• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.63-69
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• 2004

In this paper, we propose a general framework for sensor data fusion applied to control systems. Since many kinds of disturbances are introduced to a control system, it is necessary to rely on multisensor data fusion to improve control performance in spite of the disturbances. Multisensor data fusion for a control system is considered a sequence of making decisions for a combination of sensor data to make a proper control input in uncertain conditions of disturbance effects on sensors. The proposed method is applied to a typical control system of a flexible link system in which reduction of oscillation is obtained using a photo sensor at the tip of the link. But the control performance depends heavily on the environmental light conditions. To overcome the light disturbance difficulties, an accelerometer is used in addition to the existing photo sensor. Improvement of control performance is possible by utilizing multisensor data fusion for various output responses to show the feasibility of the proposed method in this paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3B
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• pp.423-430
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• 2000

Future microcellular systems will require distributed network control. A packet-switched network is suitable for this requirement. The packet reservation multiple access(PRMA) is a Reservation-ALOHA like protocol for wireless terminals to transmit packet speech to a base station. It allows spatially distributed users in cellular systems to transmit packeted voice and data to a common base station using a shared channel. In the existing PRMA, the problem is that the voice packets may collide with the data packets due to simultaneous channel access. the problem may be a major performance degradation factor to a voice and data mixed system. We propose a new PRMA method that integrates voice and data traffic efficiently by resolving the collision problem between data and voice packets. The proposed PRMA method gives a performance improvement than the existing PRAMA method in terms of voice packet dropping probability and data delay characteristic. From analytic results, we can confirm that the proposed PRMA method show a performance improvement than the existing PRMA protocol.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.268-273
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• 2021

In this paper, we propose a sentiment analysis model that improves performance on small-scale data. A sentiment analysis model for small-scale data is proposed and verified through experiments. To this end, we propose Bagging-Bi-GRU, which combines Bi-GRU, which learns GRU, which is a variant of LSTM (Long Short-Term Memory) with excellent performance on sequential data, in both directions and the bagging technique, which is one of the ensembles learning methods. In order to verify the performance of the proposed model, it is applied to small-scale data and large-scale data. And by comparing and analyzing it with the existing machine learning algorithm, Bi-GRU, it shows that the performance of the proposed model is improved not only for small data but also for large data.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.195-204
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• 2024

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

• Journal of Communications and Networks
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• v.10 no.4
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• pp.444-450
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• 2008

This paper studies the performance of multiuser data networks with transmit diversity under correlated fading channels. Previous work shows that correlated fading reduces the link performance of multiple antenna systems, but how correlated fading affects the throughput of multiuser data networks is still unknown since the throughput depends not only on the link performance but also on the multiuser diversity. We derive the throughput of the multiuser data networks with various transmit diversity schemes under correlated fading channels. The impact of correlated fading on the throughput is investigated. Analytical and simulation results show that, although correlated fading is harmful for link performance, it increases the throughput of the multiuser data networks if the transmit scheme is appropriately selected.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.188-192
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• 2021

In this paper, we apply data mining techniques and machine learning algorithms using R software, which is used to predict, here we applied a regression model to test some factor on the dataset for which we assumed that it effects student performance. Model was built on an existing dataset which contains many factors and the final grades. The factors tested are the attention to higher education, absences, study time, parent's education level, parent's jobs, and the number of failures in the past. The result shows that only study time and absences can affect the students' performance. Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a student's data.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.983-997
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• 2010

This study is to explore the relationship between trust in local administrative organization and organizational performance. Local administrative organizations provide the citizens with administrative services. Heightening the organizational performance contributes the citizens' happiness and the stream of times through organizations' change. To provide high quality of administrative service to citizens, trust in organizations is more important than any other capital. The improvement of organizational performance needs through this social capital. Factors about trust variables and organizational performance variables are extracted through the theoretical discussions. To do the research, public servants in 7 local administrative organizations of Gangwondo were asked to do the survey about how trust in organizations affects organizationa performances. The results explain that trust variables are related to organizational performance, and the local administrative organization which is high in trust is high in organizational performance. Trust in local administrative organizations improves the organizational performance internally and the organization will obtain trust from the citizens externally.

• Journal of Information Technology Applications and Management
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• v.30 no.4
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• pp.29-38
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• 2023

A single-learner model which integrates the user's positive and negative perceptions is proposed by augmenting counterfactual data to the interaction data between users and items, which are mainly used in collaborative filtering in this study. The proposed CausRec showed superior performance compared to the existing NCF model in terms of F1 value and AUC in experiments using three published datasets: MovieLens 100K, Amazon Gift Card, and Amazon Magazine. Compared to the existing NCF model, the F1 and AUC values of CausRec showed 1.2% and 2.6% performance improvement in MovieLens 100K data, and 2.2% and 10% improvement in Amazon Gift Card data, respectively. In particular, in experiments using Amazon Magazine data, F1 and AUC values were improved by 11.7% and 21.9%, respectively, showing a significant performance improvement effect. The performance of CausRec is improved because both positive and negative perceptions of the item were reflected in the recommendation at the same time. It is judged that the proposed method was able to improve the performance of the collaborative filtering because it can simultaneously alleviate the sparsity and imbalance problems of the interaction data.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.3
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• pp.228-232
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• 2005

The Korea Electric Power Research Institute (KEPRI) has developed performance demonstration programs for non-destructive testing personnel who analyze ECT(eddy current testing) data for steam generator tubing since 2001 The purpose of these performance demonstration programs is to ensure a uniform knowledge and skill level of data analysts and contribute to safe operation of nuclear power plants. Many changes have occurred in non-destructive testing of steam generator tubing such as inspection scope, plugging criteria and qualification requirements. According to the Notice 2004-13 revised by the Ministry of Science and Technology (MOST), the analyst for steam generator tubing shall be qualified as the qualified data analyst (QDA), and the site specific performance demonstration (SSPD) program shall be implemented. KEPRI developed these performance demonstration programs and they are being successfully implemented. The analyst's performance is expected to be improved by the implementation of these programs.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.59-65
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• 2016

Performance optimization of applications with massive stream data processing has been performed by considering I/O data coherency problem where a memory is shared between processors and hardware accelerators. A formula for performance analyses is derived based on profiling results of system-level simulations. Our experimental results show that overall performance was improved by 1.40 times on average for various image sizes. Also, further optimization has been performed based on the parameters appeared in the derived formula. The final performance gain was 3.88 times comparing to the original design and we can find that the performance of the design with cacheable shared memory is not always.