• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.5
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• pp.265-277
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• 2019

Using the measured data of waves and shore-line, we reviewed the grand circulation process and seasonal variation of beach cusp at the Mang-Bang beach from the perspective of trapped mode Edge waves known as the driving mechanism of beach cusp. In order to track the temporal and spatial variation trends of beach cusp, we quantify the beach cusp in terms of its wave length and amplitude detected by threshold crossing method. In doing so, we also utilize the spectral analysis method and its associated spectral mean sand wave number. From repeated period of convergence and ensuing splitting of sand waves detected from the yearly time series of spectral mean sand wave number of beach cusp, it is shown that the grand circulation process of beach cusp at Mang-Bang beach are occurring twice from 2017. 4. 26 to 2018. 4. 20. For the case of beach area, it increased by $14,142m^2$ during this period, and the shore-line advanced by 18 m at the northen and southern parts of the Mang-Bang beach whereas the shore-line advanced by 2.4 m at the central parts of Mang-Bang beach. It is also worthy of note that the beach area rapidly increased by $30,345m^2$ from 2017.11.26. to 2017.12.22. which can be attributed to the nature of coming waves. During this period, mild swells of long period were prevailing, and their angle of attack were next to zero. These characteristics of waves imply that the main transport mode of sediment would be the cross-shore. Considering the facts that self-healing capacity of natural beaches is realized via the cross-shore sediment once temporarily eroded. it can be easily deduced that the sediment carried by the boundary layer streaming toward the shore under mild swells which normally incident toward the Mang-Bang beach makes the beach area rapidly increase from 2017.11.26. to 2017.12.22.

• 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.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.37-48
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• 2021

A new high speed rotation type-passive sampling device (HSR-PSD), which can rotate seawater at high speed and absorb easily and quickly the freely dissolved hydrophobic organic contaminants from seawater, was developed and then applied around the Korean Peninsula. Freely dissolved concentrations (Cfree) of polycyclic aromatic hydrocarbons (PAHs) were determined using the HSR-PSD with low density polyethylene (LDPE) sheets as a passive sampler. Furthermore, dissolved concentrations (Cdissolved) of PAHs in seawater were also obtained from high volume water sampling as a conventional method to account for actual bioavailability. When the LDPE sheets were rotated in the HSR-PSD at 900 rpm, PAHs with log KOW 3.4 ~ 5.2 were equilibrated between the LDPE and water in 5 hours. Although the high molecular weight PAHs with log KOW 5.6 ~ 6.8 was expected to be 2 to 30 days to reach the equilibrium, the Cfree of the PAHs at equilibrium could be corrected using performance reference compounds in 5 hours. Meanwhile, the total Cfree of PAHs were from 0.32 to 1.2 ng/L, which were higher than reported values in other oceans, but lower than in coastal water such as estuary, harbor, or shore. A bioavailability from the detected PAHs was highest at the sampling line near the dumping site of the Yellow Sea. Predicted residual concentrations in biota were relatively higher in offshore including the dumping site than in coastal regions.

• Korean Journal of Heritage: History & Science
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• v.55 no.2
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• pp.168-184
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• 2022

