• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1908-1918
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• 2022

Recently, ASEAN is not only a new production base but also a consumer market for Korea, and interest in the utilization of production networks in each region is increasing. In particular, urbanization in ASEAN countries is progressing at a relatively fast pace. Each country is promoting smart city projects combined with ICT to improve outdated basic infrastructure facilities such as housing, transportation, logistics, crime prevention, and disaster prevention. The purpose of this study is to develop a web-based smart city export HUB platform so that companies with excellent domestic smart city solutions can participate in smart city construction through networks with ASEAN countries. These platforms can secure the demand for smart city construction in ASEAN countries, and through the establishment of the Korea-ASEAN public-private network, smart cities planned in ASEAN countries can be promoted more innovative. In addition, it is expected to be positioned as a Global smart city platform model by applying to real cities through collaboration with excellent domestic companies.

• Asian Journal of Atmospheric Environment
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• v.9 no.1
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• pp.12-21
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• 2015

Air pollution is a challenging issue in some of the large cities in developing countries. In this regard, data interpretation is one of the most important parts of air quality management. Several methods exist to analyze air quality; among these, we applied the Multilayer Perceptron (MLP) and Radial Basis Function (RBF) methods to predict the hourly air concentration of benzene in 14 districts in the municipality of Tehran. Input data were hourly temperature, wind speed and relative humidity. Both methods determined reliable results. However, the RBF neural network performance was much closer to observed benzene data than the MLP neural network. The correlation determination resulted in 0.868 for MLP and 0.907 for RBF, while the Index of Agreement (IA) was 0.889 for MLP and 0.937 for RBF. The sensitivity analysis related to the MLP neural network indicated that the temperature had the greatest effect on prediction of benzene in comparison with the wind speed and humidity in the study area. The temperature was the most significant factor in benzene production because benzene is a volatile liquid.

• Advances in environmental research
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• v.4 no.4
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• Journal of the Korean Geographical Society
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• v.38 no.4
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• pp.591-609
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• 2003

The purpose of this study is to exhibit the characteristics and changes of city network in China through the analyses railway structures and movement of passengers and goods in China. On the basis of accessibility analysis., central China area shows the highest level of accessibility, and it declines toward periphery areas. The center of City network was transfered from the central area to the east. Three main lines from north to south and the central status of main cities remain unchanged. The city network stratum structure of China can be divided into three levels. The first level is in a pattern of linear distribution within provices along main lines. The second level, shows a pattern of strengthened linear distribution and crossed influencial regions. The third level includes 5 areas.

• Journal of Korean Society of Rural Planning
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• v.24 no.3
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• pp.127-135
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• 2018

In countries and regions population plays an important role. Recently the importance of population migration increased as population growth slowed. Researches on population migration are mainly focused on the analysis of the population movement factors and the regional structure analysis using the network analysis method. Analysis of regional structure through population movement is not enough to explain the phenomenon of migration of small cities and rural regions. In this study, to overcome the limit of previous studies the characteristics of the population movement rate according to the size of the population were analyzed. Also network analysis using the population movement OD (Origin and Destination) and population movement rate OD were conducted and the results of them were compared. As the results of analysis by the regional population scale, the population movement by population size showed a big difference in the areas with more than 100 thousand people and less than 100 thousand people. Migration to the outside of the province was the most frequent in regions with 30,000~50,000 people. The population migration rate network analysis result showed that the new area with large population inflow capacity was identified, which could not be found in the population movement network analysis because population movement number is small. The population movement rate irate is expected to be used to identify the central regions of the province and to analyze the difference in resident attractiveness.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.765-784
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• 2019

The smart city is one of the most promising, prominent, and challenging applications of the Internet of Things (IoT). Smart cities rely on everything connected to each other. This in turn depends heavily on technology. Technology literacy is essential to transform a city into a smart, connected, sustainable, and resilient city where information is not only available but can also be found. The smart city vision combines emerging technologies such as edge computing, blockchain, artificial intelligence, etc. to create a sustainable ecosystem by dramatically reducing latency, bandwidth usage, and power consumption of smart devices running various applications. In this research, we present a comprehensive survey of emerging technologies for a sustainable smart city network. We discuss the requirements and challenges for a sustainable network and the role of heterogeneous integrated technologies in providing smart city solutions. We also discuss different network architectures from a security perspective to create an ecosystem. Finally, we discuss the open issues and challenges of the smart city network and provide suitable recommendations to resolve them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.932-945
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• 2018

Everything in the world is becoming connected and interactive due to the Internet. The future of interactive smart environments such as smart cities, smart industries, or smart farms demand high network bandwidth, high network flexibility, and self-organization systems without costly hardware upgrades, and they provide a sustainable, scalable, and replicable smart environment backbone infrastructure. This paper presents a new Hybrid Software-Defined architecture for integrating Internet-of-Things technologies that are essential technologies for smart environments. It combines a software-defined networking infrastructure and a real-time distributed network framework with an advanced optimization to enable self-configuration, self-management, and self-adaption for providing seamless communication and efficiently managing a vast number of smart heterogeneous devices.

• MALSORI
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• no.51
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• pp.137-149
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• 2004

A weather forecast service with speech recognition is described. This service allows users to get the weather information of all the cities by saying the city names with just one phone call, which was not provided in the previous weather forecast service. Speech recognition is implemented in the intelligent peripheral (IP) of the advanced intelligent network (AIN). The AIN is a telephone network architecture that separates service logic from switching equipment, allowing new services to be added without having to redesign switches to support new services. Experiments in speech recognition show that the recognition accuracy is 90.06% for the general users' speech database. For the laboratory members' speech database, the accuracies are 95.04% and 93.81%, respectively in simulation and in the test on the developed system.

• Journal of Contemporary Eastern Asia
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• v.15 no.1
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• pp.64-77
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• 2016

This case study takes a look at the hyperlink networks extracted from the websites of 367 international non-governmental organizations (NGOs) with datasets from 2010, 2012 and 2014. The first level of evaluation focuses on connections between the NGOs, identifying important nodes, groups and their relations. The second level takes into account the broad range of networked websites from the World Wide Web delivering insights into general networking patterns. The third level explores the underlying spatial configurations of the network which offers a great variety of geographic insights on information flows between and within continents, countries and cities. The most interesting findings of this study are a low level of interconnectedness between the NGOs and at the same time a strong spatial concentration of all embedded network actors.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.