• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1852-1857
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• 2007

The SSB modem performs the modulation process which converts the digital voltage level to the audible frequency band signal and the demodulation process which converts reversely the audible frequency signal to the digital voltage level. The modulator and the demodulator are implemented with a single DSP chip. Because of the SSB specific character, the distortion occurs when the frequency is changed. This distortion has no effect on voice communication but it has an significant effect on data communication. In other words, it is impossible to send data stream with adjacent 2 periods. Therefore, in case of using 2-tone FSK, it is needed to send at least 3 periods to transmit 1 bit. Therefore we implemented the modem using modified phase-delay shift keying to transmit 1 tone signal for high speed transmission. In the 1200[bps] mode, it generates 0, $187{\mu}s$, delay time at 1.3kHz symbol frequency, and in the 2400[bps] mode, 0, $70{\mu}s\;130{\mu}s\;200{\mu}s$, delay time at 1.5kHz symbol frequency. Finally, in the maximum 3600[bps] mode, it generates 0, $100{\mu}s\;160{\mu}s\;250{\mu}s$ 2.0kHz symbol frequency. The measured results of the implemented SSB modem shows a good transfer functional characteristic by spectrum analyzer, almost same bandwidth in pass band and 20dB higher SNR comparing the emu FACTOR and American CLOVER and in the experimental transmitting test, we verified the transmitted data is received correctly in platform.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Fisheries and Aquatic Sciences
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• v.24 no.3
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• pp.109-120
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• 2021

Protein hydrolysates and krill meal (KM) are used as protein sources in aquafeeds. The study was conducted to examine the supplemental effects of shrimp protein hydrolysates (SH) or KM in a high-plant-protein diet for red seabream (Pagrus major). A fish meal (FM)-based diet (40%) was considered as the high-FM diet (HFM) and a diet containing 25% FM and soy protein concentrate, in the expense of FM protein from HFM diet, was considered as the low fish meal (LFM) diet. Two other experimental diets (SH and KM) were prepared by including SH and KM into LFM diet at 5% inclusion levels in exchange of 5% FM from the LFM diet. A feeding trial was conducted for fifteen weeks using triplicate group of fish (Initial mean body weight, 8.47 ± 0.05 g) for a diet. Growth performance and feed efficiency of fish were significantly enhanced by HFM, KM and SH supplemented diets over those of fish fed LFM diet. Interestingly, these parameters of fish fed SH diet showed better performance than KM and HFM groups. Liver IGF-I expression of fish fed SH diet was comparable to HFM group and higher than KM and LFM diets. Protein digestibility of SH diet was significantly higher than KM, HFM, and LFM diets. Dry matter digestibility of SH diet was comparable to HFM diet and significantly higher than KM and LFM diets. Nitro blue tetrazolium and superoxide dismutase activities of HFM, SH and KM groups were significantly elevated than the LFM group and SH diet increased catalase and glutathione peroxidase activities of fish compared to KM and LFM groups. Hemoglobin level and hematocrit of fish fed SH and KM diets were significantly higher than LFM group. A diet containing 20% FM with KM is comparable to a HFM diet which contains 40% FM for red seabream. SH can be used to replace FM from red seabream diet down to 20% and fish performance can be improved better than a diet containing 40% FM. Overall, it seems that SH is more effective ingredient in red seabream diet compared to KM.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.403-410
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• 2022

Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.

• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of Platform Technology
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• v.11 no.2
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• pp.54-65
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• 2023

Due to the nature of electric vehicles, the batteries used for electric vehicles have a very large rated capacity. If an electric vehicle runs for a long time or an electric vehicle is abandoned due to a traffic accident, the electric vehicle battery becomes a waste battery. Even in vehicles that are being abandoned, the remaining capacity of waste batteries for electric vehicles is sufficient for other purposes. Waste batteries for automobiles are very expensive, so they need to be recycled and reused, but there was a problem that the standards for measuring the performance grade of waste batteries for recycling and reuse were insufficient. As a method for measuring the remaining capacity of waste battery, the most stable and reliable method is to measure the remaining capacity of battery using full charge and discharge. However, the inspection method by the full charging and discharging method varies depending on the capacity of the battery, but it takes more than a day to inspect, and many people are making great efforts to solve this problem. In this paper, an electric vehicle battery residual capacity analysis technique using voltage deviation between cells was studied and analyzed as a method to reduce inspection time for electric vehicle batteries. To this end, a full charging and discharging-based capacity measurement system was constructed, experimental data were collected using a nose or waste battery, and the correlation between the voltage deviation and the remaining capacity of the battery pack was analyzed to verify whether it can be used for battery inspection.

