• Proceedings of the Korean Society of Crop Science Conference
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• 2020.06a
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• pp.158-158
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• 2020

• Bulletin of the Korean Chemical Society
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• v.27 no.10
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• pp.1613-1617
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• 2006

Optimum analytical conditions for cyclic voltammetry (CV) and square wave (SW) stripping voltammetry were determined using mercury-mixed carbon nanotube paste electrode (PE). The results approached the microgram working ranges of SW: 10.0-80.0 $ugL^{-1}$ and CV: 100-700 $ugL^{-1}$ Cd (II); working conditions of 300-Hz frequency, 100 mV amplitude, 1.6 V accumulation potential, 400 sec accumulation time, and 40 mV increment potential. First, analysis was performed through direct assay of cadmium ions deep into the fishs brain core and plant tissue in real time with a preconcentration time of 400 sec. The relative standard deviation of 10.0 $mgL^{-1}$ Cd (II) observed was 0.064 (n = 12) at optimum conditions. The low detection limit (S/N) was set at 0.6 $ugL^{-1}$ ($5.33{\times}10^{-9}$ M). The methods can be used in direct analysis in vivo or in real-time monitoring of plant tissue.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.734-743
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• 1999

In this paper, a real-time communication method using a PICNET-NP(Plant instrumentation and Control Network for Nuclear Power plant) is proposed with an analysis of the control network requirements of DCS(Distributed Control System) in nuclear power plants. The method satisfies deadline in case of worst data traffics by considering aperiodic and periodic real-time data and others. In addition, the method was used to analyze the data characteristics of the DCS in existing nuclear power plant. The result shows that use of this method meets the response time requirement(100ms).

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.480-483
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• 1997

A fossil power plant can be modeled by a lot of algebraic equations and differential equations. When we simulate a large, complicated fossil power plant by a computer such as workstation or PC, it takes much time until overall equations are completely calculated. Therefore, new processing systems which have high computing speed is ultimately needed to develope real-time simulators. Vital points of real-time simulators are accuracy, computing speed, and deadline observing. In this paper, we present a enhanced strategy in which we can provide powerful computing power by parallel processing of DSP processors with communication links. We designed general purpose DSP modules, and a VME interface module. Because the DSP module is designed for general purpose, we can easily expand the parallel system by just connecting new DSP modules to the system. Additionally we propose methods about downloading programs, initial data to each DSP module via VME bus, DPRAM and processing sequences about computing and updating values between DSP modules and CPU30 board when the simulator is working.

• 연구논문집
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• s.27
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• pp.167-174
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• 1997

Point of Production/Manufacturing Execution Systems are an essential component of operations in today's competitive business environments, which require greater production efficiency and effectiveness. POP/MES focuses on the valuing-adding processes, helping to reduce manufacturing cycle time, improve product quality, reduce WIP, reduce or eliminate paperwork between shifts, reduce lead time and empowering plant operations staff. In this paper, we implement POP/MES to manage real-time plant floor data which is gathered by I/O server into database management system. I/O server is a software allows data exchange between factory real-time database and several hardware devices such as PLC, DCS, robot and sensor through ethernet TCP/IP protocol.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Research in Plant Disease
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• v.14 no.3
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• pp.219-222
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• 2008

Real time PCR was used to discriminate Colletotrichum acutatum and C. gloeosporioides for analysis of population density. Two primers, caInt2 and cgint, used for conventional PCR to discriminate two species were modified with fluorescent dye to make probe for real time PCR. Fluorescence signals were successfully detected by fCaInt2 and vCgint probe coupled with primer pair Unicon and Unicor1 resulting in discrimination of C. acutatum and C. gloeosporioides by comparison of delta Rn value.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.488-491
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• 1997

The PMGS(Plant Model Generating System) was developed based on modular modeling method and fluid network calculation concept. Fluid network calculation is used as a method of real-time computation of fluid network, and the module which has a topology with node and branch is defined to take advantages of modular modeling. Also, the database which have a shared memory as an instance is designed to manage simulation data in real-time. The applicability of the PMGS was examined implementing the HRSG(Heat Recovery Steam Generator) control logic on DCS.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.10a
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• pp.250-250
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• 2017

• Korean Journal of Plant Resources
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• v.21 no.3
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• pp.210-215
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• 2008

Gene-expression analysis is increasingly important in biological research, with real-time reverse PCR (RTPCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. However, this technique requires important preliminary work for standardizing and optimizing the many parameters involved in the analysis. Plant stress studies are more and more based on gene expression. The analysis of gene expression requires sensitive and reproducible measurements for specific mRNA sequence. Several genes are regulated in response to abitoic stresses, such as salinity, and their gene products function in stress response and tolerance. The design of the primers and TaqMan probes for real-time PCR assays were carried out using the Primer $Express^{TM}$ software 3.0. The PCR efficiency was estimated through the linear regression of the dilution curve. To understand the expression pattern of various genes under salt stressed condition, we have developed a unique public resource of 9 stress-related genes in poplar. In this study, real-time RT-PCR was used to quantify the transcript level of 10 genes (9 stress-related genes and 1 house keeping gene) that could play a role in adaptation of Populus davidiana. Real-time RT-PCR analyses exhibited different expression ratios of related genes. The data obtained showed that determination of mRNA levels could constitute a new approach to study the stress response of P. davidiana after adaptation during growth in salinity condition.