• The KIPS Transactions:PartC
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• v.11C no.7 s.96
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• pp.905-916
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• 2004

Self-organizing hierarchical ad hoc network (SOHAN) is a new ad-hoc network architecture designed to improve the scalability properties of conventional 'flat' ad hoc networks. This network architecture consists of three tiers of ad-hoc nodes, i.e.. access points, forwarding nodes and mobile nodes. This paper presents a topology discovery and routing protocol for the self-organization of SOHAN. We propose a cross-layer path metric based on link quality and MAC delay which plays a key role in producing an optimal cluster-based hierarchical topology with high throughput capacity. The topology discovery protocol provides the basis for routing which takes place in layer 2.5 using MAC addresses. The routing protocol is based on AODV with appropriate modifications to take advantage of the hierarchical topology and interact with the discovery protocol. Simulation results are presented which show the improved performance as well as scalability properties of SOHAN in terms of through-put capacity, end-to-end delay, packet delivery ratio and control overhead.

• Journal of KIISE
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• v.44 no.10
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• pp.1071-1080
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• 2017

With today's complex networks, asset identification of network devices is becoming an important issue in management and security. Because these assets are connected to the network, it is also important to identify the network structure and to verify the location and connection status of each asset. This can be used to identify vulnerabilities in the network architecture and find solutions to minimize these vulnerabilities. However, in an IoT(Internet of Things) network with a small amount of resources, the Traceroute packets sent by the monitors may overload the IoT devices to determine the network structure. In this paper, we describe how we improved the existing the well-known double-tree algorithm to effectively reduce the load on the network of IoT devices. To balance the load, this paper proposes a new destination-matching algorithm and attempts to search for the path that does not overlap the current search path statistically. This balances the load on the network and additionally balances the monitor's resource usage.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1248-1253
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• 2019

Using current evacuation methods, a crew describes the physical location of an accident and guides evacuation using alarms and emergency guide lights. However, in case of an accident on a large and complex building, an intelligent and effective emergency evacuation system is required to ensure the safety of evacuees. Therefore, several studies have been performed on intelligent path finding and emergency evacuation algorithms which are centralized guidance methods using gathered data from distributed sensor nodes. However, another important aspect is effective rescue guidance in an emergency situation. So far, there has been no consideration on the efficient rescue guidance scheme. Therefore, this paper proposes the genetic algorithm based emergency rescue guidance method using distributed wireless sensor networks. Performance evaluation using a computer simulation shows that the proposed scheme guarantees efficient path finding. The fitness converges to the minimum value in reasonable time. The density of each exit node is remarkably decreased as well.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.143-144
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• 2018

Edge Computing은 분담을 통해 네트워크의 부담을 줄일 수 있는 IoT 네트워크에 적합한 방법으로, 데이터를 전송하고 받는 과정에서 네트워크의 대역폭을 사용하는 대신 서로 연결된 노드들이 협력해서 데이터를 처리하고, 네트워크 말단에서의 데이터 처리가 허용되어 데이터 센터의 부담을 줄일 수 있다. 트리구조는 데이터 구조의 하나로, 데이터 항목의 한 묶음인 세그먼트를 나뭇가지처럼 연결한 것을 의미하여 분산된 데이터를 군집할 수 있다. 본 논문에서는 Edge Computing 환경에서 트리 구조를 이용하여 인덱스를 관리하는 모델을 알아보기 위해 이진 탐색 트리 중 AVL tree와 Paged Binary tree에 대해 서술하였다.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.6
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• pp.179-188
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• 2014

Due to the tremendous amount and its rapid increase of network traffic, the performance of network equipments are becoming an important issue. Network filtering is one of primary functions affecting the performance of the network equipment such as a firewall or a load balancer to process the packet. In this paper, we propose a cache based tri method to improve the performance of the existing tri method of searching for network filtering. When several packets are exchanged at a time between a server and a client, the tri method repeats the same search procedure for network filtering. However, the proposed method can avoid unnecessary repetition of search procedure by exploiting cache so that the performance of network filtering can be improved. We performed network filtering experiments for the existing method and the proposed method. Experimental results showed that the proposed method could process more packets up to 790,000 per second than the existing method. When the size of cache list is 11, the proposed method showed the most outstanding performance improvement (18.08%) with respect to memory usage increase (7.75%).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.288-291
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• 2004

