• Bulletin of the Korean Chemical Society
• /
• v.31 no.12
• /
• pp.3525-3529
• /
• 2010

Tyrosine kinase inhibitors (TKIs) are currently used in the treatment of patients with advanced lung cancer. Recent studies on non-small cell lung cancer have shown that some patients carry somatic mutations in the epidermal growth factor receptor (EGFR) gene. Such mutations correlate with the effectiveness of certain TKIs. To detect a small amount of mutant EGFR among an abundance of wild-type EGFR, we have developed a highly sensitive and simple method using PNA-mediated real-time PCR clamping. The PNA-mediated real-time PCR clamping enables detection of EGFR mutants down to approximately 1% mutant -to- wild type. The total assay time was short as it required only 2.0 hr. Thus, PNA-mediated real-time PCR clamping can easily be applied to clinical samples for identification of DNA carrying EGFR mutations and also appear to be the best assay to detect somatic mutations.

• Smart Media Journal
• /
• v.1 no.3
• /
• pp.48-55
• /
• 2012

In general, background subtraction based methods are used to detect the moving objects in visual tracking applications. In this paper we employed background subtraction based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection and we compare those in terms of detection performance and computational complexity. In the first approach we used single background and in the second approach we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped object. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short time fully occlusion and illumination changes, as well as it can operate in real time.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.12
• /
• pp.79-85
• /
• 2017

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.2S
• /
• pp.629-637
• /
• 2000

The content-based information retrieval for a multimedia database uses feature information extracted from the compressed videos. This paper presents an effective method to detect scene changes from compressed videos. Scene changes are detected with DC values of DCT coefficients in MPEG-1 encoded video sequences. Instead of decoding full frames. partial macroblocks of each frame, horizontal and vertical macroblocks, are decoded to detect scene changes. This method detects abrupt scene changes by decoding minimal number of blocks and saves a lot of computation time. The performance of the proposed algorithm is analyzed based on the precision and the recall. The experimental results show the effectiveness in computation time and detection rate to detect scene changes of various MPEG-1 video streams.

• Journal of Information Processing Systems
• /
• v.15 no.6
• /
• pp.1392-1405
• /
• 2019

The cathode voltage of aluminum electrolytic cell is relatively stable under normal conditions and fluctuates greatly when it has an anomaly. In order to detect the abnormal range of cathode voltage, an anomaly detection algorithm based on sliding window was proposed. The algorithm combines the time series segmentation linear representation method and the k-nearest neighbor local anomaly detection algorithm, which is more efficient than the direct detection of the original sequence. The algorithm first segments the cathode voltage time series, then calculates the length, the slope, and the mean of each line segment pattern, and maps them into a set of spatial objects. And then the local anomaly detection algorithm is used to detect abnormal patterns according to the local anomaly factor and the pattern length. The experimental results showed that the algorithm can effectively detect the abnormal range of cathode voltage.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.11
• /
• pp.1770-1781
• /
• 1989

Two robots working in a common workspace may collide with each other. In this paper, a collision-free motion planning algorithm using view time concept is proposed to detect and avoid collision before robot motion. Collision may occur not only at the robot end effector but also at robot links. To detect and avoid potential collisions, the trajectory of the first robot is sampled periodically at every view time and the region in Cartesian space swept by the first robot is viewed as an obstacle during a single sampling period. The forbidden region in the joint constraint map (JCM). The JCM's are obtained in this way at every view time. An algorithm is established for collision-free motion planning of the two robot system from the sequence of JCM's and it is verified by simulations.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.190-194
• /
• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

• Journal of the Korean Institute of Telematics and Electronics T
• /
• v.35T no.2
• /
• pp.28-34
• /
• 1998

In this paper, I'd like to make a suggestion for boundary line detect algorithm which is used 3-D image processing system in order to track moving object. Through this study, more than anything else, difference image method was adopted to detect moving object in input image. To detect moving object, I made use of detect windows constructed by 4's predictive areas and object area for the purpose of reducing processing time and its size was determined by the size of moving object and prediction parameter directed center position. And also, tracking camera was movable toward the direction of X, Y by DC motor. As a conclusion of the study proposed algorithm, I found out the following results that tracking error was less than 6% of total moving object size and maximum tracking time 2 seconds by toy-car simulation.

• Journal of Advanced Navigation Technology
• /
• v.7 no.1
• /
• pp.38-50
• /
• 2003

In this paper, a new high resolution reflectometry scheme, time-frequency domain reflectometry (TFDR), is proposed to detect and locate fault in wiring. Traditional reflectometry methods have been achieved in either the time domain or frequency domain only. However, time-frequency domain reflectometry utilizes time and frequency information of a transient signal to detect and locate the fault. The time-frequency domain reflectometry approach described in this paper is characterized by time-frequency reference signal design and post-processing of the reference and reflected signals to detect and locate the fault. Design of the reference signal in time-frequency domain reflectometry is based on the determination of the frequency bandwidth of the physical properties of cable under test. The detection and estimation of the fault on the time-frequency domain reflectometry relies on the time-frequency domain reflectometry is compared with commercial time domain reflectomtery (TDR) instrument. In these experiments provided in this paper, TFDR locates the fault with smaller error than TDR. Knowledge of time and frequency localized information for the reference and reflected signal gained via time-frequency analysis, allows one to detect the fault and estimate the location accurately.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.403-403
• /
• 2000

Generally estimation of driving direction uses the way which uses lane detection and vanishing point in autonomous-driving system. Especially we use Sub-window for decreasing Process time when we detect lane, but fixed sub-window can not detect lane because of some factors in road image. So we suggest algorithm using one-dimension line scan method to detect an exact position of lane.