• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.587-597
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• 2017

Recently, the demand for real-time performance in mobile environment is increasing due to the improvement of hardware performance, however a GPOS(General-Purpose Operating System) such as Android and Linux do not provide real-time performance. We developed RTiK(Real-Time implant Kernel) for this problem, but it has the disadvantage of supporting only x86 Architecture. In this paper, we designed and implemented a RTiKA(Real-Time implanted Kernel for ARM) to support real-time in ARM Linux. We used MCT(Multi-Core Timer) timer which replaces Local APIC Timer for real-time support, and we measured the period of generated real-time task for performance verification and evaluation. As the recent the RTiKA can guarantee the operating of several real-time tasks based on the cycle of 1ms.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• The Journal of Korean Academy of Sensory Integration
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• v.15 no.2
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• pp.46-65
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• 2017

Objective : The purpose of this research is to find clinical effects of application of weighted vest during task-oriented training focused on gross motor performance and balance abilities of children with spastic diplegia. Methods : 34 subjects were divided by simple random sampling into two groups; experimental group (male : 9, female : 8, average age : 8.12) and placebo group (male : 9, female : 9, average age : 7.53). Both two groups underwent to 40 minute intervention, twice a week for 12 weeks. The intervention was task-oriented training focused on facilitating closed kinematic chain and multi-joint functional movement pattern. During the training, the experimental group received loaded-resistance weighted vest and placebo group also received weighted vest but without loaded-resistance. Participants in both groups underwent 8 to 10 reps of the task-oriented training and there were 3 minutes break time between tasks. There were pre-test of gross motor performance and balance abilities, and two times of post-tests were performed upon 6 weeks and 12 weeks after the intervention completed. And in final, an additional follow-up test was performed 12 weeks after the evaluation was finished in order to find any difference between the two groups over time. Results : There was significant difference in Gross Motor Performance Measure (GMPM) between two groups. It is found that average score of the experimental group increased more than the placebo group after 6 weeks and 12 weeks intervention (p<.05). There was significant difference in Pediatric Berg's Balance Scale (PBS) between two groups. It is found that average score of the experimental group increased more than the placebo group after 6 weeks and 12 weeks intervention (p<.05). Conclusion : Based on the results in this study, it is proposed that application of weighted vest into task-oriented training to facilitating closed kinematic chain and multi-joint movement can improve gross motor performance and balance abilities of children with cerebral palsy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1754-1766
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• 2015

Packet classification carried out in Internet routers is one of the challenging tasks, because it has to be performed at wire-speed using five header fields at the same time. In this paper, we propose a leaf-pushed AQT bitmap trie. The proposed architecture applies the leaf-pushing to an area-based quad-trie (AQT) to reduce unnecessary off-chip memory accesses. The proposed architecture also applies a bitmap trie, which is a kind of multi-bit tries, to improve search performance and scalability. For performance evaluation, simulations are conducted by using rule sets ACL, FW, and IPC, with the sizes of 1k, 5k, and 10k. Simulation results show that the number of off-chip memory accesses is less than one regardless of set types or set sizes. Additionally, since the proposed architecture applies a bitmap trie, the required number of on-chip memory accesses is the 50% of the leaf-pushed AQT trie. In addition, our proposed architecture shows good scalability in the required on-chip memory size, where the scalability is identified by the stable change in the required memory sizes, as the size of rule sets increases.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.3
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• pp.76-84
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• 2003

Software agents reduce human involvement to a certain extent by automating routine tasks. However, most of agents have assisted with only a few steps in the multi-steps process of electronic transactions. In order to help users with the important steps in the electronic transactions, software agents need to persuade other parties to act in particular ways. While negotiations have many shapes and forms, this paper focuses on a particular class of negotiation, that is competitive business environment based negotiation. For negotiation with other parties in this contort, it is necessary for autonomous agents to consider environmental variables-the number of competitors, the number of negotiation parties, the maximum time by which they must finish their jobs, and user's preferences. Previous negotiation decision functions for the automated negotiation have used only time or the static numbs of negotiating parties as negotiation criteria, although competitive business environment should include potential competitors who can snatch negotiation parties away. This paper attempts to evaluate the performance of a negotiation decision function that considers the potential competitors in competitive market environment as well as that of a negotiation decision function that does not. For this evaluation, this study adopts the electronic marketplace as an application domain because many buyers and sellers compete for limited resources in the marketplace.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.