• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.774-777
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• 2015

This paper introduces the world's first programmable video-processing platform for the enhancement of the video quality of the 8K(7680x4320) UHD(Ultra High Definition) TV operating up to 60 frames per second. In order to support required computing capacity and memory bandwidth, the proposed platform implemented several key features such as symmetric multi-cluster architecture for parallel data processing, a ring-data path between the clusters for data pipelining and hardware accelerators for computing filter operations. The proposed platform based on RP(Reconfigurable Processor) processes video quality enhancement algorithms and handles effectively new UHD broadcasting standards and display panels.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1023-1025
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• 2019

Coal and Natural gas are two biggest contributors to a generation of energy throughout the world. Most of these resources create environmental pollution while making energy affecting the natural habitat. Many approaches have been proposed as alternatives to these sources. One of the leading alternatives is Solar Energy which is usually harnessed using solar farms. In artificial intelligence, the most researched area in recent times is machine learning. With machine learning, many tasks which were previously thought to be only humanly doable are done by machine. Neural networks have two major subtypes i.e. Convolutional neural networks (CNN) which are used primarily for classification and Recurrent neural networks which are utilized for time-series predictions. In this paper, we predict energy generated by solar fields and optimal angles for solar panels in these farms for the upcoming seven days using environmental and historical data. We experiment with multiple configurations of RNN using Vanilla and LSTM (Long Short-Term Memory) RNN. We are able to achieve RSME of 0.20739 using LSTMs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.68-74
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• 2014

Recent research on mild cognitive impairment (MCI) has shown that cognitive and memory decline in this disease is accompanied by disruptive changes in the brain functional network. However, there have been no graph-theoretical studies using $^{11}C$-PIB PET data of the Alzheimer's Disease or mild cognitive impairment. In this study, we acquired $^{18}F$-FDG PET and $^{11}C$-PIB PET images of twenty-four normal aging control participants and thirty individuals with MCI from ADNI (Alzheimer's Disease Neuroimaging Initiative) database. Brain networks were constructed by thresholding binary correlation matrices using graph theoretical approaches. Both normal control and MCI group showed small-world property in $^{11}C$-PIB PET images as well as $^{18}F$-FDG PET images. $^{11}C$-PIB PET images showed significant difference between NC (normal control) and MCI over large range of sparsity values. This result will enable us to further analyze the brain using established graph-theoretical approaches for $^{11}C$-PIB PET images.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.169-185
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• 2017

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.2
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• pp.68-72
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• 2009

The suffix array is a data structure storing all suffixes of a string in lexicographical order. It is widely used in string problems instead of the suffix tree, which uses a large amount of memory space. Many researches have shown that not only the suffix array can be built in O(n), but also it can be constructed with a small time and space usage for real-world inputs. In this paper, we analyze a practical suffix sorting algorithm due to Maniscalco and Puglisi [1], and we propose an efficient algorithm which improves Maniscalco-Puglisi's running time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.105-110
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• 2022

Domestically, we are capable of designing high-end memory semiconductors, but not in processors, resulting in unbalance. Using Vivado as a development enivronment and implementing the processor on a Xilinx FPGA reduces time and cost dramatically. In this paper, the popular language VHDL which is widely used in Europe, universities, and research centers around the world for the digital system design is used for designing a pipelined 32-bit ARM processor, implemented on FPGA and verified by Integrated Logic Analyzer. As a result, the ARM processor implemented on FPGA could execute ARM instructions successfully.

• Journal of Popular Narrative
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• v.26 no.2
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• pp.87-122
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• 2020