• Journal of Platform Technology
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• v.11 no.5
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• pp.59-71
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• 2023

This paper proposes a method of estimating the illumination chromaticity of a scene in which an image is taken. Storring and Bianco proposed a method of estimating illuminant chromaticity using skin color. Storring et al. used skin color distribution characteristics and black body locus, but there is a problem that the link between the locus and CIE-xy data is reduced. Bianco et al. estimated the illuminant chromaticity by comparing the skin color distribution in standard lighting with the skin color distribution in the input image. This method is difficult to measure and secure as much skin color as possible in various illumination. The proposed method can estimate the illuminant chromaticity for any input image by analyzing the relationship between the skin color information and the illuminant chromaticity. The estimation method is divided into an analysis stage and a test stage, and the data set was classified into an analysis group and a test group and used. Skin chromaticity is calculated by obtaining skin color areas from all input images of the analysis group, respectively. A mapping is obtained by analyzing the correlation between the average set of skin chromaticity and the reference illuminant chromaticity set. The calculated mapping is applied to all input images of the analysis group to estimate the illuminant chromaticity, calculate the error with the reference illuminant chromaticity, and repeat the above process until there is no change in the error to obtain a stable mapping. The obtained mapping is applied to the test group images similar to the analysis stage to estimate the illuminant chromaticity. Since there is no independent data set containing skin area and illuminant reference information, the experimental data set was made using some of the images of the Intel TAU data set. Compared to Finlayson, a similar theory-based existing method, it showed performance improvement of more than 40%, Zhang 11%, and Kim 16%.

• Journal of Platform Technology
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• v.11 no.5
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• pp.72-83
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• 2023

ChatGPT, commercial launch in late 2022, has shown successful results in various professional exams, including US Bar Exam and the United States Medical Licensing Exam (USMLE), demonstrating its ability to pass qualifying exams in professional domains. However, further experimentation and analysis are required to assess ChatGPT's scholastic capability, such as logical inference and problem-solving skills. This study evaluated ChatGPT's scholastic performance utilizing the Korean College Scholastic Ability Test (KCSAT) subjects, including Korean, English, and Mathematics. The experimental results revealed that ChatGPT achieved a relatively high accuracy rate of 69% in the English exam but relatively lower rates of 34% and 19% in the Korean Language and Mathematics domains, respectively. Through analyzing the results of the Korean language exam, English exams, and TOPIK II, we evaluated ChatGPT's strengths and weaknesses in comprehension and logical inference abilities. Although ChatGPT, as a generative language model, can understand and respond to general Korean, English, and Mathematics problems, it is considered weak in tasks involving higher-level logical inference and complex mathematical problem-solving. This study might provide simple yet accurate and effective evaluation criteria for generative artificial intelligence performance assessment through the analysis of KCSAT scores.

• Trans-
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• v.4
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• pp.37-63
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• 2018

Dance films, which started together as a combination of dance and video technology, have evolved into a new genre. Overseas, from early 1950s, experimental dance films have been produced and the dance film association is organized along with many dance film festivals are being held. However, it has not been long since the perception and creativity of the genre of dance films in Korea. Under these circumstances, holding of the 'Seoul Dance Film Festival' and experimental approaches by 'Dance Film Project' are important for the development of domestic dance films. Therefore, this paper explores the concept and development of dance films, the status and features of various overseas dance film festivals and the 'Seoul Dance Film Festival'. This paper also explores the roll and function of 'Seoul Dance Film Festival' and 'Dance Film Project', and their expected benefit along with possibilities of prosperity of domestic dance film. 'Dance Film Project' is the educational and experimental venue for producing of dance films, and the 'Seoul Dance Film Festival' is a platform for producing dance film makers, show of works, dialogue with audiences, international exchanges, and distributions. The dance film festival embodies the value of the past and current flow of the dance film and is intrinsic to the existence of a live content that can be predicted the future aspect of its roll. The two groups mutual growth and development are expected to play a positive role in the development of domestic dance films.