• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.4
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• pp.341-348
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• 2016

Micro-algae, one of the biological resources for alternative energy, has been heavily studied. Among various methods to analyze the status of the micro-algae including counting, screening, and flocculation, the flocculation approach has been widely accepted in many critical applications such as red tide removal study or microalgae resource study. To characterize the flocculation status of the micro-alga. A traditional optical modality, i.e., photospectrometry, measuring the optical density of the flocs has been frequently employed. While this traditional optical method needs shorter time than the counting method in flocculation status analysis, it has relatively lower detection accuracy. To address this issue, a novel real-time micro-algae flocculation analysis method based on the lens-free shadow imaging technique (LSIT) is introduced. Both single cell detection and floc detection are simultaneously available with a proposed lens-free shadow image, confirmed by comparing the results with optical microscope images. And three shadow parameters, e.g., number of flocs, effective area of flocs, and maximum size of floc, enabling quantification of the flocculation phenomenon of micro-alga, are firstly demonstrated in this article. The efficacy of each shadow parameter is verified with the real-time flocculation monitoring experiments using custom developed cohesive agents.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.251-260
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• 1998

The Holter monitoring system is a widely used noninvasive diagnostic tool for ambulatory patient who may be at risk from latent life-threatening cardiac abnormalities. In this paper, we design a high performance intelligent holter monitoring system which is characterized by the small-sized and the low-power consumption. The system hardware consists of one-chip microcontroller(68HC11E9), ECG preprocessing circuit, and flash memory card. ECG preprocessing circuit is made of ECG preamplifier with gain of 250, 500 and 1000, the bandpass filter with bandwidth of 0.05-100Hz, the auto-balancing circuit and the saturation-calibrating circuit to eliminate baseline wandering, ECG signal sampled at 240 samples/sec is converted to the digital signal. We use a linear recursive filter and preprocessing algorithm to detect the ECG parameters which are QRS complex, and Q-R-T points, ST-level, HR, QT interval. The long-term acquired ECG signals and diagnostic parameters are compressed by the MFan(Modified Fan) and the delta modulation method. To easily interface with the PC based analyzer program which is operated in DOS and Windows, the compressed data, that are compatible to FFS(flash file system) format, are stored at the flash memory card with SBF(symmetric block format).

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.36-45
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• 2007

Today Human Personal Trainers are becoming very famous in this health conscious world. They teach user to achieve fitness goals in managed way. Due to their high fee and tight schedule they are unavailable to mass number of people. Another solution to this problem is to develop digital personal trainer portable instrument that may replace human personal trainers. We developed a portable digital exercise trainer device - BIOFIT that manages, monitors and records the user's physical status and workout during exercise session. It guides the user to exercise efficiently for specific fitness goal. It keeps the full exercise program i.e. exercises start date and time, duration, mode, control parameter, intensity in its memory which helps the user in managing his exercise. Exercise program can be downloaded from the internet. During exercise it continuously monitors the user's physiological parameters: heart rate, number of steps walked, and energy consumed. If these parameters do not range within prescribed target zone, the BIOFIT will alarm the user as a feedback to control exercise. The BIOFIT displays these parameters on graphic LCD. During exercise it continuously records the heart rate and number of steps walked every 10 seconds along with exercise date and time. This stored information can be used as treatment for the user by an exercise expert. Real-time ECG monitoring can be viewed wirelessly (RF Communication) on a remote PC.

• Composites Research
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• v.36 no.6
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• pp.429-434
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• 2023

In this study, aimed at improving the existing acoustic emission sensor for real time monitoring, a macro-fiber composite (MFC) transducer was employed as the acoustic emission sensor in the gas leak detection system. Prior to implementation, structural analysis was conducted to optimize the MFC's design. Consequently, the flexibility of the MFC facilitated excellent adherence to curved pipes, enabling the reception of acoustic emission (AE) signals without complications. Analysis of AE signals revealed substantial variations in parameter values for both high-pressure and low-pressure leaks. Notably, in the parameters of the Fast Fourier Transform (FFT) graph, the change amounted to 120% to 626% for high-pressure leaks compared to the case without leaks, and approximately 9% to 22% for low-pressure leaks. Furthermore, depending on the distance from the leak site, the magnitude of change in parameters tended to decrease as the distance increased. As the results, in the future, not only will it be possible to detect a leak by detecting the amount of parameter change in the future, but it will also be possible to identify the location of the leak from the amount of change.

• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.711-732
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• 2019

Many unmanned aerial vehicle (UAV) applications have been employed for performing data collection in facilitating tasks such as surveillance and monitoring objectives in remote and dangerous environments. In light of the fact that most of the existing UAV relaying applications operate in conventional half-duplex (HD) mode, a full-duplex (FD) based UAV relay aided wireless network is investigated, in which the UAV relay helps forwarding information from the source (S) node to the destination (D). Since the activated UAV relays are always floating and flying in the air, its channel state information (CSI) as well as channel capacity is a time-variant parameter. Considering decode-and-forward (DF) relaying protocol in UAV relays, the cooperative relaying channel capacity is constrained by the relatively weaker one (i.e. in terms of signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR)) between S-to-relay and relay-to-D links. The channel capacity can be optimized by adaptively optimizing the transmit power of S and/or UAV relay. Furthermore, a hybrid HD/FD mode is enabled in the proposed UAV relays for adaptively optimizing the channel utilization subject to the instantaneous CSI and/or remaining self-interference (SI) levels. Numerical results show that the channel capacity of the proposed UAV relay aided wireless networks can be maximized by adaptively responding to the influence of various real-time factors.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.8
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• pp.441-448
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• 2017

A method for measuring the concentration of surfactant in water was developed. In this technique, microbubbles were used as light scatterers. The polarization change of light scattered by microbubbles was analyzed by Mueller matrix analysis. $M_{11}$, one of the Mueller matrix elements, was found to be a key parameter inferring the surfactant concentration within the concentration range of 0 ppm to 60 ppm. The best results for this measurement were obtained when the scattering angle was $150^{\circ}$ and the extinction ratio was 56.2. This experimental result shows that the EPLS can be effectively used as a real time inspection method for water quality monitoring in lakes or rivers.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.1
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• pp.81-88
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• 2011

A real time monitoring system from a PC has been developed which can be accessed through transmitted data, which incorporates an established low powered transport system equipped with a single chip combined with wireless sensor network technology from a three-axis acceleration sensor. In order to distinguish between static posture and dynamic posture, the extracted parameter from the rapidly transmitted data needs differentiation of movement and activity structures and status for an accurate measurement. When results interpret a static formation, statistics referring to each respective formation, known as the K-mean algorithm is utilized to carry out a determination of detailed positioning, and when results alter towards dynamic activity, fuzzy algorithm (fuzzy categorizer), which is the relationship between speed and ISVM, is used to categorize activity levels into 4 stages. Also, the ISVM is calculated with the instrumented acceleration speed on the running machine according to various speeds and its relationship with kinetic energy goes through correlation analysis. With the evaluation of the proposed system, the accuracy level stands at 100% at a static formation and also a 96.79% accuracy with kinetic energy and we can easily determine the energy consumption through the relationship between ISVM and kinetic energy.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1290-1293
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• 2021

Currently, blind people are experiencing a lot of inconvenience in their daily lives. In order to provide helpful service for the visually impaired, this study was carried out to make a new smart glasses that transmit information monitoring walking environment in real-time object recognition. In terms of object recognition, YOLOv4 was used as the artificial intelligence model. The objects, that should be identified during walking of the visually impaired, were selected, and the learning data was populated from them and re-learning of YOLOv4 was performed. As a result, the accuracy was average of 68% for all objects, but for essential objects (Person, Bus, Car, Traffic_light, Bicycle, Motorcycle) was measured to be 84%. In the future, it is necessary to secure the learning data in more various ways and conduct CNN learning with various parameters using darkflow rather than YOLOv4 to perform comparisons in the various ways.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.194-204
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• 2020

Cultivar parameters, which are key inputs to a crop growth model, have been estimated using observation data in good quality. Observation data with high quality often require considerable labor and cost, which makes it challenging to gather a large quantity of data for calibration of cultivar parameters. Alternatively, data in sufficient quantity can be collected from the reports on the evaluation of cultivars by region although these data are of questionable quality. The objective of our study was to assess the quality of crop and management data available from the reports on the regional adaptation trials for rice cultivars. We also aimed to propose the measures for improvement of the data quality, which would aid reliable estimation of cultivar parameters. DatasetRanker, which is the tool designed for quantitative assessment of the data for parameter calibration, was used to evaluate the quality of the data available from the regional adaptation trials. It was found that these data for rice cultivars were classified into the Silver class, which could be used for validation or calibration of key cultivar parameters. However, those regional adaptation trial data would fall short of the quality for model improvement. Additional information on management, e.g., harvest and irrigation management, can increase the quantitative quality by 10% with the minimum effort and cost. The quality of the data can also be improved through measurements of initial conditions for crop growth simulations such as soil moisture and nutrients. In addition, crop model improvement can be facilitated using crop growth data in time series, which merits further studies on development of approaches for non-destructive methods to monitor the crop growth.