• Smart Structures and Systems
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• v.6 no.3
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• pp.309-333
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• 2010

Civil infrastructure, in both its construction and maintenance, represents the largest societal investment in this country, outside of the health care industry. Despite being the lifeline of US commerce, civil infrastructure has scarcely benefited from the latest sensor technological advances. Our future should focus on harnessing these technologies to enhance the robustness, longevity and economic viability of this vast, societal investment, in light of inherent uncertainties and their exposure to service and even extreme loadings. One of the principal means of insuring the robustness and longevity of infrastructure is to strategically deploy smart sensors in them. Therefore, the objective is to develop novel, durable, smart sensors that are especially applicable to major infrastructure and the facilities to validate their reliability and long-term functionality. In some cases, this implies the development of new sensing elements themselves, while in other cases involves innovative packaging and use of existing sensor technologies. In either case, a parallel focus will be the integration and networking of these smart sensing elements for reliable data acquisition, transmission, and fusion, within a decision-making framework targeting efficient management and maintenance of infrastructure systems. In this paper, prudent and viable sensor and health monitoring technologies have been developed and used in several large structural systems. Discussion will also include several practical bridge health monitoring applications including their design, construction, and operation of the systems.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1780-1787
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• 2014

Arterial oxygen saturation ($SpO_2$) monitoring for newborns requires special attention in neonatal intensive care units (NICUs). Newborns have very low photo-plethysmogram (PPG) amplitudes and their body movements are difficult to contain. Hardware design and its associated signal processing algorithms should be robust enough so that faulty measurements can be avoided. In this study, improved designs were implemented to deal with low perfusion, motion artifact, and the influence of ambient light. Dynamic range was increased by using different LED intensities and a feedback system. To minimize the effects of motion artifact and to discard other unqualified data, four additional algorithms were used, which were based on dual-trace detection, continuity of DC level, morphology of PPG, and simultaneity check of $SpO_2$. Our $SpO_2$ system was tested with newborns with normal respiration in the NICU. Our system provided fast, real-time responses and 100% artifact detection was accomplished under 84% of $SpO_2$.

• Molecules and Cells
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• v.27 no.4
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• pp.403-408
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• 2009

Skeletal muscle regeneration is a highly orchestrated process initiated by activation of adult muscle satellite cells. Upon muscle injury, the inflammatory process is always accompanied by muscle regeneration. Leukotriene $B_4$ is one of the essential inflammatory mediators. We isolated and cultured primary satellite cells. RT-PCR showed that myoblasts expressed mRNA for $LTB_4$ receptors BLT1 and BLT2, and $LTB_4$ promoted myoblast proliferation and fusion. Quantitative real-time PCR and immunoblotting showed that $LTB_4$ treatment expedited the expression process of differentiation markers MyoD and M-cadherin. U-75302, a specific BLT1 inhibitor, but not LY2552833, a specific BLT2 inhibitor, blocked proliferation and differentiation of myoblasts induced by $LTB_4$, which implies the involvement of the BLT1 pathway. Overall, the data suggest that $LTB_4$ contributes to muscle regeneration by accelerating proliferation and differentiation of satellite cells.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.1-14
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• 2019

The 4th Industrial Revolution, intelligent automobile application technology is evolving beyond the limit of the mobile device to a variety of application software and multi-media collective technology with big data-based AI(artificial intelligence) technology. with the recent commercialization of 5G mobile communication service, artificial intelligent automobile technology, which is a fusion of automobile and IT technology, is evolving into more intelligent automobile service technology, and each multimedia platform service and application developed in such distributed environment is being developed Accordingly, application software technology developed with a single system SoC of a portable terminal device through various service technologies is absolutely required. In this paper, smart device design for ontology design of intelligent automobile open platform enables to design intelligent automobile middleware software design technology such as Android based SVC Codec and real time video and graphics processing that is not expressed in single ASIC application software technology as SoC based application designWe have experimented in smart device environment through researches, and newly designed service functions of various terminal devices provided as open platforms and application solutions in SoC environment and applied standardized interface analysis technique and proved this experiment.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.55-63
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• 2022

Although research on the leader's or speaker's voice has been continuously conducted, existing research has a single point of view. Sound analysis of voice characteristics has been studied from engineering perspectives, and leadership trait theory has been studied from a business perspective. Convergence studies on leader voice and member cognition are being attempted today. Convergence research on voice has a positive effect on refinement of voice analysis, diversification of voice use, and establishment of voice utilization strategy. This study explains the current flow of research on convergence between speaker's voice and listener's perception, and suggests a direction for the future development of voice fusion research. Furthermore, in connection with AI in the 4th industrial age, new attempts for voice research are sought. First, advances in AI focus on strategically generating the voices needed for individual situations. Second, the voice corrected in real time will support the leader and speaker to utilize the desired voice type. Third, voices through AI based on big data will affect the cognition, attitude and behavior of individual listeners who members, customers, and students in more diverse situations. The purpose and significance of this study is to suggest the way to research the leader's voice recognized by members, and to suggest a method that can be applied in various situations.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.153-158
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• 2023

Most agriculture plantings are based on traditional farming and demand a lot of human work processes. In order to improve the efficiency as well as the productivity of their farms, modern agricultural technology was proven to be better than traditional practices. Internet of Things (IoT) is usually related in modern agriculture which provides the farmer with a real-time monitoring condition of their farm from anywhere and anytime. Therefore, the application of IoT with a sensor to measure and monitors the humidity and the temperature in the mushroom farm that can overcome this problem. This paper proposes an IoT based monitoring system forindoor Ganoderma lucidum cultivation at a minimal cost in terms of hardware resources and practicality. The results show that the data of temperature and humidity are changing depending on the weather and the preliminary experimental results demonstrated that all parameters of the system were optimized and successful to achieve the objective. In addition, the analysis results show that the quality of Ganoderma lucidum produced on the research method conforms to regulations in Vietnam.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.574-579
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• 2016

Unlike general unmanned aerial vehicles, the quad-rotor is attracting the attention of many people because of simple structure and very useful value. However, as the interest in drones increases, the safety and location of vehicles are becoming more important provide against aviation safety accidents or lost accidents. Therefore, in this paper, we propose a tracking system that stabilizes the model with a simple controller by linearized modeling and grasp tilt angle data from various sensor through the filter. The developed tracking system transmits the position of the quad-rotor in flight to the computer and shows it through the route, so it can check the flight path and various information such as flight speed and altitude at the same time. Then the sensor used in the actual quad-rotor can not measure exact sensor data for disturbance and vibration. So we use sensor fusion of Kalman filter and Complementary filter to overcome this problem and the stability of the quad-rotor hovering is realized by PID control. Through simulation, various information such as the speed, position, and altitude of the quad-rotor were confirmed in real time.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.