• Korean Journal of Ecology and Environment
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• v.37 no.3 s.108
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• pp.344-354
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• 2004

A pilot study was performed from September 2000 to April 2004 to examine the feasibility of the wetland-pond system for the agricultural reuse of reclaimed water. The wetland system was a subsurface flow type, with a hydraulic residence time of 3.5 days, and the subsequent pond was 8 $m^3$ in volume (2 m ${\times}$ 2 m ${\times}$ 2 m) and operated with intermittent-discharge and continuous flow types. The wetland system was effective in treating the sewage; median removal efficiencies of $BOD_5$ and TSS were above 70.0%, with mean effluent concentrations of 27.1 and 16.8 mg $L^{-1}$, respectively, for these constituents. However, they did often exceed the effluent water quality standards of 20 mg $L^{-1}$. Removal of T-N and T-P was relatively less effective and mean effluent concentrations were approximately 103.2 and 7.2 mg $L^{-1}$, respectively. The wetland system demonstrated high removal rate (92 ${\sim}$ 90%) of microorganisms, but effluent concentrations were in the range of 300 ${\sim}$ 16,000 MPN 100 $mL^{-1}$ which is still high for agricultural reuse. The subsequent pond system provided further treatment of the wetland effluent, and especially additional microorganisms removal in addition to wetland-pond system could reduce the mean concentration to 1,000 MPN 100 $mL^{-1}$ from about $10^5$ MPN 100 $mL^{-1}$ of wetland influent. Other parameters in the pond system showed seasonal variation, and the upper layer of the pond water column became remarkably clear immediately after ice melt. Overall, the wetland system was found to be adequate for treating sewage with stable removal efficiency, and the subsequent pond was effective for further polishing. This study concerned agricultural reuse of reclaimed water using natural systems. Considering stable performance and effective removal of bacterial indicators as well as other water quality parameters, low maintenance, and cost-effectiveness, wetland- pond system was thought to be an effective and feasible alternative for agricultural reuse of reclaimed water in rural area.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.34 no.3
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• pp.157-165
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• 2008

In the previous study, the anti-oxidant activity of oxtract/fraction of Sueada aspparagoides(SA) and the stability test for the cream containing SA extract were investigated respectively[1,2]. In this study, the components of SA extract were analyzed by TLC, HPLC, and LC/ESI-MS/MS, $^1H$-NMR. TLC chromatogram of ethyl acetate fraction of SA extract revealed 5 bands $(SA1{\sim}SA5)$. HPLC chromatogram of aglycone fractions obtained from deglycoylation reaction of ethyl acetate fraction showed 2 bands (SAA 2 and SAA 1), which were identified as quercetin (composition ratio, 16.88%) and kaempferol (83.12%) in the order of elution time. Among 5 bands of TLC chromatogram, 4 bands $(SA2{\sim}SA5)$ also were Identified as kaempferol-3-O-glucoside (SA 2), quercetin-3-O-glucoside (SA3), kaempferol-3-O-rutinoside (SA 4), quercetin-3-O-rutinoside (SA 5) by LC/ESI-MS/MSMS/MS. respectively. The spectrum generated for SAA 1 by LC/ESI-MS/MS in the negative ion mode also gave the ion corresponding to the deprotonated aglycone $[M-H]^-$ (285m/z), the $^1H$-NMR spectrum contained signals [${\delta}$ 6.19 (1H, d, J=1.8Hz, H-6), ${\delta}$ 6.44 (1H, d, J=1.8Hz, H-8), ${\delta}$ 6.92 (2H, d, J=9.0Hz, H-3', 5'), ${\delta}$ 8.04 (2H, d, J=9.0Hz, H-2', 6', thus SAA 1 was identified as kaempferol. SAA 2 yielded the deprotonated agycone ion $[M-H]^-$ (301m/z), $^1H$-NMR spectrum showed signals [${\delta}$ 6.20 (1H, d, J=2.0Hz, H-6), ${\delta}$ 6.42 (1H, d, J=2.0Hz, H-8), ${\delta}$ 6.90 (1H, d, J=8.6Hz, H-5'), ${\delta}$ 7.55 (1H, dd, J=8.6, 2.2Hz, H-6'), ${\delta}$ 7.69 (1H, d, J=2.2Hz, H-2', thus SAA 2 was Identified as quercetin. In conclusion, with the anti-oxidant activity and the stability test reported previously, component analysis of SA extracts could be applicable to new cosmeceuticals.

