• Journal of Contemporary Eastern Asia
• /
• v.17 no.2
• /
• pp.176-198
• /
• 2018

Environmental issues are among the most critical issues nowadays. These issues are no longer confined to individual countries, and international society has been progressing in building global dialogues since the early 1970s. Within these international efforts, Germany and Japan have played essential roles in global environmental governance. However, there are major differences in nation-level environmental policies in both countries. Governance based on network structure is more efficient than that based on hierarchy for solving complex problems. The network structure is formed through horizontal cooperation among various autonomous actors, and the relationship intensity among actors is one of the key concepts in the governance. Using social network analysis as a framework to explain complicated societal structures explains how interaction among actors creates networks, and these networks further affect their interactions. The purpose of this study is to investigate the structure of environmental policy governance as collaborative governance in Germany and Japan. To address this goal, this paper analyzes the relationship between the informational dimension of governance networks and its complement resource-sharing networks in both countries. The results show that the information-sharing networks have lower-level network influence on the resource-sharing networks as higher-level networks even if not all of the information factors have singular influences. The results suggest that the information-sharing networks may be one of the pieces of the puzzle for explaining this phenomenon in environmental governance in Germany and Japan.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4326-4344
• /
• 2021

The SinGAN is one of generative adversarial networks that can be trained on a single nature image. It has poor ability to learn more global features from nature image, and losses much local detail information when it generates arbitrary size image sample. To solve the problem, a non-linear function is firstly proposed to control downsampling ratio that is ratio between the size of current image and the size of next downsampled image, to increase the ratio with increase of the number of downsampling. This makes the low-resolution images obtained by downsampling have higher proportion in all downsampled images. The low-resolution images usually contain much global information. Therefore, it can help the model to learn more global feature information from downsampled images. Secondly, the attention mechanism is introduced to the generative network to increase the weight of effective image information. This can make the network learn more local details. Besides, in order to make the output image more natural, the TVLoss function is introduced to the loss function of SinGAN, to reduce the difference between adjacent pixels and smear phenomenon for the output image. A large number of experimental results show that our proposed model has better performance than other methods in generating random samples with fixed size and arbitrary size, image harmonization and editing.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.4
• /
• pp.325-330
• /
• 2001

It has been realized that the results of circuit level simulation of neural networks, used for optimization problems, arc much different from those of algorism level simulation. In other words, the outputs converges asymptotically as time elapes, however, the input convergence depends on the value of parasitic conductance connected between input node and ground. Also, this conductance affects system performance. This paper discusses the influence of input conductance on the convergece of the continuous Hopfield neural networks. The convergence has been analyzed for the input and output nodes of neurons. Also, the characteristics of equilibrium points has been analyzed depending on different values of the input conductance.

• Journal of applied mathematics & informatics
• /
• v.26 no.5_6
• /
• pp.961-972
• /
• 2008

In this paper, we consider the global exponential stability of cellular neural networks with time-varying delays. Based on the Lyapunov function method and convex optimization approach, a novel delay-dependent criterion of the system is derived in terms of LMI (linear matrix inequality). In order to solve effectively the LMI convex optimization problem, the interior point algorithm is utilized in this work. Two numerical examples are given to show the effectiveness of our results.

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

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.187-188
• /
• 2006

In CDMA based mobile communication network, signaling messages are routed through Signaling Transfer Point(STP) which is responsible for MTP layer 3 switching. As WCDMA based UMTS network is considered as next generation technology allowing global roaming services, all nodes in networks have their own identity number called Global Title(GT). Therefore, it is essential to introduce Signaling Gateway(SG) responsible for SCCP layer switching to solve the problem each node has all GT tables including even all overseas nodes. In this paper, we propose the signaling network architecture in UMTS networks using SG and we show that we can reduce CAPEX and OPEX in each node. To show the validity of the proposed method some simulations are given in which the results can be expected by intuitive observation.

• Communications for Statistical Applications and Methods
• /
• v.29 no.4
• /
• pp.487-498
• /
• 2022

Social network analysis (SNA) techniques have recently been developed to monitor and detect abnormal behaviors in social networks. As a useful tool for process monitoring, control charts are also useful for network monitoring. In this paper, the degree and closeness centrality measures, in which each has global and local perspectives, respectively, are applied to an exponentially weighted moving average (EWMA) chart and a multinomial cumulative sum (CUSUM) chart for monitoring undirected weighted networks. In general, EWMA charts monitor only one variable in a single chart, whereas multinomial CUSUM charts can monitor a categorical variable, in which several variables are transformed through classification rules, in a single chart. To monitor both degree centrality and closeness centrality simultaneously, we categorize them based on the average of each measure and then apply to the multinomial CUSUM chart. In this case, the global and local attributes of the network can be monitored simultaneously with a single chart. We also evaluate the performance of the proposed procedure through a simulation study.

• Journal of Positioning, Navigation, and Timing
• /
• v.13 no.2
• /
• pp.137-147
• /
• 2024

This paper presents a novel Long Short-Term Memory (LSTM) network architecture for the integration of an Inertial Measurement Unit (IMU) and Global Navigation Satellite Systems (GNSS). The proposed algorithm consists of two independent LSTM networks and the LSTM networks are trained to predict attitudes and velocities from the sequence of IMU measurements and mechanization solutions. In this paper, three GNSS receivers are used to provide Real Time Kinematic (RTK) GNSS attitude and position information of a vehicle, and the information is used as a target output while training the network. The performance of the proposed method was evaluated with both experimental and simulation data using a lowcost IMU and three RTK-GNSS receivers. The test results showed that the proposed LSTM network could improve positioning accuracy by more than 90% compared to the position solutions obtained using a conventional Kalman filter based IMU/GNSS integration for more than 30 seconds of GNSS outages.

• Journal of Information Processing Systems
• /
• v.7 no.1
• /
• pp.17-28
• /
• 2011

Despite the fact that the deployment of sensor networks and target tracking could both be managed by taking full advantage of Voronoi diagrams, very little few have been made in this regard. In this paper, we designed an optimized barrier coverage and an energy-efficient clustering algorithm for forming Vonoroi-based Wireless Sensor Networks(WSN) in which we proposed a mobile target tracking scheme (CTT&MAV) that takes full advantage of Voronoi-diagram boundary to improve detectability. Simulations verified that CTT&MAV outperforms random walk, random waypoint, random direction and Gauss-Markov in terms of both the average hop distance that the mobile target moved before being detected and lower sensor death rate. Moreover, we demonstrate that our results are robust as realistic sensing models and also validate our observations through extensive simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.10
• /
• pp.3771-3788
• /
• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.