• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.127-135
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• 2020

The purpose of this study is to develop a human activity recognition (HAR) system, which distinguishes 13 activities, including five activities commonly dealt with in conventional HAR researches and eight activities from the Eastern-Asian culture. The eight special activities include floor-sitting/standing, chair-sitting/standing, floor-lying/up, and bed-lying/up. We used a 3-axis accelerometer sensor on the wrist for data collection and designed a machine learning model for the activity classification. Data clustering through preprocessing and feature extraction/reduction is performed. We then tested six machine learning algorithms for recognition accuracy comparison. As a result, we have achieved an average accuracy of 99.7% for the 13 activities. This result is far better than the average accuracy of current HAR researches based on a smartwatch (89.4%). The superiority of the HAR system developed in this study is proven because we have achieved 98.7% accuracy with publically available 'pamap2' dataset of 12 activities, whose conventionally met the best accuracy is 96.6%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.125-129
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• 2015

The most important factor within the wireless sensor network is to have effective network usage and increase the lifetime of the individual nodes in order to operate the wireless network more efficiently. Therefore, many routing protocols have been developed. The LEACH protocol presented by Wendi Heinzelman, especially well known as a simple and efficient clustering based routing protocol. However, because LEACH protocol in an irregular network is the total data throughput efficiency dropped, the stability of the cluster is declined. Therefore, to increase the stability of the cluster head, in this paper, it proposes a stochastic cluster head selection method for improving the LEACH protocol. To this end, it proposes a SH-LEACH(Stochastic Cluster Head Selection Method-LEACH) that it is combined to the HEED and LEACH protocol and the proposed algorithm is verified through the simulation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.3
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• pp.203-216
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• 2003

In this paper a new feature extraction and target classification method is proposed for the recognition part of FLIR(Forwar Looking Infrared)-image-based ATR system. Proposed feature extraction method is "cluster(=set of classes)-based"version of previous fisherfaces method that is known by its robustness to illumination changes in face recognition. Expecially introduced class clustering and cluster-based projection method maximizes the performance of fisherfaces method. Proposed target image classification method is based on the mixture of experts model which consists of RBF-type experts and MLP-type gating networks. Mixture of experts model is well-suited with ATR system because it should recognizee various targets in complexed feature space by variously mixed conditions. In proposed classification method, one expert takes charge of one cluster and the separated structure with experts reduces the complexity of feature space and achieves more accurate local discrimination between classes. Proposed feature extraction and classification method showed distinguished performances in recognition test with customized. FLIR-vehicle-image database. Expecially robustness to pixelwise sensor noise and un-wanted intensity variations was verified by simulation.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1189-1199
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• 2003

This paper presents a new method for the motion detection of vehicles using hierarchical motion estimation and parallel processing. It captures the road image by using a CMOS sensor. It divides the captured image into small blocks and detects the motion of each block by using a block-matching method which is based on a hierarchical motion estimation and parallel processing for the real-time processing. The parallelism is achieved by using tile pipeline and the data flow technique. The proposed method has been implemented by using an embedded system. The proposed block matching algorithm has been implemented on PLDs(Programmable Logic Device) and clustering algorithm has been implemented by ARM processor. Experimental results show that the proposed system detects the motion of vehicles in real-time.

• Journal of Advanced Navigation Technology
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• v.13 no.2
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• pp.252-256
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• 2009

In this paper, the emergency light where is being scattered always inspection and will be able to manage from the management center. Is not interfered in data neck of a bottle actual condition and the data communication will be possible and the cluster monitor network will grow and uses establishes the emergency light monitoring system. Will not be interfered in location of emergency light and not to be will be able to establish the system. And the monitoring network there is by a destroyer and the communication relay system is born breakdown but the dead zone without condition of emergency light proposes the emergency light management system where the monitoring and management are possible.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.255-260
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• 2008

