• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.133-138
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• 2015

This paper presents a performance improvement of gray level co-occurrence matrix(GLCM) based on the nonuniform quantization, which is generally used to analyze the texture of images. The nonuniform quantization is given by Lloyd algorithm of recursive technique by minimizing the mean square error. The nonlinear intensity levels by performing nonuniformly the quantization of image have been used to decrease the dimension of GLCM, that is applied to reduce the computation loads as a results of generating the GLCM and calculating the texture parameters by using GLCM. The proposed method has been applied to 30 images of $120{\times}120$ pixels with 256-gray level for analyzing the texture by calculating the 6 parameters, such as angular second moment, contrast, variance, entropy, correlation, inverse difference moment. The experimental results show that the proposed method has a superior computation time and memory to the conventional 256-level GLCM method without performing the quantization. Especially, 16-gray level by using the nonuniform quantization has the superior performance for analyzing textures to another levels of 48, 32, 12, and 8 levels.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.6
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• pp.179-188
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• 2014

Due to the tremendous amount and its rapid increase of network traffic, the performance of network equipments are becoming an important issue. Network filtering is one of primary functions affecting the performance of the network equipment such as a firewall or a load balancer to process the packet. In this paper, we propose a cache based tri method to improve the performance of the existing tri method of searching for network filtering. When several packets are exchanged at a time between a server and a client, the tri method repeats the same search procedure for network filtering. However, the proposed method can avoid unnecessary repetition of search procedure by exploiting cache so that the performance of network filtering can be improved. We performed network filtering experiments for the existing method and the proposed method. Experimental results showed that the proposed method could process more packets up to 790,000 per second than the existing method. When the size of cache list is 11, the proposed method showed the most outstanding performance improvement (18.08%) with respect to memory usage increase (7.75%).

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.726-735
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• 2013

This paper proposed face recognition methods about performance improvement of the face recognition using the properties of wavelet transform. Using discrete wavelet transform is Daubechies D4 filter that is similar to mother wavelet transform. For discrete wavelet transform method, In this case, by using LL subband only we can reduce processing time and amount of memory in recognition processing. To improve recognition ratio without further loss of 2 dimensional data changing, We applies 2D LDA. We perform SVM training algorithm to the feature vector obtained by 2D LDA. Experiment is performed using ORL database set and Yale database set by Matlab program. Test result shows that proposed method is superior to existence methods in recognition rate and performance time.

• Design Convergence Study
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• v.18 no.4
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• pp.1-15
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• 2019

As the game industry is steadily becoming the spotlight industry, the importance of user experience design in game industry is increasing. This study tried to approach game from the viewpoint of user experience design and aimed to analyze the onboarding process focusing on user accessibility and retention in an online game. First, through literature review, onboarding process was devided into three stages. Then each stages were analyzed into experience design, game design elements to derive the key UX factors. Second, based on the UX elements, the game experience and cognitive element analysis frame was presented. With this frame, five domestic online games were qualitatively analyzed and cognitive elements of each game's onboarding process were derived. Key cognitive factors in each stages were, selective attention in the discovery stage, working memory and active learning in the learning stage, and participation and motivation in the immersion stage. Finally, improvement direction were presented, focusing on the key cognitive factors. These studies highlight the importance of the user entry process in online games and suggests improvements to lower entry barriers.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.1-9
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• 2023

This study was conducted for the purpose of improving the usability of the product through the usability evaluation of the voice tag reader to improve the life convenience of the visually impaired. Perceived usability evaluation was conducted for 19 evaluation items based on the evaluation model considering the usability principle and the characteristics of the visually impaired. A total of 50 participants were included for the analysis. As a result of the perceived usability evaluation of the visually impaired, the safety of the voice tag reader, voice and sound quality, and accuracy of voice information were relatively satisfactory. It was found that the reader received a low evaluation in terms of efficiency in use, including the size and weight of the reader, and the convenience of carrying and storing. For the usability improvement, the procedure for using a product needs to be more simplified, and it would be helpful to input and supply tags for commonly used objects in advance.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.186-197
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• 2023

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.

• Journal of Home Health Care Nursing
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• v.30 no.3
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• pp.252-263
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• 2023

Purpose: To assess the feasibility, safety, and preliminary estimates of effectiveness of Tai Chi on the functional outcomes of older adults in the community. Methods: This was a mixed-method study that employed a single-group repeated measure design and in-depth interviews. Nine older adults were recruited from the community were recruited to participate in a Tai Chi program, conducted twice weekly for 6 months. Research outcomes included physical function, cognitive function, and quality of life, measured at intervals of 3 and 6 months. Findings: Tai Chi exercises were gradually conducted based on the health status of the older adults. All participants actively participated in the program with an average attendance of 90%. Consequently, the participants showed significant improvements in mobility and their memory recall ability at both 3 and 6 months. Additionally, the results of the Stroop test exhibited improvement 3 months after the commencement of the study program. Quality of life of the participants improved according to the mild cognitive impairment questionnaire, but it did not show significant improvement in health-related quality of life. Conclusion: The Tai Chi exercise program was a safe and, feasible program to improve the physical function, cognitive function, quality of life among the older adults in the community.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.463-476
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• 2006

R-trees have been traditionally optimized for the I/O performance with the disk page as the tree node. Recently, researchers have proposed cache-conscious variations of R-trees optimized for the CPU cache performance in main memory environments, where the node size is several cache lines wide and more entries are packed in a node by compressing MBR keys. However, because there is a big difference between the node sizes of two types of R-trees, disk-optimized R-trees show poor cache performance while cache-optimized R-trees exhibit poor disk performance. In this paper, we propose a cache and disk optimized R-tree, called the PR-tree (Prefetching R-tree). For the cache performance, the node size of the PR-tree is wider than a cache line, and the prefetch instruction is used to reduce the number of cache misses. For the I/O performance, the nodes of the PR-tree are fitted into one disk page. We represent the detailed analysis of cache misses for range queries, and enumerate all the reasonable in-page leaf and nonleaf node sizes, and heights of in-page trees to figure out tree parameters for best cache and I/O performance. The PR-tree that we propose achieves better cache performance than the disk-optimized R-tree: a factor of 3.5-15.1 improvement for one-by-one insertions, 6.5-15.1 improvement for deletions, 1.3-1.9 improvement for range queries, and 2.7-9.7 improvement for k-nearest neighbor queries. All experimental results do not show notable declines of the I/O performance.