• Parasites, Hosts and Diseases
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• v.30 no.2
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• pp.65-74
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• 1992

The tegumental ultrastructure of juvenile and adult Echinostoma cinetorchis (Trematoda: Echinostomatidae) was observed by scanning electron microscopy. Three-day (juvenile) and 16-day (adult) worms were harvested from rats (Sprague-Dawley) experimentally fed the metacercariae from the laboratory-infected fresh water snail, Hippeutis cantori. The worms were fifed with 2.5% glutaraldehyde, processed routinely, and observed by an ISI Korea DS-130 scanning electron microscope. The 3-day old juvenile worms were elongated and ventrally curved, with their ventral sucker near the anterior two-fifths of the body. The head crown was bearing 37∼38 collar spines arranged in a zigzag pattern. The lips of the oral and ventral suckers had 8 and 5 type II sensory papillae respectively, and bewteen the spines, a few type III papillae were observed. Tongue or spade-shape spines were distributed anteriorly to the ventral sucker, whereas peg-like spines were distributed posteriorly and became sparse toward the posterior body. The spines of the dorsal surface were similar to those of the ventral surface. The 16-day old adults were leaf-like, and their oral and ventral suckers were located very closely. Aspinous head crown, oral and ventral suckers had type II and type III sensory papillae, and numerous type I papillae were distributed on the tegument anterior to the ventral sucker. Scale-like spines, with broad base and round tip, were distributed densely on the tegument anterior to the ventral sucker but they became sparse posteriorly. At the dorsal surface, spines were observed at times only at the anterior body. The results showed that the tegument of E. cinetorchis is similar to that of other echinostomes, but differs in the number and arrangement of collar spines, shape and distribution of tegumenal spines, and type and distribution of sensory papillae.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.117-130
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• 2005

In both deteministic user Optimal Traffic Assignment Model (UOTAM) and stochastic UOTAM, travel time, which is a major ccriterion for traffic loading over transportation network, is defined by the sum of link travel time and turn delay at intersections. In this assignment method, drivers actual route perception processes and choice behaviors, which can become main explanatory factors, are not sufficiently considered: therefore may result in biased traffic loading. Even though there have been some efforts in Stochastic UOTAM for reflecting drivers' route perception cost by assuming cumulative distribution function of link travel time, it has not been fundamental fruitions, but some trials based on the unreasonable assumptions of Probit model of truncated travel time distribution function and Logit model of independency of inter-link congestion. The critical reason why deterministic UOTAM have not been able to reflect route perception cost is that the route perception cost has each different value according to each origin, destination, and path connection the origin and destination. Therefore in order to find the optimum route between OD pair, route enumeration problem that all routes connecting an OD pair must be compared is encountered, and it is the critical reason causing computational failure because uncountable number of path may be enumerated as the scale of transportation network become bigger. The purpose of this study is to propose a method to enable UOTAM to reflect route perception cost without route enumeration between an O-D pair. For this purpose, this study defines a link as a least definition of path. Thus since each link can be treated as a path, in two links searching process of the link label based optimum path algorithm, the route enumeration between OD pair can be reduced the scale of finding optimum path to all links. The computational burden of this method is no more than link label based optimum path algorithm. Each different perception cost is embedded as a quantitative value generated by comparing the sub-path from the origin to the searching link and the searched link.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• 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 Korea Game Society
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• v.4 no.4
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• pp.11-18
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• 2004

The on-line games in the past were played by only two persons exchanging data based on one-to-one connections, whereas recent ones (e.g. MMORPG: Massively Multi-player Online Role-playings Game) enable tens of thousands of people to be connected simultaneously. Specifically, Korea has established an excellent network infrastructure that can't be found anywhere in the world. Almost every household has a high-speed Internet access. What made this possible was, in part, high density of population that has accelerated the formation of good Internet infrastructure. However, this rapid increase in the use of on-line games may lead to surging traffics exceeding the limited Internet communication capacity so that the connection to the games is unstable or the server fails. expanding the servers though this measure is very costly could solve this problem. To deal with this problem, the present study proposes the load distribution technology that connects in the form of local clustering the game servers divided by their contents used in each on-line game reduces the loads of specific servers using the load balancer, and enhances performance of sewer for their efficient operation. In this paper, a cluster system is proposed where each Game server in the system has different contents service and loads are distributed efficiently using the game server resource information such as CPU utilization. Game sewers having different contents are mutually connected and managed with a network file system to maintain information consistency required to support resource information updates, deletions, and additions. Simulation studies show that our method performs better than other traditional methods. In terms of response time, our method shows shorter latency than RR (Round Robin) and LC (Least Connection) by about 12%, 10% respectively.

• Tunnel and Underground Space
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• v.13 no.1
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• pp.52-63
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• 2003

Discontinuities such as faults, fractures and joints in rock mass play the dominant role in the mechanical and hydraulic properties of the rock mass. The key factors that influence on the flow of groundwater are hydraulic and geometric characteristics of discontinuities and their connectivity. In this study, a program that analyzes groundwater flow in the 3D discontinuity network was developed on the assumption that the discontinuity characteristics such as density, trace length, orientation and aperture have particular distribution functions. This program generates discontinuities in a three-dimensional space and analyzes their connectivity and groundwater flow. Due to the limited computing capacity In this study, REV was not exactly determined, but it was inferred to be greater than 25$\times$25$\times$25 ㎥. By calculating the extent of aperture that influences on the groundwater flow, it was found that the discontinuities with the aperture smaller than 30% of the mean aperture had little influence on the groundwater flow. In addition, there was little difference in the equivalent hydraulic conductivity for the the two cases when considering and not considering the boundary effect. It was because the groundwater flow was mostly influenced by the discontinuities with large aperture. Among the parameters considered in this study, the length, aperture, and orientation of discontinuities had the greatest influence on the equivalent hydraulic conductivity of rock mass in their order. In case of existence of a fault in rock mass, elements of the equivalent hydraulic conductivity tensor parallel to the fault fairly increased in their magnitude but those perpendicular to the fault were increased in a very small amount at the first stage and then converged.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.197-208
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• 2020

