• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.611-613
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• 2021

Recently, research and development of quantum computers, which exceed the limits of classical computers, have been actively carried out in various fields. Quantum computers, which use quantum mechanics principles in a way different from the electrical signal processing of classical computers, have various quantum mechanical phenomena such as quantum superposition and quantum entanglement. It goes through a very complicated calculation process compared to the calculation of a classical computer for performing an operation using its characteristics. In order to utilize each element efficiently and accurately, it is necessary to visualize the data before driving the actual quantum computer and perform error verification, optimization, reliability, and verification. However, when visualizing all the data of various elements configured inside the quantum computer, it is difficult to intuitively grasp the necessary data, so it is necessary to visualize the data selectively. In this paper, we visualize the data of various elements that make up a quantum computer, and hierarchically visualize the internal circuit components of a quantum computer that are complicatedly configured so that the data can be observed and utilized intuitively.

• Analytical Science and Technology
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• v.21 no.6
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• pp.443-458
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• 2008

Brominated flame retardants (BFRs) are chemical compounds that inhibit the combustion of organic materials by scavenging free radicals that would otherwise encourage the spread of flames. These compounds are found in a wide variety of materials including paints, plastics, textiles, furniture and electronics. Mounting evidence, however, suggests that the non-reactive BFRs can easily leach into the environment and pose significant environmental and health concerns. PBDDs/PBDFs are often formed in the process of manufacturing brominated flame retardants and from the combustion of waste products containing flame retardants BFR. Therefore, this paper describes the general characteristics, management status, residual concentration in environments and analytical method.

• Journal of IKEEE
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• v.27 no.4
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• pp.644-649
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• 2023

As the cell spacing decreases during the scaling process of DRAM(Dynamic Random Access Memory), the reduction in STI(Shallow Trench Isolation) thickness leads to an increase in sub-threshold leakage due to the passing word line effect. The increase in sub-threshold leakage current caused by the voltage applied to adjacent passing word lines affects the data retention time and increases the number of refresh operations, thereby contributing to higher power consumption in DRAM. In this paper, we identify the causes of the passing word line effect through TCAD Simulation. As a result, we confirm the DRAM operational conditions under which the passing word line effect occurs, and observe that this effect alters the proportion of the total leakage current attributable to different causes. Through this, we recognize the necessity to consider not only leakage currents due to GIDL(Gate Induced Drain Leakage) but also sub-threshold leakage currents, providing guidance for improving DRAM structure.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.338-346
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• 2023

During isometric elbow flexion, forearm length should be an important factor to determine not only joint torque but also maximum endurance time (MET), when the forearm is perpendicular to the direction of the force. The purpose of this paper is to examine the effect of forearm length as an additional factor on empirical models of MET such as an exponential model and a power model during isometric elbow flexion. Thirty volunteers participated in our experiment to measure factor variables such as circumferences and lengths of their upper and lower arms. Their METs were measured according to the percent of maximum voluntary contraction intensity (%MVC). For the multiple linear regression model of ln(MET) using these measurements, significant variables could be observed in %MVC and forearm lengths (P<0.05). The empirical models were assessed by these models using forearm length as the additional factor. Mean absolute deviations (MAD) between the measured METs amd the two empirical models were about 19.4 [s], but MAD using models applied forearm lengths were reduced to about 16.2 [s]. The correlation coefficients and intraclass correlation coefficients were about 0.87, but those applied forearm lengths were increased to about 0.91. These results demonstrated that forearm length was a significant additional factor to the empirical model.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.217-222
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• 2023

The purpose of this paper was to synthesize a polymer organic hard mask that simplifies the manufacturing process, reduces process time significantly, and thereby lowers manufacturing costs. The results of measuring residual metals through vapor refining showed that 9-Naphthalen-1-ylcarbazole(9-NC) measured 101.75ppb in the 4th zone, 2-Naphthol (2-NA) measured 306.98ppb in the 5th zone, and 9-Fluorenone(9-F) measured between 129.05ppb across the 4th and 5th zones. After passing through a filtration system, the synthesized organic hard mask measured residual metals in the range of 9 to 7ppb. Additionally, the thermal analysis indicated a decrease of 2.78%, a molecular weight of 942, carbon content of 89.74%, and a yield of 72.4%. The etching rate was measured at an average of 18.22Å/s, and the coating thickness deviation was averaged at 1.19. For particle sizes below 0.2㎛ in the organic hard mask, no particles were observed. By varying the coating speed at 1,000, 1,500, and 1,800rpm and measuring the resulting coating thickness, the shrinkage rate ranged from 17.9% to 20.8%. The coating results demonstrated excellent adhesion to SiON, and it was evident that the organic hard mask was uniformly applied.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.315-324
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• 2023

