• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• Journal of dental hygiene science
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• v.15 no.2
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• pp.106-112
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• 2015

This study was conducted to provide with baseline data with the purpose of increasing the values of medical services. Self-administered questionnaire survey was conducted on 236 patients at a dental clinic follow-up visit in dentist clinic Gyeongnam area from June 2013 to August 2013. All statistical analyses were performed using SPSS. The motivation visiting the dental clinic is that the first one is introduction from the family and friends, the second is accessibility, the third is conspicuity and the last one is awareness of the dentist. The main variables in the process of treatment are service system, kindness, satisfaction of service, efficient of re-call system. The relief of discomfort at revisit show the highest score in the process of implant treatment and intention of revisit hereafter do in the prostheses process. In the correlation between main variables, service system and relief of discomfort at revisit (r=0.440, p<0.001), kindness and satisfaction of medical service (r=0.675, p<0.001), revisit hereafter and satisfaction of service (r=0.387, p<0.001) and efficiency of re-call system and revisit showed the highest correlation. The influence on satisfaction of dental service show meaningful level in kindness (p<0.001) and efficiency of re-call system (p<0.05). The intention of revisit is affected meaningfully by relief of uncomfort (p<0.05), service system (p<0.05), kindness (p<0.01) and efficiency of re-call system (p<0.01). In summary, the personal network of patients is most important variable at intention for revisit of dental clinic. As satisfaction of kindness and efficiency of re-call system is higher, satisfaction of medical service and intention for revisit are shown higher. Therefore further research for improvement of satisfaction for medical service and of intention of revisit at the dental clinic should be carried out.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.

• Korean Journal of Ecology and Environment
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• v.52 no.3
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• pp.245-265
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• 2019

Biotope, which represents the characteristic habitats of living organisms, need to be identified as essential for the efficient creation and sustainable management of urban ecosystems. This study was carried out to provide the basic information for ecological urban planning by analyzing types and attributes of the biotop established throughout the whole area of the Pohang city, a representative industrial city in Korea. The biotop established in Pohang city is composed of 12 types including forests (coniferous, deciduous, and mixed forests), agricultural fields (rice paddy and upland field), green facilities, river, reservoir, bare ground, residential area, public facilities, commercial area, industrial area, roads, and schools. As a result of analyzing the properties according to biotop types, industrial, commercial and residential areas, which represent urban areas, was dominated by introduced vegetation. Moreover the introduced vegetation is usually composed of exotic plants or modified forms for landscape architecture and horticulture rather than native plants, which reflects ecological property of both region and site. As the distance from the urban center increases, the agricultural field showed a form of typical farmland, whereas the closer it is, the more form of greenhouse farming. Natural green spaces were divided into riparian vegetation established along the stream and forest vegetation. Forest vegetation is consisted of secondary forests (seven communities) and plantations (three communities). The urban landscape of Pohang city is dominated by the industrial area. Among them, the steel industry, which occurs large amounts of heat pollution and carbon dioxide, occupies a large proportion. On the other hand, green space is very insufficient in quantity and inferior in quality. This study proposed several restoration plans and further, a green network, which ties the existing green spaces and the green space to be restored as a strategy to improve the environmental quality in this area.

• Journal of Korea Port Economic Association
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• v.38 no.3
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• pp.105-131
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• 2022

Even after the improvement in 2019, UNCTAD's Liner Shipping Connectivity Index (LSCI), which evaluates the performance of the global container port market, has limited use. In particular, since the liner shipping connectivity index evaluates the performance based only on the distance of the relationship, the performance index combining the port attractiveness of calling would be more efficient. This study used the modified Huff model, the hub-authority algorithm and the eigenvector centrality of social network analysis, and correlation analysis for 2007, 2017, and 2019 data of Ocean-Commerce, Japan. The findings are as follows: Firstly, the port attractiveness of calling and the overall performance of the port did not always match. However, according to the analysis of the attractiveness of a port calling, Busan remained within the top 10. Still, the attractiveness among other Korean ports improved slowly from the low level during the study period. Secondly, Global container ports are generally specialized for long-term specialized inbound and outbound ports by the route and grow while maintaining professionalism throughout the entire period. The Korean ports continue to change roles from analysis period to period. Lastly, the volume of cargo by period and the extended port connectivity index (EPCI) presented in this study showed a correlation from 0.77 to 0.85. Even though the Atlantic data is excluded from the analysis and the ship's operable capacity is used instead of the port throughput volume, it shows a high correlation. The study result would help evaluate and analyze global ports. According to the study, Korean ports need a long-term strategy to improve performance while maintaining professionalism. In order to maintain and develop the port's desirable role, it is necessary to utilize cooperation and partnerships with the complimentary port and attract shipping companies' services calling to the complementary port. Although this study carried out a complex analysis using a lot of data and methodologies for an extended period, it is necessary to conduct a study covering ports around the world, a long-term panel analysis, and a scientific parameter estimation study of the attractiveness analysis.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.1
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• pp.33-52
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• 2022

The History and Culture Park and the Lee Kun-hee Donation Hall will be built in Songhyeon-dong, Seoul. Political games from the Joseon Dynasty to the present greatly influenced the historicity of Songhyeon-dong. However, place analysis was limited to changes in landowners and land uses rather than a historical context. Therefore, this study analyzed the context in which the placeness of Songhyeon-dong changed according to the emergence of cultural hegemony using the perspective of modern cultural geography and comparative history. As a result of the analysis, cultural hegemony in historical transitions, such as Sinocentrism, maritime expansion, civil revolutions, imperialism, nationalism, popular art, and neoliberalism, was found to have created new intellectuals in Bukchon, including Songhyeon-dong, and influenced social systems and spatial policies. In this social relations, the placeness of Songhyeon-dong changed as follows. First, the founding forces of Joseon created pine forests as Bibo Forests to invocate the permanence of the dynasty. In the late Joseon dynasty, it was an era of maritime expansion, and as Joseon's yeonhaeng increased, a garden for the Gyeonghwasejok, who enjoyed the culture of the Qing dynasty, was built. Although pine forests and gardens disappeared due to the development of housing complexes as the population soared during the Japanese colonial era, Cha Gyeong's landscape aesthetics, which harmonized artificial gardens and external nature, are worth reinterpreting in modern times. Second, the wave of modernization created a new school in Bukchon and a boarding house in Songhyeon-dong owned by a pro-Japanese faction. Angukdongcheon-gil, next to Songhyeon-dong, was where thinkers who promoted civil revolution and national self-determination exchanged ideas. Songhyeon-dong, the largest boarding house, served as a residence for students to participate in the March 1st Movement and was the cradle of the resulting culture of student movements. The appearance of the old road is preserved, so it is a significant part of the regeneration of walking in the historic city center, connecting Gwanghwamun-Bukchon-Insadong -Donhwamunro. Third, from the cultural rule of the Government General of Joseon to the Military Government, Songhyeon-dong acted as a passage to western culture with the Joseon Siksan Bank's cultural housing and staff accommodations at the U.S. Embassy. Ancient and contemporary art coexisted in the surrounding area, so the modern and contemporary art market was formed. The Lee Kun-hee Donation Hall is expected to form a cultural belt for citizens with the gallery, Bukchon Hanok Village, the Craft Museum, and the Modern Museum of Art. Discourses and challenges are needed to recreate the place in harmony with the forests, gardens, the street of citizens' birth, history and culture park, the art museum, and the surrounding walking network.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.101-114
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• 2023

In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.