• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Journal of Life Science
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• v.19 no.8
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• pp.1055-1061
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• 2009

${\alpha}$-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) receptors are widespread throughout the central nervous system and appear to serve as synaptic receptors for fast excitatory synaptic transmission mediated by glutamate. Their modulation is believed to affect learning and memory. To identify the interaction proteins for the AMPA receptor subunit glutamate receptor-interacting protein 1 (GRIPl), GRIP1 interactions with armadillo family protein p0071/plakophilin-4 were investigated. GRIP1 protein bound to the tail region of p0071/plakophilin-4 but not to other armadillo family protein members in a yeast two-hybrid assay. The "S-X-V" motif at the carboxyl (C)-terminal end of p0071/plakophilin-4 is essential for interaction with GRIP1. p0071/plakophilin-4 interacted with the Postsynaptic density-95/Discs large/Zona occludens-1 (PDZ) domains of GRIPI in the yeast two-hybrid assay, as is indicated also by Glutathione S-transferase (GST) pull-down, and co-immunoprecipitated with GRIP1 antibody in brain fraction. The findings of this study provide evidence that p0071/plakophilin-4 is an interactor of GRIP1.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.4
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• pp.189-198
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• 2015

Most IoT-related approaches have tried to establish the relation by connecting the network between things. The proposed research will present how the pervasive interaction of eco-system formed by touching the objects between humans and things can be recognized on purpose. By collecting and sharing the detected patterns among all kinds of things, we can construct the environment which enables individualized interactions of different objects. To perform the aforementioned, we are going to utilize technical procedures such as event-driven signal processing, pattern matching for signal recognition, and hardware in the loop simulation. We will also aim to implement the prototype of sensor processor based on Arduino MCU, which can be integrated with system using Arduino-Matlab/Simulink hybrid-interoperation environment. In the experiment, we use piezo transducer to detect the vibration or vibrates the surface using acoustic wave, which has specific frequency spectrum and individualized signal shape in terms of time axis. The signal distortion in time and frequency domain is recorded into memory tracer within sensor processor to extract the meaningful pattern by comparing the stored with lookup table(LUT). In this paper, we will contribute the initial prototypes for the acoustic touch processor by using off-the-shelf MCU and the integrated framework based on Matlab/Simulink model to provide the individualization of the touch-sensing for the user on purpose.

• Journal of KIISE
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• v.42 no.6
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• pp.691-698
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• 2015

Application launch time is an important performance metric to user experience in desktop and laptop environment, which mostly depends on the performance of secondary storage. Application launch times can be reduced by utilizing solid-state drive (SSD) instead of hard disk drive (HDD). However, considering a cost-performance trade-off, utilizing SSDs as caches for slow HDDs is a practicable alternative in reducing the application launch times. We propose a new SSD caching scheme which migrates data blocks from HDDs to SSDs. Our scheme operates entirely in the file system level and does not require an extra layer for mapping SSD-cached data that is essential in most other schemes. In particular, our scheme does not incur mapping overheads that cause significant burdens on the main memory, CPU, and SSD space for mapping table. Experimental results conducted with 8 popular applications demonstrate our scheme yields 56% of performance gain in application launch, when data blocks along with metadata are migrated.

• The KIPS Transactions:PartC
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• v.11C no.3
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• pp.301-308
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• 2004

Misuse IDS is known to have an acceptable accuracy but suffers from high rates of false alarms. We show a behavior based alarm reduction with a memory-based machine learning technique. Our extended form of IBL, (XIBL) examines SNORT alarm signals if that signal is worthy sending signals to security manager. An experiment shows that there exists an apparent difference between true alarms and false alarms with respect to XIBL behavior This gives clear evidence that although an attack in the network consists of a sequence of packets, decisions over Individual packet can be used in conjunction with misuse IDS for better performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.9
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• pp.1702-1709
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• 1994

In the postprocessing of Hangul recognition system, the storage structure of contextual information is an important matter for the recognition rate and speed of the entire system. Trie in general is used to represent the context as word dictionary, but the memory space efficiency of the structure is low. Therefore we propose a new structure for word dictionary that has better space efficiency and the equivalent merits of trie. Because Hangul is a compound language, the language can be represented by phonemes or by characters. In the representation by phonemes(P-mode) the retrieval is fast, but the space efficiency is low. In the representation by characters(C-mode) the space efficiency is high, but the retrieval is slow. In this paper the two representation methods are combined to form a hybrid representation(H-mode). At first an optimal level for the combination is selected by two characteristic curves of node utilization and dispersion. Then the input words are represented with trie structure by P-mode from the first to the optimal level, and the rest are represented with sequentially linked list structure by C-mode. The experimental results for the six kinds of word set show that the proposed structure is more efficient. This result is based on the fact that the retrieval for H-mode is as fast as P-mode and the space efficiency is as good as C-mode.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.689-698
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• 2005

