• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.5
• /
• pp.521-528
• /
• 2019

Epilepsy is one of the most prevalent neurological diseases. Electroencephalogram (EEG) signals are widely used for monitoring and diagnosis tool for epileptic seizure. Typically, a huge amount of EEG signals is needed, where they are visually examined by experienced clinicians. In this study, we propose a simple automatic seizure detection framework using intracranial EEG signals. We suggest a sparse approximation based classification (SAC) scheme by solving overdetermined system. L1-norm minimization algorithms are utilized for efficient sparse signal recovery. For evaluation of the proposed scheme, the public EEG dataset obtained by five healthy subjects and five epileptic patients is utilized. The results show that the proposed fast L1-norm minimization based SAC methods achieve the 99.5% classification accuracy which is 1% improved result than the conventional L2 norm based method with negligibly increased execution time (42msec).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.3
• /
• pp.161-166
• /
• 2023

In recent years, new and variant hacking of binary codes has increased, and the limitations of techniques for detecting malicious codes in source programs and defending against attacks are often exposed. Advanced software security vulnerability detection technology using machine learning and deep learning technology for binary code and defense and response capabilities against attacks are required. In this paper, we propose a malware clustering method that groups malware based on the characteristics of the taint information after entering dynamic taint information by tracing the execution path of binary code. Malware vulnerability detection was applied to a three-layered Few-shot learning model, and F1-scores were calculated for each layer's CPU and GPU. We obtained 97~98% performance in the learning process and 80~81% detection performance in the test process.

• Journal of Korea Multimedia Society
• /
• v.24 no.7
• /
• pp.890-902
• /
• 2021

Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig's bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.1960-1963
• /
• 2003

For applications such as video surveillance and human computer interface, we propose an efficiently integrated method to detect and track faces. Various visual cues are combined to the algorithm: motion, skin color, global appearance and facial pattern detection. The ICA (Independent Component Analysis)-SVM (Support Vector Machine based pattern detection is performed on the candidate region extracted by motion, color and global appearance information. Simultaneous execution of detection and short-term tracking also increases the rate and accuracy of detection. Experimental results show that our detection rate is 91％ with very few false alarms running at about 4 frames per second for 640 by 480 pixel images on a Pentium IV 1㎓.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.336-338
• /
• 2006

One of the important topics concerning the safety of electrical and electronic system is the reliability of the wiring system. The Time-Frequency Domain Reflectometry(TFDR) is a state-of-the-art system for detection and estimation of the fault on a wiring/cable. The purpose of this paper is to implement a Labview based TFDR Real Time system though the instruments of PCI extensions for Instrumentation(PXI). The TFDR Real Time system consists of the five parts: Reference signal design, signal generation, signal acquisition, algorithm execution, results diplay part. In the signal generation and acquisition parts we adopt the Arbitrary Waveform Generator(AWG) and Digital Storage Oscilloscope(DSO) PXI modules which offer commonality, compatibility and easy integration at low cost. And execution of the PXI modules not only is controlled by the Labview programing but also the total system process is executed by the Labview application software.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.3
• /
• pp.117-124
• /
• 2023

In the machine vision inspection equipment, diameter measurement is important process in inspection of cylindrical object. However, machine vision inspection equipment requires complex algorithm processing such as camera distortion correction and perspective distortion correction, and the increase in processing time and cost required for precise diameter measurement. In this paper, we proposed the algorithm for diameter measurement of cylindrical object using the laser displacement sensor. In order to fit circle for given four input outer points, grid search algorithms using root-mean-square error and mean-absolute error are applied and compared. To solve the limitations of the grid search algorithm, we finally apply the singular-value-decomposition based circle fitting algorithm. In order to compare the performance of the algorithms, we generated the pseudo data of the outer points of the cylindrical object and applied each algorithm. As a result of the experiment, the grid search using root-mean-square error confirmed stable measurement results, but it was confirmed that real-time processing was difficult as the execution time was 10.8059 second. The execution time of mean-absolute error algorithm was greatly improved as 0.3639 second, but there was no weight according to the distance, so the result of algorithm is abnormal. On the other hand, the singular-value-decomposition method was not affected by the grid and could not only obtain precise detection results, but also confirmed a very good execution time of 0.6 millisecond.

• Journal of information and communication convergence engineering
• /
• v.19 no.3
• /
• pp.142-147
• /
• 2021

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.

• The KIPS Transactions:PartD
• /
• v.14D no.2
• /
• pp.145-156
• /
• 2007

Recently, the needs for effective real time change detection algorithms for XML/HTML documents and increased in such fields as the detection of defacement attacks to web documents, the version management, and so on. Especially, those applications of real time change detection for large number of XML/HTML documents require fast heuristic algorithms to be used in real time environment, instead of algorithms which compute minimal cost-edit scripts. Existing heuristic algorithms are fast in execution time, but do not provide satisfactory edit script. In this paper, we present existing algorithms XyDiff and X-tree Diff, analyze their problems and propose algorithm X-tree Diff which improve problems in existing ones. X-tree Diff+ has similar performance in execution time with existing algorithms, but it improves matching ratio between nodes from two documents by refining matching process based on the notion of efficiency of matching.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.1
• /
• pp.1-10
• /
• 2015

In this paper, we propose an efficient parallel implementation method of a widely used complex four-stage fire detection algorithm using a graphics processing unit (GPU) to improve the performance of the algorithm and analyze the performance of the parallel implementation method. In addition, we use seven different resolution videos (QVGA, VGA, SVGA, XGA, SXGA+, UXGA, QXGA) as inputs of the four-stage fire detection algorithm. Moreover, we compare the performance of the GPU-based approach with that of the CPU implementation for each different resolution video. Experimental results using five different fire videos with seven different resolutions indicate that the execution time of the proposed GPU implementation outperforms that of the CPU implementation in terms of execution time and takes a 25.11ms per frame for the UXGA resolution video, satisfying real-time processing (30 frames per second, 30fps) of the fire detection algorithm.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.13 no.5
• /
• pp.415-422
• /
• 2013

In this paper, we present Self-Checking look-up-table (LUT) based on Scalable Error Detection Coding (SEDC) scheme for use in fault-tolerant reconfigurable architectures. SEDC scheme has shorter latency than any other existing coding schemes for all unidirectional error detection and the LUT execution time remains unaffected with self-checking capabilities. SEDC scheme partitions the contents of LUT into combinations of 1-, 2-, 3- and 4-bit segments and generates corresponding check codes in parallel. We show that the proposed LUT with SEDC performs better than LUT with traditional Berger as well as Partitioned Berger Coding schemes. For 32-bit data, LUT with SEDC takes 39% less area and 6.6 times faster for self-checking than LUT with traditional Berger Coding scheme.