• Biomedical Engineering Letters
• /
• v.8 no.4
• /
• pp.373-382
• /
• 2018

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

• Journal of Broadcast Engineering
• /
• v.4 no.2
• /
• pp.155-163
• /
• 1999

In this paper, we propose novel scene change detection and key frame selection techniques, which use two feature images, i.e., DC and edge images, extracted directly from MPEG compressed video. For fast edge image extraction. we suggest to utilize 5 lower AC coefficients of each DCT. Based on this scheme, we present another edge image extraction technique using AC prediction. Although the former is superior to the latter in terms of visual quality, both methods all can extract important edge features well. Simulation results indicate that scene changes such as cut. fades, and dissolves can be correctly detected by using the edge energy diagram obtained from edge images and histograms from DC images. In addition. we find that our edge images are comparable to those obtained in the spatial domain while keeping much lower computational cost. And based on HVS, a key frame of each scene can also be selected. In comparison with an existing method using optical flow. our scheme can select semantic key frames because we only use the above edge and DC images.

• Journal of Microbiology
• /
• v.44 no.4
• /
• pp.403-408
• /
• 2006

The purpose of this study was to determine the efficacy of a nucleic acid sequence-based amplification (NASBA) method of detecting noroviruses in artificially and naturally contaminated shellfish. We used 58 fecal samples that tested positive for noroviruses with electron microscopy (EM) to develop an NASBA assay for these viruses. Oligonucleotide primers targeting the polymerase coding region were used to amplify the viral RNA in an isothermal process that resulted in the accumulation of RNA amplicons. These amplicons were detected by hybridization with digoxigenin-labeled oligonucleotide probes that were highly specific for genogroup I (GI) and genogroup II (GII) of noroviruses. The expected band of 327bp appeared in denaturing agarose gel without any nonspecific band. The specific signal for each amplicon was obtained through Northern blotting in many repeats. All fecal samples of which 46(79.3%) belonged to GII and 12(20.6%) belonged to GI were positive for noroviruses by EM and by NASBA. Target RNA concentrations as low as 5pg/ml were detected in fecal specimens using NASBA. When the assay was applied to artificially contaminated shellfish, the sensitivity to nucleic acid was 100pg/1.5g shellfish tissue. The potential use of this assay was also confirmed in naturally contaminated shellfish collected from different ponds in Guangzhou city of China, of which 24 (18.76%) out of 128 samples were positive for noroviruses; of these, 19 (79.6%) belonged to GII and 5 (20.4%) belonged to GI. The NASBA assay provided a more rapid and efficient way of detecting noroviruses in fecal samples and demonstrated its potential for detecting noroviruses in food and environmental samples with high specificity and sensitivity.

• Journal of KIISE:Information Networking
• /
• v.33 no.2
• /
• pp.131-138
• /
• 2006

To achieve security in wireless sensor networks(WSN), it is important to be able to encrypt and authenticate messages sent among sensor nodes. Due to resource constraints, many key agreement schemes used in general networks such as Diffie-Hellman and public-key based schemes are not suitable for wireless sensor networks. The current pre-distribution of secret keys uses q-composite random key and it randomly allocates keys. But there exists high probability not to be public-key among sensor nodes and it is not efficient to find public-key because of the problem for time and energy consumption. To remove problems in pre-distribution of secret keys, we propose a new cryptographic key management protocol, which is based on the clustering scheme but does not depend on probabilistic key. The protocol can increase efficiency to manage keys because, before distributing keys in bootstrap, using public-key shared among nodes can remove processes to send or to receive key among sensors. Also, to find outcompromised nodes safely on network, it selves safety problem by applying a function of lightweight attack-detection mechanism.

• Bulletin of the Korean Chemical Society
• /
• v.34 no.6
• /
• pp.1684-1688
• /
• 2013

A rapid and sensitive method for determining usnic acid of Lethariella cladonioides in rat was established using high performance liquid chromatography (HPLC) quadrupole time-of-flight (QTOF) tandem mass (MS/MS). Rat plasma was pretreated by mixture of acetonitrile and chloroform to precipitate plasma proteins. Chromatographic separation was achieved on a column ($50{\times}2.1$ mm, $5{\mu}m$) with a mobile phase consisting of water (containing $5{\times}10^{-3}$ M ammonium formate, pH was adjusted to 3.0 with formic acid) and acetonitrile (20:80, v/v) at a flow rate of 0.3 mL/min. A tandem mass spectrometric detection with an electrospray ionization (ESI) interface was conducted via collision induced dissociation (CID) under negative ionization mode. The MS/MS transitions monitored were m/z 343.0448 ${\rightarrow}$ m/z 313.2017 for usnic acid and m/z 153.1024 ${\rightarrow}$ m/z 136.2136 for protocatechuic acid (internal standard). The linear range was calculated to be 2.0-160.0 ng/mL with a detection limit of 3.0 pg/mL. The inter- and intra-day accuracy and precision were within ${\pm}7.0%$. Pharmacokinetic study showed that the apartment of usnic acid in vivo confirmed to be a two compartment open model. The method was fully valid and will probably be an alternative for pharmacokinetic study of usnic acid.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.4
• /
• pp.1137-1144
• /
• 2012

