• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.1
• /
• pp.9-14
• /
• 2006

This paper presents methods of robot localization using recent radio frequency identification technology. If the absolute position and orientation of a tag are given in an indoor environment where RFID tags are installed, a robot can estimate its location using the relationship of the identified tag and the robot in a relative coordinate. To derive this relationship, we propose three estimation techniques using a model of a RFID reader, the direction of identification and the detection range. In this algorithm, a suitable estimation method is selected out of the three proposed techniques depending on the situations and trajectory of robot in the detection range. Simulation and experimental results show that the proposed methods can provide good performance for localization.

• YAKHAK HOEJI
• /
• v.29 no.5
• /
• pp.260-267
• /
• 1985

A gas chromatography with flame ionization detection was developed to measure tranylcypromine in rat urine. The method involves extraction of the drug and the internal standard, phenylpropylamine from the urine using ethyl acetate and back extraction into 0.5N $H_{2}SO_{4}$. Following final extraction using dichloromethane, both the drug and the internal standard were converted to trifluoroacetyl derivatives and analyzed using a column of 3% SE-30 on 80/100 mesh Chromosorb W(HP). A calibration curve was constructed in the range of $5~50{\mu}g$tranylcypromine sulfate in 0.5ml urine and found to be linear. The detection limit was $2{\mu}g$. The tranylcypromine could be analyzed with the percent recovery of $100.81{\pm}8.13$ (SD) ina concentration range of $8-40{\mu}g$ in 0.5ml urine. When 0.4mmol/kg dose of the drug was administered through, an oral route, excretion percent of tranylcypromine in rat urine over 36hr was found to be $11.90{\pm}6.04$ (SD) for tranyleypromine sulfate and $2.23{\pm}0.63$ (SD) for benzyl trans-2-phenylcyclopropanecarbamate.

• Journal of the Korean Electrochemical Society
• /
• v.14 no.1
• /
• pp.44-49
• /
• 2011

This paper describes an electrochemical immunosensor for simple, fast and quantitative detection of a urinary hippuric acid which is one of major biological indicator in toluene-exposed humans. The feature of this electrochemical system for immunoassay of hippuric acid is based on the direct conjugation of ferrocene to a hippuric acid. With the competition between the ferrocene-hippuric acid complex and hippuric acid for binding to the anti-hippuric acid monoclonal antibody coated onto gold nanoparticles, the electrical signals are turned out to be proportional to urinary hippuric acid in the range of 0.01-10 mg/mL, which is enough to be used for the point-of-care. The proposed electrochemical method could extend its applications to detect a wide range of different small molecules of antigens in the health care area.

• Bulletin of the Korean Chemical Society
• /
• v.19 no.5
• /
• pp.524-526
• /
• 1998

A sensitive and specific method is described for the determination of midazolam in human plasma. The drug was extracted from 1 mL of carbonate buffered plasma (pH 9.6) with 8 mL of diethyl ether. Famprofazone was used as internal standard. The organic phase was evaporated to dryness. The residue was dissolved in methanol for the direct analysis by gas chromatograph-nitrogen phosphorus detector system. In the concentration range of 1-5000 ng/mL, the calibration curve was linear. The coefficients of variation from the precision test were <6% at the range of the concentration of 0.10-2.00 μg/mL and the detection limit for midazolam in 1 mL of plasma was o.5 ng. This assay is more sensitive, selective, simple and rapid than earlier methods. Plasma midazolam concentrations were determined by this method after administration of midazolam.

• Journal of Sensor Science and Technology
• /
• v.33 no.2
• /
• pp.98-104
• /
• 2024

This study proposes a method to overcome the limited detection range of short-baseline stereo cameras (SBSCs). The proposed method includes two steps: (1) predicting an unscaled initial depth using monocular depth estimation (MDE) and (2) adjusting the unscaled initial depth by a scale factor. The scale factor is computed by triangulating the sparse visual keypoints extracted from the left and right images of the SBSC. The proposed method allows the use of any pre-trained MDE model without the need for additional training or data collection, making it efficient even when considering the computational constraints of small platforms. Using an open dataset, the performance of the proposed method was demonstrated by comparing it with other conventional stereo-based depth estimation methods.

• Journal of information and communication convergence engineering
• /
• v.4 no.4
• /
• pp.174-179
• /
• 2006

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

• International Journal of Contents
• /
• v.14 no.1
• /
• pp.1-6
• /
• 2018

Text, as one of the most influential inventions of humanity, has played an important role in human life since ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications such as the text data extracted from images that can provide information for automatic annotation, indexing, language translation, and the assistance systems for impaired persons. Therefore, natural-scene text detection with active research topics regarding computer vision and document analysis is very important. Previous methods have poor performances due to numerous false-positive and true-negative regions. In this paper, a fully-convolutional-network (FCN)-based method that uses supervised architecture is used to localize textual regions. The model was trained directly using images wherein pixel values were used as inputs and binary ground truth was used as label. The method was evaluated using ICDAR-2013 dataset and proved to be comparable to other feature-based methods. It could expedite research on text detection using deep-learning based approach in the future.

• Proceedings of KOSOMES biannual meeting
• /
• 2006.05a
• /
• pp.41-45
• /
• 2006

From the 1978 Seasat synthetic aperture radar(SAR) to present systems, spaceborne SAR has demonstrated the capability to image the Earth's ocean and land features over broad areas, day and night, and under most weather conditions. The application of SAR for surveillance of commercial fishing grounds can did in the detection of illegal fishing activities and provides more efficient use cf limited aircraft or patron craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which uses the ground-based radar system has some difficulties in detecting moving ships due to the limited detection range cf about 10 miles. This paper introduces the field testing results of ship detection by RADARSAT SAR imagery, and proposes a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.11 no.5
• /
• pp.267-276
• /
• 2016

Human detection technologies are widely used in smart homes and autonomous vehicles. However, in order to detect human, autonomous vehicle researchers have used a high-resolution LIDAR and smart home researchers have applied a camera with a narrow detection range. In this paper, we propose a novel method using a low-cost and low-resolution LIDAR that can detect human fast and precisely without complex learning algorithm and additional devices. In other words, human can be distinguished from objects by using a new human characteristics function which is empirically extracted from the characteristics of a human. In addition, we verified the effectiveness of the proposed algorithm through a number of experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.174-177
• /
• 1995

Detecting a small threat object either fast moving or floating on shallow water presents a formidable challenge to shipboard sensor systems, which must determine whether or not to launch defensive weapons in a timely manner. An integrated multisensor concept is envisioned wherein the combined use of active and passive sensor is employed for the detection of short duration targets in dense ocean surface clutter to maximize detection range. The objective is to develop multisensor integration techniques that operate on detection data prior to track formation while simultaneously fusing contacts to tracks. In the system concept, detections from a low grazing angle search radar render designations to a sensor-search infrared sensor for target classification which in turn designates an active electro-optical sensor for sector search and target verification.