security detection technology can be divided into passive and active approaches. In this paper, we will first survey the railway security systems and compare them. We will also propose a subway security detection system with computer vision technology, which can detect three kinds of problems: the spark problem, the obstacle problem, and the lost screw problem. The spark and obstacle detection methods are unique in our system. In our experiment using about 900 input test images, we obtained about a 99.8% performance in F- measure for the spark detection problem, and about 94.7% for the obstacle detection problem.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.992-999
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• 2011

This paper proposed a human body posture recognition program based on haar-like feature and hand activity detection. Its distinguishing features are the combination of face detection and motion detection. Firstly, the program uses the haar-like feature face detection to receive the location of human face. The haar-like feature is provided with the advantages of speed. It means the less amount of calculation the haar-like feature can exclude a large number of interference, and it can discriminate human face more accurately, and achieve the face position. Then the program uses the frame subtraction to achieve the position of human body motion. This method is provided with good performance of the motion detection. Afterwards, the program recognises the human body motion by calculating the relationship of the face position with the position of human body motion contour. By the test, we know that the recognition rate of this algorithm is more than 92%. The results show that, this algorithm can achieve the result quickly, and guarantee the exactitude of the result.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1200-1207
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• 2008

In this paper, a novel fault detection method is proposed when the neutral-point-clamped inverter has a open-circuit fault in the switching device. This proposed method is configured with simple circuit and is achieved by a simple algorithm using the inherent characteristic of the continuous Pulse Width Modulation. Also, this method has the fast fault detection ability and is much simpler to embody, in comparison with conventional fault detection methods. This ability to detect fault minimizes harmful effect which are such as DC-link voltage unbalance and overstress to other switching devices. Therefore, this proposed fault detection method can improve reliability of NPC inverter system. Experimental results are presented to verify the validity of proposed fault detection method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.161-170
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• 2010

This paper provides a novel approach for a user oriented system for face detection system for application developers. Even though there are many open source or commercial libraries to solve the problem of face detection, they are still hard to use because they require specific knowledge on detail algorithmic techniques. The purpose of this paper is to come up with a high-level system for face detection with which users can develop systems easily and even without specific knowledge on face detection theories and algorithms. Important conditions are firstly considered to categorize the large problem space of face detection. The conditions identified here are then represented as expressions so that application developers can use them to express various problems. Once the conditions are expressed by developers, the interpreter proposed take the role to interpret the conditions, find and organize the optimal algorithms to solve the represented problem with corresponding conditions. A proof-of-concept is implemented and some example problems are tested and analyzed to show the ease of use and usability.