• Journal of the Korean Statistical Society
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• v.14 no.1
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• pp.39-47
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• 1985

In this paper, we propose some nonparametric subset selection procedures for scale parameters based on rank-likes. The proposed procedures are compared to the Gupta-Sobel's parametric prcedure through a small-sample Monte Carlo study. The results show that the nonparametric procedures are quite robust for heavy-tailed distributions, but they have somewhat low efficiencies.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.613-616
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• 2007

Characteristics of dark image retention and address discharge time lag were investigated simultaneously. It was found that reset waveforms with low black luminance did not guarantee lower image retention. Improved address discharge time lag due to modified reset waveforms similarly did not show improved image retention. The address discharge time lag and the image retention are in a trade-off relation.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.743-746
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• 2000

In this paper we propose a new network called Dynamic network for symmetric block ciphers. Dynamic cipher has the property that the key-size, the number of round, and the plaintext-size are scalable simultaneously We present the method for designing secure Dynamic cipher against meet-in-the-middle attack and linear cryptanalysis. Also, we show that the differential cryptanalysis to Dynamic cipher is hard.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.847-848
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• 2007

In this work, we analyze the role of reputation in ubiquitous healthcare system as well as the relationship of security, trust and reputation in this environment in details. In addition, an example is given to show how to use reputation system in ubiquitous healthcare and how to use reputation system on decision making.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Communications and Networks
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• v.3 no.4
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• pp.365-373
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• 2001

We propose a new channel reservation protocol which can reduce message delay by using a counter for detection of d source conflict in a WDM single-hop network with non-equivalent propagation delay. A source convict occurs when a source node has the right to transmit more than or equal to two messages simultaneously, which are transmitted using different wavelengths. In such a case, the source node has to newly obtain the right to transmit the message. In the proposed protocol, by using a source conflict counter a source node can detect a source conflict before a wave-length assignment is performed. Therefore, the source node can start a procedure to newly obtain the right to transmit the message which cannot be transmitted due to a source conflict. We analyse the throughput performance by taking the effect of a source conflict into account, and show that the approximate analysis and the computer simulated results are close. Also, from computer simulated results, we show that our proposed protocol can reduce mean message delay dramatically without degrading throughput performance as the offered load becomes large.

• Journal of Computing Science and Engineering
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• v.8 no.3
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• pp.173-179
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• 2014

In an opportunistic network, one of the most challenging issues is the equilibrium of the network traffic and transmission delay for forwarding messages. To resolve this problem, we propose a new forwarding scheme, called the direction entropy-based forwarding scheme (DEFS), using the main direction and direction entropy based on the information collected about the directions of the nodes in the network. Since each node sends a message to another node with a different location and less direction entropy, DEFS utilizes those nodes that are more likely to travel to various locations to forward the messages to the destination nodes. Experiments were performed on the network simulator NS-2. The results show that DEFS provides better balance than the typical forwarding schemes, such as Epidemic, PRoPHET, and WAIT.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.68-71
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• 2001

This paper has been studied for a comparison of the propagation and noise characteristics between ultrasonic and electromagnetic wave for the high speed communication of the short range telemetry. We analyze the propagation depth of electromagnetic and ultrasonic wave by skin depth effect and by ultrasonic loss ratio. We also studied several effects such as near field effect in electromagnetic wave and Rayleigh scattering noise of ultrasonic wave, etc. We show the experimental results of their propagation loss and modulation experiments in water. The experimental results show that both method is good for the implementation of short range telemetry.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.387-388
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• 2006

In this paper, we propose an efficient algorithm based on PCA and LDA combining K-means clustering method, which has better accuracy of face recognition than Eigenface and Fisherface. In this algorithm, PCA is firstly used to reduce the dimensionality of original face image. Secondly, a truncated face image data are sub-clustered by K-means clustering method based on Euclidean distances, and all small subclusters are labeled in sequence. Then LDA method project data into low dimension feature space and group data easier to classify. Finally we use nearest neighborhood method to determine the label of test data. To show the recognition accuracy of the proposed algorithm, we performed several simulations using the Yale and ORL (Olivetti Research Laboratory) database. Simulation results show that proposed method achieves better performance in recognition accuracy.