• Journal of Korean Theatre Studies Association
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• no.52
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• pp.319-357
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• 2014

Formation of 'body schema' is the start for actor to create role and becomes the root and the foundation of existing as a role on the stage. For this, an actor needs to form 'scheme of role' with escaping from own 'body schema.' 'Schema of role' is formed by acquiring through synthesizing daily basic actions, namely, walking, standing, sitting, hand stretching, bending, and touching. The body schema, which was made with simple and usual actions, has fundamental significance in a sense of becoming the body in which the past traces in a role are habituated while energy as a role flows. As for the process of forming body schema, an actor first needs to obtain the visualized materials like photo, magazine, picture and image available for seeing a role specifically and clearly based on what analyzed a character. An actor needs to have three-dimensional image available for always recalling it in the head during acting. To do this, image data available for fundamentally capturing routine actions along with body structure are still more useful. Next, the body schema is formed by interaction with environment. Thus, there is a need of passing through the two-time process of forming body schema. Firstly, the body schema is made on routine actions in a role as physical condition of a role in actor's own everyday life. Secondly, the body schema is made on routine actions available for moving efficiently and economically in line with the environment of performance. A theatrical stage is the temporal space of rhythm and rule different from routine space. What forms body schema immediately in the second phase without body schema in the first phase ultimately becomes what exists as actor's own body, not the body of a role. The body schema, which was formed as the second process, is what truly has identity as a role in the ontological aspect, comes to experience the oppositional force in muscle, a qualitative change in energy, and emotional agitation in the physical aspect, and experiences perception, thinking, volition, and even consciousness with the entire body in the cognitive dimension. Thus, the formation of body schema can be known to be just a method of changing even spiritual and emotional layer. Body schema cannot be made if there is no process of embodiment and habit. Embodiment and habit are not simply the repeated, empty and mechanical action in the body. But, habit itself has very important meanings for forming body schema for role creating. First, habit allows the body itself to learn and understand a meaning. Second, habit relies upon environment, thereby allowing an actor of making the habituated body schema to recognize environment. Third, habit makes the mind. The habituated body schema is just the mind and the ego of a person who possesses the body schema. Fourth, habit comes to experience the expansion in energy and the expansion in existence. It may be experienced through interrelation among actor's body, tool, and environment. Fifth, habit makes identity of the body. Hence, this just becomes what secures identity of a role. These implications of habit are the formation of body schema, which is maintained with the body of being remembered firmly through being closely connected with the process of neural adaptation. Finally, it sought for possibility of practice as one method of forming body schema for role creating through Deleuze's '-becoming' theory. As 'actual animal-becoming' is real '-becoming' of forming structural transformation in the physical dimension, it meets with what the formation of body schema pursues actuality and reality. This was explained with a concept as saying of 'all '-becoming' molecular' by Deleuze/Guattari. 'Animal of having imitated animal's characteristic- becoming' is formed by which the body schema relies upon environment. In this way, relationship among the body, tool and environment has influence even upon a change in consciousness, thinking, and emotion, thereby being able to be useful for forming body schema in a sense of possibly experiencing ultimately expansion in role, namely, expansion in existence.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.3
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• pp.1-7
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• 2021

