• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.649-655
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• 2021

This study purposes to cross-validate its performance by applying the optimal seismic vulnerability assessment model based on previous studies conducted in Gyeongju to other regions. The test area was Pohang City, the occurrence site for the 2017 Pohang Earthquake, and the dataset was built the same influencing factors and earthquake-damaged buildings as in the previous studies. The validation dataset was built via random sampling, and the prediction accuracy was derived by applying it to a model based on a random forest (RF) of Gyeongju. The accuracy of the model success and prediction in Gyeongju was 100% and 94.9%, respectively, and as a result of confirming the prediction accuracy by applying the Pohang validation dataset, it appeared as 70.4%.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.27-32
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• 2020

In this paper, we propose an algorithm to measure the accumulated status of scrap boxes where metal scraps are accumulated. The accumulated status measuring is defined as a multi-class classification problem, and the method with deep learning classify the accumulated status using only the scrap box image. The learning was conducted by the Transfer Learning method, and the deep learning model was NASNet-A. In order to improve the accuracy of the model, we combined the Random Forest classifier with the trained NASNet-A and improved the model through post-processing. Testing with 4,195 data collected in the field showed 55% accuracy when only NASNet-A was applied, and the proposed method, NASNet with Random Forest, improved the accuracy by 88%.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.319-326
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• 2020

PRNGs(Pseudorandom number generators) are essential for generating encryption keys for to secure online communication. A bitstream generated by the PRNG must be generated at high speed to encrypt the big data effectively in a symmetric key cryptosystem and should ensure the randomness of the level to pass through the several statistical tests. CA(Cellular Automata) based PRNGs are known to be easy to implement in hardware and to have better randomness than LFSR based PRNGs. In this paper, we design PRNGs based on PMLCA(Programable Maximum Length CA) that can generate effective key sequences in symmetric key cryptosystem. The proposed PRNGs generate bit streams through nonlinear control method. First, we design a PRNG based on an (m,n)-cell PMLCA ℙ with a single complement vector that produces linear sequences with the long period and analyze the period and the generating polynomial of ℙ. Next, we design an (m,n)-cell PC-MLCA based PRNG with two complement vectors that have the same period as ℙ and generate nonlinear sequences, and analyze the location of outputting the nonlinear sequence.

• Journal of IKEEE
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• v.24 no.2
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• pp.543-549
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• 2020

As the importance of security increases in various fields, research on a random number generator (RNG) used for generating an encryption key, has been actively conducted. A high-quality RNG is essential to generate a high-performance encryption key, but the initial pseudo-random number generator (PRNG) has the possibility of predicting the encryption key from the outside even though a large amount of hardware resources are required to generate a sufficiently high-performance random number. Therefore, the demand of high-quality true random number generator (TRNG) generating random number through various noises is increasing. This paper examines and compares the representative TRNG methods based on metastable-based and ring-oscillator-based TRNGs. We compare the methods how the random sources are generated in each TRNG and evaluate its performances using NIST SP 800-22 tests.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.7
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• pp.36-45
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• 1999

In order to control the flow of traffics in ATM networks and optimize the usage of network resources, an efficient control mechanism is necessary to cope with congestion and prevent the degradation of network performance caused by congestion. In this paper, Buffered Leaky Bucket which applies the same control scheme to a variety of traffics requiring the different QoS(Quality of Service) and Neural Networks lead to the effective buffer utilization and QoS enhancement in aspects of cell loss rate and mean transfer delay. And the cell scheduling algorithms such as DWRR and DWEDF for multiplexing the incoming traffics are enhanced to get the better fair delay. The network congestion information from cell scheduler is used to control the predicted traffic loss rate of Neural Leaky Bucket, and token generation rate and buffer threshold are changed by the predicted values. The prediction of traffic loss rate by neural networks can enhance efficiency in controlling the cell loss rate and cell transfer delay of next incoming cells and also be applied for other traffic controlling schemes. Computer simulation results performed for random cell generation and traffic prediction show that QoSs of the various kinds of traffcis are increased.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.4
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• pp.315-320
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• 2016

In this study, we developed a response time measuring apparatus for fencing which can use fencing athlete's own electric sword and connectors. The measuring apparatus designed to be connected to upper back of dummy body and it's position can be changed by spinning itself. And it operates like a closed circuit composed of dummy helmet, the apparatus, reel signal connection, connector and electric sword of athlete. This measuring apparatus can provide response time to athlete immediately which response time is measured from the time of indicating random signal of light and sound to the time of attacking dummy by athlete. we carried out pilot test for couch and athlete of national fencing team and could validate availability of this system.

• Journal of IKEEE
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• v.19 no.3
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• pp.385-391
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• 2015

In this paper, we analyze the communication performance in a time synchronous multi-channel environment and deal with a packet collision avoidance technique to improve it based on IEEE1609.4 for increasing the efficiency of the control channel IEEE802.11p WAVE communication system. In previous works, they tried to solve this problem by message scheduling method on application layer software or changing the value of the random back-off optionally Contention Window. In this paper, we propose a method for adjusting the Channel Guard Interval for packet collision avoidance. The performance was evaluated by the actual vehicle test. The result was confirmed performance over 90% PDR(Packet Delivery Ratio).

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1133-1144
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• 2021

Zero-copy is a technology that is also called no-memory copy, and through its use, context switching between the user space and the kernel space can be reduced to minimize the load on the CPU. However, this technology is only used to transmit small random files, and has not yet been widely used for large file transfers. This paper intends to discuss the practical application of zero-copy in processing large files via a network. To this end, we first developed a small test bed and program that can transmit and store data based on zero-copy. Afterwards, we intend to verify the usefulness of the applied technology in detail through detailed performance evaluation

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.661-668
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• 2018

Biomedical signals using skin resistance have different characteristics according to stress diseases. Biological diagnostic devices for diagnosing stress diseases have been developed by using these characteristics, and devices have been developed so that the signals measured by the skin storage meter can be easily analyzed. Experts in the field will look directly at the output signal to determine the likelihood of any stress disorder. However, it is very difficult for a person to accurately determine whether a person to be measured has a stress disorder by analyzing a bio-signal measured by each person to be measured, and the result of the judgment is very likely to be wrong. In order to solve these problems, we implemented the function of determining the signal of a stress disorder by using the machine learning technique. SVM was used as a classification method in consideration of low computing ability of measurement equipment. Training data and test data were randomly generated for each disease using error range 5 based on 13 diseases. Simulation results showed more than 90% decision accuracy. In the future, if the measurement equipment is actually applied to the patients, we can retrain the classifier with the newly generated data.