• Journal of electromagnetic engineering and science
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• v.12 no.3
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• pp.196-202
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• 2012

Spectrum sensing is one of the most important functions in cognitive radio systems. In this paper, we focus on reducing the sensing time in a cooperative spectrum sensing paradigm. In the proposed scheme, a sequential detection technique is employed to provide a robust and quick detection system. Each of the secondary users measures the log-likelihood probability of the received signals and then sequentially reports to the base station. Here, the maximum ratio combining (MRC) technique is employed to reduce the average sample number (ASN) in order to reduce the sensing time. This proposed scheme is analyzed and simulated to illustrate the performance in comparison with the other given methods. Analysis and simulation are provided to validate the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.1 no.1
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• pp.19-31
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• 2007

RFID tag is detected by an RFID antenna and information is read from the tag detected, by an RFID reader. RFID tag detection by an RFID reader is very important at the deployment stage. Tag detection is influenced by factors such as tag direction on a target object, speed of a conveyer moving the object, and the contents of an object. The water content of the object absorbs radio waves at high frequencies, typically approximately 900 MHz, resulting in unstable tag signal power. Currently, finding the best conditions for factors influencing the tag detection requires very time consuming work at deployment. Thus, a quick and simple RFID tag detection scheme is needed to improve the current time consuming trial-and-error experimental method. This paper proposes a back-propagation learning-based RFID tag detection prediction scheme, which is intelligent and has the advantages of ease of use and time/cost savings. The results of simulation with the proposed scheme demonstrate a high prediction accuracy for tag detection on a water content, which is comparable with the current method in terms of time/cost savings.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.72-75
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• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.989-997
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• 2018

Massive event stream brings us great challenges in its volume, velocity, variety, value and veracity. Picking up some valuable information from it often faces with long detection time, high memory consumption and low detection efficiency. Aiming to solve the problems above, an efficient complex event detection method based on NFA_HTS (Nondeterministic Finite Automaton_Hash Table Structure) is proposed in this paper. The achievement of this paper lies that we successfully use NFA_HTS to realize the detection of complex event from massive RFID event stream. Specially, in our scheme, after using NFA to capture the related RFID primitive events, we use HTS to store and process the large matched results, as a result, our scheme can effectively solve the problems above existed in current methods by reducing lots of search, storage and computation operations on the basis of taking advantage of the quick classification and storage technologies of hash table structure. The simulation results show that our proposed NFA_HTS scheme in this paper outperforms some general processing methods in reducing detection time, lowering memory consumption and improving event throughput.

• Textile Coloration and Finishing
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• v.33 no.2
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• pp.49-63
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• 2021

In this study, a convenient approach for sensitive, quick and simple detection of hazardous acids was investigated. A series of azo dyes were synthesized and applied as a chemosensor for the acid detection both on fibers and in solution. Various aniline, benzothiazole or isoxazole derivatives were used as diazo component and coupled with N-benzyl-N-ethylaniline or 2,2'-(phenylimino)bis-ethanol to give azo based dyes. The acid sensing phenomenon was observed by naked-eye and detection was further confirmed by UV-Vis spectrophotometer and hue difference(ΔH^*) evaluation. The developed sensors showed a distinct and quick color change from yellow to magenta by addition of trace amounts of the hazardous acids. The absorption maxima was shifted to a longer wavelength by 70 ~ 150nm and hue difference(ΔH^*) was 60 ~ 120°. A cotton fiber coated with Dye 1 exhibited excellent storage stability under various temperature(-30 ~ 43℃) and humidity(30 ~ 80%) conditions without discoloration and fading of the fiber sensors. Also the acid sensing properties were maintained.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.530-535
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• 2002

An approach for quickly updating GIS building data using high resolution remotely sensed data is proposed in this paper. High resolution remotely sensed data could be aerial photographs, satellite images and airborne laser scanning data. Data from different types of sensors are integrated in building extraction. Based on the extracted buildings and the outdated GIS database, the change-detection-template can be automatically created. Then, GIS building data can be fast updated by semiautomatically processing the change-detection-temp late. It is demonstrated that this approach is quick, effective and applicable.

• Food Science and Biotechnology
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• v.17 no.3
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• pp.651-654
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• 2008

In order to enhance the efficacy of norovirus detection by reverse transcriptase-polymerase chain reaction (RT-PCR) and nested PCR, this study developed a norovirus mRNA concentration method using poly oligo dT-conjugated magnetic beads. An efficient norovirus detection protocol was performed on commercial ham using 2 viral elution buffers (glycine buffer and Tris beef extract buffer) and 2 concentration solutions [polyethylene glycol (PEG) and zirconium hydroxide]. The different approaches were verified by RT-PCR and nested PCR. This method was performed on ham in less than 8 hr by artificial inoculation of serial dilutions of the virus ranging from 1,000 to 1 RT-PCR unit/mL. The viral extraction and concentration method had 10-fold higher sensitivity using the combination of Tris beef extract buffer and PEG as compared to glycine buffer and zirconium hydroxide. This method proved that RT-PCR and nested PCR have the sensitive ability to detect norovirus in commercial ham, in that norovirus was successfully detected in artificially contaminated samples at a detection level as low as 1-10 RT-PCR unit/mL. Overall, such a detection limit suggests this protocol is both quick and efficient in terms of its potential use for detecting norovirus in meat products.

• Journal of the Korean Institute of Navigation
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• v.22 no.4
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• pp.59-67
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• 1998

A quick and automatic detection with no harm to the goods is very important task for improving quality control, process control and labour reduction. In real fields of industry, defect detection is mostly accomplished by skillful workers. A narrow band eliminating spatial filter having characteristics of removing the specified spatial frequency is developed by the author, and it is proved that the filter has an excellent ability for on-line and real time detection of surface defect. By the way,. this spatial filter shows a ripple phenominum in filtering characteristics. So, it is necessary to remove the ripple component for the improvement of filter gain, moreover efficiency of defect detection. The spatial filtering method has a remarkable feature which means that it is able to set up weighting function for its own sake, and which can to obtain the best signal relating to the purpose of the measurement. Hence, having an eye on such feature, theoretical analysis is carried out at first for optimal design of narrow band eliminating spatial filter, and secondly, on the basis of above results spatial filter is manufactured, and finally advanced effectiveness of spatial filter is evaluated experimentally.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.