• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.76-82
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• 2019

The development of Internet technology and the deployment of smart devices provide a convenient environment for people, and this is becoming common with the technology called the Internet of Things (IoT). But the development of, and demand for, IoT technology is causing various problems, such as personal information leaks due to the attacks of hackers who exploit it. A number of devices are connected to a network, and network attacks that have been exploited in the existing PC environment are occurring in the IoT environment. When it comes to IP cameras, security incidents (such as distributed denial of service [DDoS] attacks, hacking someone's personal information, and monitoring without consent) are occurring. However, it is difficult to install and implement existing security solutions because memory space and power are limited owing to the characteristics of small devices in the IoT environment. Therefore, this paper proposes a security protocol that can look at and prevent IoT security threats. A security assessment verified that the proposed protocol is able to respond to various security threats that could arise in a network. Therefore, it is expected that efficient operation of this protocol will be possible if it is applied to the IoT environment.

• Korean Journal of Environmental Agriculture
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• v.35 no.1
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• pp.55-61
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• 2016

BACKGROUND: This study was carried out to survey the levels of heavy metals in perennial root vegetables and to assess dietary exposure and risk to the Korean population health.METHODS AND RESULTS: Perennial root vegetables (n=214) including Panax ginseng C.A mayer, Woodcultivated ginseng, Codonopsis lanceolata, and Platycodon granditloum were collected from markets or harvested from farmhouse in Korea. Lead(Pb), cadmium(Cd) and arsenic (As) analysis were performed with microwave device and inductively coupled plasma mass spectrometer. Limit of detection for heavy metals were 0.010~0.050 μg/kg, while limit of quantitation were 0.035~0.175 μg/kg. The recovery results were in the range of 76~102%. The average contents of heavy metals in perennial root vegetables were in the range of Pb 0.013(Panax ginseng C.A Mayer)~0.070 (Wood-cultivated ginseng) mg/kg, Cd 0.009(Panax ginseng C.A Mayer)~0.034(Codonopsis lanceolata) mg/kg, and As 0.002(Panax ginseng C.A Mayer)~0.004(Plafycodon grandiflorum) mg/kg, respectively. For risk assessment, daily intakes of heave metals were estimated and risk indices were calculated in comparison with reference dose. The dietary exposures of heavy metals through usual intake were Pb 0.070 μg/day, Cd 0.041 μg/day and As 0.008 μg/day, taking 0.03%, 0.08% and 0.0003% as risk indices, respectively.CONCLUSION: The risk level for Korean population exposed to heavy metals through intake of perennial root vegetables was far low, indicating of little possibility of concern.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.4
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• pp.344-351
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• 2009

Purpose: Optical imaging is providing great advance and improvement in genetic and molecular imaging of animals and humans. Optical imaging system consists of optical imaging devices, which carry out major function for monitoring, tracing, and imaging in most of molecular in-vivo researches. In bio-luminescent imaging, small animals containing luciferase gene locally irradiate light, and emitted photons transmitted through skin of the small animals are imaged by using a high sensitive charged coupled device (CCD) camera. In this paper, we introduced optical imaging system for the image acquisition of bio-luminescent signals emitted from small animals. Materials and Methods: In the system, Nikon lens and four LED light sources were mounted at the inside of a dark box. A cooled CCD camera equipped with a control module was used. Results: We tested the performance of the optical imaging system using effendorf tube and light emitting bacteria which injected intravenously into CT26 tumor bearing nude mouse. The performance of implemented optical imaging system for bio-luminescence imaging was demonstrated and the feasibility of the system in small animal imaging application was proved. Conclusion: We anticipate this system could be a useful tool for the molecular imaging of small animals adaptable for various experimental conditions in future.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• The Journal of Engineering Geology
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• v.18 no.3
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• pp.331-338
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• 2008

A new ground water sampler was developed and evaluated for target depth sampling under most rigorous field conditions. This new concept sampler comprises an air-cylinder, a hypodermic needle and a sampling bottle. Pressurized air or nitrogen gas can be used as a mechanical power source to operate the sampler. The air-cylinder is used to jab the hypodermic needle into the rubber cap of the sampling bottle. The hypodermic needle functions as a pathway to inject groundwater into the sampling bottle. Field test was conducted in a seawater intrusion monitoring well located at Handong district of Jeju Island. Water qualities in this well are periodically changed from the effects of sea water. Water sampling fir the same target depth in this well were tried at various times, and variations in electrical conductivity and pressure at the inside and outside of the sampler were measured using CTD divers. We found that the device could collect water samples only when it was actuated, and the pattern and range of variations in electrical conductivities and pressures measured at the inside and outside of the sampler were nearly identical. These results indicate that water samples using the sampler presented in this study represent correctly water qualities in which the samplings were made at a specific target depth in a well.

