• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.131-142
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• 2023

In this paper, we propose a new station selection algorithm to solve the illegal parking problem of shared electric scooters and improve the service quality. Recently, as a solution to the urban transportation problem, shared electric scooters are attracting attention as the first and last mile means between public transportation and final destinations. As a result, the shared electric scooter market grew rapidly, problems caused by electric scooters are becoming serious. Therefore, in this study, text data are collected to understand the nature of the problem, and the problems related to shared scooters are viewed from the perspective of pedestrians and users in 'LDA Topic Modeling', and a station extension algorithm is based on this. Some parking lots have already been installed, but the existing parking lot location is different from the actual area of tow. Therefore, in this study, we propose an algorithm that can install stations at high actual tow density using mixed clustering technology using K-means after primary clustering by DBSCAN, reflecting the 'current state of electric scooter tow in Seoul'.

• Korean Journal of Environmental Biology
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• v.22 no.2
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• pp.341-350
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• 2004

We examined estuarine macrobenthos in Namdaecheon estuary, Gangneung, Korea, September 22, 1997. A total of 56 species were found together with an abundance of 378 individuals $m^{-2}$/ and a biomass of 20.79 gWWt $m^{-2}$. The major dominant species were unidentified oligochaete followed by different species of polychaetes Hediste japonica, Rhynchospio glutaea, Poecilochaetus trilobatus, Scoloplos armiger, Spiophanes bombyx and a talitrid amphipod Platorchestia crassicornis. The study area was divided into two different groups of stations and species, based on the q -mode and r-mode clustering analysis. In case of q-mode, there are two groups: one is a marine station group and the other is estuarine group. The r-mode clustering analysis showed the two main communities as follows; 1) Marine species group occurred only at station 7, 8 and 9 and 2) Estuarine species group composed of the species present at station 1 to 6 and 10 to 15. In the number of species, the estuarine station group includes 13 species, whereas 43 species were presented in the marine station group. Total macrofaunal abundance and total biomass were higher in the marine station group than those in the estuarine group. The species diversity index was also high in the marine group (> 2), whereas it was less than ca. 1 in the estuarine group.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.1984-1989
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• 2008

In this study, fire simulations were performed to analyze the characteristics of the fire driven flow and the effects of the platform screen door on the smoke flow in the station, when the fire occurred in the center of the platform. Soongsil Univ. station (line number 7, 47m in depth underground) was chosen which was the one of the deepest underground subway stations in the Seoul metro, SMRT. The parallel computational method was employed to compute the heat and mass transfer eqn's with 6 CPUs of the linux clustering machine. The fire driven flow was simulated with using FDS code in which LES method was applied. The Heat release rate was 10MW and The Ultrafast model was applied for the growing model of the fire source. The 10,000,000 structured grids were used.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.659-671
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• 2009

Fuzzy c-means clustering technique is applied to improve the accuracy of G/R ratio used for rainfall estimation by radar reflectivity. G/R ratio is computed by the ground rainfall records at AWS(Automatic Weather System) sites to the radar estimated rainfall from the reflectivity of Kwangduck Mt. radar station with 100km effective range. G/R ratio is calculated by two methods: the first one uses a single G/R ratio for the entire effective range and the other two different G/R ratio for two regions that is formed by clustering analysis, and absolute relative error and root mean squared error are employed for evaluating the accuracy of radar rainfall estimation from two G/R ratios. As a result, the radar rainfall estimated by two different G/R ratio from clustering analysis is more accurate than that by a single G/R ratio for the entire range.

