• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4006-4024
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• 2017

Internet of Things (IoT) will transform our daily life by making different aspects of life smart like smart home, smart workplace, smart health and smart city etc. IoT is based on network of physical objects equipped with sensors and actuators that can gather and share data with other objects or humans. Secure communication is required for successful working of IoT. In this paper, a total of 13 lightweight cryptographic algorithms are evaluated based on their implementation results on 8-bit, 16-bit, and 32-bit microcontrollers and their appropriateness is examined for resource-constrained scenarios like IoT. These algorithms are analysed by dissecting them into their logical and structural elements. This paper tries to investigate the relationships between the structural elements of an algorithm and its performance. Association rule mining is used to find association patterns among the constituent elements of the selected ciphers and their performance. Interesting results are found on the type of element used to improve the cipher in terms of code size, RAM requirement and execution time. This paper will serve as a guideline for cryptographic designers to design improved ciphers for resource constrained environments like IoT.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5654-5668
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• 2018

Driven by security and real-time demands of Internet of Things (IoT), the timing of fog computing and edge computing have gradually come into place. Gateways bear more nearby computing, storage, analysis and as an intelligent broker of the whole computing lifecycle in between local devices and the remote cloud. In fog computing, the edge broker requires X-aware capabilities that combines software programmability, stream processing, hardware optimization and various connectivity to deal with such as security, data abstraction, network latency, service classification and workload allocation strategy. The prosperous of Field Programmable Gate Array (FPGA) pushes the possibility of gateway capabilities further landed. In this paper, we propose a software-defined gateway (SDG) scheme for fog computing paradigm termed as Fog Computing Zero-Knowledge Gateway that strengthens data protection and resilience merits designed for industrial internet of things or highly privacy concerned hybrid cloud scenarios. It is a proxy for fog nodes and able to integrate with existing commodity gateways. The contribution is that it converts Privacy-Enhancing Technologies rules into provable statements without knowing original sensitive data and guarantees privacy rules applied to the sensitive data before being propagated while preventing potential leakage threats. Some logical functions can be offloaded to any programmable micro-controller embedded to achieve higher computing efficiency.

• Science of Emotion and Sensibility
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• v.21 no.3
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• pp.29-48
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• 2018

Although the concept of "common sense" is often taken for granted, judging whether behavior or knowledge is common sense requires a complex series of mental processes. Additionally, different perceptions of common sense can lead to social conflicts. Thus, it is important to understand how we perceive common sense and make relevant judgments. The present study investigated the dynamics of neural representations underlying judgments of what common sense is. During functional magnetic resonance imaging, participants indicated the extent to which they thought that a given sentence corresponded to common sense under the given perspective. We incorporated two different decision contexts involving different cultural perspectives to account for social variability of the judgments, an important feature of common sense judgments apart from logical true/false judgments. Our findings demonstrated that common sense versus non-common sense perceptions involve the amygdala and a brain network for episodic memory recollection, including the hippocampus, angular gyrus, posterior cingulate cortex, and ventromedial prefrontal cortex, suggesting integrated affective, mnemonic, and social functioning in common sense processing. Furthermore, functional connectivity multivariate pattern analysis revealed that interactivity among the amygdala, angular gyrus, and parahippocampal cortex reflected representational features of common sense perception and not those of non-common sense perception. Our study demonstrated that the social memory network is exclusively involved in processing common sense and not non-common sense. These results suggest that intergroup exclusion and misunderstanding can be reduced by experiencing and encoding long-term social memories about behavioral norms and knowledge that act as common sense of the outgroup.

