• Korean Journal of Microbiology
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• v.36 no.3
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• pp.242-248
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• 2000

Since the official allowance of bottled water at Korean domestic market in 1995, Pseudomo~zas aemginosu has been detected from 2.3% and 1.2% of source and products of bottled water sa~nple tested, respectively. according to the nation-wide dala froin May 1995 to December 1996. Therefore, P aeivginosa was the second most important parameter, next to colifoi~ns, anlong the bacieriological parameters regulated for bonled water. The official standard method initially adopted the Japanese officlal method and Standard Methods of the US, w~hich is using aspai-agiii-acetamid mnedia(A-A method). how eve^; the method showed low specificity regardless of the high sensitivity. The $42^{\circ}C$ growth test was the best biochemical featu1-e differentiating the P uelarginosu 6-om P aei-uginosa-like species such as P puririir and P Jhoi.escens amo1zg the other characierisiics such as fluorescence pigment_ pyocyanin, casein hy&olysis, etc. Thel-efore, addition of the $42^{\circ}C$ growth Lest in advance ofthe biochemical identification test, when sainple is positive by A-A method, should strengthen the spec~IiclQ w~tli ~ninin~um addition of testing load.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.41-50
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• 2008

Recent progress in state-of-the-art geospatial information technologies such as digital mapping, LiDAR(Light Detection And Ranging), and high-resolution satellite imagery provides various data sources fer Digital Elevation Model(DEM). DEMs are major source to extract elements of the hydrological terrain property that are necessary for efficient watershed management. Especially, watersheds extracted from DEM are important geospatial database to identify physical boundaries that are utilized in water resource management plan including water environmental survey, pollutant investigation, polluted/wasteload/pollution load allocation estimation, and water quality modeling. Most of the previous studies related with watershed extraction using DEM are mainly focused on the hydrological elements analysis and preprocessing without considering grid size of the DEMs. This study aims to analyze accuracy of the watersheds extracted from DEMs with various grid sizes generated by LiDAR data and digital map, and appropriate preprocessing methods.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.609-615
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• 2013

As with any ships, adequate power provision is crucial, especially on the ocean navigating ships far from the land. In order to resolve the effective and economic power supply system of any ship in operation, in this paper, we propose a power management system that intelligently controls the power supply in ships. Power management systems in this design consist of a power load detection system, a generator configuration system, and a power monitoring system respectively. The CT / PT sensor is used to measure amount of current and power in the power detection system, and according to the collected information from various sensor, the generator configuration system will switch on and off the main / sub generator effectively. Finally, the power monitoring system will display all status information of this power management system at a glance for users. This power management systems implemented in this paper is evaluated via real-time experiments, which works well as designed, and certified by KSCIEC61892-1:2012 and KSCIEC60950-1:2008.

• Structural Monitoring and Maintenance
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• v.2 no.3
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• pp.253-267
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• 2015

Corrosion has significant adverse effects on the durability of reinforced concrete (RC) structures, especially those exposed to a marine environment and subjected to mechanical stress, such as bridges, jetties, piers and wharfs. Previous studies have been carried out to investigate the corrosion behaviour of steel rebar in various concrete structures, however, few studies have focused on the corrosion monitoring of RC structures that are subjected to both mechanical stress and environmental effects. This paper presents an exploratory study on the development of corrosion monitoring and detection techniques for RC structures under the combined effects of external loadings and corrosive media. Four RC beams were tested in 3% NaCl solutions under different levels of point loads. Corrosion processes occurring on steel bars under different loads and under alternative wetting - drying cycle conditions were monitored. Electrochemical and microscopic methods were utilised to measure corrosion potentials of steel bars; to monitor galvanic currents flowing between different steel bars in each beam; and to observe corrosion patterns, respectively. The results indicated that steel corrosion in RC beams was affected by local stress. The point load caused the increase of galvanic currents, corrosion rates and corrosion areas. Pitting corrosion was found to be the main form of corrosion on the surface of the steel bars for most of the beams, probably due to the local concentration of chloride ions. In addition, visual observation of the samples confirmed that the localities of corrosion were related to the locations of steel bars in beams. It was also demonstrated that electrochemical devices are useful for the detection of RC beam corrosion.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.123-132
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• 2021

Malicious activities of Botnets are responsible for huge financial losses to Internet Service Providers, companies, governments and even home users. In this paper, we try to confirm the possibility of detecting botnet traffic by applying the deep learning model Convolutional Neural Network (CNN) using the CTU-13 botnet traffic dataset. In particular, we classify three classes, such as the C&C traffic between bots and C&C servers to detect C&C servers, traffic generated by bots other than C&C communication to detect bots, and normal traffic. Performance metrics were presented by accuracy, precision, recall, and F1 score on classifying both known and unknown botnet traffic. Moreover, we propose a stackable botnet detection system that can load modules for each botnet type considering scalability and operability on the real field.