Introduction: Recent studies have shown that circulating tumor cells (CTCs) play potential roles as diagnostic and prognostic biomarkers with various cancer types. The aim of this study was to comprehensively and quantitatively summarize the evidence for the use of CTCs to predict the survival outcome of lung cancer patients. Materials and Methods: Relevant literature was identified using Medline and EMBASE. Patients' clinical characteristics, overall survival (OS) and progression-free survival (PFS) together with CTC positive rates at different time points (before, during and after treatment) were extracted. A meta-analysis was performed to clarify the prognostic role of CTCs and the correlation between the CTC appearance and clinical characteristics. Results: A total of 12 articles containing survival outcomes and clinical characteristics and 15 articles containing only clinical characteristics were included for the global meta-analysis. The hazard ratio (HR) for OS predicted by pro-treatment CTCs was 2.61 [1.82, 3.74], while the HR for PFS was 2.37 [1.41, 3.99]. The HR for OS predicted by post-treatment CTCs was 4.19 [2.92, 6.00], while the HR for PFS was 4.97 [3.05, 8.11]. Subgroup analyses were conducted according to histological classification and detection method. Odds ratio (OR) showed the appearance of pro-treatment CTCs correlated with the lymph node status, distant metastasis, and TNM staging, while post-treatment CTCs correlated with TNM staging only. Conclusion: Detection of CTCs in the peripheral blood indicates a poor prognosis in patients with lung cancer.

• The Journal of the Korea Contents Association
• /
• v.16 no.10
• /
• pp.467-476
• /
• 2016

With the development of information technology, Small office Home office(SOHO) is picking up. SOHO generally uses Wi-Fi. The wireless LAN environment using 802.11 protocol is easily affected by DoS attacks. To deal with these threats, there is Wireless Intrusion Detection System(WIDS). However, legacy products of WIDS cannot be easily used by SOHO because they are expensive and require management burden. In this paper, Smart WIDS for SOHO is proposed and implemented on Raspberry Pi2. And, it provides the interface for attack detection notice to android smart phone. Smart WIDS detects Masquerading DoS and Resource Depletion DoS based on IEEE 802.11 so that we notice the attempt of cracking Pre-shared Key(PSK), Man-In-The-Middle(MITM), and service failure.

• Convergence Security Journal
• /
• v.14 no.4
• /
• pp.3-8
• /
• 2014

Wireless Ad hoc Network is threatened from many types of attacks because of its open structure, dynamic topology and the absence of infrastructure. Attacks by malicious nodes inside the network destroy communication path and discard packet. The damage is quite large and detecting attacks are difficult. In this paper, we proposed attack detection technique using secure authentication infrastructure for efficient detection and prevention of internal attack nodes. Cluster structure is used in the proposed method so that each nodes act as a certificate authority and the public key is issued in cluster head through trust evaluation of nodes. Symmetric Key is shared for integrity of data between the nodes and the structure which adds authentication message to the RREQ packet is used. ns-2 simulator is used to evaluate performance of proposed method and excellent performance can be performed through the experiment.

• Journal of the Korean Society of Safety
• /
• v.31 no.6
• /
• pp.121-128
• /
• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.430-436
• /
• 2021

The non-contact body temperature measurement system is one of the key factors, which is manage febrile diseases in mass facilities using optical and thermal imaging cameras. Conventional systems can only be used for simple body temperature measurement in the face area, because it is used only a deep learning-based face detection algorithm. So, there is a limit to detecting abnormal symptoms of the infants and young children, who have difficulty expressing their opinions. This paper proposes an improved facial expression recognition algorithm for detecting abnormal symptoms in infants and young children. The proposed method uses an object detection model to detect infants and young children in an image, then It acquires the coordinates of the eyes, nose, and mouth, which are key elements of facial expression recognition. Finally, facial expression recognition is performed by applying a selective sharpening filter based on the obtained coordinates. According to the experimental results, the proposed algorithm improved by 2.52%, 1.12%, and 2.29%, respectively, for the three expressions of neutral, happy, and sad in the UTK dataset.