기존의 SOFPNN은 데이터 수가 적고 비선형 요소가 많은 시스템에 대한 체계적이고 효율적인 최적 모델 을 구축할 수 있었으며 각 층 노드의 선택 입력을 변화시킴으로써 네트워크 구조 전체의 적응능력을 향상 시켰다. SOFPNN의 구조는 퍼지 다항식 뉴론(FPN)들로 구성되어 있으며, 층이 진행하는 동안 모델 스스로 노드의 선택과 제거를 통해 최적의 네트워크 구조를 생성할 수 있는 유연성을 가지고 있다. 그러나, 노드의 입력변수의 수와 규칙 후반부 다항식 차수 그리고 입력변수는 설계자의 경험 또는 반복적인 학습을 통해 선호된 네트워크 구조를 선택하였으나, 최적의 네트워크 구조를 구축하는데는 어려옴이 내재되어 있었다. 본 논문에서는 자기구성 퍼지 다항식 뉴럴네트워크(Self-Organizing Fuzzy Polynomial Neural Networks： SOFPNN)을 최적화시키기 위해 유전자 알고리즘을 이용하여 자기구성 퍼지 다항식 뉴럴 네트워크의 입력변수의 수와 이에 해당되는 입력변수 그리고 규칙 후반부 다항식의 차수를 탐색하여 최적 의 자기구성 퍼지 다항식 뉴럴 네트워크를 구축한다. 따라서 모델 구축에 있어서 유연성과 정확성을 가지며 객관적이고 좀 더 정확한 예측 능력을 가진 SOFPNN 모델 구조를 구축할 수가 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1193-1195
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• 2013

최근 정보 기술의 발달로 XML, 화학 복합물, 소셜 네트워크 등과 같은 구조적 정보를 갖는 빅 데이터들이 대량으로 축적되고 있다. 이러한 구조적 정보를 갖는 그래프 데이터에서 메디안을 찾기 위한 연구가 진행되고 있다. 기존에는 그래프 메디안을 효율적으로 계산하기 위해 하한값을 이용한 그래프 메디안 탐색 기법이 제안되었다. 그러나 탐색을 시작하는 버텍스를 선정하는 데에 따라 가지치기 효과가 다르게 발생하는 문제점이 있다. 본 논문에서는 버텍스의 그래프 구조 정보를 이용한 효율적인 메디안 탐색 기법을 제안한다. 제안하는 탐색 기법은 버텍스의 차수와 에지 가중치를 이용하여 그래프 메디안 예측 값을 정의하고, 그래프 메디안과 유사한 버텍스들부터 우선적으로 탐색한다. 실험을 통하여 제안하는 기법이 기존의 방법보다 최대 10%까지 수행시간을 단축함을 보인다.

• Journal of the Korean association of regional geographers
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• v.12 no.6
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• pp.693-703
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• 2006

Keeping the balance between exploration of new possibilities and exploitation of existing certainties in organizational innovation is getting its significance as business environments become more turbulent This paper focused on the relationship between two different types of innovation and network configuration. For this purpose, we conducted the empirical studies of 168 IT SMEs located in Gyeongbuk. For this analysis, we defined two innovation types as exploratory innovation and exploitative innovation. Also, we considered network scope and strength of tie as network configuration. The results showed that the exploratory innovation had sparse network of network scope and weak tie of strength. On the contrary the exploitative innovation had dense network and strong tie.

• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.474-492
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• 2007

In the protein interaction network, there are many meaningful functional modules, each involving several protein interactions to perform discrete functions. Pathways and protein complexes are the examples of the functional modules. In this paper, we propose a new method for detecting the functional modules based on concept. A conceptual functional module, briefly concept module is introduced to match the modules taking them as its instances. It is defined by the corresponding rule composed of triples and operators between the triples. The triples represent conceptual relations reifying the protein interactions of a module, and the operators specify the structure of the module with the relations. Furthermore, users can define a composite concept module by the counterpart rule which, in turn, is defined in terms of the predefined rules. The concept module makes it possible to detect functional modules that are conceptually similar as well as structurally identical to users' queries. The rules are managed in the XML format so that they can be easily applied to other networks of different species. In this paper, we also provide a visualized environment for intuitionally describing complexly structured rules.