• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2398-2415
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• 2017

Content-Centric Networking (CCN) is a new Internet architecture with routing and caching centered on contents. Through its receiver-driven and connectionless communication model, CCN natively supports the seamless mobility of nodes and scalable content acquisition. In-network caching is one of the core technologies in CCN, and the research of efficient caching scheme becomes increasingly attractive. To address the problem of unbalanced cache load distribution in some existing caching strategies, this paper presents a neighbor cooperation based in-network caching scheme. In this scheme, the node with the highest betweenness centrality in the content delivery path is selected as the central caching node and the area of its ego network is selected as the caching area. When the caching node has no sufficient resource, part of its cached contents will be picked out and transferred to the appropriate neighbor by comprehensively considering the factors, such as available node cache, cache replacement rate and link stability between nodes. Simulation results show that our scheme can effectively enhance the utilization of cache resources and improve cache hit rate and average access cost.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.212-220
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• 2024

When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.

• Journal of the Korean Society for Heat Treatment
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• v.34 no.5
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• pp.239-244
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• 2021

Following Silicon Carbide, single crystal diamond continues to attract attention as a next-generation semiconductor substrate material. In addition to excellent physical properties, large area and productivity are very important for semiconductor substrate materials. Research on the increase in area and productivity of single crystal diamonds has been carried out using various devices such as HPHT (High Pressure High Temperature) and MPECVD (Microwave Plasma Enhanced Chemical Vapor Deposition). We hit the limits of growth rate and internal defects. However, HFCVD (Hot Filament Chemical Vapor Deposition) can be replaced due to the previous problem. In this study, HFCVD confirmed the distance between the substrate and the filament, the accompanying growth rate, the surface shape, and the Raman shift of the substrate after vapor deposition according to the vapor deposition temperature change. As a result, it was confirmed that the difference in the growth rate of the single crystal substrate due to the change in the vapor deposition temperature was gained up to 5 times, and that as the vapor deposition temperature increased, a large amount of polycrystalline diamond tended to be generated on the surface.

• Journal of the Korean Society of Child Welfare
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• no.56
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• pp.253-281
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• 2016

The purpose of this study was to verify the clinical utility of th Korea Child Behavior Checklist 16-18(K-CBCL 6-18) in diagnosing ADHD among children with psychological disorders in child welfare institutions. The participants were 509 elementary school children(309 boys and 200 girls) who lived in child welfare institutions. They were assessed using the Korean ADHD Rating Scale(K-ARS) and K-CBCL 6-18. Only five scales of the K-CBCL 6-18 related with attention were used for analysis: syndrom total, externalizing total, aggressive behavior, attention problems and DSM-oriented ADHD scales. The results were as follows. First, K-ARS and K-CBCL 6-18 had significantly positive correlations with all five scales. Second, as a result of a t-test on the ADHD and the non-ADHD groups, which were divided using K-ARS, the mean scores of ADHD group were significantly higher than the non-ADHD group for all five scales of the K-CBCL 6-18. The hit rate of all five scales of the K-CBCL 6-18 was 60 to 70 percent. The syndrom total and externalizing total scales had high sensitivity, whereas the aggressive behavior, attention problems, and the DSM-oriented ADHD scales had high specificity. In addition, all scales had high positive predictive values. Third, as the result of a t-test on the ADHD group and the emotional disorder group, there were significant difference in the mean scores of the attention problems and the DSM-oriented ADHD scales. The attention problems and the DSM-oriented ADHD scales had a similar percentage of hit rate, high specificity and low sensitivity. Especially, the DSM-oriented ADHD scale revealed higher specificity than the attention problems scale. The results of this study suggested that the five scales related to attention of the K-CBCL 6-18 are useful in diagnosing ADHD in child welfare institutions.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.72-81
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• 2008

High Performance DSP usually supports cache and internal memory. For an optimal implementation of a multimedia stream application on such a high performance DSP, one needs to utilize the cache and internal memory efficiently. In this paper, we investigate performance analysis of cache, and internal memory configuration and placement necessary to achieve an optimal implementation of multimedia stream applications like motion picture encoder on high performance DSP, TMS320C6000 series, and propose strategies to improve performance for cache and internal memory placement. From the results of analysis and experiments, it is verified that 2-way L2 cache configuration with the remaining memory configured as internal memory shows relatively good performance. Also, it is shown that L1P cache hit rate is enhanced when frequently called routines and routines having caller-callee relationships with them are continuously placed in the internal memory and that L1D cache hit rate is enhanced by the simple change of the data size. The results in the paper are expected to contribute to the optimal implementation of multimedia stream applications on high performance DSPs.

