• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.35-44
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• 2023

In recent, there are many research cases that analyze trends or research trends with text analysis. When collecting documents by searching for keywords in abbreviations for data analysis, it is necessary to disambiguate abbreviations. In many studies, documents are classified by hand-work reading the data one by one to find the data necessary for the study. Most of the studies to disambiguate abbreviations are studies that clarify the meaning of words and use supervised learning. The previous method to disambiguate abbreviation is not suitable for classification studies of documents looking for research data from abbreviation search documents, and related studies are also insufficient. This paper proposes a method of semi-automatically classifying documents collected by abbreviations by going topic modeling with Non-Negative Matrix Factorization, an unsupervised learning method, in the data pre-processing step. To verify the proposed method, papers were collected from academic DB with the abbreviation 'MSA'. The proposed method found 316 papers related to Micro Services Architecture in 1,401 papers. The document classification accuracy of the proposed method was measured at 92.36%. It is expected that the proposed method can reduce the researcher's time and cost due to hand work.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.334-343
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• 2023

Purpose: In this study, the cavity data found through ground cavity exploration was combined with underground facilities to derive a correlation, and the ground subsidence prediction map was verified based on the AI algorithm. Method: The study was conducted in three stages. The stage of data investigation and big data collection related to risk assessment. Data pre-processing steps for AI analysis. And it is the step of verifying the ground subsidence risk prediction map using the AI algorithm. Result: By analyzing the ground subsidence risk prediction map prepared, it was possible to confirm the distribution of risk grades in three stages of emergency, priority, and general for Busanjin-gu and Saha-gu. In addition, by arranging the predicted ground subsidence risk ratings for each section of the road route, it was confirmed that 3 out of 61 sections in Busanjin-gu and 7 out of 68 sections in Sahagu included roads with emergency ratings. Conclusion: Based on the verified ground subsidence risk prediction map, it is possible to provide citizens with a safe road environment by setting the exploration section according to the risk level and conducting investigation.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.21-30
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• 2024

As the self-driving car market continues to grow, the need for charging infrastructure is growing. However, in the case of a wireless charging system, stability issues are being raised because it requires a large amount of power compared with conventional wired charging. SAE J2954 is a standard for building autonomous vehicle wireless charging infrastructure, and the standard defines a communication method between a vehicle and a power transmission system. SAE J2954 recommends using physical media such as Wi-Fi, Bluetooth, and UWB as a wireless charging communication method for autonomous vehicles to enable communication between the vehicle and the charging pad. In particular, UWB is a suitable solution for indoor and outdoor charging environments because it exhibits robust communication capabilities in indoor environments and is not sensitive to interference. In this standard, the process for building a wireless power transmission system is divided into several stages from the start to the completion of charging. In this study, UWB technology is used as a means of fine alignment, a process in the wireless power transmission system. To determine the applicability to an actual autonomous vehicle wireless power transmission system, experiments were conducted based on distance, and the distance information was collected from UWB. To improve the accuracy of the distance data obtained from UWB, we propose a Single Model and Multi Model that apply machine learning and deep learning techniques to the collected data through a three-step preprocessing process.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.

• Journal of Food Hygiene and Safety
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• v.27 no.2
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• pp.161-168
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• 2012

This study was conducted to evaluate microbial contamination levels of Korea traditional rice cakes such as Sirutteok, Garaetteok and Gyeongdan in the manufacturing process and environment. The microbial contamination levels such as total aerobic bacteria, fungi, coliforms, Escherichia coli, Staphylococcus aureus, Bacillus cereus and Clostridium perfringens of rice cake products were analyzed. The contamination levels of total aerobic bacteria, coliforms, fungi and B. cereus in raw materials were in the range of 2.4~4.5, ND~1.9, 1.2~2.1 and 1.0~2.1 log CFU/g, respectively. The microbial contamination levels of total aerobic bacteria, coliforms, fungi and B. cereus in manufacturing process of rice cakes were increased in the soaking and grinding steps and were decreased in steaming step. E. coli, S. aureus and C. perfringens were not detected in any manufacturing process and environment. The microbial contamination levels of raw materials and final products of rice cake were suitable for microbial safety standard in Korea. However, the manufacturing environment such as equipments and employee's sanitation were in trouble for microbial safety. The results of this study suggest that safety educatio n for personal hygiene and safetymanagement in processing environment are continuously required to assure safety in working environment and employee's individual hygiene.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.573-597
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• 2016

