• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.25-38
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• 2017

Climate change and environmental pollution are becoming serious due to the use of fossil energy. For this reason, renewable energy systems are increasing, especially photovoltaic systems being more popular. The photovoltaic system has characteristics that are affected by ambient weather conditions such as insolation, outside temperature, wind speed. Particularly, it has been confirmed that the performance of the photovoltaic system decreases as the module temperature increases. In order to grasp the influence of the module temperature in advance, several researchers have proposed the prediction models on the module temperature. In this paper, we predicted the module temperature using the aforementioned prediction model on the basis of the weather conditions in Incheon, South Korea during July and August. The influence of weather conditions (i.e. insolation, outside temperature, and wind speed) on the accuracy of the prediction models was also evaluated using the standard statistical metrics such as RMSE, MAD, and MAPE. The results show that the prediction accuracy is reduced by 3.9 times and 1.9 times as the insolation and outside temperature increased respectively. On the other hand, the accuracy increased by 6.3 times as the wind speed increased.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.3
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• pp.28-34
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• 2015

An analysis method of trajectory prediction has been suggested and the developed trajectory prediction module, which is an important functional component of the Arrival Manager (AMAN) of Jeju airport, has been tested by applying the suggested method. The objective of this method is to improve prediction performance of the trajectory prediction module. The trajectory prediction module predicts the trajectories based on the real-time track data and flight plans. Therefore, the suggested analysis method includes the simulation framework which is based on real-time playback, recording, and graphic display systems for testing. Besides, the definition of time error, which is a important index for the time based scheduling system, such as AMAN, is included in the suggested analysis method. An example of arrival time prediction accuracy improvement through the suggested analysis method has also been presented.

• IE interfaces
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• v.21 no.1
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• pp.85-95
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• 2008

Reliability prediction serves many purposes during the life of a system, so several methods have been developed to predict the parts and systems reliability. MIL-HDBK-217F, among the those methods, has been widely used as a requisite tool for the reliability prediction which is applied to nuclear power plants and their safety regulations. This paper presents the reliability prediction for the DSP(Digital Signal Processor) module composed of three assemblies. One of the assemblies has a monitoring and self test function which is used to enhance the module reliability. The reliability of each assembly is predicted by MIL-HDBK-217F. Based on these predicted values, Markov modelling is finally used to predict the module reliability. Relax 7.7 software of Relax software corporation is used because it has many part libraries and easily handles Markov processes modelling.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

• Journal of Ocean Engineering and Technology
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• v.35 no.1
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• pp.91-97
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• 2021

Marine accidents caused by ships have brought about economic and social losses as well as human casualties. Most of these accidents are caused by small and medium-sized ships and are due to their poor conditions and insufficient equipment compared with larger vessels. Measures are quickly needed to improve the conditions. This paper discusses a video-integrated collision prediction and fall detection system to support the safe navigation of small- and medium-sized ships. The system predicts the collision of ships and detects falls by crew members using the CCTV, displays the analyzed integrated information using automatic identification system (AIS) messages, and provides alerts for the risks identified. The design consists of an object recognition algorithm, interface module, integrated display module, collision prediction and fall detection module, and an alarm management module. For the basic research, we implemented a deep learning algorithm to recognize the ship and crew from images, and an interface module to manage messages from AIS. To verify the implemented algorithm, we conducted tests using 120 images. Object recognition performance is calculated as mAP by comparing the pre-defined object with the object recognized through the algorithms. As results, the object recognition performance of the ship and the crew were approximately 50.44 mAP and 46.76 mAP each. The interface module showed that messages from the installed AIS were accurately converted according to the international standard. Therefore, we implemented an object recognition algorithm and interface module in the designed collision prediction and fall detection system and validated their usability with testing.

• ETRI Journal
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• v.27 no.5
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• pp.511-524
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• 2005

In this paper, we propose a novel hardware architecture for an intra-prediction, integer transform, quantization, inverse integer transform, inverse quantization, and mode decision module for the macroblock engine of a new video coding standard, H.264. To reduce the cycle of intra prediction, transform/quantization, and inverse quantization/inverse transform of H.264, a reduction method for cycle overhead in the case of I16MB mode is proposed. This method can process one macroblock for 927 cycles for all cases of macroblock type by processing $4{\times}4$ Hadamard transform and quantization during $16{\times}16$ prediction. This module was designed using Verilog Hardware Description Language (HDL) and operates with a 54 MHz clock using the Hynix $0.35 {\mu}m$ TLM (triple layer metal) library.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.2 s.17
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• pp.5-12
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• 2004

The erosion wear of gun barrel occurs due to heat and chemical reactions. The high pressure and temperature in chamber increase the erosion wear. It is known that the metal phase transfer is the primary wear factor in a gun barrel under high temperature. In this paper, the tool of wear prediction in high pressure gun tube has been developed. The program developed has three modules such as DIRECT(interior ballistics analysis module), INVERSE(gun design module), and WEAR(wear prediction module). The prediction of wear was compared with the experimental data which was collected in the field unit. The prediction results shows good trend with the collected data.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.794-802
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• 2022

The recovery of reasonable depth information from different scenes is a popular topic in the field of computer vision. For generating depth maps with better details, we present an efficacious monocular depth prediction framework with coordinate attention and feature fusion. Specifically, the proposed framework contains attention, multi-scale and feature fusion modules. The attention module improves features based on coordinate attention to enhance the predicted effect, whereas the multi-scale module integrates useful low- and high-level contextual features with higher resolution. Moreover, we developed a feature fusion module to combine the heterogeneous features to generate high-quality depth outputs. We also designed a hybrid loss function that measures prediction errors from the perspective of depth and scale-invariant gradients, which contribute to preserving rich details. We conducted the experiments on public RGBD datasets, and the evaluation results show that the proposed scheme can considerably enhance the accuracy of depth prediction, achieving 0.051 for log10 and 0.992 for δ<1.25³ on the NYUv2 dataset.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.55-57
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• 2015

The tracking system plays a key role in accurate estimation and prediction of maneuvering vessel's position and velocity in a bid to enhance safety by taking avoiding action against collision. Therefore, in order to achieve this, many ocean- going vessels are equipped with radar and the ARPA system. However, the accuracy of prediction highly depends on the choice of the gain parameters, ${\alpha}$, ${\beta}$ and ${\gamma}$ employed in the tracking filter. P revious research of this paper was based on theoretically developing an algorithm for a tracking module. This research paper is hence a continuation by the authors to determine the optimal values of the gain parameters used in the tracking module. A tracking algorithm is developed using the ${\alpha}-{\beta}-{\gamma}$ filter to carry out prediction and smoothing of the positions and velocities. Numerical simulations are then performed to evaluate the optimal values of the smoothing parameters that will improve the performance of the tracking module and reduce measurement noise. The twice distance root mean square (2drms) is then calculated to determine error variation.