• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.383-400
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• 2023

Air pollution-related diseases are escalating worldwide, with the World Health Organization (WHO) estimating approximately 7 million annual deaths in 2022. The rapid expansion of industrial facilities, increased emissions from various sources, and uncontrolled release of odorous substances have brought air pollution to the forefront of societal concerns. In South Korea, odor is categorized as an independent environmental pollutant, alongside air and water pollution, directly impacting the health of local residents by causing discomfort and aversion. However, the current odor management system in Korea remains inadequate, necessitating improvements. This study aims to enhance the odor management system by analyzing 1,010,749 data points collected from odor sensors located in Osong, Chungcheongbuk-do, using an Ensemble-Based Multi-Region Integrated Odor Concentration Prediction Model. The research results demonstrate that the model based on the XGBoost algorithm exhibited superior performance, with an RMSE of 0.0096, significantly outperforming the single-region model (0.0146) with a 51.9% reduction in mean error size. This underscores the potential for increasing data volume, improving accuracy, and enabling odor prediction in diverse regions using a unified model through the standardization of odor concentration data collected from various regions.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.260-268
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• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.278-286
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• 2021

In this study, based on the Computational Fluid Dynamics (CFD) simulation model developed through previous study, inner environmenct of the modified glass greenhouse was predicted. Also, suggested the optimal shape of the greenhouse and location of the heat exchangers for heat energy management of the greenhouse using the developed model. For efficient heating energy management, the glass greenhouse was modified by changing the cross-section design and the location of the heat exchanger. The optimal cross-section design was selected based on the cross-section design standard of Republic of Korea's glass greenhouse, and the Fan Coil Unit(FCU) and the radiating pipe were re-positioned based on "Standard of greenhouse environment design" to enhance energy saving efficiency. The simulation analysis was performed to predict the inner temperature distribution and heat transfer with the modified greenhouse structure using the developed inner environment prediction model. As a result of simulation, the mean temperature and uniformity of the modified greenhouse were 0.65℃, 0.75%p higher than those of the control greenhouse, respectively. Also, the maximum deviation decreased by an average of 0.25℃. And the mean age of air was 18 sec. lower than that of the control greenhouse. It was confirmed that efficient heating energy management was possible in the modified greenhouse, when considered the temperature uniformity and the ventilation performance.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.1-14
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• 2017

This paper proposes adaptive reference structure decision method to improve the performance of HEVC (High Efficiency Video Coding) encoder. When an event occurs in the input sequence, such as scene change, scene rotation, fade in/out, or light on/off, the proposed algorithm changes the reference structure to improve the inter prediction performance. The proposed algorithm divides GOP (Group Of Pictures) into two sub-groups based on the picture that has such event and decides the reference pictures in the divided sub-groups. Also, this paper proposes fast encoding method which changes the picture type of first encoded picture in the GOP that has such event to CRA (Clean Random Access). With the statistical feature that intra prediction is selected by high probability for the first encoded picture in the GOP carrying such event, the proposed fast encoding method does not operate inter prediction. The experimental result shows that the proposed adaptive reference structure decision method improves the BD-rate 0.3% and reduces encoding time 4.9% on average under the CTC (Common Test Condition) for standardization. In addition, the proposed reference structure decision method with the picture type change reduces the average encoding time 12.2% with 0.11% BD-rate loss.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.12
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• pp.9-16
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• 2005

A new online routing algerian is proposed in this paper, which use the interference-prediction to solve the network congestion originated from extension of Internet scope and increasing amount of traffic. The end-to-end QoS has to be guaranteed in order to satisfy service level agreements (SLAs) in the integrated networks of next generation. For this purpose, bandwidth is allocated dynamically and effectively, moreover the path selection algorithm is required while considering the network performance. The proposed algorithm predicts the level of how much the amount of current demand interferes the future potential traffic, and then minimizes it. The proposed algorithm considers the bandwidth on demand, link state, and the information about ingress-egress pairs to maximize the network performance and to prevent the waste of the limited resources. In addition, the interference-prediction supports the bandwidth guarantee in dynamic network to accept more requests. In the result, the proposed algorithm performs the effective admission control and QoS routing. In this paper, we analyze the required conditions of routing algorithms, the aspect of recent research, and the representative algorithms to propose the optimized path selection algorithm adequate to Internet franc engineering. Based on these results, we analyze the problems of existing algorithms and propose our algorithm. The simulation shows improved performance by comparing with other algorithms and analyzing them.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1994-2000
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• 1993

A new lattice structure using decision feedback and augmented prediction for estimating and suppressing the narrowband interference is presented. The performance of the proposed interference canceller is compared to the conventional interference cancellation filter. The reference signal of the interference canceller is formed by using the chip decisions, which is correlated with the narrowband interference components of the received signal. The decision feedback technique reduce the distortion of the desired signal which is introduced by the interference canceller through the use of feedback chip decisions. And by linear prediction of the error signal, the residual interference component of can be eliminated, Using this unconteminated error signal to update the adaptive filter coefficients, the performance of the rejection can be improved. In the simulation, it is assumed that the processing gains are 7 and 15, signal to interference ratio is -10[dB], and 5% interference band. The results show that the BER performance of the proposed filter structure is improved by 1~3dB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.842-849
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• 2000

In this paper, We analyzed the protocol to improve the performance of the multicast traffic processing in WDM networks. In the protocol, control channels are devided into contention-less minislots and contention minislots. And the packets which fail to have successful reservation in the present time slot have priority to have successful reservation in the contention-less minislots of the next time slot. Therefore, control channel contentions and destination conflicts can be reduced with the use of contention-less minislots. For the multicast traffic processing, the theoretical analysis and computer simulation are important to estimate the network performance and to calculate the optimized number of contention-less minislots. In this paper, the state transition probability of the number of contention-less minislots and arrival packets are calculated using 4-dimension matrix. The maximum number of contention-less minislots is equal to the number of channels for maximum performance improvement of the system. It is theoretical analysis and prove to computer simulation the performance of the protocol.

• 전자공학회논문지 IE
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• v.45 no.1
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• pp.15-23
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• 2008

In this paper, an algorithm, that provisions absolute differentiation of packet delays is proposed, simulated, and implemented with VHDL on XPC 860 CPU based test board with an objective for enhancing quality of service (QoS) in future packet networks. It features a scheme that compensates the deviation for prediction on the traffic to be arrived continuously. It predicts the traffic to be arrived at the beginning of a time slot and measures the actual arrived traffic at the end of the time slot and derives the difference between them. The deviation is utilized to the delay control operation for the next time slot to offset it. As it compensates the prediction error continuously, it shows superior adaptability to the bursty traffic as well as the exponential traffic. It is demonstrated through both simulation and the real traffic test on the board that the algorithm meets the quantitative delay bounds and shows superiority to the traffic fluctuation in comparison with the conventional non-adaptive mechanism.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.6
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• pp.415-423
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• 2018

We herein investigate the E1 pulse for evaluating the conducted performance of transmission lines connected to the electromagnetic pulse protection facilities against a conducted high-altitude electromagnetic pulse threat exposed to an external electromagnetic environment. The existing E1 pulse generator uses the Marx generator high-voltage step-up method; however, in this research, we used the Tesla transformer method to easily change the broadband output voltage(30 to 350 kV). We also analyzed the controller, power supply, high-voltage booster, and pulse-shaping device. The E1 pulse performance using the Tesla transformer was predicted through simulations and validated by measurements.