• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.691-708
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• 2022

As the technology of machine learning and deep learning became common, it began to be applied to research on anomaly(abnormal) detection of industrial control systems. In Korea, the HAI dataset was developed and published to activate artificial intelligence research for abnormal detection of industrial control systems, and an AI contest for detecting industrial control system security threats is being conducted. Most of the anomaly detection studies have been to create a learning model with improved performance through the ensemble model method, which is applied either by modifying the existing deep learning algorithm or by applying it together with other algorithms. In this study, a study was conducted to improve the performance of anomaly detection with a post-processing method that detects abnormal data and corrects the labeling results, rather than the learning algorithm and data pre-processing process. Results It was confirmed that the results were improved by about 10% or more compared to the anomaly detection performance of the existing model.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.182-189
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• 2023

CT is a medical device that acquires medical images based on Attenuation coefficient of human organs related to X-rays. In addition, using this theory, it can acquire sagittal and coronal planes and 3D images of the human body. Then, CT is essential device for universal diagnostic test. But Exposure of CT scan is so high that it is regulated and managed with special medical equipment. As the special medical equipment, CT must implement quality control. In detail of quality control, Spatial resolution of existing phantom imaging tests, Contrast resolution and clinical image evaluation are qualitative tests. These tests are not objective, so the reliability of the CT undermine trust. Therefore, by applying an artificial intelligence classification model, we wanted to confirm the possibility of quantitative evaluation of the qualitative evaluation part of the phantom test. We used intelligence classification models (VGG19, DenseNet201, EfficientNet B2, inception_resnet_v2, ResNet50V2, and Xception). And the fine-tuning process used for learning was additionally performed. As a result, in all classification models, the accuracy of spatial resolution was 0.9562 or higher, the precision was 0.9535, the recall was 1, the loss value was 0.1774, and the learning time was from a maximum of 14 minutes to a minimum of 8 minutes and 10 seconds. Through the experimental results, it was concluded that the artificial intelligence model can be applied to CT implements quality control in spatial resolution and contrast resolution.

• The Journal of the Acoustical Society of Korea
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• v.26 no.7
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• pp.328-333
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• 2007

In this paper, we describe characteristics of a movable bridge called the Anjok and propose an improved string model which has delay line controller in physical modeling of the Gayageum. Movable bridge, the Anjok determines the length of vibrating string and transmits the vibration of each string to the body of the Gayageum. We analyze the variations in frequency domain and implement the Anjok model as parametric form using the first-order polynomial fitting in logarithmic scale graph, because the length of string changes fundamental frequency. In order to implement the Anjok model, frequency fitting, tension fitting and frequency fitting using leaky integrator are used. The frequency fitting using leaky integrator has the best results among those. Proposed string model with the Anjok model can represent real tuning system of the real Gayageum and the proposed model could synthesize sounds which is similar to original sounds.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.742-757
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• 2001

Since database management systems(DBMSS) have limited lock resources, transactions requesting locks beyond the limit mutt be aborted. In the worst carte, if such transactions are aborted repeatedly, the DBMS can become paralyzed, i.e., transaction execute but cannot commit. Lock escalation is considered a solution to this problem. However, existing lock escalation methods do not provide a complete solution. In this paper, we prognose a new lock escalation method, adaptive lock escalation, that selves most of the problems. First, we propose a general model for lock escalation and present the concept of the unescalatable look, which is the major cause making the transactions to abort. Second, we propose the notions of semi lock escalation, lock blocking, and selective relief as the mechanisms to control the number of unescalatable locks. We then propose the adaptive lock escalation method using these notions. Adaptive lock escalation reduces needless aborts and guarantees that the DBMS is not paralyzed under excessive lock requests. It also allows graceful degradation of performance under those circumstances. Third, through extensive simulation, we show that adaptive lock escalation outperforms existing lock escalation methods. The results show that, compared to the existing methods, adaptive lock escalation reduces the number of aborts and the average response time, and increases the throughput to a great extent. Especially, it is shown that the number of concurrent transactions can be increased more than 16 ~256 fold. The contribution of this paper is significant in that it has formally analysed the role of lock escalation in lock resource management and identified the detailed underlying mechanisms. Existing lock escalation methods rely on users or system administrator to handle the problems of excessive lock requests. In contrast, adaptive lock escalation releases the users of this responsibility by providing graceful degradation and preventing system paralysis through automatic control of unescalatable locks Thus adaptive lock escalation can contribute to developing self-tuning: DBMSS that draw a lot of attention these days.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.8-15
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• 2020

