• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.55-61
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• 2009

This paper describes a point of production(POP) system which collects and manages real-time shop floor machining information in a wafer dicing process. The system are composed of POP terminal, line controller and network. In the configuration of the system, LAN and RS485 network are used for connection with the upper management system and down stratum respectively. As a bridge between POP terminal and server, a line controller is used. The real-time information which is the base of production management are collected from information resources such as machine, product and worker. The collected information are used for the calculation of optimal cutting condition. The collection of the information includes cutting speed, spout of pure water, accumulated count of cut in process for blade and wafer defect. In order to manage machining information in wafer dicing process, production planning information is delivered to the shop floor, and production result information is collected from the shop floor, delivered to the server and used for managing production plan. From the result of the system application, production progress status, work and non-working hour analysis for each machine, and wafer defect analysis are available, and they are used for quality and productivity improvements in wafer dicing process. A case study is implemented to evaluate the performance of the system.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.95-104
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• 2009

This paper proposes the noise robust algorithm to detect the starting point of music. Detection of starting point of music is necessary to solve computational-waste problem and retrieval-comparison problem with inconsistent input data in music content based retrieval system. In particular, such detection is even more necessary in time sequential retrieval method that compares data in the sequential order of time in contents based music retrieval system. Whereas it has the long point that the retrieval is fast since it executes simple comparison in the order of time, time sequential retrieval method has the short point that data starting time to be compared should be the same. However, digitalized music cannot guarantee the equity of starting time by bit rate conversion. Therefore, this paper ensured that recognition rate shall not decrease even while executing high speed retrieval by applying time sequential retrieval method through detection of music starting point in the pre-processing stage of retrieval. Starting point detection used minimum wave model that can detect effective sound, and for strength against noise, the noises existing in mute sound were swapped. The proposed algorithm was confirmed to produce about 38% more excellent performance than the results to which starting point detection was not applied, and was verified for the strength against noise.

• Convergence Security Journal
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• v.22 no.2
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• pp.131-141
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• 2022

Future battlefield domains are expanding to ground, sea, air, space, and cyber, so future military operations are expected to be carried out simultaneously and complexly in various battlefield domains. In addition, the application of convergence technologies that create innovations in all fields of economy, society, and defense, such as artificial intelligence, IoT, and big data, is being promoted. However, since the current Korean military C4I system manages warfighting function DBs in one DB server, the efficiency of combat performance is reduced utilization and distribution speed of data and operation response time. To solve this problem, research is needed on how to apply the 4th industrial revolution technologies such as AI, IoT, 5G, big data, and cloud to the Korean military C4I system, but research on this is insufficient. Therefore, this paper analyzes the problems of the current Korean military C4I system and proposes to apply the 4th industrial revolution technology in terms of operational mission, network and data link, computing environment, cyber operation, interoperability and interlocking capabilities.

• Geomechanics and Engineering
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• v.34 no.4
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• pp.449-459
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• 2023

The design and development of underground nuclear waste repositories should cover the performance evaluation of the different components such as the construction materials because the long term stability will depend on their response to the surrounding conditions. In South Korea, Gyeonju bentonite has been proposed as a candidate to be used as buffer and backfilling material, especially in the form of blocks to speed up the construction process. In this study, various cylindrical samples were prepared with different dry density and water content, and their physical and mechanical properties were analyzed and correlated with X-ray CT observations. The main objective was to characterize the samples and establish correlations for non-destructive estimation of physical and mechanical properties through the utilization of X-ray CT images. The results showed that the Uniaxial Compression Strength and the P-wave velocity have an increasing relationship with the dry density. Also, a higher water content increased the values of the measure parameters, especially for the P-wave velocity. The X-ray CT analysis indicated a clear relation between the mean CT value and the dry density, Uniaxial Compression Strength, and P-wave velocity. The effect of the higher water content was also captured by the mean CT value. Also, the relationship between the mean CT value and the dry density was used to plot CT dry densities using CT images only. Moreover, the histograms also provided information about the samples heterogeneity through the histograms' full width at half maximum values. Finally, the particle size and heterogeneity were also analyzed using the Madogram function. This function identified small particles in uniform samples and large particles in some samples as a result of poor mixing during preparation. Also, the μ_max value correlated with the heterogeneity, and higher values represented samples with larger ranges of CT values or particle densities. These image-based tools have been shown to be useful on the non-destructive characterization of bentonite samples, and the establishment of correlations to obtain physical and mechanical parameters solely from CT images.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

