• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.939-940
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• 2008

In this paper, we propose a novel image clustering method based on weighted sub-sampling to reduce clustering time and the number of clusters for target detection and tracking. Our proposed method first obtain sub-sampling image with specific weights which is the number of target pixels in sampling region. After performing clustering procedure, the cluster center position is properly obtained using weights of target pixels in the cluster. Therefore, our proposed method can not only reduce clustering time, but also obtain proper cluster center.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.556-578
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• 2020

There are many different researches about how to analyze issues based on real-time news streams. But, there are few researches which analyze issues hierarchically from news articles and even a previous research of hierarchical issue analysis make clustering speed slower as the increment of news articles. In this paper, we propose a hierarchical and incremental clustering for semi real-time issue analysis on news articles. We trained siamese neural network based weighted cosine similarity model, applied this model to k-means algorithm which is used to make word clusters and converted news articles to document vectors by using these word clusters. Finally, we initialized an issue cluster tree from document vectors, updated this tree whenever news articles happen, and analyzed issues in semi real-time. Through the experiment and evaluation, we showed that up to about 0.26 performance has been improved in terms of NMI. Also, in terms of speed of incremental clustering, we also showed about 10 times faster than before.

• Journal of Korea Multimedia Society
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• v.7 no.2
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• pp.264-271
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• 2004

A load-sharing algorithm must deal with load imbalance caused by characteristics of a network and heterogeneity of nodes in Internet-based clustering systems. This paper has proposed the Efficient Load-Sharing algorithm. Efficient-Load-Sharing algorithm creates a scheduler based on the WF(Weighted Factoring) algorithm and then allocates tasks by an adaptive granularity strategy and the refined fixed granularity algorithm for better performance. In this paper, adaptive granularity strategy is that master node allocates tasks of relatively slower node to faster node and refined fixed granularity algorithm is to overlap between the time spent by slave nodes on computation and the time spent for network communication. For the simulation, the matrix multiplication using PVM is performed on the heterogeneous clustering environment which consists of two different networks. Compared to other algorithms such as Send, GSS and Weighted Factoring, the proposed algorithm results in an improvement of performance by 75%, 79% and 17%, respectively.

• Journal of Information Technology Applications and Management
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• v.20 no.3_spc
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• pp.219-230
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• 2013

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer's data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.427-432
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• 2005

I studied about existent load distribution algorithms and the WLC(Weighted Least Connection) algerian that is using much at present to distribute the connection request of users to real servers efficiently in web cluster system. The efficiency of web cluster system is fallen by load imbalance between servers, because there is problem In inaccurate load status measuring of servers and measuring timing at these load distribution algorithms. In this paper, I suggest an algorithm that distributes load base on various load state of servers by real time using broadcasting RPC(Remote Procedure Call) when a user requests connection, and implement a prototype and experiment its performance. The experiment result shows that load imbalance phenomenon between reai sowers was improved greatly than existing method, and performance of web cluster system was improved by efficiency that response time is shortened.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.73-78
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• 2019

This paper proposes polynomial-time rule for maximum weighted independent set(MWIS) problem that is well known NP-hard. The well known distributed algorithm selects the maximum weighted node as a element of independent set in a local. But the merged independent nodes with less weighted nodes have more weights than maximum weighted node are frequently occur. In this case, existing algorithm fails to get the optimal solution. To deal with these problems, this paper constructs maximum weighted independent set in local area. Application result of proposed algorithm to various networks, this algorithm can be get the optimal solution that fail to existing algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.504-510
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• 2009

Recently, personalized-adaptive services became the center of interest in the world. However the services about music are not widely diffused out. That is because the analyzing of music information is more difficult than analyzing of text information. In this paper, we propose a music recommendation system which provides personalized services. The system keeps a user's listening list and analyzes it to select pieces of music similar to the user's preference. For analysis, the system extracts properties from the sound wave of music and the time when the user listens to music. Based on the properties, a piece of music is mapped into a point in the property space and the time is converted into the weight of the point. At this time, if we select and analyze the group which is selected by user frequently, we can understand user's taste. However, it is not easy to predict how many groups are formed. To solve this problem, we apply the K-means clustering algorithm to the weighted points. We modified the K-means algorithm so that the number of clusters is dynamically changed. This manner limits a diameter so that we can apply this algorithm effectively when we know the range of data. By this algorithm we can find the center of each group and recommend the similar music with the group. We also consider the time when music is released. When recommending, the system selects pieces of music which is close to and released contemporarily with the user's preference. We perform experiments with one hundred pieces of music. The result shows that our proposed algorithm is effective.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.171-176
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• 2020

This paper proposes polynomial-time algorithm for maximum weighted independent set(MWIS) problem that is well known as NP-hard. The known algorithms for MWIS problem are polynomial-time to specialized in particular graph type, distributed, or clustering method. But there is no unified algorithm is suitable to all kinds of graph types. Therefore, this paper suggests unique polynomial-time algorithm that is suitable to all kinds of graph types. The proposed algorithm merges the maximum weighted vertex v_i and maximum weighted vertex v_j that is not adjacent to v_i. As a result of apply to undirected graphs and trees, this algorithm can be get the optimal solution. This algorithm improves previously known solution to new optimal solution.