In the recently years, there has been a big interest in ad hoc wireless network as they have tremendous military and commercial potential. An Ad hoc wireless network is composed of mobile computing devices that use having no fixed infrastructure of a multi-hop wireless network formed. So, the fact that limited resource could support the network of robust, simple framework and energy conserving etc. In this paper, we propose a new ad hoc multicast routing protocol for based on the ontology scheme called inference network. Ontology knowledge-based is one of the structure of context-aware. And the ontology clustering adopts a tree structure to enhance resilient against mobility and routing complexity. This proposed multicast routing protocol utilizes node locality to be improve the flexible connectivity and stable mobility on local discovery routing and flooding discovery routing. Also attempts to improve route recovery efficiency and reduce data transmissions of context-awareness. We also provide simulation results to validate the model complexity. We have developed that proposed an algorithm have design multi-hierarchy layered networks to simulate a desired system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2B
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• pp.269-278
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• 2010

In the clustering protocol, lifetime of the cluster members radically decrease because frequency interference between clusters make every cluster member consume a lot of energy to maintain or increase its transmission rate. In this paper, we analyze the frequency interference among the clusters with the game theory which deals with resource bargaining problems between players, and present a rational power allocation strategy. Both the cases that each cluster tries to selfishly occupy and cooperatively share the resource are analyzed in terms of non-cooperative and cooperative games. In simulation, we compare the cooperative game with non-cooperative game in terms of the node lifetime.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8A
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• pp.810-819
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• 2010

This paper proposes an algorithm to improve total network lifetime for Wireless Sensor Network (WSN) application such as environmental condition monitoring systems based on LEACH protocol. Firstly, the algorithm had equal number of nodes allocated to each cluster at cluster set-up phase where it abided by LEACH protocol. Secondly, at cluster set-up phase, each node was determined the order to be cluster header of the cluster which it had joined. After then, the role of a cluster head delivers to the next node according to determined order when the cluster head has received certain number of packets. With above method energy consumption of each node made equal and overall network lifetime was increased. Simulation results shows that overall network lifetime of proposed algorithm was increased two times than that of LEACH and total energy consumption was one forth of that of LEACH protocol.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2099-2100
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• 2006

제한된 전력의 노드들로 구성된 무선 센서 네트워크에서 효율적인 정보 수집이 이루어지기 위해서는 전체 네트워크의 Life Time을 늘리는 게 중요하다. 각각의 센서 노드들이 멀리 떨어져 있는 BS(Base Station)으로 직접 데이터를 전송하면 전력소비가 매우 크고 비효율 적이다. 그리하여 네트워크의 life time을 늘리기 위한 많은 연구가 이루어지고 있다. 그중에 클러스터링 기법은 가장 널리 연구되는 기법 중에 하나이다. 대표적인 클러스터링 기법 LEACH(Low-Energy Adaptive Clustering Hierarchy)[1]는 전체 노드 수의 5%클 클러스터 헤드로 결정하여 나머지 노드들로부터 데이터를 수집하여 BS로 전송함으로써 에너지를 효율적으로 사용하는 알고리즘이다. 그러나 클러스터 헤드를 결정하는데 있어서 잔여 에너지를 고려하지 않고 순환적으로 결정하는 문제점을 가지고 있다. 그래서 본 논문에서는 SVM(Supprt Vector Machine)을 이용하여 FND(First Node Dic)가 발생했을 때 각 노드들의 에너지 잔량 정도를 따져서 영역을 나눈 후, 에너지가 더 많은 영역에서 클러스터 헤드를 선정하는 방법을 제안한다. 잔량 에너지가 많은 노드를 클러스터 헤드로 결정함으로써 전체 네트워크의 life time을 늘릴 수 있다.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.373-380
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• 2014

Safety-related parameters are very important for confirming the status of a nuclear power plant. In particular, the reactor vessel water level has a direct impact on the safety fortress by confirming reactor core cooling. In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN). The prediction model was developed using training data, and validated using independent test data. The data was generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The informative data for training the FNN model was selected using the subtractive clustering method. The prediction performance of the reactor vessel water level was quite satisfactory, but a few large errors were occasionally observed. To check the effect of instrument errors, the prediction model was verified using data containing artificially added errors. The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised. Furthermore, if the developed FNN model can be optimized using a variety of data, it should be possible to predict the reactor vessel water level precisely.