This paper examined the construction techniques of the earthen fortifications in the Hanseong Period of Baekje Kingdom, which has been researched most frequently among the Three Kingdoms. The construction processes of the Earthen Fortifications were reviewed and dividing into 'selection of location and construction of the base', 'construction of the wall', and 'finish, extension and repair'. The results show that various techniques were mobilized for building these earthen fortifications. Techniques which were adequate for the topography were utilized for reinforcing the base, and several other techniques were used for constructing the wall. In particular, techniques for wall construction may be clearly divided into those of the fill(盛土) and panchuk(版築) techniques. The fill method has been assumed since the 2000s to have been more efficient than the panchuk technique. This method never uses the structure of the panchuk technique and is characterized by a complex soil layer line, an alternate fill, use of 'earth mound(土堤)'/'clay clod(土塊)', and junctions of oval fill units. The fill method allows us to understand active technological sharing and application among the embankment structures in the period of the Three Kingdoms. The panchuk technique is used to construct a wall using a stamped earthen structure. This technique is divided into types B1 and B2 according to the height, scale, and extension method of the structure. Type B1 precedes B2, which was introduced in the late Hanseong Period. Staring with the Pungnap Earthen Fortification in Seoul, the panchuk technique seems to have spread throughout South Korea. The techniques of the fill and panchuk techniques coexisted at the time when they appeared, but panchuk earthen fortifications gradually dominated. Both techniques have completely different methods for the soil layers, and they have opposite orders of construction. Accordingly, it is assumed that both have different technical systems. The construction techniques of the earthen fortifications began from the Hanseong Period of Baekje Kingdom and were handed down and developed until the Woongjin-Sabi Periods. In the process, it seems that there existed active interactions with other nations. Recently, since studies of the earthen fortifications have been increasing mainly in the southern areas, it is expected that comparative analysis with neighboring countries will be done intensively.

• Journal of Naturopathy
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• v.11 no.1
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• pp.18-30
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• 2022

Background: It was to develop an air purification system (APS) using an underground air purification layer to verify the effect of basalt forest's underground air (sumgol) on a volcanic Jeju. Finally, it is necessary to analyze these purified air components and their usefulness to the human body in an air experience center. Purpose: It was to collect basalt forest air, analyze its composition, and explore its effect on the human body. Methods: We APS devices installed at four points in the Papaville area of Jeju. The air discharged from the APS was collected and analyzed the recycling components. An installed experience room filled with negative ions is about 5,000 ions/m³. After allowing the participants to stay for 60 to 120 minutes, we investigated the state of blood vessels. Results: In the analysis of the underground air, the O₂ concentration was 21.18%, which was higher than the average oxygen concentration of 20.94% in the atmosphere. However, Formaldehyde was not detected, and the CO₂ was 419 ppm, which was lower than that of indoor air. The PM2.5 concentration was less than 24 ㎍/m³ and detected anions over 5.000 /m³. The experiencer's vascular states improved, and the increase in pulse rate and stress relief were high. Conclusions: The valuable ingredients identified by analyzing the air were precious for natural healing. The experience results showed that it effectively improved the pulse rate, blood vessels, and stress. These conditions may be highly beneficial as a new area for expanding the basalt lava forest in the Jeju area into the natural healing and wellness industry.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.651-662
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• 2022

The electrochemical impedance spectroscopy(EIS) method was used to evaluate the concrete deterioration process related to chloride-induced steel corrosion with various corrosion levels(initiation, rust propagation and acceleration periods). The impressed current technique, with four total current levels of 0C, 13C, 65C and 130C, was used to accelerate steel corrosion in concrete cylinder samples with w/c ratio of 0.4, 0.5, and 0.6, immersed in a 0.5M NaCl solution. A series of EIS measurements was performed to monitor concrete deterioration during the accelerated corrosion test in this study. Some critical parameters of the equivalent circuit were obtained through the EIS analysis. It was observed that the charge transfer resistance(R_c) dropped sharply as the impressed current increased from 0C to 13C, indicating a value of approximately 10kΩcm². However, the sensitivity of R_c significantly decreased when the impressed current was further increased from 13C to 130C after corrosion of steel had been initiated. Meanwhile, the double-layer capacitance value(C_dl) linearly increased from 50×10^-6μF/cm² to 250×10^-6μF/cm² as the impressed current in creased from 0C to 130C. The results in this study showed that monitoring C_dl is an effective measurement parameter for evaluating the progress of internal concrete damages(de-bonding between steel and concrete, micro-cracks, and surface-breaking cracks) induced by steel corrosion. The findings of this study provide a fundamental basis for developing an embedded sensor and signal interpretation method for monitoring concrete deterioration due to steel corrosion at various corrosion levels.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Korean Society of Forest Science
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• v.21 no.1
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• pp.1-34
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• 1974

