• Journal of Digital Convergence
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• v.7 no.3
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• pp.25-33
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• 2009

Historically, performance prediction and sizing of server systems have been the key purchasing argument for customer. To accurate server's sizing and performance prediction, it is necessary to correctness guideline for sizing and performance prediction. But existing guidelines have many errors. So, we examine the benchmarks of performance organization such as SPEC and TPC. And then we consider to TPC-C and TPC-E benchmarks for OLTP server's sizing and performance prediction that is a basic concept of guidelines. Eventually, we propose improvement of errors in guidelines.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.59-64
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• 2002

An expert system, to assist dam managers for five dams along the Saikawa River, has been developed with a primary objective of achieving swift and accurate reservoir operation decision-makings during floods. The expert system is capable of supporting on decision-makings upon establishment of flood management procedure and release/storage planning. Furthermore, an attempt was made to improve reservoir inflow prediction models for better supporting capability. As a result, accuracy on prediction of inflow up to 7 hours ahead was improved, which is important for flood management of the five dams, using neural network. The neural network inflow prediction models were developed for each types of floods caused by frontal rainfalls, snowmelt and typhoons, after extracting relevant meteorological factors for each.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.59-60
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• 2018

The purpose of this study is to predict and classify the accident types based on the KOSHA (Korea Occupational Safety & Health Agency) and weather data. We also have an effort to suggest an important management method according to accident types by deriving feature importance. We designed two models based on accident data and weather data (model(a)) and only weather data (model(b)). As a result of random forest method, the model(b) showed a lack of accuracy in prediction. However, the model(a) presented more accurate prediction results than the model(b). Thus we presented safety management plan based on the results. In the future, this study will continue to carry out real time prediction to occurrence types to prevent safety accidents by supplementing the real time accident data and weather data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1997.10a
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• pp.111-124
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• 1997

With the combination of existing tidal predictio model and numerical tidal model, the efficient tidal prediction system was formulated and applied to the neighboring area of Pusan port. Because all tidal constituents for prediction (normally 69 constituents are used) can't be considered due to difficulties on computing efforts, some errors between the observed and predicted values were inevitably occurred. But it was confirmed that the practical results with about 10% of relative errors were obtained if four major tidal constituents(M$_2$, S$_2$. $K_1$, $O_1$) are used at least. Thus, if other constituents than four major tidal constituents are additornaly used, more accurate results will be obtained . Furthermore, if the databases of harmonic constants in coastal waters is made in advance using the numberical tidal model, prompt tidal prediction could be achieved whenever required.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.917-923
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• 2010

The local area weather information is very important element to estimate where the air-pollutant will flow. But the existing NBC hazard prediction model does not consider the local area weather information. So, in this paper, we present SN-HPM that uses the local area wether information to perform more accurate and reliable estimate, and embody it to program.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.55-58
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• 2005

This study was Performed to grasp of the problem and improvement in traffic noise environmental impact assessment(EIA). National institute of environmental research(NIER) traffic noise prediction model is in general use in internal EIA. In this study, NIER noise prediction model need to improve in that the predicted results lower than the measured results. The other predict model(KLC KEI) is more accurate. Also the volume and speed of traffic is need to standardize in traffic noise prediction.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• ETRI Journal
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• v.45 no.2
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• pp.329-337
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• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• The korean journal of orthodontics
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• v.29 no.3 s.74
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• pp.285-301
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• 1999

There is a need for more accurate prediction in surgical orthodontic treatment. Video imaging is an important technology in planning orthognathic surgery and educating patients about the esthetic results after treatment. Preoperative and postoperative lateral cephalogram of 30 patients who had one piece Le Fort I osteotomy advancement and mandibular set back by bilateral intraoral vertical ramal osteotomy with or without genioplasty were used in this study. The computer generated soft tissue line drawing prediction were compared with the actual postoperative cephalograms .The results are as follows. 1. 14 variables showed Statistically significant differences from 24 variables between computer predicted profile and post operative profile 2. Most of the differences were found in the maxilla-related soft tissue landmarks. 3. The predicted results were more accurate in the groups who had small amount of mandibular set back. 4. The predicted results were more accurate in the groups who had no genioplasty. Most of these differences were within 2mm ranges. Therefore profile change prediction by video imaging could be considered clinically acceptable.