• Journal of Platform Technology
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• v.8 no.4
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• pp.11-19
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• 2020

In an autonomous driving environment, dynamic recognition of objects is essential as the situation changes in real time. In addition, as the number of sensors and control modules built into an autonomous vehicle increases, the amount of data the central control unit has to process also rapidly increases. By minimizing the output data from the sensor, the load on the central control unit can be reduced. This study proposes a dynamic object recognition algorithm solely using the embedded processor on a LiDAR sensor. While there are open source algorithms to process the point cloud output from LiDAR sensors, most require a separate high-performance processor. Since the embedded processors installed in LiDAR sensors often have resource constraints, it is essential to optimize the algorithm for efficiency. In this study, an embedded processor based object recognition algorithm was developed for autonomous vehicles, and the correlation between the size of the point clouds and processing time was analyzed. The proposed object recognition algorithm evaluated that the processing time directly increased with the size of the point cloud, with the processor stalling at a specific point if the point cloud size is beyond the threshold

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2614-2624
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• 2022

Monte Carlo simulations were used to model a portable Neutron backscattering (NBT) sensor suitable for detecting plastic anti-personnel mines (APMs) buried in dry and moist soils. The model consists of a 100 MBq ²⁵²Cf source encapsulated in a neutron reflector/shield assembly and centered between two ³He detectors. Multi-parameter optimization was performed to investigate the efficiency of Be/Zr(BH₄)₄ and Be/Be(BH₄)₂ assemblies in terms of increasing the signal-to-background (S/B) ratio and reducing the total dose equivalent rate. The MCNP results showed that 2 cm Be/3 cm Zr(BH₄)₄ and 2 cm Be/3 cm Be(BH₄)₂ are the optimal configurations. However, due to portability requirements and abundance of Be, the ²⁵²Cf-2 cm Be/3 cm Be(BH₄)₂ NBT model was selected to scan the center of APM buried 3 cm deep in dry and moist soils. The selected NBT model has positively identified the APM with a S/B ratio of 886 for dry soils of 1 wt% hydrogen content and with S/B ratios of 615, 398, 86, and 12 for the moist soils containing 4, 6, 10, and 14 wt% hydrogen, respectively. The total dose equivalent rate reached 0.0031 mSv/h, suggesting a work load of 8 h/day for 806 days within the permissible annual dose limit of 20 mSv.

• Journal of Industrial Convergence
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• v.20 no.7
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• pp.65-71
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• 2022

With the development of ICT, as the era of the 4th industrial revolution arrives, the amount of data is enormous, and as big data technologies emerge, technologies for processing, storing, and processing data are becoming important. In this paper, we propose a system that detects events through monitoring and judges them using hash values because the damage to important files in case of leakage in industries and public places is serious nationally and property. As a research method, an optional event method is used to compare the hash value registered in advance after performing the encryption operation in the event of a file leakage, and then determine whether it is an important file. Monitoring of specific events minimizes system load, analyzes the signature, and determines it to improve accuracy. Confidentiality is improved by comparing and determining hash values pre-registered in the database. For future research, research on security solutions to prevent file leakage through networks and various paths is needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.125-134
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• 2023

In this paper, we propose an efficient surveillance RADAR(RAdio Detection And Ranging) interface control unit(ICU) design in the naval combat system. The proposed design applied a standardized architecture for modules that can be shared in ship combat system software. An error detection function for each link was implemented to increase the recognition speed of disconnection. Messages that used to be sent periodically for human-computer interaction(HCI) are now only transmitted when there is a change in the datagram. This can reduce the processing load of the console. The proposed design supplements the radar with the waterfall scope and time-limited splash recognition in relation to the hit check and zeroing of the shot when the radar processing ability is low due to the adoption of a low-cost commercial radar in the ship. Therefore, it is easy for the operator to determine whether the shot is hit or not, the probability of wrong recognition can be reduced, and the radar's resources can be obtained more effectively.