In this paper, by embedding a bare-die chip-type drive amplifier into the PCB composed of ABF and FR-4, it implements an embedded active device that can be applied in 28 GHz band modules. The ABF has a dielectric constant of 3.2 and a dielectric loss of 0.016. The FR-4 where the drive amplifier is embedded has a dielectric constant of 3.5 and a dielectric loss of 0.02. The proposed embedded module is processed into two structures, and S-parameter properties are confirmed with measurements. The two process structures are an embedding structure of face-up and an embedding structure of face-down. The fabricated module is measured on a designed test board using Taconic's TLY-5A(dielectric constant : 2.17, dielectric loss : 0.0002). The PCB which embedded into the face-down expected better gain performance due to shorter interconnection-line from the RF pad of the Bear-die chip to the pattern of formed layer. But it is verified that the ground at the bottom of the bear-die chip is grounded Through via, resulting in an oscillation. On the other hand, the face-up structure has a stable gain characteristic of more than 10 dB from 25 GHz to 30 GHz, with a gain of 12.32 dB at the center frequency of 28 GHz. The output characteristics of module embedded into the face-up structure are measured using signal generator and spectrum analyzer. When the input power (P_in) of the signal generator was applied from -10 dBm to 20 dBm, the gain compression point (P_1dB) of the embedded module was 20.38 dB. Ultimately, the bare-die chip used in this paper was verified through measurement that the oscillation is improved according to the grounding methods when embedding in a PCB. Thus, the module embedded into the face-up structure will be able to be properly used for communication modules in millimeter wave bands.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.93-99
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• 2022

With the rapid development of ultra-small and wearable device technology, continuous electricity supply without spatiotemporal limitations for driving electronic devices is required. Accordingly, Triboelectric nanogenerator (TENG), which utilizes static electricity generated by the contact and separation of two different materials, is being used as a means of effectively harvesting various types of energy dispersed without complex processes and designs due to its simple principle. However, to apply the TENG to real life, it is necessary to increase the electrical output. In addition, stable generation of electrical output, as well as increase in electrical output, is a task to be solved for the commercialization of TENG. In this study, we proposed a method to not only improve the output of TENG but also to stably represent the improved output. This was solved by using the contact layer, which is one of the components of TENG, as an electret for improved output and stability. The utilized electret was manufactured by sequentially performing corona charging-thermal annealing-corona charging on the Fluorinated ethylene propylene (FEP) film. Electric charges artificially injected due to corona charging enter a deep trap through the thermal annealing, so an electret that minimizes charge escape was fabricated and used in TENG. The output performance of the manufactured electret was verified by measuring the voltage output of the TENG in vertical contact separation mode, and the electret treated to the corona charging showed an output voltage 12 times higher than that of the pristine FEP film. The time and humidity stability of the electret was confirmed by measuring the output voltage of the TENG after exposing the electret to a general external environment and extreme humidity environment. In addition, it was shown that it can be applied to real-life by operating the LED by applying an electret to the clap-TENG with the motif of clap.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.30-36
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• 2022

A sensor is needed to continuously and automatically measure the change in HNS concentration in industrial facilities that directly discharge to the sea after water treatment. The basic function of the sensor is to be able to detect ppb levels even at room temperature. Therefore, a method for increasing the sensitivity of the existing sensor is proposed. First, a method for increasing the conductivity of a film using a conductive carbon-based additive in a nanoparticle thin film and a method for increasing ion adsorption on the surface using a catalyst metal were studied.. To improve conductivity, carbon black was selected as an additive in the film using ITO nanoparticles, and the performance change of the sensor according to the content of the additive was observed. As a result, the change in resistance and response time due to the increase in conductivity at a CB content of 5 wt% could be observed, and notably, the lower limit of detection was lowered to about 250 ppb in an experiment with organic solvents. In addition, to increase the degree of ion adsorption in the liquid, an experiment was conducted using a sample in which a surface catalyst layer was formed by sputtering Au. Notably, the response of the sensor increased by more than 20% and the average lower limit of detection was lowered to 61 ppm. This result confirmed that the chemical resistance sensor using metal oxide nanoparticles could detect HNS of several tens of ppb even at room temperature.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.