In this paper, we propose a real-time pose estimation method for a monocular camera using a particle filter integrated with UKF (unscented Kalman filter). While conventional camera tracking techniques combine camera images with data from additional devices such as gyroscopes and accelerometers, the proposed method aims to use only two-dimensional visual information from the camera without additional sensors. This leads to a significant simplification in the hardware configuration. The proposed approach is based on a particle filter integrated with UKF. The pose of the camera is estimated using UKF, which is defined individually for each particle. Statistics regarding the camera state are derived from all particles of the particle filter, from which the real-time camera pose information is computed. The proposed method demonstrates robust tracking, even in the case of rapid camera shakes and severe scene occlusions. The experiments show that our method remains robust even when most of the feature points in the image are obscured. In addition, we verify that when the number of particles is 35, the processing time per frame is approximately 25ms, which confirms that there are no issues with real-time processing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.331-337
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• 2023

In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.239-246
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• 2023

In this paper, we propose an open-loop algorithm to classify speech and music signals using the spectral flux parameters and Mel Frequency Cepstral Coefficients(MFCC) parameters for the audio coder. To increase responsiveness, the MFCC was used as a short-term feature parameter and spectral fluxes were used as a long-term feature parameters to improve accuracy. The overall voice/music signal classification decision is made by combining the short-term classification method and the long-term classification method. The Gaussian Mixed Model (GMM) was used for pattern recognition and the optimal GMM parameters were extracted using the Expectation Maximization (EM) algorithm. The proposed long-term and short-term combined speech/music signal classification method showed an average classification error rate of 1.5% on various audio sound sources, and improved the classification error rate by 0.9% compared to the short-term single classification method and 0.6% compared to the long-term single classification method. The proposed speech/music signal classification method was able to improve the classification error rate performance by 9.1% in percussion music signals with attacks and 5.8% in voice signals compared to the Unified Speech Audio Coding (USAC) audio classification method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.354-361
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• 2023

Big data analysis systems apply NoSQL databases like MongoDB to store, process, and analyze diverse forms of large-scale data. MongoDB offers scalability and fast data processing speeds through distributed processing and data replication, depending on its configuration. This paper investigates the suitable MongoDB environment configurations for implementing big data analysis systems. For performance evaluation, we configured both single-node and multi-node environments. In the multi-node setup, we expanded the number of data nodes from two to three and measured the performance in each environment. According to the analysis, the processing speeds for complex data structures with three or more dimensions are approximately 5.75% faster in the single-node environment compared to an environment with two data nodes. However, a setting with three data nodes processes data about 25.15% faster than the single-node environment. On the other hand, for simple one-dimensional data structures, the multi-node environment processes data approximately 28.63% faster than the single-node environment. Further research is needed to practically validate these findings with diverse data structures and large volumes of data.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.137-144
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• 2023

Cyberattack technology is evolving to an unpredictable degree, and it is a situation that can happen 'at any time' rather than 'someday'. Infrastructure that is becoming hyper-connected and global due to cloud computing and the Internet of Things is an environment where cyberattacks can be more damaging than ever, and cyberattacks are still ongoing. Even if damage occurs due to external influences such as cyberattacks or natural disasters, intelligent self-recovery must evolve from a cyber resilience perspective to minimize downtime of cyber assets (OS, WEB, WAS, DB). In this paper, we propose an intelligent self-recovery technology to ensure sustainable cyber resilience when cyber assets fail to function properly due to a cyberattack. The original and updated history of cyber assets is managed in real-time using timeslot design and snapshot backup technology. It is necessary to secure technology that can automatically detect damage situations in conjunction with a commercialized file integrity monitoring program and minimize downtime of cyber assets by analyzing the correlation of backup data to damaged files on an intelligent basis to self-recover to an optimal state. In the future, we plan to research a pilot system that applies the unique functions of self-recovery technology and an operating model that can learn and analyze self-recovery strategies appropriate for cyber assets in damaged states.