Various systems are used to provide the location based services. But, the existing systems have some problems which have difficulties in dealing with faster services for above million people. In order to solve it, a multi-level DBMS which supports both fast data processing and large data management support should be used. The multi-level DBMS with snapshots has all the data existing in disk database and the data which are required to be processed for fast processing are managed in main memory database as snapshots. To optimize performance of this system for location based services, the query classification component which classifies the queries for efficient snapshot usage is needed. In this paper, the query classification component in multi-level DBMS for location based services is designed and implemented. The proposed component classifies queries into three types: (1) memory query, (2) disk query, (3) hybrid query, and increases the rate of snapshot usage. In addition, it applies division mechanisms which divide aspatial and spatial filter condition for partial snapshot usage. Hence, the proposed component enhances system performance by maximizing the usage of snapshot as a result of the efficient query classification.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.183-184
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• 2013

Two main MBE growth techniques have been used: plasma-assisted MBE (PA-MBE), which utilizes a rf plasma to supply active nitrogen, and ammonia MBE, in which nitrogen is supplied by pyrolysis of NH3 on the sample surface during growth. PA-MBE is typically performed under metal-rich growth conditions, which results in the formation of gallium droplets on the sample surface and a narrow range of conditions for optimal growth. In contrast, high-quality GaN films can be grown by ammonia MBE under an excess nitrogen flux, which in principle should result in improved device uniformity due to the elimination of droplets and wider range of stable growth conditions. A drawback of ammonia MBE, on the other hand, is a serious memory effect of NH3 condensed on the cryo-panels and the vicinity of heaters, which ruins the control of critical growth stages, i.e. the native oxide desorption and the surface reconstruction, and the accurate control of V/III ratio, especially in the initial stage of seed layer growth. In this paper, we demonstrate that the reliable and reproducible growth of GaN on Si (110) substrates is successfully achieved by combining two MBE growth technologies using rf plasma and ammonia and setting a proper growth protocol. Samples were grown in a MBE system equipped with both a nitrogen rf plasma source (SVT) and an ammonia source. The ammonia gas purity was ＞99.9999% and further purified by using a getter filter. The custom-made injector designed to focus the ammonia flux onto the substrate was used for the gas delivery, while aluminum and gallium were provided via conventional effusion cells. The growth sequence to minimize the residual ammonia and subsequent memory effects is the following: (1) Native oxides are desorbed at $750^{\circ}C$ (Fig. (a) for [$1^-10$] and [001] azimuth) (2) 40 nm thick AlN is first grown using nitrogen rf plasma source at $900^{\circ}C$ nder the optimized condition to maintain the layer by layer growth of AlN buffer layer and slightly Al-rich condition. (Fig. (b)) (3) After switching to ammonia source, GaN growth is initiated with different V/III ratio and temperature conditions. A streaky RHEED pattern with an appearance of a weak ($2{\times}2$) reconstruction characteristic of Ga-polarity is observed all along the growth of subsequent GaN layer under optimized conditions. (Fig. (c)) The structural properties as well as dislocation densities as a function of growth conditions have been investigated using symmetrical and asymmetrical x-ray rocking curves. The electrical characteristics as a function of buffer and GaN layer growth conditions as well as the growth sequence will be also discussed. Figure: (a) RHEED pattern after oxide desorption (b) after 40 nm thick AlN growth using nitrogen rf plasma source and (c) after 600 nm thick GaN growth using ammonia source for (upper) [110] and (lower) [001] azimuth.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.421-434
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• 2007

Among many other CG generated natural scenes, the representation of ocean surfaces is one of the most complicated and time-consuming problem because of its large extent and complex surface movement. We present a hybrid method to represent and animate unbound deep-water ocean surfaces by utilizing graphics processor as both simulation and rendering core. Our technique is mainly based on spectral approaches that generate a high-detailed height field using Fourier transform on a 2D regular grid. Additionally, we incorporate Gerstner model and generate low-detailed height field on a 2D projected grid in order to represent large waves and main structure of ocean surface. There is no interruption between CPU and GPU, and no need to transfer simulation results from the system memory to graphics hardware because the entire simulation and rending processes are done on graphics processor. As a result we can synthesize and render realistic water surfaces in real-time. Proposed techniques are readily adoptable to real-time applications such as computer games that have heavy work load on CPU but still demand plausible natural scenes.