• Journal of Plant Biotechnology
• /
• v.48 no.3
• /
• pp.148-155
• /
• 2021

Gibberellins (GAs) are essential phytohormones for plant growth that influence developmental processes and crop yields. Recent functional genomic analyses of model plants have yielded good characterizations of the canonical GA signaling pathways and related genes. Although Panax ginseng has long been considered to have economic and medicinal importance, functional genomic studies of the GA signaling pathways in this crucial perennial herb plant have been rarely conducted. Here, we identified and performed functional analysis of the GA signaling-related genes, including PgGID1s, PgSLY1s, and PgRGAs. We confirmed that the physiological role of GA signaling components in P. ginseng was evolutionarily conserved. In addition, the important functional domains and amino acid residues for protein interactions among active GA, GID1, SCF^SLY1, and RGA were also functionally conserved. Prediction and comparison of crystallographic structural similarities between PgGID1s and AtGID1a supported their function as GA receptors. Moreover, the subcellular localization and GA-dependent promotion of DELLA degradation in P. ginseng was similar to the canonical GA signaling pathways in other plants. Finally, we found that overexpression of PgRGA2 and PgSLY1-1 was sufficient to complement the GA-related phenotypes of atgid1a/c double- and rga quintuple-mutants, respectively. This critical information for these GA signaling genes has the potential to facilitate future genetic engineering and breeding of P. ginseng for increased crop yield and production of useful substances.

• The Journal of the Korea Contents Association
• /
• v.21 no.7
• /
• pp.13-19
• /
• 2021

While watching the movie, the audience shows various emotional reactions. Emotional reactions such as sadness and anger, joy and anger appear depending on the storyline of the film. This aspect can be seen through the audience's brain wave response. This study is to examine the relationship between the movie story development and the movie story development through brain wave measurement of the emotional reaction of the audience in situations and events occurring in the movie development. Four American films, which represent each genre and are well known to many people, were selected for the study. These are of the adventure genre, of the animation genre, of the action genre, and of the drama genre. In order to measure the emotional response of these movies, four cases were set centered on the PPG of EEG and analyzed as a time series graph pattern. It can be seen that the emotional response on the graph has a certain relationship with the story development. It is expected that this study will help in selecting a genre when making a movie in the future, especially when deciding how to compose and develop a story, and it will help to induce the emotions of the audience.

• Journal of Convergence for Information Technology
• /
• v.9 no.3
• /
• pp.8-15
• /
• 2019

Recently, the importance of pre-shipment quality inspection has been emphasized due to the increase of STB supply. In this paper, we propose a method to support automation of quality inspection through simultaneous multi-channel input of STB video signal. The proposed method extracts a fingerprint using the center scan line of the image after stable video shot boundary detection using CeLDP combining color information and LDP code and performs synchronization between input video channels. The proposed method shows stronger shot boundary detection performance than the conventional shot detection method. Through the experiments applied to the real environment, it is possible to secure reliability and real-time quality check for synchronization between multi-channel inputs required for STB quality inspection. Also, based on the proposed method, we intend to study a large-scale quality inspection method in the future and propose a more effective quality inspection system.

• Electronics and Telecommunications Trends
• /
• v.34 no.5
• /
• pp.14-25
• /
• 2019

As the Internet of Things, artificial intelligence and big data have received a lot of attention as key growth engines in the era of the fourth industrial revolution, data acquisition and utilization in mobile, automotive, robotics, manufacturing, agriculture, health care and national defense are becoming more important. Due to numerous data-based industrial changes, demand for sensor technologies is exploding, especially for intelligent sensor technologies that combine control, judgement, storage and communication functions with the sensors's own functions. Intelligent sensor technology can be defined as a convergence component technology that combines intelligent sensor units, intelligent algorithms, modules with signal processing circuits, and integrated plaform technologies. Intelligent sensor technology, which can be applied to variety of smart IT convergence services such as smart devices, smart homes, smart cars, smart factory, smart cities, and others, is evolving towards intelligent and convergence technologies that produce new high-value information through recognition, reasoning, and judgement based on artificial intelligence. As a result, development of intelligent sensor units is accelerating with strategies for miniaturization, low-power consumption and convergence, new form factor such as flexible and stretchable form, and integration of high-resolution sensor arrays. In the future, these intelligent sensor technologies will lead explosive sensor industries in the era of data-based artificial intelligence and will greatly contribute to enhancing nation's competitiveness in the global sensor market. In this report, we analyze and summarize the recent trends in intelligent sensor technologies, especially those for four core technologies.

• Convergence Security Journal
• /
• v.18 no.4
• /
• pp.73-79
• /
• 2018

Since 1910s, the drones were mainly used for military purposes for reconnaissance and attack targets, but they are now being used in various fields such as disaster prevention, exploration, broadcasting, and surveillance of risk areas. As drones are widely used from military to civilian field, hacking into the drones such as radio disturbance, GPS spoofing, hijacking, etc. targeting drones has begun to occur. Recently, the use of drones in hacking into wireless network has been reported. If the artificial intelligence technology is applied to the drones in the military, hacking into unmanned combat system using drones will occur. In addition, a drone with a hacking program may be able to relay a hacking program to the hacking drone located far away, just as a drone serves as a wireless communication station. And the drones will be equipped with a portable GPS jamming device, which will enable signal disturbance to unmanned combat systems. In this paper, we propose security threats and the anticipated hacking scenarios using the drones on the battlespace to know the seriousness of the security threats by hacking drones and prepare for future cyberspace.

