• KIPS Transactions on Computer and Communication Systems
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• v.3 no.5
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• pp.155-162
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• 2014

We proposed a 2 level tree based cluster based routing protocol for mobile ad hoc networks. it is our crucial goal to establish improved clustering's structure in order to extend average node life-time and elevate the average packet delivery ratio. Because of insufficient wireless resources and energy, the method to form and manage clusters is useful for increasing network stability. but cluster-head fulfills roles as a host and a router in clustering protocol of Ad hoc networks environment. Therefore energy exhaustion of cluster-head causes communication interruption phenomenon. Effective management of cluster-head is key-point which determines the entire network performance. The scheme focuses on improving the performance the life time of the network and throughput through the management of cluster-heads and its neighbor nodes. In simulation, we demonstrated that it would obtain averagely better 17% performance than LS2RP.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.5
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• pp.199-202
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• 2013

In the cluster mechanism of the wireless sensor network, the network lifetime is affected by how cluster heads are selected. One of the representative clustering mechanisms, the low-energy adaptive clustering hierarchy (LEACH), selects cluster heads periodically, resulting in high energy consumption in cluster reconstruction. On the other hand, the adaptive clustering algorithm via waiting timer (ACAWT) proposes a non-periodic re-clustering mechanism that reconstructs clusters if the remaining energy level of a cluster head reaches a given threshold. In this paper, we propose a re-clustering mechanism that uses multiple remaining node energy levels and does re-clustering when the remaining energy level of a cluster head reaches one level lower. Also, in determining cluster heads, both of the number of neighbor nodes and the remaining energy level are considered so that cluster heads can be more evenly placed. From the simulations based on the Qualnet simulator, we validate that our proposed mechanism outperforms ACAWT in terms of the network lifetime.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.424-434
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• 2017

In industrial wireless sensor networks, many applications require integrated QoS supporting. This paper proposes a reliable protocol for real-time monitoring in industrial wireless sensor networks. Retransmission is well-known to recover the transmission failure, however, this might cause the time delay to violate the real-time requirement. The proposed protocol exploits broadcasting feature of wireless networks and the temporal opportunity concept. The opportunities to relay the data packets are shared by the broadcasting feature and the temporal opportunity concept maximize the number of candidates in communication. Simulation results show that the proposed protocol is superior to the existing real-time protocols in term of real-time service and reliability.

• Journal of Internet Computing and Services
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• v.5 no.5
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• pp.27-37
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• 2004

The existing ad hoc network protocols suffer the scalability problem due to the inherent characteristics of node mobility. Cluster-based routing protocols divide the member nodes into a set of clusters and perform a hierarchical routing between these clusters. This hierarchical feature help to improve the scalability of ad hoc network routing. However, previous k-hop cluster-based routing protocols face another problems, that is, control overhead of the cluster headers. This paper proposes a novel k-hop cluster-based routing scheme with delegation functions for mobile ad hoc networks. The scheme employs is based on tree topology to manage cluster members in effectively. The cluster headers do not manage the routing table for whole members, while the header keeps the routing table for its neighbor members and the member list for one hop over nodes within k-hop cluster. Then the in-between leveled nodes manage the nested nodes which is structured in the lower level. Therefore, the proposed mechanism can reduce some control overhead of the cluster leaders.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.9
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• pp.965-970
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• 2014

In an effort to improve the convergence speed in k-means clustering, we introduce the notion of the birds' movement in a flock. Their motion is characterized by the observation that each bird runs after his nearest neighbor. We utilize this feature in clustering procedure. Once the class of a vector is determined, then a number of vectors in the vicinity of it are assigned to the same class. Experiments have shown that the required number of iterations for termination is significantly lower in the proposed method than in the conventional one. Furthermore, the time of calculation per iteration is more than 5% shorter in the proposed case. The quality of the clustering, as determined from the total accumulated distance between the vector and its centroid vector, was found to be practically the same. It might be phrased that we may acquire practically the same clustering result with shorter computational time.

• Journal of Life Science
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• v.14 no.5
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• pp.882-890
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• 2004

Gap junction is a membrane structure facilitating the direct transmission of several ions and small molecules between two cells. It is also called an 'intercellular channel' to distinguish it from other well-known cellular channels (e.g. sodium and potassium channels). Gap junction channels are not passive conduits, rather the ion channels modulated by several stimuli including pH, calcium ion, voltage, and a chemical modification (mainly known as phosphorylation). Among them, the effects of voltage on the gating of gap junction channels have been well studied. Gap junction channels are more sensitive to the transjunctional potential ($V_j$) between two cells rather than the membrane potential($V_m$) between inside and outside the cell. In this review, I will summarize the general properties of gap junction channel and discuss the gating mechanism for the gap channels.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.671-681
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• 2019

Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.575-589
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• 2020

In recent years, Shield TBM construction has been continuously increasing in domestic tunnels. The main excavation tool in the shield TBM construction is a disc cutter which naturally wears during the excavation process and significantly degrades the excavation efficiency. Therefore, it is important to know the appropriate time of the disc cutter replacement. In this study, it is proposed a predictive model that can determine yes/no of disc cutter replacement using machine learning algorithm. To do this, the shield TBM machine data which is highly correlated to the disc cutter wears and the disc cutter replacement from the shield TBM field which is already constructed are used as the input data in the model. Also, the algorithms used in the study were the support vector machine, k-nearest neighbor algorithm, and decision tree algorithm are all classification methods used in machine learning. In order to construct an optimal predictive model and to evaluate the performance of the model, the classification performance evaluation index was compared and analyzed.

• Korean Journal of Human Ecology
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• v.24 no.2
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• pp.185-204
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• 2015

The purpose of this study is to suggest factors for village activation and maintenance of urban village community, based on the survey of residents' interaction (neighbors relationship, using community facilities, and community activities) and their community consciousness of Seongmisan village, a successful case in Seoul. 171 residents answered the questionnaire, and the data were analyzed by the SPSS program. Major findings are as follows. 1) Families got social with neighbors, caused by common use of community facilities, nearness of houses, and kids' friendship; the number of neighbors to be expected to give help was mostly under 5. 2) Facilities used by residents more than 3 times a week were cooperative association and cafe 'Little Tree'. Facilities considered to be important by residents were living cooperative association('Doore'), kids daycare center, village school, cafe, and education center. 3) 'Village News' was mostly interested community activities; 'Village Festival' and 'Village Sports Day' were also mostly participated. 4) As for the village consciousness factors, more than 3 points of 5 marked in all the 3 categories; emotional intimacy, neighbor homogeneity, and village attribution. The village consciousness, however, showed statistically meaningful difference in residents' motivation to move into village, number of family members, age, income, period of residence, neighbors relationship, whether they participate in group activities and events or not.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.472-477
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• 2018

In this paper, we develop interactive content services for preventing depression of users through an intelligent Internet of Things(IoT) mirror system. For interactive content services, an IoT mirror device measures attention and meditation data from an EEG headset device and also measures facial expression data such as "sad", "angery", "disgust", "neutral", " happy", and "surprise" classified by a multi-layer perceptron algorithm through an webcam. Then, it sends the measured data to an oneM2M-compliant IoT server. Based on the collected data in the IoT server, a machine learning model is built to classify three levels of depression (RED, YELLOW, and GREEN) given by a proposed merge labeling method. It was verified that the k-nearest neighbor (k-NN) model could achieve about 93% of accuracy by experimental results. In addition, according to the classified level, a social network service agent sent a corresponding alert message to the family, friends and social workers. Thus, we were able to provide an interactive content service between users and caregivers.