Recent fMRI and EEG research for neural representations of action concepts insist that processing of action concepts evoke the simulation of sensory-motor information. Moreover, there are several behavioral studies showing that understanding of action verbs or sentences describing actions interfere or facilitate current action performance. However, it is unclear that online interaction between processing of action concepts and current action is based on the simulation of sensory-motor information, or other neural mechanisms. The present research aims to explore the underlying neural mechanism that how the perception of action language influence the performance of current action using high-spacial temporal resolution EEG and multiple source analysis techniques. For this, participants were asked to perform a cued-motor reaction task in which button-pressing hand action and pedal-stepping foot action were required according to the color of the cue, and we presented auditorily action verbs describing the responding actions (i.e., /press/, /step/, /stop/) just before the color cue and examined the interaction effect from the semantic congruency between the action verbs and the current action. Behavioral results revealed consistently a facilitatory effect when action verbs and responding actions were semantically congruent in both button-pressing and pedal-stepping actions, and an inhibitory effect when semantically incongruent in the button-pressing action condition. In the results of EEG source waveform analysis, the semantic congruency effects between action verbs and the responding actions were observed in the Wernicke's area during the perception of action verbs, in the anterior cingulate gyrus and the supplementary motor area (SMA) at the time when the motor-cue was presented, and in the SMA and primary motor cortex (M1) during action execution stage. Based on the current findings, we argue that perceived action verbs evoke the facilitation/inhibition effect by influencing the expectation and preparation stage of following actions rather than the directly activating the particular motor cortex. Finally we discussed the implication on the neural representation of action concepts and methodological limitations of the current research.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.975-976
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• 1993

This talk presents the overview of the author's research and development activities on fuzzy inference hardware. We involved it with two distinct approaches. The first approach is to use application specific integrated circuits (ASIC) technology. The fuzzy inference method is directly implemented in silicon. The second approach, which is in its preliminary stage, is to use more conventional microprocessor architecture. Here, we use a quantitative technique used by designer of reduced instruction set computer (RISC) to modify an architecture of a microprocessor. In the ASIC approach, we implemented the most widely used fuzzy inference mechanism directly on silicon. The mechanism is beaded on a max-min compositional rule of inference, and Mandami's method of fuzzy implication. The two VLSI fuzzy inference chips are designed, fabricated, and fully tested. Both used a full-custom CMOS technology. The second and more claborate chip was designed at the University of North Carolina(U C) in cooperation with MCNC. Both VLSI chips had muliple datapaths for rule digital fuzzy inference chips had multiple datapaths for rule evaluation, and they executed multiple fuzzy if-then rules in parallel. The AT & T chip is the first digital fuzzy inference chip in the world. It ran with a 20 MHz clock cycle and achieved an approximately 80.000 Fuzzy Logical inferences Per Second (FLIPS). It stored and executed 16 fuzzy if-then rules. Since it was designed as a proof of concept prototype chip, it had minimal amount of peripheral logic for system integration. UNC/MCNC chip consists of 688,131 transistors of which 476,160 are used for RAM memory. It ran with a 10 MHz clock cycle. The chip has a 3-staged pipeline and initiates a computation of new inference every 64 cycle. This chip achieved an approximately 160,000 FLIPS. The new architecture have the following important improvements from the AT & T chip: Programmable rule set memory (RAM). On-chip fuzzification operation by a table lookup method. On-chip defuzzification operation by a centroid method. Reconfigurable architecture for processing two rule formats. RAM/datapath redundancy for higher yield It can store and execute 51 if-then rule of the following format: IF A and B and C and D Then Do E, and Then Do F. With this format, the chip takes four inputs and produces two outputs. By software reconfiguration, it can store and execute 102 if-then rules of the following simpler format using the same datapath: IF A and B Then Do E. With this format the chip takes two inputs and produces one outputs. We have built two VME-bus board systems based on this chip for Oak Ridge National Laboratory (ORNL). The board is now installed in a robot at ORNL. Researchers uses this board for experiment in autonomous robot navigation. The Fuzzy Logic system board places the Fuzzy chip into a VMEbus environment. High level C language functions hide the operational details of the board from the applications programme . The programmer treats rule memories and fuzzification function memories as local structures passed as parameters to the C functions. ASIC fuzzy inference hardware is extremely fast, but they are limited in generality. Many aspects of the design are limited or fixed. We have proposed to designing a are limited or fixed. We have proposed to designing a fuzzy information processor as an application specific processor using a quantitative approach. The quantitative approach was developed by RISC designers. In effect, we are interested in evaluating the effectiveness of a specialized RISC processor for fuzzy information processing. As the first step, we measured the possible speed-up of a fuzzy inference program based on if-then rules by an introduction of specialized instructions, i.e., min and max instructions. The minimum and maximum operations are heavily used in fuzzy logic applications as fuzzy intersection and union. We performed measurements using a MIPS R3000 as a base micropro essor. The initial result is encouraging. We can achieve as high as a 2.5 increase in inference speed if the R3000 had min and max instructions. Also, they are useful for speeding up other fuzzy operations such as bounded product and bounded sum. The embedded processor's main task is to control some device or process. It usually runs a single or a embedded processer to create an embedded processor for fuzzy control is very effective. Table I shows the measured speed of the inference by a MIPS R3000 microprocessor, a fictitious MIPS R3000 microprocessor with min and max instructions, and a UNC/MCNC ASIC fuzzy inference chip. The software that used on microprocessors is a simulator of the ASIC chip. The first row is the computation time in seconds of 6000 inferences using 51 rules where each fuzzy set is represented by an array of 64 elements. The second row is the time required to perform a single inference. The last row is the fuzzy logical inferences per second (FLIPS) measured for ach device. There is a large gap in run time between the ASIC and software approaches even if we resort to a specialized fuzzy microprocessor. As for design time and cost, these two approaches represent two extremes. An ASIC approach is extremely expensive. It is, therefore, an important research topic to design a specialized computing architecture for fuzzy applications that falls between these two extremes both in run time and design time/cost. TABLEI INFERENCE TIME BY 51 RULES {{{{Time }}{{MIPS R3000 }}{{ASIC }}{{Regular }}{{With min/mix }}{{6000 inference 1 inference FLIPS }}{{125s 20.8ms 48 }}{{49s 8.2ms 122 }}{{0.0038s 6.4㎲ 156,250 }} }}

