• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1379-1382
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• 2004

The use of a cache for storing and processing of Web objects is becoming larger. Also, many studies on the efficient management of the storing scope of caches are being done. Web caching algorithms have many differences from traditional algorithms. Particularly, heterogeneity of Web objects that are processing units of Web caching, and a variation of Web object reference characteristic with time are the important causes of the decrease the performance of existing algorithms. In this study, we proposed the new web-caching algorithm. A heterogeneity variation of an object can be reduced as the proposed method dividedly managing Web objects and a cache scope with heterogeneity, and it is adaptively reflecting a variation of object reference characteristics with the flowing of time. In the experiments, we verified that the performance of the proposed method was more improved than existing algorithms through the two experiment models which considered heterogeneity of an object.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.79-90
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• 2019

Maximizing energy efficiency minimizes the energy consumption of computation, storage and communications required for IT services, resulting in economic and environmental benefits. Recent advancement of flash and next generation non-volatile memory technology and price decrease of those memories have led to the rise of so-called AFA (All-Flash Array) storage devices made of flash or next generation non-volatile memory. Currently, the AFA devices are rapidly replacing traditional storages in the high-performance servers due to their fast input/output characteristics. However, it is not well known how effective the energy efficiency of the AFA devices in the real world. This paper shows input/output performance and power consumption of the AFA devices measured on the Linux XFS file system via experiments and discusses energy efficiency of the AFA devices in the real world.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.5
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• pp.119-123
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• 2006

We present a new face recognition method, which selects eigenfaces by our algorithm instead of the existing eigenfaces selection method that chooses eigenfaces by the value of corresponding eigenvalues. We justify our method by comparing our method with traditional one by experiments with YALE, ORL database. By using our algorithm in selecting the eigenfaces, we obtain higher recognition rate than the existing schemes.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.95.1-95.1
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• 2003

Sopungsungi-won has been used as a traditional medicine for diabetes and it has been proved evidently as a potential remedy for type 2 diabetes mellitus. Both in vivo and in vitro experiments with water extract of Sopungsungi-won have been reported to exhibit anti-diabetic effects in our previous studies. In the present study, we have chosen Rheum undulatum (RU), which is the main component of Sopungsungi-won, to examine its anti-apoptotic effect on pancreatic b-cells, HIT-T15, against oxidative stress induced by hydrogen peroxide (H$_2$O$_2$). (omitted)

• Structural Engineering and Mechanics
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• v.77 no.2
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• pp.273-291
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• 2021

The strut-and-tie method (STM) has been widely accepted and used as a rational approach for the design of disturbed regions ('D' regions) of reinforced concrete members such as in corbels and deep beams, where traditional flexure theory does not apply. This paper evaluates the applicability of the equilibrium based STM in strength predictions of deep beams (with rectangular and circular cross-section) and corbels using the available experiments in literature. STM is found to give fairly good results for corbel and deep beams. The failure modes of these deep members are also studied, and an optimum amount of distribution reinforcement is suggested to eliminate the premature diagonal splitting failure. A comparison with existing empirical and semi empirical methods also show that STM gives more reliable results. The nonlinear finite element analysis (NLFEA) of 50 deep beams and 20 corbels could capture the complete behaviour of deep members including crack pattern, failure load and failure load accurately.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.423-430
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• 2021

Aiming Graph convolutional networks (GCNs) have achieved outstanding performances on skeleton-based action recognition. However, several problems remain in existing GCN-based methods, and the problem of low recognition rate caused by single input data information has not been effectively solved. In this article, we propose a Dual-stream fusion method that combines video data and skeleton data. The two networks respectively identify skeleton data and video data and fuse the probabilities of the two outputs to achieve the effect of information fusion. Experiments on two large dataset, Kinetics and NTU-RGBC+D Human Action Dataset, illustrate that our proposed method achieves state-of-the-art. Compared with the traditional method, the recognition accuracy is improved better.

• Journal of Korean Medicine for Obesity Research
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• v.21 no.1
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• pp.42-48
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• 2021

Objectives: The purpose of this study was to review the studies of the wild ginseng complex pharmacopuncture related to obesity treatment. Methods: We searched the papers with key words of 'wild ginseng complex pharmacopuncture', 'wild ginseng pharmacopuncture', 'obesity', 'fat', 'weight' in Oriental medicine Advanced Searching Integrated System, KRpia, Koreanstudies Information Service System, Research Information Sharing Service, KoreaMed and PubMed, Scopus. We classified the papers by year, content and study type. Results: There were 7 studies about the wild ginseng pharmacopuncture related to obesity treatment. Cell studies were excluded because they were not related to obesity. Analysis of 3 animal experiments and 4 clinical studies were conducted to describe each research subject, method, and research results. Conclusion: More interest and further research will be needed on wild ginseng pharmacopuncture related to obesity treatment in the Korean medicine to achieve clinical application and to develop treatment protocols for the obesity disease.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2359-2376
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• 2022

With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3283-3292
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• 2022

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.