• Journal of IKEEE
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• v.22 no.4
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• pp.1012-1018
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• 2018

In this paper, CMOS SAR (Successive Approximation Register) A/D converter with 1.8V supply voltage is designed for IoT sensor processing. This paper proposes design of a 12-bit SAR A/D converter with two A / D converters in parallel to improve the sampling rate. A/D converter1 of the two A/D converters determines all the 12-bit bits, and another A/D converter2 uses the upper six bits of the other A/D converters to minimize power consumption and switching energy. Since the second A/D converter2 does not determine the upper 6 bits, the control circuits and SAR Logic are not needed and the area is minimized. In addition, the switching energy increases as the large capacitor capacity and the large voltage change in the C-DAC, and the second A/D converter does not determine the upper 6 bits, thereby reducing the switching energy. It is also possible to reduce the process variation in the C-DAC by proposed structure by the split capacitor capacity in the C-DAC equals the unit capacitor capacity. The proposed SAR A/D converter was designed using 0.18um CMOS process, and the supply voltage of 1.8V, the conversion speed of 10MS/s, and the Effective Number of Bit (ENOB) of 10.2 bits were measured. The area of core block is $600{\times}900um^2$, the total power consumption is $79.58{\mu}W$, and the FOM (Figure of Merit) is 6.716fJ / step.

• Journal of IKEEE
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• v.23 no.1
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• pp.35-43
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• 2019

A capacitor self-calibration is proposed to improve the linearity of the capacitor digital-to-analog converter (CDAC) for an asynchronous successive approximation register (SAR) analog-to-digital converter (ADC) with 10-bit resolution. The proposed capacitor self-calibration is performed so that the value of each capacitor of the upper 5 bits of the 10-bit CDAC is equal to the sum of the values of the lower capacitors. According to the behavioral simulation results, the proposed capacitor self-calibration improves the performances of differential nonlinearity (DNL) and integral nonlinearity (INL) from -0.810/+0.194 LSBs and -0.832/+0.832 LSBs to -0.235/+0.178 LSBs and -0.227/+0.227 LSBs, respectively, when the maximum capacitor mismatch of the CDAC is 4%. The proposed 10-bit 20-MS/s asynchronous SAR ADC is implemented using a 110-nm CMOS process with supply of 1.2 V. The area and power consumption of the proposed asynchronous SAR ADC are $0.205mm^2$ and 1.25 mW, respectively. The proposed asynchronous SAR ADC with the capacitor calibration has a effective number of bits (ENOBs) of 9.194 bits at a sampling rate of 20 MS/s about a $2.4-V_{PP}$ differential analog input with a frequency of 96.13 kHz.

• Smart Structures and Systems
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• v.24 no.6
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• pp.769-781
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• 2019

Dynamic displacement response of civil structures is an important index for in-construction and in-service structural condition assessment. However, accurately measuring the displacement of large-scale civil structures such as high-rise buildings still remains as a challenging task. In order to cope with this problem, a vision-based system with the use of industrial digital camera and image processing has been developed for long-distance, remote, and real-time monitoring of dynamic displacement of supertall structures. Instead of acquiring image signals, the proposed system traces only the coordinates of the target points, therefore enabling real-time monitoring and display of displacement responses in a relatively high sampling rate. This study addresses the in-situ experimental verification of the developed vision-based system on the Canton Tower of 600 m high. To facilitate the verification, a GPS system is used to calibrate/verify the structural displacement responses measured by the vision-based system. Meanwhile, an accelerometer deployed in the vicinity of the target point also provides frequency-domain information for comparison. Special attention has been given on understanding the influence of the surrounding light on the monitoring results. For this purpose, the experimental tests are conducted in daytime and nighttime through placing the vision-based system outside the tower (in a brilliant environment) and inside the tower (in a dark environment), respectively. The results indicate that the displacement response time histories monitored by the vision-based system not only match well with those acquired by the GPS receiver, but also have higher fidelity and are less noise-corrupted. In addition, the low-order modal frequencies of the building identified with use of the data obtained from the vision-based system are all in good agreement with those obtained from the accelerometer, the GPS receiver and an elaborate finite element model. Especially, the vision-based system placed at the bottom of the enclosed elevator shaft offers better monitoring data compared with the system placed outside the tower. Based on a wavelet filtering technique, the displacement response time histories obtained by the vision-based system are easily decomposed into two parts: a quasi-static ingredient primarily resulting from temperature variation and a dynamic component mainly caused by fluctuating wind load.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.671-679
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• 2020

In order to increase the food self-sufficiency rate, monitoring and analysis of crop conditions in the cultivated area is important, and the existing measurement methods in which agricultural personnel perform measurement and sampling analysis in the field are time-consuming and labor-intensive for this reason inefficient. In order to overcome this limitation, it is necessary to develop an efficient method for monitoring crop information in a small area where many exist. In this study, RGB images acquired from unmanned aerial vehicles and vegetation index calculated using RGB image were applied as deep learning input data to classify complex upland crops in small farmland. As a result of each input data classification, the classification using RGB images showed an overall accuracy of 80.23% and a Kappa coefficient of 0.65, In the case of using the RGB image and vegetation index, the additional data of 3 vegetation indices (ExG, ExR, VDVI) were total accuracy 89.51%, Kappa coefficient was 0.80, and 6 vegetation indices (ExG, ExR, VDVI, RGRI, NRGDI, ExGR) showed 90.35% and Kappa coefficient of 0.82. As a result, the accuracy of the data to which the vegetation index was added was relatively high compared to the method using only RGB images, and the data to which the vegetation index was added showed a significant improvement in accuracy in classifying complex crops.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.87-103
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• 2021