• Korean Journal of Food Science and Technology
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• v.41 no.4
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• pp.392-398
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• 2009

This study identified risk factors of cross-contamination of foodborne pathogens and established a good agricultural practice (GAP) system for an agricultural products processing center (APC) for perilla leaves. All samples were collected before and after a standard work shift at the APC, while perilla leaves were also collected after each step in the APC. In addition, the workers and their surroundings were sampled by swabbing. The total plate count (TPC) and coliform count in the water samples increased significantly (p<0.05) to 3.36 and 1.73 log CFU/mL after work, respectively. However, no Escherichia coli and Listeria monocytogenes were detected. The bacterial populations of the workers and their surroundings did not differ significantly (p${\geq}$0.05) before and after work. However, Staphylococcus aureus (<1.66 log CFU) was detected at a high rate (13-50%) in the basket, packing table, gloves and cloth. Although perilla leaves passed through the washing steps, the TPC and coliform bacterial populations on the final products were higher (p${\geq}$0.05) than those of unwashed perilla leaves, which indicates that the washing system was not functioning properly. Accordingly, a GAP system with a better washing system should be employed at this facility.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.46-55
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• 2016

This work proposes a 13b 100MS/s 45nm CMOS ADC with a high dynamic performance for IF-domain high-speed signal processing systems based on a four-step pipeline architecture to optimize operating specifications. The SHA employs a wideband high-speed sampling network properly to process high-frequency input signals exceeding a sampling frequency. The SHA and MDACs adopt a two-stage amplifier with a gain-boosting technique to obtain the required high DC gain and the wide signal-swing range, while the amplifier and bias circuits use the same unit-size devices repeatedly to minimize device mismatch. Furthermore, a separate analog power supply voltage for on-chip current and voltage references minimizes performance degradation caused by the undesired noise and interference from adjacent functional blocks during high-speed operation. The proposed ADC occupies an active die area of $0.70mm^2$, based on various process-insensitive layout techniques to minimize the physical process imperfection effects. The prototype ADC in a 45nm CMOS demonstrates a measured DNL and INL within 0.77LSB and 1.57LSB, with a maximum SNDR and SFDR of 64.2dB and 78.4dB at 100MS/s, respectively. The ADC is implemented with long-channel devices rather than minimum channel-length devices available in this CMOS technology to process a wide input range of $2.0V_{PP}$ for the required system and to obtain a high dynamic performance at IF-domain input signal bands. The ADC consumes 425.0mW with a single analog voltage of 2.5V and two digital voltages of 2.5V and 1.1V.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.