• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.65-72
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• 2020

Today, the Internet of Things is used in many places, including homes, industrial sites, and hospitals, to give us convenience. Many services generate new value through real-time data collection, storage and analysis as devices are connected to the network. Many of these fields are creating services and applications that utilize sensors and communication functions within IoT devices. However, since everything can be hacked, it causes a huge privacy threat to users who provide data. For example, a variety of sensitive information, such as personal information, lifestyle patters and the existence of diseases, will be leaked if data generated by smarwatches are abused. Development of IoT must be accompanied by the development of security. Recently, Differential Privacy(DP) was adopted to privacy-preserving data processing. So we propose the method that can aggregate health data safely on smartwatch platform, based on DP.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.86-96
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• 2015

In mobile communication, mobile services [MSs] (e.g., phone calls, short/multimedia messages, and Internet data) incur a cost to both mobile users (MUs) and mobile service providers (MSPs). The proposed model MobPrice consists of dynamic data pricing schemes for mobile communication in order to achieve optimal usage of MSs at minimal prices. MobPrice inspires MUs to subscribe MSs with flexibility of data sharing and intra-peer exchanges, thereby reducing overall cost. The main contributions of MobPrice are three-fold. First, it proposes a novel k-level data-pricing (kDP) scheme for MSs. Second, it extends the kDP scheme with the notion of service-sharing-based pricing schemes to a collaborative peer-to-peer data-pricing (pDP) scheme and a cluster-based data-pricing (cDP) scheme to incorporate the notion of 'cluster' (made up of two or more MUs) in mobile communication. Third, our performance study shows that the proposed schemes are indeed effective in maximizing MS subscriptions and minimizing MS's price/user.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.285-290
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• 2006

This paper presents an adaptive neural network based controller and its application to Dynamic Positioning (DP) control system of ship. The proposed neural network based controller is developed for station-keeping and low-speed maneuvering control of ship. At first, the DP system configuration is described. And then, to validate the proposed DP system, computer simulations of station-keeping and low-speed maneuvering performance of a multi-purpose supply ship are presented under the influence of measurement noise, external disturbances such as sea current, wave, and wind. The simulations have shown the feasibility of the DP system in various maneuvering situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.489-498
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• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.38 no.11
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• pp.30-40
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• 2001

In this paper, we propose a dynamic slot allocation algorithm for efficient transmission of VBR services in wireless ATM networks. The proposed algorithm is based on a hybrid dynamic parameter(DP) control which combines the strength of in-band control and out-of-band control by considering the variation characteristics of buffer length in distributed mobile terminals. This algorithm consists of four sub-algorithms: dynamic parameter determination algorithm, dynamic parameter transmission algorithm, estimation algorithm of the number of request slots, and prorated-allocation algorithm. As the proposed allocation algorithm based on the hybrid DP control scheme can offer nearly precise MAC level estimations of the requirements for each VBR, the algorithm makes it possible to obtain ideal allocation efficiency. The allocation efficiency of the algorithm is shown by numerical analysis. Simulation results show that the proposed algorithm has better performance than conventional schemes in terms of allocation efficiency, delay and cell loss ratio under VBR traffic.

• Proceedings of the KIPE Conference
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• 2012.11a
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• pp.33-34
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• 2012

As the distributed power system with PV and ESS is highlighted to be one of the most prominent structure to replace the traditional electric power system, power flow scheduling is expected to bring better system efficiency. Optimal energy management system (EMS) where the power from PV and the grid is managed in time-domain using ESS needs an optimization process. In this paper, main optimization method is implemented using dynamic programming (DP). To overcome the drawback of DP in which ideal future information is required, prediction stage precedes every EMS execution. A simple auto-regressive moving-average (ARMA) forecasting followed by a PI-controller updates the prediction data. Assessment of the on-line optimal EMS scheme has been evaluated on several cases.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1039-1045
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• 1994

The speech recognition systems using VQ have usually the problem decreasing recognition rate, MSVQ assigning the dissimilar vectors to a segment. In this paper, applying One-stage DMS/DP algorithm to the recognition experiments, we can solve these problems to what degree. Recognition experiment is peformed for Korean DDD area names with DMS model of 20 sections and word unit template. We carried out the experiment in speaker dependent and speaker independent, and get a recognition rates of 97.7% and 81.7% respectively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.166-174
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• 1994

This paper presents the algorithm of an on-line cursive Hangeul recognition by means of the DP matching with structural information. In the proposed method, the penalty represents the similarity between input character and reference character. The penalty is dynamically computed with types of strokes, directions of strokes, and the length rations of fragment during the process that searchs for an optimal path using 3$\times$3 matrix. As the result, this method can be exactly matched for even greatly deformed characters. The computer simulation shows that the proposed algorithm can be utilized in recognizing cursive Hangeul as well as correct Hangeul.

• Journal of the military operations research society of Korea
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• v.24 no.2
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• pp.130-145
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• 1998

This paper is concerned with the optimization of a system reliability. Two kinds of the reliability model for optimal allocation of parallel redundancy are considered. The algorithm for solving the optimal redundancy problem is proposed by the use of dynamic programming(DP) method. The problem is approached with a standard DP formulation and the DP algorithm is applied to the model and then the optimal solution is found by the backtracking method. The method is applicable to the models having no constraints or having a cost constraint subject to a specified minimum requirement of the system reliability. A consequence of this study is that the developed computer program package are implemental for the optimization of the system reliability.