• The Journal of the Acoustical Society of Korea
• /
• v.43 no.2
• /
• pp.225-233
• /
• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.05a
• /
• pp.329-336
• /
• 2008

Nowadays with advancement in technology and aging society, the number of disabled citizens is increasing. The disabled citizens always need a caretaker for daily life routines especially for mobility. In future, the need is considered to increase more. To reduce the burden from the disabled, various devices for healthcare are introduced using computer technology. The power wheelchair is an important and convenient mobility device. The demand of power wheelchair is increasing for assistance in mobility. In this paper we proposed a robotic wheelchair for mobility aid to reduce the burden from the disabled. The main issue in an autonomous wheelchair is the automatic detection and avoidance of obstacles and going to the pre-designated place. The proposed algorithm detects the obstacles and avoids them to drive the wheelchair to the desired place safely. By this way, the disabled will not always have to worry about paying deep attention to the surroundings and his path.

• Journal of Broadcast Engineering
• /
• v.2 no.2
• /
• pp.146-155
• /
• 1997

The new video coding standard Iv1PEG-4 is enabling content-based functionalities. It requires a prior decomposition of sequences into video object planes (VOP's) so that each VOP represents moving objets. This paper addresses an image segmentation method for separating moving objects from still background (non-moving area) in video sequences using a statistical hypothesis test. In the proposed method. three consecutive image frames are exploited and a hypothesis testing is performed by comparing two means from two consecutive difference images. which results in a T-test. This hypothesis test yields a change detection mask that indicates moving areas (foreground) and non-moving areas (background), Moreover. an effective method for extracting

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.1
• /
• pp.129-135
• /
• 2003

Optical fiber has many advantages, such as high reliability, long lifetime, immunity to the electromagnetic interference, high speed response and low cost. In this study, we proposed and developed an optical fiber impact and pressure sensor for prevention of accident which occurs in the automatic system or auto door. The principle of the sensor is to detect different optical intensity caused by variation of a speckle pattern due to the external perturbation. Speckle pattern appears at the end of a multimode fiber in which coherent beam propagates. The fabricated sensor in this study was tested. As a result of experiments, amplitude of the output signal isn't linear, but it has sufficient sensitivity for a sensor. Moreover, we can control sensitivity of the sensor by an amplifier at receiver. It has several advantages which are ability of detection at all point on the multimode fiber, large sensitive area, and many application areas for a sensing impact and pressure.

• Journal of Advanced Navigation Technology
• /
• v.21 no.4
• /
• pp.325-331
• /
• 2017

As a GPS stability problem arises, MLAT system is spotlighted as an alternative technology of ADS-B. MLAT system has a high position accuracy as much as ADS-B. Also, MLAT receives the mode A,C,S, and 1090ES(ADS-B) signals from the mounted aircraft transponder. MLAT receives signals from several receiver units and calculates aircraft positions. MLAT has ADS-B level positioning accurarcy using GPS and can calculate the position information with objects independently. According to global environment changes, Local area multiltilateration(LAM) surveillance system is under development for moving vehicles and aircraft detection in airport. These are still under testing in Tae-an Airfield. In the paper, we analyzed the performance by comparing the calculated position data from MLAT to RTK. In order to confirm the position accuracy of MLAT and the deviation of position data between fixed target and moving target on the ground during the field test in Tae-an Airfield.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.6
• /
• pp.3230-3253
• /
• 2017

Android malware steals users' private information, and embedded unsafe advertisement (ad) libraries, which execute unsafe code causing damage to users. The majority of such traffic is HTTP and is mixed with other normal traffic, which makes the detection of malware and unsafe ad libraries a challenging problem. To address this problem, this work describes a novel HTTP traffic flow mining approach to detect and categorize Android malware and unsafe ad library. This work designed AndroCollector, which can automatically execute the Android application (app) and collect the network traffic traces. From these traces, this work extracts HTTP traffic features along three important dimensions: quantitative, timing, and semantic and use these features for characterizing malware and unsafe ad libraries. Based on these HTTP traffic features, this work describes a supervised classification scheme for detecting malware and unsafe ad libraries. In addition, to help network operators, this work describes a fine-grained categorization method by generating fingerprints from HTTP request methods for each malware family and unsafe ad libraries. This work evaluated the scheme using HTTP traffic traces collected from 10778 Android apps. The experimental results show that the scheme can detect malware with 97% accuracy and unsafe ad libraries with 95% accuracy when tested on the popular third-party Android markets.

• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1325-1333
• /
• 2016

Software classification has several applications such as copyright infringement detection, malware classification, and software automatic categorization in software repositories. It can be also employed by software filtering systems to prevent the transmission of illegal software. If illegal software is identified by measuring software similarity in software filtering systems, the average number of comparisons can be reduced by shrinking the search space. In this study, we focused on the classification of Windows executables using API call information and machine learning. We evaluated the classification performance of machine learning-based classifier according to the refinement method for API information and machine learning algorithm. The results showed that the classification success rate of SVM (Support Vector Machine) with PolyKernel was higher than other algorithms. Since the API call information can be extracted from binary executables and machine learning-based classifier can identify tampered executables, API call information and machine learning-based software classifiers are suitable for software filtering systems.

• Science of Emotion and Sensibility
• /
• v.11 no.2
• /
• pp.235-244
• /
• 2008

This study is to develop automatic extraction system of continuous blood pressure using ECG (Electrocardiogram) and PPG(Photoplethysmography) for u-health care technology. PTT (Pulse Transit Time) was determined from peak difference between ECG and PPG and its inverse made to get blood pressure. Since the peaks were vulnerable to be contaminated from noise and variation of amplitude, this study developed the adaptive algorithm for peak calculation in any noise condition. The developed method of the adaptive peak calculation was proven to make the standard deviations of PPT decrease to 28% and the detection of noise increase to 18%. Also, the correlation model such as blood pressure = -0.044 $\cdot$ PTT + 133.592 has successfully been determined for predicting the continuous pressure measured without using cuff but with using PPG and ECG, only.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.2 s.308
• /
• pp.57-64
• /
• 2006

Tn this paper, We propose a user friendly object-based multimedia retrieval system using the HCNN(HippoCampus Neural Network. Most existing approaches to content-based retrieval rely on query by example or user based low-level features such as color, shape, texture. In this paper we perform a scene change detection and key frame extraction for the compressed video stream that is video compression standard such as MPEG. We propose a method for automatic color object extraction and ACE(Adaptive Circular filter and Edge) of content-based multimedia retrieval system. And we compose multimedia retrieval system after learned by the HCNN such extracted features. Proposed HCNN makes an adaptive real-time content-based multimedia retrieval system using excitatory teaming method that forwards important features to long-term memories and inhibitory learning method that forwards unimportant features to short-term memories controlled by impression.

• Journal of the Korean Chemical Society
• /
• v.43 no.1
• /
• pp.104-109
• /
• 1999

A number of leukocytes increases when infected by a germ or virus. Detection of leukocyte levels can indicate of such medical informations as urogenital tract infection or other dysfunction. In this study, pentyl-3-thiophene-carboxlyate (PTC), pentyl-8-quinolinecarboxylate (PQC), and 2-Phenyl-4(N-tosyl-alanyloxyl)-thiazole (PTT) were synthesized, and the test strips were prepared with these substrates for quantifying leukocytes in urine. Among these substrates, the PTT test strip prepared in 0.5% borate buffer pH 8.0, 0.03% PTT, 0.1-0.8% PVP, and 1% decanol showed not only better color reaction but also an excellent application possibility to be used in automatic urine analyzer.