• The KIPS Transactions:PartA
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• v.19A no.1
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• pp.35-44
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• 2012

Traditional technologies that are used to improve the performance of hard disk drives show many negative cases if they are applied to solid state drives (SSD). Access time and block sequence in hard disk drives that consist of mechanical components are very important performance factors. Meanwhile, SSD provides superior random read performance that is not affected by block address sequence due to the characteristics of flash memory. Practically, it is recommended to disable prefetching if a SSD is installed in a personal computer. However, this paper presents a combinational method of a prefetching scheme and a memory management that consider the internal structure of SSD and the characteristics of NAND flash memory. It is important that SSD must concurrently operate multiple flash memory chips. The I/O unit size of NAND flash memory tends to increase and it exceeded the block size of operating systems. Hence, the proposed prefetching scheme performs in an operating unit of SSD. To complement a weak point of the prefetching scheme, the proposed memory management scheme adaptively evicts uselessly prefetched data to maximize the sum of cache hit rate and prefetch hit rate. We implemented the proposed schemes as a Linux kernel module and evaluated them using a commercial SSD. The schemes improved the I/O performance up to 26% in a given experiment.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.223-228
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• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

• Journal of Veterinary Clinics
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• v.24 no.3
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• pp.422-425
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• 2007

A 6-month-old intact male Jindo dog was underwent surgery for hip fracture caused by hit by a car. Routine laboratory tests performed prior to surgery found no significant abnormalities, which might increase risks associated with general anesthesia. The dog was premedicated with atropine, induced general anesthesia with thiopental sodium and maintained with isoflurane. Forty minutes after surgery, the dog was suddenly bradycardic. Atropine (18 ug/kg) was slowly infused intravenously to normalize heart rate. However, paradoxically the dog showed slower heart rate with intermittent atrioventricular block ($2^{nd}$ degree type I) after atropine infusion. The dog's rhythm was returned to normal rate 7 minutes after ephedrine was infused. This is a rare case of paradoxical atrioventricular block induced by high dose of atropine in a dog.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of Korean Library and Information Science Society
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• v.21
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• pp.253-289
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• 1994

The purpose of this study is about the search pattern of LINNET (Library Information Network System) OPAC users by transaction log, maintained by POSTECH(Pohang University of Science and Technology) Central Library, to provide feedback information of OPAC system design. The results of this study are as follows. First, for the period of this analysis, there were totally 11, 218 log-ins, 40, 627 transaction logs and 3.62 retrievals per a log-in. Title keyword was the most frequently used, but accession number, bibliographic control number or call number was very infrequently used. Second, 47.02% of OPAC, searches resulted in zero retrievals. Bibliographic control number was the least successful search. User displayed 2.01% full information and 64.27% local information per full information. Third, special or advanced retrieval features are very infrequently used. Only 22.67% of the searches used right truncation and 0.71% used the qualifier. Only 1 boolean operator was used in every 22 retrievals. The most frequently used operator is 'and (&)' with title keywords. But 'bibliographical control number (N) and accessionnumber (R) are not used at all with any operators. The causes of search failure are as follows. 1. The item was not used in the database. (15, 764 times : 79.42%). 2. The wrong search key was used. (3, 761 times : 18.95%) 3. The senseless string (garbage) was entered. (324 times : 1.63%) On the basis of these results, some recommendations are suggested to improve the search success rate as follows. First, a n.0, ppropriate user education and online help function let users retrieve LINNET OPAC more efficiently. Second, several corrections of retrieval software will decrease the search failure rate. Third, system offers right truncation by default to every search term. This methods will increase success rate but should considered carefully. By a n.0, pplying this method, the number of hit can be overnumbered, and system overhead can be occurred. Fourth, system offers special boolean operator by default to every keyword retrieval when user enters more than two words at a time. Fifth, system assists searchers to overcome the wrong typing of selecting key by automatic korean/english mode change.