A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.7
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• pp.580-586
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• 2016

The ultra-long stroke engine was developed to generate greater power at lower speeds than previous designs to enhance the propulsion efficiency. The torsional exciting force, on the other hand, was increased significantly. Therefore, it is possible to control the torsional vibration of its shaft system equipped with the fuel efficient ultra-long stroke engine by adopting a damper although the torsional vibration could be controlled adequately by applying tuning and turning wheels on the engine previously. In this paper, the dynamic characteristics of a viscous-spring damper used to control the torsional vibration of the corresponding shaft system are reviewed and then examined to determine what vibration characteristics might be used to optimize the viscous-spring damper. In some cases, operators of eco-ships have recently experienced the problem of delayed RPM acceleration. It has been suggested that the proper measures for controlling the torsional vibration in the shaft system should involve adjusting the design parameters of its damper determined by the optimum damper design theory to avoid the fatigue damage of shafts.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.59-64
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• 2002

In the RFID system using ISM-band, The tag mounted at the object has used the DC power by rectifying the RF signals of the small antenna for operating the micro-controller and memory. The performance of the tag would be reduced because of the second harmonics generated by the nonlinearity of the semiconductor and the spurious signal excited the high order mode of the antenna. This paper has realized the novel type low-pass filter with "the Stub-I type DGS slot structure" to improve the efficiency of the tag by suppressing the harmonics. The optimized frequency character at the pass-band/stop-band has obtained by tuning the stub width and slit width of I type slot. The measured result of the LPF has the cutoff frequency 3.25 GHz, the insertion loss about -0.29~-0.3 dB at pass-band 2.4 GHz~2.5 GHz, the return loss about -27.688~-33.665 dB at pass-band with a good performance, and the suppression character is about -19.367 dB at second harmonics frequency 4.9 GHz. This DGS LPF may be applied the various application as the RFID, WLAN to improve the efficiency of the system by suppressing the harmonics and spurious signals. 

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.755-768
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• 2002

We investigate the effect of the data skew of join attributes on the performance of a pipelined multi-way hash join method, and propose two new hash join methods with load balancing capabilities. The first proposed method allocates buckets statically by round-robin fashion, and the second one allocates buckets adaptively via a frequency distribution. Using hash-based joins, multiple joins can be pipelined so that the early results from a join, before the whole join is completed, are sent to the next join processing without staying on disks. Unless the pipelining execution of multiple hash joins includes some load balancing mechanisms, the skew effect can severely deteriorate system performance. In this paper, we derive an execution model of the pipeline segment and a cost model, and develop a simulator for the study. As shown by our simulation with a wide range of parameters, join selectivities and sizes of relations deteriorate the system performance as the degree of data skew is larger. But the proposed method using a large number of buckets and a tuning technique can offer substantial robustness against a wide range of skew conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.17-25
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• 2017

To resolve the problem of the temperature rise in LED or OLED lighting, until now a thick metal film has been used as a heat-sink. Conventionally, this thick metal film is made by the electroplating method and used as the heat-dissipating plate of the electronic devices. However, nowadays there is increasing need for a Cu metal film with a thickness of several hundred micrometers that can be formed by the dry deposition method. In this work, we designed and fabricated a Cu film deposition system where the heating element is separated from the ceramic crucible, which makes ultra-rapid deposition possible by preventing heat loss. In addition, the resulting induction heating also contributes to the high deposition rate. By tuning the various parameters, we obtained a $100-{\mu}m$ thick Cu film whose heat conductivity is high and whose thickness uniformity is better than 2%, while the deposition rate is as high as $1000{\AA}/s$.