• Journal of Advanced Navigation Technology
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• v.13 no.4
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• pp.551-558
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• 2009

Most of clustering schemes focusing on the energy efficiency have only a cluster head in each cluster, thus the energy consumption of cluster head in a cluster can rapidly increase. To reduce the energy consumption, recently, the dual-layered clustering which is separated a cluster ranges into two parts, i.e., data aggregation layer and data transmission layer, based on a sensor equipped with geographical positioning system (GPS), was proposed. However, the logical regions existing within the dual-layered clustering range can not distinguish efficiently. This scheme leads to many messages collision and transmission delay among member nodes. In this paper, to solve these problems, we propose a separated dual-layered routing scheme using the position information and the cluster radius. Proposed scheme clearly distinguish the dual-layered clustering range and gets the balanced number of member nodes in each cluster. Therefore, the proposed routing scheme could prolong the overall network life time about 10% compared to the previous two layered clustering scheme and LEACH.

• The Transactions of the Korea Information Processing Society
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• v.4 no.5
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• pp.1231-1242
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• 1997

This paper deals with an IP mukticast protocol in support of host mobility. Most of the previous multicast schemes have utilized an underlying logical strucuture of network topology,in order to provide a certain degree of order and predictability.On the other hand,mobility implies that a host location relaative to the rest of the net-work changes with time;the physical connectivity of the entire network is thus modified as move.In this case.some multicast datagrams nay not delivered properly,or may delivered twice or more,to a mobile host because the destinations will keep moving whlist datagrams are dekivered with different time delay.This paper first describes the relation between host mobility and multicast, by exploring the possible interactions,and presents a multicast scheme in support of Internet host mobility.A revised scheme is then proposed to adapt the multicast semantic and to optimize the communication overhead.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.352-362
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• 2010

The simultaneous operation of multiple UAV's makes it possible to enhance the mission accomplishment efficiency. In order to achieve this, easily scalable control algorithms are required, and swarm intelligence having such characteristics as flexibility, robustness, decentralized control, and self-organization based on behavioral model comes into the spotlight as a practical substitute. Recently, evolutionary robotics is applied to the control of UAV's to overcome the weakness of difficulties in the logical design of behavioral rules. In this paper, a neural network controller evolved by evolutionary robotics is applied to the control of multiple UAV's which have the mission of searching limited area. Several numerical demonstrations show the proposed algorithm has superior results to those of behavior based neural network controller which is designed by intuition.

• Journal of the Korean Geographical Society
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• v.39 no.4
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• pp.633-642
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• 2004

This study tries to generalize the stream network by constructing rule-based modelling. A study on the map generalization tends to be concentrated on development of algorithms for modification of linear features and evaluations to the limited cartographic elements. Rule-based modelling can help to improve previous algorithms by application of generalization process with the results that analyzing mapping principles and spatial distribution patterns of geographical phenomena. Rule-based modelling can be applied to generalize various cartographic elements, and make an effective on multi-scaling mapping in the digital environments. In this research, nile-based modelling for stream network is composed of generalization rule, algorithm for centerline extraction and linear features. Before generalization, drainage pattern was analyzed by the connectivity with lake to minimize logical errors. As a result, 17 streams with centerline are extracted from 108 double-lined streams. Total length of stream networks is reduced as 17% in 1:25,000 scale, and as 29% in 1:50,000. Simoo algorithm, which is developed to generalize linear features, is compared to Douglas-Peucker(D-P) algorithm. D-P made linear features rough due to the increase of data point distance and widening of external angle. But in Simoo, linear features are smoothed with the decrease of scale.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1320-1332
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• 1998

We propose Multiph which is a virtual topology for a reliable group communication platform on dynamic high speed network. We also define group communication primitives for transmission of messages and group management. Multiph consists of a set of nodes which are process groups and a set of edges which are the connection between nodes providing the route of messages. Processes can send and receive messages overlapped in a several multicast groups. Overlapped process organizes non-overlapped logical group, which called meta-group. Multiph guarantees ordering of messages that received at the destination group. It has no extra node that decreases system performance. Although the group has dynamic group change to leave and add process, it does not reconstruct the existing platform structure.

• Journal of KIISE
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• v.45 no.1
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• pp.22-29
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• 2018

Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.