• International journal of advanced smart convergence
• /
• v.8 no.3
• /
• pp.69-77
• /
• 2019

Today, most of elevators have an emergency call facility for emergency situations. However, if the network installed in the elevator is also out of power, it cannot be used for the elevator remote monitoring and management. So, we develop an integrated and unified emergency call system, which can transmit not only telephone call but also data signals using PSTN(Public Switched Telephone Network) in order to remote monitoring and management of elevators, even though a power outage occurs. The proposed integrated emergency call system to process multiple data such as voice and operational information is a multi-channel board system which is composed of an emergency phone signal processing module and an operational information processing module in the control box of elevator. In addition, the RMS(remote management server) systems based on the Web consist of a dial-up server and a remote monitoring server where manages the elevator's operating information, status records, and operational faults received via the proposed integrated and unified emergency call system in real time. So even if there's a catastrophic emergency, the proposed RMS systems shall ensure and maintain the safety of passengers inside the elevator. Also, remote control of the elevator by this system should be more efficient and secure. In near future, all elevator emergency call system need to support multifunctional capabilities to transmit operational data as well as phone calls for the safety of passengers. In addition, for safer elevators, it is necessary to improve them more efficiently by combining them with high-tech technologies such as the Internet of Things and artificial intelligence.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.684-694
• /
• 2021

Resting-state Functional Magnetic Resonance Imaging(fMRI) data detects the temporal correlations in Blood Oxygen Level Dependent(BOLD) signal and these temporal correlations are regarded to reflect intrinsic cortical connectivity, which is deactivated during attention demanding, non-self referential tasks, called Default Mode Network(DMN). The relationship between fMRI and anatomical connectivity has not been studied in detail, however, the preceded studies have tried to clarify this relationship using Diffusion Tensor Imaging(DTI) and fMRI. These studies use method that fMRI data assists DTI data or vice versa and it is used as guider to perform DTI tractography on the brain image. In this study, we hypothesized that functional connectivity in resting state would reflect anatomical connectivity of DMN and the combined images include information of fMRI and DTI showed visible connection between brain regions related in DMN. In the previous study, functional connectivity was determined by subjective region of interest method. However, in this study, functional connectivity was determined by objective and advanced method through Independent Component Analysis. There was a stronger connection between Posterior Congulate Cortex(PCC) and PHG(Parahippocampa Gyrus) than Anterior Cingulate Cortex(ACC) and PCC. This technique might be used in several clinical field and will be the basis for future studies related to aging and the brain diseases, which are needed to be translated not only functional connectivity, but structural connectivity.

• Journal of agriculture & life science
• /
• v.53 no.3
• /
• pp.147-157
• /
• 2019

In this study, we tried to find out the most appropriate pre-processing method and to verify the feasibility of developing a low-price sensing system for predicting the hardy kiwis sugar content based on VNIRS and subsequent spectral analysis. A total of 495 hardy kiwi samples were collected from three farms in Muju, Jeollabukdo, South Korea. The samples were scanned with a spectrophotometer in the range of 730-2300 nm with 1 nm spectral sampling interval. The measured data were arbitrarily separated into calibration and validation data for sugar content prediction. Partial least squares (PLS) regression was performed using various combinations of pre-processing methods. When the latent variable (LV) was 8 with the pre-processing combination of standard normal variate (SNV) and orthogonal signal correction (OSC), the highest R2 values of calibration and validation were 0.78 and 0.84, respectively. The possibility of predicting the sugar content of hardy kiwi was also examined at spectral sampling intervals of 6 and 10 nm in the narrower spectral range from 730 nm to 1200 nm for a low-price optical sensing system. The prediction performance had promising results with R2 values of 0.84 and 0.80 for 6 and 10 nm, respectively. Future studies will aim to develop a low-price optical sensing system with a combination of optical components such as photodiodes, light-emitting diodes (LEDs) and/or lamps, and to locate a more reliable prediction model by including meteorological data, soil data, and different varieties of hardy kiwi plants.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.4
• /
• pp.515-522
• /
• 2021

In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.34 no.6
• /
• pp.363-370
• /
• 2021

In this paper, we presented an optimized crack and flexural strength analysis of a glass-fiber reinforced polymer (GFRP) rebar, used as reinforcements for in-site railway concrete slabs. The insulation performance of a GFRP rebar has the advantage of avoiding the loss of signal current in an audio frequency (AF) track circuit. A full-scale experiment, and three-dimensional finite element simulation results were compared to validate our approaches. Parametric numerical results revealed that the diameters and arrangements of the GFRP rebar had a significant effect on the flexural strength and crack control performances of the concrete track slabs. The results of this study could serve as a benchmark for future guidelines in designing more efficient, and economical concrete slabs using the GFRP rebar.