• Journal of KIISE:Information Networking
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• v.35 no.2
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• pp.139-148
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• 2008

Many types of sensor data exhibit strong correlation in both space and time. Suppression, both temporal and spatial, provides opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not correlation of sensor data. In this paper, we propose a novel clustering algorithm with suppression techniques. To guarantee independent communication among clusters, we allocate multiple channels based on sensor data. Also, we propose a spatio-temporal suppression technique to reduce the network traffic. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the site of data which have been collected in the base-station. As a result, our experimental results show that the size of data was reduced by $4{\sim}40%$, and whole network lifetime was prolonged by $20{\sim}30%$.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.13-18
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• 2018

In this study, we classify Seoul metro stations according to boarding and alighting patterns using machine earning technique. The target data is the number of boarding and alighting passengers per hour every day at 233 subway stations from 2008 to 2017 provided by the public data portal. Gaussian mixture model (GMM) and K-means clustering are used as machine learning techniques in order to classify subway stations. The distribution of the boarding time and the alighting time of the passengers can be modeled by the Gaussian mixture model. K-means clustering algorithm is used for unsupervised learning based on the data obtained by GMM modeling. As a result of the research, Seoul metro stations are classified into four groups according to boarding and alighting patterns. The results of this study can be utilized as a basic knowledge for analyzing the characteristics of Seoul subway stations and analyzing it economically, socially and culturally. The method of this research can be applied to public data and big data in areas requiring clustering.

• Smart Media Journal
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• v.8 no.1
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• pp.43-50
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• 2019

As numerous devices are connected through a wireless network, there exist many studies conducted to efficiently connect the devices. While earlier studies often use clustering for efficient device management, there is a load-intensive cluster node which may lead the entire network to be unstable. In order to solve this problem, we propose a scheduling model for centralized unequal chain clustering for efficient management of sensor nodes. For the cluster configuration, this study is based on the cluster head range and the distance to the base station(BS). The main vector projection technique is used to construct clustering with concentricity where the positions of the base stations are not the same. We utilize a multiple radio access interface, multiple-input multiple-output (MIMO), for data transmission. Experiments show that cluster head energy consumption is reduced and network lifetime is improved.

• The Journal of the Acoustical Society of Korea
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• v.31 no.3
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• pp.170-178
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• 2012

In the paper, phonetic decision tree of the triphone unit was built for the phoneme-based speech recognition of 640 stations which run by the Korail. The clustering rate was determined by Pearson and Regression analysis to decide threshold used in node splitting. Using the determined the clustering rate, thresholds are automatically decided by the threshold value according to the average clustering rate. In the recognition experiments for verifying the proposed method, the performance improved 1.4~2.3 % absolutely than that of the baseline system.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.136-141
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• 2021

Research on wireless sensor networks has focused on the monitoring and characterization of large-scale physical environments and the tracking of various environmental or physical conditions, such as temperature, pressure, and wind speed. We propose a stochastic mobility model that can be applied to a MANET (Mobile Ad-hoc NETwork). environment, and apply this mobility model to a newly proposed clustering-based routing protocol. To verify its stability and durability, we compared the proposed stochastic mobility model with a random model in terms of energy efficiency. The FND (First Node Dead) was measured and compared to verify the performance of the newly designed protocol. In this paper, we describe the proposed mobility model, quantify the changes to the mobile environment, and detail the selection of cluster heads and clusters formed using a fuzzy inference system. After the clusters are configured, the collected data are sent to a base station. Studies on clustering-based routing protocols and stochastic mobility models for MANET applications have shown that these strategies improve the energy efficiency of a network.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.230-238
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• 2021

A sensor network is made up of many sensors deployed in different areas to be monitored. They communicate with each other through a wireless medium. The routing of collected data in the wireless network consumes most of the energy of the network. In the literature, several routing approaches have been proposed to conserve the energy at the sensor level and overcome the challenges inherent in its limitations. In this paper, we propose a new low-energy routing protocol for power grids sensors based on an unsupervised clustering approach. Our protocol equitably harnesses the energy of the selected cluster-head nodes and conserves the energy dissipated when routing the captured data at the Base Station (BS). The simulation results show that our protocol reduces the energy dissipation and prolongs the network lifetime.