This study explores the method of representation of traumatic history in 2D horror game Detention developed by Taiwan game production company Red Candle through an analysis of its method of storytelling. Unlike conventional public media, video/digital games are based on interactivity, in which game players engage in the narratives. Thus, the relationship between players and the history in the game world may also change. This research investigates how the players build their way of remembering and recognizing the past in a different relationship. Detention, which was well received, not only in Taiwan but also around the world upon its launch in 2017, is set in a middle school during the martial law era in Taiwan in the 1960s. In the game, the main character encounters her lost memories in the process of following clues and game rules, and finally realizes she is implicated in the 1960s' event. Detention was cinematized after the success of the game. The film achieved enormous popularity both in terms of box office success and criticism. In this paper, the strategy of the game's storytelling is introduced in comparison to the film's approach in the representation of historical events. In particular, the paper explores elements such as the interactivity of the game medium, narrative fragmentation, quests, hints and cues, and the horror genre, that asks users to understand history beyond the game world differently from the point of view of other media. Though this study, it can be considered that the digital game is a medium exploring history in a serious manner. In particular, Detention invokes the matter of game-mnemonics as well as cine-mnemonics. Compared to plentiful research in cine-mnemonics, game-mnemonics has not been extensively studied to date. Therefore, through the analysis of Detention, this paper explores the relationship between digital games, history and memory.

• (The)Study of the Eastern Classic
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• no.59
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• pp.93-120
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• 2015

The purpose of this study is to explore the source of appealing which Gusadang Kim Nakhaeng's writing for ancestral rites is equipped with. Gusadang was one of the Confucianists in Yeongnam during the 18th century and was praised for his scholarly virtue of jihaenghapil and silcheongunghaeng. Although Gusadang's writing for ancestral rites and his teacher Milam Lee Jaeui's letters were even specially named as 'gujemilchal', there has been almost no research on Gusadang's writing for ancestral rites yet. Therefore, this study selects three pieces of Gusadang's writing for ancestral rites which are especially rich in emotional expression for discussion. Chapter 2 titled as 'the Reconstruction of Memory in a Microscopic Perspective' presents the reason why Gusadang's writing for ancestral rites is recognized even as a piece of work equipped with appealing. Writing for ancestral rites begins from the point that there exists memory that can be shared by both the living and the dead. In reconstructing the anecdote with the dead on the stage of ritual writing in detail, the writer's memory plays an important role. Chapter 3 titled as 'the Rhetorical Reconstruction of Elevated Sensitivity' examines rhetorical devices needed for writing for ancestral rites. Proper rhetoric is needed to upgrade the dignity of the ritual writing and arouse sympathy from the readers. Although writing for ancestral rites is supposed to express sadness in terms of its formal characteristics, it should not end up being a mere outlet of emotion. Chapter 4 looks into 'the Descriptive Reconstruction of Lamenting Sentiment'. There should be a clear focus of description to make the gesture of the living towards the being not existing in the world any longer an appealing story. While maintaining a distinct way of description, Gusadang organizes the noble character of the dead, pitiable death, the precious bond in the past, and the longing of those left for the dead systematically. Writing for ancestral rites is a field to mourn over the death and reproduce the sadness of the living through writing. To make the text written in that way get to work as ritual writing properly, it should be appealing necessarily. This study has found the fact that such appealing that gives life to ritual writing is grounded on authenticity.

• Journal of Yeungnam Medical Science
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• v.16 no.1
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• pp.25-33
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• 1999

Benzodiazepines(BZDs) are among the most widely prescribed drugs in the world. They are potent anxiolytic, antiepileptic, hypnotic, and muscle relaxing agents. There is an emerging model of the role of several neural systems in anxiety and their relation to the mechanism of action of BZDs. It has been postulated that BZD drugs exert their anxiolytic action by regulating GABAergic transmission in limbic areas such as the amygdala, in the posterior hypothalamus, and in the raphe nuclei. The involvement of the amygdala in the behaviors triggered by fear and stress has been suggested by many previous studies. In this review, reports about regulatory effects of endogenous BZD receptor ligands on the perception of anxiety and memory consolidation were summerized. These findings further support the contention that BZD receptor ligands modulate memory consolidation of averse learning tasks by influencing the level of stress and/or anxiety that accompanies a learning experience. The findings suggest that the decrease in the limbic levels of BZD-like molecules seen after the various behavioral procedures represent a general response to stress and/or anxiety, since it occurs in proportion to the level of stress and/or anxiety that accompany these tasks. In addition, these findings further support the hypothesis that the $GABA_A$/BZD receptor complex in limbic structures plays a pivotal role in the stress and anxiety.