In order to increase the use of electric vehicles (EVs) and minimize grid strain, microgrid using renewable energy must take an important role. Microgrid may use fossil fuels such as small diesel power, but in many cases, they can be supplied with energy from renewable energy, which is an eco-friendly energy source. However, renewable energy such as solar and wind power have variable output characteristics. Therefore, in order to meet the charging and discharging energy demands of electric vehicles and at the same time supply load power stably, it is necessary to review the configuration of electric vehicle charging infrastructure that utilizes diesel power or electric vehicle-to-grid (V2G) as a parallel energy source in the microgrid. Against this background, this study modelized a microgrid that can stably supply power to loads using solar power, wind power, diesel power, and V2G. The proposed microgrid uses solar power and wind power generation as the primary supply energy source to respond to power demand, and determines the operation type of the load's electric vehicles and the rotation speed of the load synchronous machine to provide stable power from diesel power for insufficient generations. In order to verify the system performance of the proposed model, we studied the stable operation plan of the microgrid by simulating it with MATLAB /Simulink.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.195-208
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• 2023

The nodular diaphragm wall (NDW) is a novel type of foundation with favorable engineering characteristics, which has already been utilized in high-rise buildings and high-speed railways. Compared to traditional diaphragm walls, the NDW offers significantly improved vertical bearing capacity due to the presence of nodular parts while reducing construction time and excavation work. Despite its potential, research on the vertical bearing characteristics of NDW requires further study, and the investigation and visualization of its displacement pattern and failure mode are scant. Meanwhile, the measurement of the force component acting on the nodular parts remains challenging. In this paper, the vertical bearing characteristics of NDW are studied in detail through the indoor model test, and the displacement and failure mode of the foundation is analyzed using particle image velocimetry (PIV) technology. The principles and methods for monitoring the force acting on the nodular parts are described in detail. The research results show that the nodular part plays an essential role in the bearing capacity of the NDW, and its maximum load-bearing ratio can reach 30.92%. The existence of the bottom nodular part contributes more to the bearing capacity of the foundation compared to the middle nodular part, and the use of both middle and bottom nodular parts increases the bearing capacity of the foundation by about 9~12% compared to a single nodular part of the NDW. The increase in the number of nodular parts cannot produce a simple superposition effect on the resistance born by the nodular parts since the nodular parts have an insignificant influence on the exertion and distribution of the skin friction of NDW. The existence of the nodular part changes the displacement field of the soil around NDW and increases the displacement influence range of the foundation to a certain extent. For NDWs with three different nodal arrangements, the failure modes of the foundations appear to be local shear failures. Overall, this study provides valuable insights into the performance and behavior of NDWs, which will aid in their effective utilization and further research in the field.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.34 no.1
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• pp.1-7
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• 2024

β-Ga₂O₃ is a material with a wide band gap of ~4.8 eV and a high breakdown-voltage of 8 MV/cm, and is attracting much attention in the field of power device applications. In addition, compared to representative WBG semiconductor materials such as SiC, GaN and Diamond, it has the advantage of enabling single crystal growth with high growth rate and low manufacturing cost [1-4]. In this study, we succeeded in growing a 10 mm thick β-Ga₂O₃ single crystal doped with 0.3 mol% SnO₂ through the EFG (Edge-defined Film-fed Growth) method using multi-slit structure. The growth direction and growth plane were set to [010]/(010), respectively, and the growth speed was about 12 mm/h. The grown β-Ga₂O₃ single crystal was cut into various crystal planes (010, 001, 100, ${\bar{2}}01$) and surface processed. The processed samples were compared for characteristics according to crystal plane through analysis such as XRD, UV/VIS/NIR/Spec., Mercury Probe, AFM and Etching. This research is expected to contribute to the development of power semiconductor technology in high-voltage and high-temperature applications, and selecting a substrate with better characteristics will play an important role in improving device performance and reliability.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.250-258
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• 2007

Current speech recognition technology s achieved high performance with the development of hardware devices, however it is insufficient for some applications where high reliability is required, such as voice control of powered wheelchairs for disabled persons. For the system which aims to operate powered wheelchairs safely by voice in real environment, we need to consider that non-voice commands such as user s coughing, breathing, and spark-like mechanical noise should be rejected and the wheelchair system need to recognize the speech commands affected by disability, which contains specific pronunciation speed and frequency. In this paper, we propose non-voice rejection method to perform voice/non-voice classification using both YIN based fundamental frequency(F0) extraction and reliability in preprocessing. We adopted a multi-template dictionary and acoustic modeling based speaker adaptation to cope with the pronunciation variation of inarticulately uttered speech. From the recognition tests conducted with the data collected in real environment, proposed YIN based fundamental extraction showed recall-precision rate of 95.1% better than that of 62% by cepstrum based method. Recognition test by a new system applied with multi-template dictionary and MAP adaptation also showed much higher accuracy of 99.5% than that of 78.6% by baseline system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.