• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2844-2851
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• 2011

The purpose of this study is to set up an acoustic prediction technique and to perform the IL test of scale down noise reducing device for the development of the noise reducing device as the development of 400km/h class high speed train. First of all, the IL prediction of noise reducing device was performed with the 2D BEM method. And the noise test of scale down noise reducing device in anechoic chamber was performed for the verification of acoustic prediction technique and IL performance evaluation. As the results, the acoustic prediction technique for the development of noise reducing device was verified because the averaged IL difference between prediction and test is in 2dB(A). And the measured IL value of noise reducing device is less than 2dB(A), and additional IL with polyester absorption material is increased about 0.5dB(A).

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.733-738
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• 2015

User demographic information is necessary in order to improve the quality of personalized services such as recommendation systems. Mobile data, especially text data, is known to be effective for prediction of user demographic information. However, mobile text data has privacy issues so that its utilization is limited. In this regard, we introduce an on-device gender prediction framework utilizing mobile text data while minimizing the privacy issue. Discriminative word and emoticon sets of each gender are constructed from web documents written by authors of each gender. After gender prediction is performed by comparing discriminative word and emoticon sets with a user's mobile text data, an ensemble method that combines two prediction results draws a final result. From experiments conducted on real-world mobile text data, the proposed on-device framework shows promising results for gender prediction.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.65-77
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• 2016

As respecting one's privacy becomes an important issue in mobile device data analysis, on-device analysis is getting attention, in which the data analysis is conducted inside a mobile device without sending data from the device to outside. One possible application of the on-device analysis is gender prediction using text data in mobile devices, such as text messages, search keyword, website bookmarks, and contact, which are highly private, and the limited computing power of mobile devices can be addressed by utilizing the word comparison method, where words are selected beforehand and delivered to a mobile device of a user to determine the user's gender by matching mobile text data and the selected words. Moreover, it is known that performing prediction after filtering instances using definite evidences increases accuracy and reduces computational complexity. In this regard, we propose a two-phase approach to on-device gender prediction, where both discriminability and popularity of a word are sequentially considered. The proposed method performs predictions using a few highly discriminative words for all instances and popular words for unclassified instances from the previous prediction. From the experiments conducted on real-world dataset, the proposed method outperformed the compared methods.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.77-84
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• 2016

This paper proposes a methodology for creating a function based reliability prediction model. Although, there are various works for reliability prediction, one of the features of their research is that the research is based on hardware-centered reliability prediction. Reliability is often defined as the probability that a device will perform its intended function, under operating condition, for a specified period of time, there is a profound irony about reliability prediction problem. In this paper, we proposed four-phase modeling procedure for function-centered reliability prediction. The proposed modeling procedure consists of four models; 1) structure block model, 2) function block model, 3) device model, and 4) reliability prediction model. We performed function-centered reliability prediction for electronic ballast using the proposed modeling procedure and MIL-HDBK-217F which is the military handbook for reliability prediction of electronic equipment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1446-1452
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• 1994

In this paper, Weibull distribution is applied to the lifetme distribution of a device. The method of Bayesian estimate used to estimate requiring parameter in order to predict lifetime of device using accelerated lifetime test data, namely failure time of device. The method of Bayesian estimate needs prior information in order to estimate parameter. But this paper proposed the method of parameter estimate without prior information. As stress is temperature, Arrhenius model is applied and the method of linear estimate is applied to predict lifetime of device at the state of normal operation.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Transactions on Electrical and Electronic Materials
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• v.16 no.6
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• pp.317-322
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• 2015

The metal oxide varistor (MOV) is a major component of the surge protection devices (SPDs) currently in use. The device is judged to be faulty when fatigue caused by the continuous inflow of lightning accumulates and reaches the damage limit. In many cases, induced lightning resulting from lightning strikes flows in to the device several times per second in succession. Therefore, the frequency or the rate at which the SPD is actually exposed to stress, called a surge, is outside the range of human perception. For this reason, the protective device should be replaced if it actually approaches the end of its life even though it is not faulty at present, currently no basis exists for making the judgment of remaining lifetime. Up to now, the life of an MOV has been predicted solely based on the number of inflow surges, irrespective of the magnitude of the surge current or the amount of energy that has flowed through the device. In this study, nonlinear data that shows the damage to an MOV depending on the count of surge and the amount of input current were collected through a high-voltage test. Then, a failure prediction algorithm was proposed by preparing a look-up table using the results of the test. The proposed method was experimentally verified using an impulse surge generator

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.164-168
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• 2023

Accurate and reliable weather forecasts for temperature, relative humidity, and precipitation using advanced transformer models and IoT are essential in various fields related to global climate change. We propose a novel weather prediction device that integrates state-of-the-art transformer models and IoT techniques to improve prediction accuracy and real-time processing. The proposed system demonstrated high reliability and performance, offering valuable insights for industries and sectors that rely on accurate weather information, including agriculture, transportation, and emergency response planning. The integration of transformer models with the IoT signifies a substantial advancement in weather and climate modeling.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.336-343
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• 2017

We propose a real time personalized recommendation algorithm on the mobile device. We use a unified collaborative filtering with reduced data. We use Fuzzy C-means clustering to obtain the reduced data and Konohen SOM is applied to get initial values of the cluster centers. The proposed algorithm overcomes data sparsity since it extends data to the similar users and similar items. Also, it enables real time service on the mobile device since it reduces computing time by data clustering. Applying the suggested algorithm to the MovieLens data, we show that the suggested algorithm has reasonable performance in comparison with collaborative filtering. We developed Android-based smart-phone application, which recommends restaurants with coupons and restaurant information.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.297-302
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• 2000

Pipe cooling method is widely used for reduction of hydration heat and control of cracking in mass concrete structures. However, in order to effectively apply pipe cooling systems to concrete structure, the coefficient of flow convection relating the thermal transfer between inner stream of pipe and concrete must be estimated. In this study, a device measuring the coefficient of flow convection is developed. Since a variation of thermal distribution caused by pipe cooling has a direct effect in internal forced flows, the developed testing device is based on the internal forced flow concept. Influencing factors on the coefficient of flow convection are mainly flow velocity, pipe diameter and thickness, and pipe material. finally a prediction model of the coefficient of flow convection is proposed using experimental results from the developed device. According to the proposed prediction model, the coefficient of flow convection increases with increase in flow velocity and decreases with increase in pipe diameter and thickness. Also, the coefficient of flow convection is largely affected by the type of pipe materials.