• Journal of the Korean Society for Railway
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• v.19 no.1
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• pp.38-45
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• 2016

The power transformation system of High Speed rolling stocks like KTX-Sancheon has shown excellent control capacities in the areas of riding comfortability, switching efficiency, safety and energy consumption due to technical developments in power-electronics, high speed & large scale integrated semiconductors and microprocessors. However, harmonics from IGBT, a high speed switching device used in the Convertor & Invertor equipment of rolling stocks have given rise to various problems in transformer substations, signaling systems, data transmission systems and facility monitoring systems. Especially, TI21 non-insulated track circuits have malfunctioned due to the influence of returning current harmonics which were generated at around of integer times of the number of power transformation equipment in the frequency domain. This paper, measures and analyzes various schemes to analyze the traveling path of the returning current harmonics generated due to the relationship between the rolling stocks and track circuits on site. Ultimately, theseschemes will be used to design high speed rolling stocks, AF track circuits and a common grounding network.

• Korean Journal of Optics and Photonics
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• v.19 no.1
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• pp.68-72
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• 2008

Fiber-optic strain sensors based on Bragg reflection gratings produce the change of reflection spectrum when an external stress is applied on the sensor. To measure the Bragg reflection wavelength in high speed, an arrayed waveguide grating device is incorporated in this work. By monitoring the output power from each channel of the AWG, the peak wavelength corresponding to the applied strain could be obtained. To enhance the accuracy of the AWG wavelength interrogation system, a chirped fiber Bragg grating with a 3-dB bandwith of 5.4 nm is utilized. The high-speed response of the proposed system is demonstrated by measuring a fast varying strain produced by the damped oscillation of a cantilever. An oscillation frequency of 17.8 Hz and a damping time constant of 0.96 second are obtained in this measurement.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.7
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• pp.183-194
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• 2018

With the rapid development of IoT(Internet of Things) technology, various convenient services such as smart home and smart city have been realized. However, IoT devices in unmanned environments are exposed to various security threats including eavesdropping and data forgery, information leakage due to unauthorized access. To build a secure IoT environment, it is necessary to use proper cryptographic technologies to IoT devices. But, it is impossible to apply the technologies applied in the existing IT environment, due to the limited resources of the IoT devices. In this paper, we survey the classification of IoT devices according to the performance and analyze the security requirements for IoT devices. Also we survey and analyze the use of cryptographic technologies in the current status of IoT open standard platform such as AllJoyn, oneM2M, IoTivity. Based on the research of cryptographic usage, we examine whether each platform satisfies security requirements. Each IoT open platform provides cryptographic technology for supporting security services such as confidentiality, integrity, authentication an authorization. However, resource constrained IoT devices such as blood pressure monitoring sensors are difficult to apply existing cryptographic techniques. Thus, it is necessary to study cryptographic technologies for power-limited and resource constrained IoT devices in unattended environments.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.429-438
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• 1997

Mechanical valve is one of the most widely used implantable artificial organs of which the reliability is so important that its failure means the death of patient. Therefore early noninvasive detection is essentially required, though mechanical valve failure with thrombosis is the most common. The objective of this paper is to detect the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter(DaqBook 100) and the periodogram is the main algorithm for obtaining spectrum. We made the thrombosis models using pellethane and silicon and they are thrombosis model on the valvular disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The performance of the measurment system was tested firstly using 1 KHz sinusoidal wave. The measurement system detected well 1KHz spectrum as expected. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. We find that the secondary peak changes according to the thrombosis model. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network, which contains 7, 000 input node, 20 hidden layer and 1 output was employed The trained neural network can distinguish normal and valve with more than 90% probability. As a conclusion, the noninvasive monitoring of implanted mechanical valve is possible by analysing the acoustical spectrum using neural network algorithm and this method will be applied to the performance evaluation of other implantable artificial organs.