This study was carried out to analyze spatial and temporal incidence characteristics of scrub typhus and predict the future incidence of scrub typhus since the incidences of scrub typhus have been rapidly increased among vector-borne diseases. A maximum entropy (MaxEnt) ecological model was implemented to predict spatial distribution and incidence rate of scrub typhus using spatial data sets on environmental and social variables. Additionally, relationships between the incidence of scrub typhus and critical spatial data were analyzed. Elevation and temperature were analyzed as dominant spatial factors which influenced the growth environment of Leptotrombidium scutellare (L. scutellare) which is the primary vector of scrub typhus. A temporal number of diseases by scrub typhus was predicted by a deep neural network (DNN). The model considered the time-lagged effect of scrub typhus. The DNN-based prediction model showed that temperature, precipitation, and humidity in summer had significant influence factors on the activity of L. scutellare and the number of diseases at fall. Moreover, the DNN-based prediction model had superior performance compared to a conventional statistical prediction model. Finally, the spatial and temporal models were used under climate change scenario. The future characteristics of scrub typhus showed that the maximum incidence rate would increase by 8%, areas of the high potential of incidence rate would increase by 9%, and disease occurrence duration would expand by 2 months. The results would contribute to the disease management and prediction for the health of residents in terms of public health.

• Journal of the Korean Geographical Society
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• v.31 no.3
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• pp.593-612
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• 1996

This study concern with the changes of street from the Choseon Dynasty to present days around Old-Boundary in Taegu, analyzing the backgrounds of change factors and development trends of the Taegu City. The basis element of a city structure is the street. Therefore, in this study, a chage of street space of a city was investigted. Historically, Taegu was a walled city and had a Mono-nucleus which was restricted by the castle, and served as a starting point of formation of spatial structuure. The form of the artery street took a "T" pattern, othe streets were formed in irregular shapes. As the city grew gradually, the castle was removed on account of diversification in traffic network, change of socio-economic organization in traffic network, change of socio-economic organization, formation of industrial bases and functional distribution. CBD of this city has been located within the area surrounded by these streets. This is a kind of general pattern of traditional walled cities through the world in both Western and Oriental societies. A s the begining of this centry, a 'Dark Ages' descended upon Korea because the country was under the Japanese-Korean Annexation, and, throughout this period, the urban planning was planned exclusively for Japanese. The street pattern within residential areas of Korea took the maze type, in contrast with Japanese residential areas which showed grid pattern of streets. This is another general pattern of almost of all colonial cities especially in Asia. High class residential areas were planned and built by Japanese, and they were located within 5-10 minutes' on-foot distance from the CBD hard core. This high prestige has continued until the 1980s when it occurred land use succession which commerical functions invaded into residential areas. Back in the colonial period, there was a between two hetrogeneous groups due to the fact that the Japanese lived mainly oriented the new railway system but that Koreans still lived along the old highway system which ran through the Korea Peninsula. Street netwook formed in the above process has maintained its shape without great changes after the liberation form the Japanese Colony. Taegu has, accordingly, developed ring-radial network system which has been a combination of radial and ring facilities. The present conditions of street patterns in Taegy mainly depend on 4 rings and 8 radius, with grid pattern street able to be found in Old Boundary.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.1-28
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• 2019

This study analyzed the density and mortality rate of Korean fir at 9 sites where individuals of Korean firs were marked into the live and dead trees with coordinates on orthorectified aerial images by digital photogrammetric system. As a result of the analysis, Korean fir in each site showed considerable heterogeneity in density and mortality rate depending on the location within site. This make it possible to assume that death of Korean fir can occur by specific factors that vary depending on the location. Based on the analyzed densities and mortality rates of Korea fir, we investigated the correlation between topographic factors such as altitude, terrain slope, drainage network, solar radiation, aspect and the death of Korean fir. The density of Korean fir increases with altitude, and the mortality rate also increases. A negative correlation is found between the terrain slope and the mortality rate, and the mortality rate is higher in the gentle slope where the drainage network is less developed. In addition, it is recognized that depending on the aspect, the mortality rate varies greatly, and the mean solar radiation is higher in live Korean fir-dominant area than in dead Korean fir-dominant area. Overall, the mortality rate of Korean fir in Mt. Halla area is relatively higher in areas with relatively low terrain slope and low solar radiation. Considering the results of previous studies that the terrain slope has a strong negative correlation with soil moisture and the relationship between solar radiation and evaporation, these results lead us to infer that excess soil moisture is the cause of Korean fir mortality. These inferences are supported by a series of climate change phenomena such as precipitation increase, evaporation decrease, and reduced sunshine duration in the Korean peninsula including Jeju Island, increase in mortality rate along with increased precipitation according to the elevation of Mt. Halla and the vegetation change in the mountain. It is expected that the spatial patterns in the density and mortality rate of Korean fir, which are controlled by topography such as altitude, slope, aspect, solar radiation, drainage network, can be used as spatial variables in future numerical modeling studies on the death or decline of Korean fir. In addition, the method of forest distribution survey using the orthorectified aerial images can be widely used as a numerical monitoring technique in long - term vegetation change research.