The author intended to investigate external and internal changes in the cone structure, changes in water content, sugar, fat and protein during the period of seed maturation which bears a proper germinability. The experimental results can be summarized as in the following. 1. Male flowers 1) Pollen-mother cells occur as a mass from late in April to early in May, and form pollen tetrads through meiosis early and middle of May. Pollen with simple nucleus reach maturity late in May. 2) Stamen number of a male flower is almost same as the scale number of cone and is 69-102 stamens. One stamen includes 5800-7300 pollen. 3) The shape is round and elliptical, both of a pollen has air-sac with $80-91{\mu}$ in length, and has cuticlar exine and cellulose intine. 4) Pollen germinate in 68 hours at $25^{\circ}C$ with distilled water of pH 6.0, 2% sugar and 0.8% agar. 2. Female flowers 1) Ovuliferous scales grow rapidly in late April, and differentiation of ovules begins early in May. Embryo-sac-mother cells produce pollen tetrads through meiosis in the middle of May, and flower in late May. 2) The pollinated female flowers show repeated divisions of embryo-sac nucleus, and a great number of free nuclei form a mass for overwintering. Morphogenesis of isolation in the mass structure takes place from the middle of March, and that forms albuminous bodies of aivealus in early May. 3. Formation of pollinators and embryos. 1) Archegonia produce archegonial initial cells in the middle and late April, and pollinators are produced in the late April and late in early May. 2) After pollination, Oespore nuclei are seen to divide in the late May forming a layer of suspensor from the diaphragm in early June and in the middle of June. Thus this happens to show 4 pro-embryos. The organ of embryos begins to differentiate 1 pro-embryo and reachs perfect maturation in late August. 4. The growth of cones 1) In the year of flowering, strobiles grow during the period from the middle of June to the middle of July, and do not grow after the middle of August. Strobiles grow 1.6 times more in length 3.3 times short in diameter and about 22 times more weight than those of female flower in the year of flowering. 2) The cones at the adult stage grow 7 times longer in diameter, 12-15 times shorter diameter than those of strobiles after flowering. 3) Cone has 96-133 scales with the ratio of scale to be 69-80% and the length of cone is 11-13cm. Diameter is 5-8cm with 160-190g weight, and the seed number of it is 90-150 having empty seed ratio of 8-15%. 5. Formation of seed-coats 1) The layers of outer seed-coat become most for the width of $703{\mu}$ in the middle of July. At the adult stage of seed, it becomes $550-580{\mu}$ in size by decreasing moisture content. Then a horny and the cortical tissue of outer coats become differentiated. 2) The outer seed-coat of mature seeds forms epidermal cells of 3-4 layers and the stone cells of 16-21 layers. The interior part of it becomes parenchyma layer of 1 or 2 rows. 3) Inner seed-coat is formed 2 months earlier than the outer seed-coat in the middle of May, having the most width of inner seed-coat $667{\mu}$. At the adult stage it loses to $80-90{\mu}$. 6. Change in moisture content After pollination moisture content becomes gradually increased at the top in the early June and becomes markedly decreased in the middle of August. At the adult stage it shows 43~48% in cone, 23~25% in the outer seed-coat, 32~37% in the inner seed-coat, 23~26% in the inner seed-coat and endosperm and embryo, 21~24% in the embryo and endosperm, 36~40% in the embryos. 7. The content compositions of seed 1) Fat contents become gradually increased after the early May, at the adult stage it occupies 65~85% more fat than walnut and palm. Embryo includes 78.8% fat, and 57.0% fat in endosperm. 2) Sugar content after pollination becomes greatly increased as in the case of reducing sugar, while non-reducing sugar becomes increased in the early June. 3) Crude protein content becomes gradually increased after the early May, and at the adult stage it becomes 48.8%. Endosperm is made up with more protein than embryo. 8. The test of germination The collected optimum period of Pinus koraiensis seeds at an adequate maturity was collected in the early September, and used for the germination test of reduction-method and embryo culture. Seeds were taken at the interval of 7 days from the middle of July to the middle of September for the germination test at germination apparatus.