• Biomedical Science Letters
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• v.21 no.4
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• pp.165-171
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• 2015

Microbial pathogens are responsible for most of the rapidly-spreading deadly infectious diseases against humans. Thus, there is an urgent need for efficient and rapid detection methods for infectious microorganisms. The detection methods should not only be targeted and specific, but they have to be encompassing of potential changes of the pathogen as it evolves and mutates quickly during an epidemic or pandemic. The existing diagnostics such as the antibody-based ELISA immunoassay and PCR methods are too selective and narrowly focused; they are insufficient to capture newly evolved mutant strains of the pathogen. Here, we introduce a fresh perspective on some new technologies, including aptamers and next generation sequencing for pathogen detection. These technologies are not in their infancy; they are reasonably mature and ready, and they hold great promise for unparalleled applications in pathogen detection.

• Journal of applied mathematics & informatics
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• v.30 no.5_6
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• pp.847-864
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• 2012

In recent years, using computer networks (wired and wireless networks) has been widespread in many applications. As computer networks become increasingly complex, the accompanied potential threats also grow to be more sophisticated and as such security has become one of the major concerns in them. Prevention methods alone are not sufficient to make them secure; therefore, detection should be added as another defense before an attacker can breach the system. Intrusion Detection Systems (IDSs) have become a key component in ensuring systems and networks security. An IDS monitors network activities in order to detect malicious actions performed by intruders and then initiate the appropriate countermeasures. In this paper, we present a survey and taxonomy of intrusion detection systems and then evaluate and compare them.

• International Journal of Oral Biology
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• v.42 no.4
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• pp.197-201
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• 2017

A novel method for the detection of hydrogen peroxide in aqueous solution was developed via reaction between $H_2O_2$, trivalent titanium ion ($Ti^{3+}$) and 4-(2-thiazolylazo) resorcinol (TAR), resulting in a ternary complex with a maximum UV absorbance at 530 nm. The CE detection of $H_2O_2$ was fast, sensitive and cost-effective without pretreatment procedures. $H_2O_2$ was detected within 15 min at 1 to $100{\mu}M$ range with the lowest detection limit at $1.0{\mu}M$. Under the optimized CE conditions, the concentration of $H_2O_2$ in coffee or tea extract was quantitatively determined. Our results show that CE detection of the ternary complex of $H_2O_2-Ti^{3+}$-TAR has potential applications for the detection of $H_2O_2$ in aqueous sources.

• The Plant Pathology Journal
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• v.39 no.1
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• pp.141-148
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• 2023

Black shoot blight disease caused by Erwinia pyrifoliae has serious impacts on quality and yield in pear production in Korea; therefore, rapid and accurate methods for its detection are needed. However, traditional detection methods require a great deal of time and fail to achieve absolute quantification. In the present study, we developed a droplet digital polymerase chain reaction (ddPCR) method for the detection and absolute quantification of E. pyrifoliae using a pair of species-specific primers. The detection range was 10³-10⁷ copies/ml (DNA templates) and cfu/ml (cell culture templates). This new method exhibited good linearity and repeatability and was validated by absolute quantification of E. pyrifoliae DNA copies from samples of artificially inoculated immature pear fruits. Here, we present the first study of ddPCR assay for the detection and quantification of E. pyrifoliae. This method has potential applications in epidemiology and for the early prediction of black shoot blight outbreaks.

• ETRI Journal
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• v.45 no.2
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• pp.329-337
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• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.5
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• pp.392-399
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• 2023

This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.

• Applied Chemistry for Engineering
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• v.31 no.3
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• pp.237-257
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• 2020

The role of nano bio-composites precursor to chitosan are innumerable and are known for having different applications in various branches of physical sciences. The application to the sensor development is relatively new, where only few literature works are available to address the specific and critical analysis of nanocomposites in the subject area. The bio-composites are potential and having greater affinity towards the heavy metals and several micro-pollutants hence, perhaps are having wider implications in the low or even trace level detection of the pollutants. The nano-composites could show good selectivity and suitability for the detection of the pollutants as they are found in the complex matrix. However, the greater challenges are associated using the bio-composites, since the biomaterials are prone to be oxidized or reduced at an applied potential and found to be a hinderance for the detection of target pollutants. In addition, the materials could proceed with a series of electrochemical reactions, which could produce different by-products in analytical applications, resulting in several complex phenomena in electrochemical processes. Therefore, this review addresses critically various aspects of an evaluation of nano bio-composite materials in the electrochemical detection of heavy metals and micro-pollutants from aqueous solutions.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.135-142
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• 1994

This paper presents average method to detect Visual-Transient Evoked Potential from the human scalp electric potential measured by the ElectroEncephaloGram. To confirm the validity of average method, the average-process is performed with both stimulated and no-stimulated potentials respectively, and both results are compared. The specific waveform, which is visual-transient evoked potential, is produced only in the case of stimulated potential. It was found that a dc-drift, due to instrumentation errors and other noises, can produce significant changes in the evoked-potential waveform. This can be removed with a high-pass filter (cut-off frequency=0.5Hz).

• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.83-90
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• 2015

Almost of current Automatic Incident Detection(AID) algorithms involve the vulnerability that detects the traffic accident in open road or in tunnel as the traffic jam not as the traffic accident. This paper proposes the improved accident detection algorithm to enhance the detection probability based on accident detection algorithms applied in open roads. The improved accident detection algorithm provides the preliminary judgment of potential accident by detecting the stopped object by Gaussian Mixture Model. Afterwards, it measures the detection area is divided into blocks so that the occupancy rate can be determined for each block. All experimental results of applying the new algorithm on a real incident was detected image without error.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.