• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.1-12
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• 2013

This paper explores design space of many-core processors for a fire feature extraction algorithm. This paper evaluates the impact of varying the number of cores and memory sizes for the many-core processor and identifies an optimal many-core processor in terms of performance, energy efficiency, and area efficiency. In this study, we utilized 90 samples with dimensions of $256{\times}256$ (60 samples containing fire and 30 samples containing non-fire) for experiments. Experimental results using six different many-core architectures (PEs=16, 64, 256, 1,024, 4,096, and 16,384) and the feature extraction algorithm of fire indicate that the highest area efficiency and energy efficiency are achieved at PEs=1,024 and 4,096, respectively, for all fire/non-fire containing movies. In addition, all the six many-core processors satisfy the real-time requirement of 30 frames-per-second (30 fps) for the algorithm.

• The KIPS Transactions:PartA
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• v.19A no.1
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• pp.51-60
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• 2012

IC(Integrated circuits)card, generally be named smard card, embedded MPU(Micro Processor Unit) of small-size, memory, EEPROM, Card Operating System(COS) and security algorithm. The IC card is used in almost all industry such as a finance(credit, bank, stock etc.), a traffic, a communication, a medical, a electronic passport, a membership management and etc. Recently, a application field of IC card is on the increase by method for payments of T-commerce, as T-commerce is becoming a new growth engine of the broadcating industry by trend of broadcasting and telecommunication convergence, smart mechanization of TV. For example, we can pay in IC credit card(or IC cash card) on T-Commerce. or we can be provided TV banking service in IC cash card such as ATM. However, so far, T-commerce payment services have weakness in security such as storage and disclosure of card information as well as dropping sharply about custom ease because of taking advantage of card information input method using remote control. To solve this problem, This paper developed processing payment module for implementing TV electronic payment system using IC credit card payment standard, EMV.

• The KIPS Transactions:PartA
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• v.16A no.2
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• pp.69-78
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• 2009

One of the most effective way to improve cache performance is to exploit both temporal and spatial locality given by any program executive characteristics. In this paper we present a high performance and low power cache structure with a bank selection mechanism that enhances exploitation of spatial and temporal locality. The proposed cache system consists of two parts, i.e., a main direct-mapped cache with a small block size and a fully associative buffer with a large block size as a multiple of the small block size. Especially, the main direct-mapped cache is constructed as two banks for low power consumption and stores a small block which is selected from fully associative buffer by the proposed bank selection algorithm. By using the bank selection algorithm and three state bits, We selectively extend the lifetime of those small blocks with high temporal locality by storing them in the main direct-mapped caches. This approach effectively reduces conflict misses and cache pollution at the same time. According to the simulation results, the average miss ratio, compared with the Victim and STAS caches with the same size, is improved by about 23% and 32% for Mibench applications respectively. The average memory access time is reduced by about 14% and 18% compared with the he victim and STAS caches respectively. It is also shown that energy consumption of the proposed cache is around 10% lower than other cache systems that we examine.

• The KIPS Transactions:PartA
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• v.16A no.5
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• pp.357-368
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• 2009

Mobile IP storage has been proposed to overcome storage limitation in the flash memory and hard disks. It provides almost capacity-free space for mobile devices over wireless IP networks. However, battery lifetime of the mobile devices is reduced rapidly because of power consumption with continuous use of a WLAN device when multimedia contents are being streamed through the mobile IP storage. This paper proposes an energy-efficient WLAN device power control technique for streaming multimedia contents with the mobile IP storage. The proposed technique consists of a prefetch buffer input/output module, a WLAN device power control module, and a reconfigurable prefetch buffer module. Besides, it adaptively determines the size of the prefetch buffer according to a quality of the multimedia contents, and it dynamically controls the power mode of the WLAN device on the basis of power on-off operations while streaming the multimedia contents. We evaluate the performance of the proposed technique on a PXA270-based mobile device that employs the embedded linux 2.6.11, Intel iSCSI reference codes, and a WLAN device. Extensive experiments reveal that the proposed technique can save the energy consumption of the WLAN device up to 8.5 times with QVGA multimedia contents, as compared with no power control.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.1-12
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• 2010

