• Journal of Communications and Networks
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• v.17 no.6
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• pp.549-557
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• 2015

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platformand the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4Gbyte of storage size (87%of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• Information and Communications Magazine
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• v.33 no.1
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• pp.49-53
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• 2015

본고에서는 4G/5G/WiFi 무선 통신의 C-RAN(Centralized/Cloud Radio Access Network)을 지원하는 xhaul망에서의 이더넷 응용 기술에 대해 분석한다. xhaul에서의 요구사항을 고찰하고, 이를 충족시키기 위한 이더넷 응용으로서 RoE(Radio over Ethernet)을 중심으로 한 기술 분석과 함께, 그 외 프론트홀 전송 기술을 알아봄으로써, xhaul에서의 이더넷의 응용과 더불어 기타 차세대 무선 망 기술과 함께 유무선 융합 전송 기술에 대한 방향을 타진해 보도록 한다.

• Water Engineering Research
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• v.3 no.2
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• pp.123-134
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• 2002

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

• Water Engineering Research
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• v.3 no.2
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.296-298
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• 1999

Five institutions, which are very active in data utilization of environmental satellites NOAA and GMS, are connected via high speed networks to construct the databases based on the observations of A AVHRR (Advanced very High Resolution Radiometer) of NOAA satellite and VISSR (Visible and Infrared Scanning Radiometer) of GMS (Geostationary Meteorological Satellite) and to create scientific data sets for land, ocean and ,atmosphere. And vegetation index, sea surface temperature, cloud distribution maps and so on are generated by high speed and huge volume data Processing for studies on long term variations of land, ocean and atmosphere in Asia. In this paper the concept of this project and the activities at the Science University of Tokyo are briefly introduced

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.139-144
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• 2022

The smart cities are evolving constantly and are responsible for the current transformation of cities and countries into a completely connected network of information and technology This interconnected network of a huge number of smart devices is capable of exchanging complex information and provides tremendous support including enhanced quality of life within urban locations. Unfortunately this set-up is vulnerable to security attacks and requires the widespread ubiquitous network to authorize access through privacy and thus offer security in order to ensure civilian participation in a country. The smart network should benefit the individuals of the country by developing potential strategies to protect the smart cities and their participating entities from the unauthorized attacks. Trustworthy data sharing strategies based on the utilization of advanced technology features via smart communication network could solve some issues of privacy and security. This paper presents the challenges and issues related to protection and highlights the important aspects of securing the smart cities and its components. It also presents the role of cloud security for building a secure smart city.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.803-812
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• 2022

With the increasing number of mobile device users worldwide, utilizing mobile edge computing (MEC) devices close to users for content caching can reduce transmission latency than receiving content from a server or cloud. However, because MEC has limited storage capacity, it is necessary to determine the content types and sizes to be cached. In this study, we investigate a caching strategy that increases the hit ratio from small base stations (SBSs) for mobile users in a heterogeneous network consisting of one macro base station (MBS) and multiple SBSs. If there are several SBSs that users can access, the hit ratio can be improved by reducing duplicate content and increasing the diversity of content in SBSs. We propose a Deep Q-Network (DQN)-based caching strategy that considers time-varying content popularity and content redundancy in multiple SBSs. Content is stored in the SBS in a divided form using maximum distance separable (MDS) codes to enhance the diversity of the content. Experiments in various environments show that the proposed caching strategy outperforms the other methods in terms of hit ratio.

• Journal of Contemporary Eastern Asia
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• v.13 no.2
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• pp.15-39
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• 2014

This research conducted a before/after naturalistic field experiment, with the early Arab Spring as the treatment. Compared to before the early Arab Spring, after the observation period the associations became stronger among the Web terms: 'Jihad, Sharia, innovation, democracy and civil society.' The Western concept of civil society transformed into a central Islamist ideological component. At another level, the inter-nation network based on Jihad-weighted Web hyperlinks between pairs of 46 Muslim Majority (MM) nations found Iran in one of the top two positions of flow betweenness centrality, a measure of network power, both before and after early Arab Spring. In contrast, Somalia, UAE, Egypt, Libya, and Sudan increased most in network flow betweenness centrality. The MM 'Jihad'-centric word co-occurrence network more than tripled in size, and the semantic structure more became entropic. This media "cloud" perhaps billowed as Islamist groups changed their material-level relationships and the corresponding media representations of Jihad among them changed after early Arab Spring. Future research could investigate various rival explanations for this naturalistic field experiment's findings.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.109-112
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• 2020

Many devices are connected on the internet to give functionalities for interconnected services. In 2020', The number of devices connected to the internet will be reached 5.8 billion. Moreover, many connected service provider such as Google and Amazon, suggests edge computing and mesh networks to cope with this situation which the many devices completely connected on their networks. This paper introduces the current state of the introduction of the wireless mesh network and edge cloud in order to efficiently manage a large number of nodes in the exploding Internet of Things (IoT) network and introduces the existing Network Time Protocol (NTP). On the basis of this, we propose a relatively accurate time synchronization method, especially in heterogeneous mesh networks. Using this NTP, multiple time coordinators can be placed in a mesh network to find the delay error using the average delay time and the delay time of the time coordinator. Therefore, accurate time can be synchronized when implementing IoT, remote metering, and real-time media streaming using IoT mesh network.