• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.2
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• pp.1-11
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• 1997

We developed a base station modulator ASIC for CDMA digital cellular system. In CDMA digital cellular system, the modulation is performed by convolutional encoding and QPSK with spread spectrum. The function blocks of base station modulator are CRC, convolutional encoder, interleaver pseudo-moise scrambler, power control bit puncturing, walsh cover, QPSK, gain controller, combiner and multiplexer. Each function block was designed by the logic synthesis of VHDL codes. The VHDL code was described at register transfer level and the size of code is about 8,000 lines. The circuit simulation and logic simulation were performed by COMPASS tools. The chip (ES-C2212B CMB) contains 25,205 gates and 3 Kbit SRAM, and its chip size is 5.25 mm * 5,45 mm in 0.8 mm CMOS cell-based design technology. It is packaged in 68 pin PLCC and the power dissipation at 10MHz is 300 mW at 5V. The ASIC has been fully tested and successfully working on the CDMA base station system.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.730-738
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• 1992

This paper alms to analyse the performance of frequency hopping /spread spectrum multiple access system by employing the channel with mixture of AWGN, partial band Jamming, fading and user interference. The performance analysis of FH /SSMA system, taking account of frequency 'hit'(user Interference ) which occurs in the presence of multiple user, produces the following numerical results by computing error probability and throughput of each code in two cases whether the side Information about channel is used or not. The numerical results are as follows : When composite interferences coexist In channel, RS code Is significantly superior to convolutional code in terms of performance. Concatenated code provides the same performance as RS code. The above results show that RS code is pertinent as error-correction code.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.165-172
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• 2019

Machine-learning techniques have been actively employed to information security in recent years. Traditional rule-based security solutions are vulnerable to advanced attacks due to unpredictable behaviors and unknown vulnerabilities. By employing ML techniques, we are able to develop intrusion detection systems (IDS) based on anomaly detection instead of misuse detection. Moreover, threshold issues in anomaly detection can also be resolved through machine-learning. There are very few datasets for network intrusion detection compared to datasets for malicious code. KDD CUP 99 (KDD) is the most widely used dataset for the evaluation of IDS. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD. In this work, we develop an IDS model using CSE-CIC-IDS 2018, a dataset containing the most up-to-date common network attacks. We employ deep-learning techniques and develop a convolutional neural network (CNN) model for CSE-CIC-IDS 2018. We then evaluate its performance comparing with a recurrent neural network (RNN) model. Our experimental results show that the performance of our CNN model is higher than that of the RNN model when applied to CSE-CIC-IDS 2018 dataset. Furthermore, we suggest a way of improving the performance of our model.

• Journal of Digital Contents Society
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• v.7 no.3
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• pp.169-174
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• 2006

In recent years, RFID is widely used in industrial applications including factory, material flow, logistics and defense areas. In this paper, The convolutional encoding and viterbi decoding is also implemented to improve the system performance. in an FPGA chip. The used convolution code is constraint length K=3 and rate R=1/2. The length of command frame and response frame is total of 48bits consisting of SOF 8 bits, command 16 bits, CRC 16 bit, and EOF 8 bits. And also the frame error rates are measured under the channel of line-of-sight and non line-of-sight, respectively. The performances are analyzed with FSK modulation only and FSK modulation added with convolutional encoding. These two measured results are compared with that of a RFID system with ASK modulation.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.76-82
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• 2017

Malware, including ransomware to quickly detect, in this study, to provide an analysis method of malicious code through the image analysis that has been learned in the deep learning of artificial intelligence. First, to analyze the 2,400 malware data, and learning in artificial neural network Convolutional neural network and to image data. Extracts subgraphs to convert the graph of abstracted image, summarizes the set represent malware. The experimentally analyzed the malware is not how similar. Using deep learning of artificial intelligence by classifying malware and It shows the possibility of accurate malware detection.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.205-208
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• 2001

According to the IMT-2000 specification of 3GPP(3rd Generation Partnership Project) and 3GPP2, Turbo codes is selected as a FEC(forward error correction) code for even higher reliable data communication. In 3GPP complied IMT-2000 system, channel coding under consideration is the selective use of convolutional coding and Turbo codes of 1/3 code rate with 4 constraint length. Suggesting a new path metric normalization method, we achieved a low complexity and high performance SOVA decoder for Turbo Codes, Further more, we analyze the decoding performance with respect to update depth and find out the optimal value of it by using computer simulation. Based on the simulation result, we designed a SOVA decoder using VHDL and implemented it into the Altera EPF10K100GC503FPGA.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.261-269
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• 2016

This paper aims to propose a novel technology of network convergence to provide ultra high speed internet services over CATV networks, by which a CMC(cable modem concentrator) and CM(cable modem) of 1Gbps level are designed. This technology not only lowers the production cost in comparison to the existing bonding technology with DOCSIS specification but also enables the adjustment of data speed based on the channel bandwidth. According to the experiments, when convolutional code rate with 128QAM is changed to 1/2, 2/3, 3/4 and 7/8, the data recorded the maximum transmission speed of up to 299 Mbps at the zero error rate. As the convolutional code rates with 256QAM is increased, it showed 334Mbps at the error rate of $10^{-5}$. Based on the findings of this paper, if we secure the channel bandwidth of 200MHz and adjust the modulation order of QAM and the convolution code rate depending on the channel status, we can get the transmission speed of more than 1Gbps, which is much more competitive in its function and price than the existing technology based on DOCSIS.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.1
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• pp.45-49
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• 2016

The performance of underwater acoustic communication systems is sensitive to inter-symbol interference caused by delay spread developed from multipath signal propagation. The multipath nature of underwater channels causes signal distortion and error floor. In order to improve the performance, it is necessary to employ an iterative coding scheme. Of the various iterative coding schemes, turbo code and convolutional code based on the BCJR algorithm have recently dominated this application. In this study, the performance of iterative codes based on turbo equalizers with equivalent coding rates and similar code word lengths were analyzed. Underwater acoustic communication system experiments using these two coding techniques were conducted on Kyeong-chun Lake in Munkyeong City. The distance between the transmitter and receiver was 400 m, and the data transfer rate was 1 Kbps. The experimental results revealed that the performance of turbo codes is better for channeling than that of convolutional codes that use a BCJR decoding algorithm.