Recently, processor performance has been improved dramatically. Unfortunately, as the process technology scales down, energy consumption in a processor increases significantly whereas the processor performance continues to improve. Moreover, peak temperature in the processor increases dramatically due to the increased power density, resulting in serious thermal problem. For this reason, performance, energy consumption and thermal problem should be considered together when designing up-to-date processors. This paper proposes three modified filter cache schemes to alleviate the thermal problem in the filter cache, which is one of the most energy-efficient design techniques in the hierarchical memory systems : Bypass Filter Cache (BFC), Duplicated Filter Cache (DFC) and Partitioned Filter Cache (PFC). BFC scheme enables the direct access to the L1 cache when the temperature on the filter cache exceeds the threshold, leading to reduced temperature on the filter cache. DFC scheme lowers temperature on the filter cache by appending an additional filter cache to the existing filter cache. The filter cache for PFC scheme is composed of two half-size filter caches to lower the temperature on the filter cache by reducing the access frequency. According to our simulations using Wattch and Hotspot, the proposed partitioned filter cache shows the lowest peak temperature on the filter cache, leading to higher reliability in the processor.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.640-645
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• 2021

The deep learning based end-to-end TTS system consists of Text2Mel module that generates spectrogram from text, and vocoder module that synthesizes speech signals from spectrogram. Recently, by applying deep learning technology to the TTS system the intelligibility and naturalness of the synthesized speech is as improved as human vocalization. However, it has the disadvantage that the inference speed for synthesizing speech is very slow compared to the conventional method. The inference speed can be improved by applying the non-autoregressive method which can generate speech samples in parallel independent of previously generated samples. In this paper, we introduce FastSpeech, FastSpeech 2, and FastPitch as Text2Mel technology, and Parallel WaveGAN, Multi-band MelGAN, and WaveGlow as vocoder technology applying non-autoregressive method. And we implement them to verify whether it can be processed in real time. Experimental results show that by the obtained RTF all the presented methods are sufficiently capable of real-time processing. And it can be seen that the size of the learned model is about tens to hundreds of megabytes except WaveGlow, and it can be applied to the embedded environment where the memory is limited.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1071-1077
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• 2022

A security SoC that can be used to implement elliptic curve cryptography (ECC) based public-key infrastructures was designed. The security SoC has an architecture in which a hardware accelerator for the elliptic curve digital signature algorithm (ECDSA) is interfaced with the Cortex-A53 CPU using the AXI4-Lite bus. The ECDSA hardware accelerator, which consists of a high-performance ECC processor, a SHA3 hash core, a true random number generator (TRNG), a modular multiplier, BRAM, and control FSM, was designed to perform the high-performance computation of ECDSA signature generation and signature verification with minimal CPU control. The security SoC was implemented in the Zynq UltraScale+ MPSoC device to perform hardware-software co-verification, and it was evaluated that the ECDSA signature generation or signature verification can be achieved about 1,000 times per second at a clock frequency of 150 MHz. The ECDSA hardware accelerator was implemented using hardware resources of 74,630 LUTs, 23,356 flip-flops, 32kb BRAM, and 36 DSP blocks.

• Journal of the Korean Geographical Society
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• v.52 no.1
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• pp.25-53
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• 2017

The ultimate goal of this research is to examine the geographical characteristics of therapeutic spaces where have been appeared in Wa-dong and Gojan-dong, Ansan-si after the disaster of the MV Sewol. As looking into the inside, the aim of the therapeutic spaces, which cover each target group (victims) individually, is various and different because the disaster of the MV Sewol generated various direct and indirect victims requiring healing. The therapeutic spaces are estimated at about 10 organizations and are leaded by private agents predominantly. Furthermore, the therapeutic spaces are located near, but are aside from Danwon high school where many students are reported killed and injured in the incident. And the therapeutic spaces provide simple and repetitive diversions, for example, having a meal, knitting and studying, rather than special programs to restore a broken daily life to the original state. On the basis of such a background, the geographical characteristics of the therapeutic spaces related to the disaster of the MV Sewol can be summarized as follows; first, it seems that target groups accept the therapeutic spaces as the concept of place gradually. Even though most of the therapeutic spaces were suggested by third parties at first, target groups are involved in the management and recollection of their own therapeutic spaces as well as the plan for a future direction now; and consider the therapeutic spaces as exclusive properties. Second, the disaster of the MV Sewol have embedded collective trauma to not only direct victims, but extensive groups such as parents, brothers and sisters, relatives, friends and neighbors as noted earlier. Therefore, the therapeutic spaces support comprehensive target groups; but each therapeutic space is not overlapped each other. However, to solve collective trauma in a local community effectively, the therapeutic spaces are networked closely and build a regular cooperative system. Third, a continuous memory is mentioned as an important point to overcome collective trauma, but some phenomena such as fatigue and conflict with neighbors, out-migrants and a faded atmosphere as time passes act as risk factors in Ansan-si. To keep a continuous memory, the therapeutic spaces attempt the recovery of local communities and devise various events, for example, cultural performances; furthermore, are closely connected with external organizations.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.