The government is trying to create jobs by providing 160 billion won in 2021 to revitalize youth start-ups, but the number of youth unemployment and potential unemployment is hitting a record high of 1.2 million due to the shock of employment due to COVID-19. Although start-ups are encouraged as an alternative to revitalizing jobs, the success rate of young start-ups is low due to lack of start-up funds and experience. The purpose of this study is to understand the need to diversify start-up education and career education by understanding start-up policies through one-time funding and short-term education. The results of the study on the factors affecting the willingness to start a business were as follows, by sampling 344 students from specialized high schools preparing for employment and 344 young people in their 20s who are seeking jobs. First, among the entrepreneurship subvariables, innovation, autonomy of job value, and desire for economic achievement are significant, and the older the person surveyed, the more positive the perception of the entrepreneur was. Second, as you get older, your will to start a business decreases, and your experience in successful start-up models and start-up education has an impact on your will to start a business. Third, perception of entrepreneurs is a partial medium effect, which indirectly influences the willingness to start a business and directly or indirectly influences the willingness to start a business through the autonomy of job values, the desire to achieve economic and entrepreneurship.

• 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.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.81-90
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• 2020

This paper describes a method of building Korean conversational speech data in the emergency medical domain and proposes an annotation method for the collected data in order to improve speech recognition performance. To suggest future research directions, baseline speech recognition experiments were conducted by using partial data that were collected and annotated. All voices were recorded at 16-bit resolution at 16 kHz sampling rate. A total of 166 conversations were collected, amounting to 8 hours and 35 minutes. Various information was manually transcribed such as orthography, pronunciation, dialect, noise, and medical information using Praat. Baseline speech recognition experiments were used to depict problems related to speech recognition in the emergency medical domain. The Korean conversational speech data presented in this paper are first-stage data in the emergency medical domain and are expected to be used as training data for developing conversational systems for emergency medical applications.

• Korean Journal of Mineralogy and Petrology
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• v.33 no.4
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• pp.391-405
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• 2020

Leaching chracteristics of Andong-dam sediment was conducted for heavy metal and nutrients. Five mixed sediment samples were prepared and leaching was conducted under aerobic and anaerobic condition for 60 days. Cd, Cu, Pb, Cr, Zn, Hg, As, Fe, Mn, phosphorus, and nitrogen were analyzed at each sampling time. The leaching rate of phosphorus was higher in anaerobic condition comparing with that of under aerobic condition. Some samples showed higher than the water-quality level IV. In case of As and Cd which showed highest contamination level in the sediment, leached concentration were 0.028 mg/L and 0.003 mg/L in maximum, respectively. The leached concentration is below than the lake water quality standard of Korea. Other heavy metals including Cu, Pb, and Cr also showed similar trend. Five step sequential extraction showed that easily extractable 1-2 step portion such as ion-exchangeable and adsorbed one was less than 10% and the most of the portion was residual. For As and Cd, the residual portion were 80% and 95% respectively indicating the risk by the heavy metal leaching into the lake for a short period was not high in comparing with the contamination levels.

• Journal of the Korean Society of School Health
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• v.34 no.3
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• pp.133-150
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• 2021

Purpose: The epidemiologic understanding of allergic diseases, including prevalence and risk factors, is needed for allergy management. This study identified the prevalence of and coping patterns for various types of allergies, such as asthma, allergic rhinitis (AR), atopic dermatitis (AD), allergic conjunctivitis (AC), food allergy (FA), and drug allergy (DA), in Nam-gu, Ulsan, one of the most industrialized districts in Korea. Methods: A cross-sectional study was performed using a self-report multiple choice questionnaire. The International Study of Asthma and Allergies in Childhood (ISAAC) and other measures were reviewed to develop the questionnaire. Data was collected from May 9th to November 30th of 2018. Descriptive and frequency analyses were performed to apprehend the prevalence of symptoms, diagnostic rates, diagnosis dates, treatment history, and others. Results: By randomized sampling, 9,102 children from 25 nurseries, 25 kindergartens, and 15 elementary schools participated in the study. The prevalence of asthma, AR, AD, AC, FA, and DA were 11.1%, 48.7%, 15.5%, 17.6%, 15.1%, and 2.0%, respectively. The respective rate of ever being diagnosed with asthma, AR, AD, AC, FA, and DA were 4.2%, 38.4%, 25.5%, 23.3%, 9.7%, and 1.1%, respectively. Last year's allergy related school absenteeism rates were 10.8%, 4.1%, and 1.3% for asthma, AR, and AD, respectively. Lastly, 59.2% of the participants considered air pollution as the most challenging factor in allergy management. Conclusion: This study comprehensively investigated the current state of various allergic diseases in Nam-gu, Ulsan. The study's findings are expected to be applied to strategies for decreasing the socioeconomic burden of allergic diseases.