• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.95-102
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• 2005

The Newton-Raphson iterative algorithm for finding a floating point reciprocal which is widely used for a floating point division, calculates the reciprocal by performing a fixed number of multiplications. In this paper, a variable latency Newton-Raphson's reciprocal algorithm is proposed that performs multiplications a variable number of times until the error becomes smaller than a given value. To find the reciprocal of a floating point number F, the algorithm repeats the following operations: '$'X_{i+1}=X=X_i*(2-e_r-F*X_i),\;i\in\{0,\;1,\;2,...n-1\}'$ with the initial value $'X_0=\frac{1}{F}{\pm}e_0'$. The bits to the right of p fractional bits in intermediate multiplication results are truncated, and this truncation error is less than $'e_r=2^{-p}'$. The value of p is 27 for the single precision floating point, and 57 for the double precision floating point. Let $'X_i=\frac{1}{F}+e_i{'}$, these is $'X_{i+1}=\frac{1}{F}-e_{i+1},\;where\;{'}e_{i+1}, is less than the smallest number which is representable by floating point number. So, $X_{i+1}$ is approximate to $'\frac{1}{F}{'}$. Since the number of multiplications performed by the proposed algorithm is dependent on the input values, the average number of multiplications per an operation is derived from many reciprocal tables $(X_0=\frac{1}{F}{\pm}e_0)$ with varying sizes. The superiority of this algorithm is proved by comparing this average number with the fixed number of multiplications of the conventional algorithm. Since the proposed algorithm only performs the multiplications until the error gets smaller than a given value, it can be used to improve the performance of a reciprocal unit. Also, it can be used to construct optimized approximate reciprocal tables. The results of this paper can be applied to many areas that utilize floating point numbers, such as digital signal processing, computer graphics, multimedia scientific computing, etc.

• Asian Journal of Turfgrass Science
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• v.23 no.2
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• pp.219-228
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• 2009

The construction procedures and artificial turf maintenance program on golf course definitely influence on the distortion of its environment. Soil microbial communities in soil profile were affected directly by those practises on turf areas. In Jeju island, the environmental impact assessment has been required to apply the first quality class granular activated carbon(GAC), which has a high absorbent character to agricultural chemicals, on the soil profiles of golf green system to reduce the pesticide leaching to ground water. This research was carried out to analyze the changes of microbial communities and chemical properties on soil profiles where GAC had been applied at the construction stage at two golf courses in Jeju. The changes of soil microbial population and chemical properties associated with construction methods of soil profile and agrochemical management program were analyzed by monthly at the surface and sub-soil profiles during April through October, 2007. The total numbers of bacteria and fungi, soil moisture content, soil physio-chemical properties were measured on greens and fairways of the both golf courses with different GAC treatment on the green and fairway soil profiles. The results showed that GAC had positive effects on the water holding capacity, pH and EC, however, it did not improved the holding capacity of available nutrients ${NO_3}^-,{NH_4}^+$, and phosphorus by its sorption phenomenon. In microbial count test, the total numbers of bacteria and fungi showed a great variation during sampling dates. That may directly relate to the agrochemical application, however, the ratio of total bacterial number versus total fungus number showed a constant value on a sub-soil of 15~30cm depth. Thus, the construction method of GAC in soil profile, and application of fertilizer and pesticide, both impacted on the changes of microbial population. It's means that the construction method of soil profile and turf management using agro-materials might greatly affect on the turfgrass culture and the environment of golf course.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.29-37
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• 2019

Textile quality management targets the physical properties of fabrics and the subjective discriminations of color and fitting. Color is the most representative quality factor that consumers can use to evaluate quality levels without any instruments. For this reason, quantification using a color discrimination device has been used for statistical quality management in the textile industry. However, small and medium-sized domestic textile manufacturers use only visual inspection for color discrimination. As a result, color discrimination is different based on the inspectors' individual tendencies and work procedures. In this research, we want to develop a textile industry-friendly quality management method, evaluating the possibility of rapid color discrimination using a digital imaging device, which is one of the office-automation instruments. The results show that an imaging process-based color discrimination method is highly correlated with conventional color discrimination instruments ($R^2=0.969$), and is also applicable to field discrimination of the manufacturing process, or for different lots. Moreover, it is possible to recognize quality management factors by analyzing color components, ${\Delta}L$, ${\Delta}a$, ${\Delta}b$. We hope that our rapid discrimination method will be a substitute technique for conventional color discrimination instruments via elaboration and optimization.

• Korean Chemical Engineering Research
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• v.56 no.6
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• pp.871-877
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• 2018

Storing the surplus energy from renewable energy resource is one of the challenges related to intermittent and fluctuating nature of renewable energy electricity production. $CO_2$ methanation is well known reaction that as a renewable energy storage system. $CO_2$ methanation requires a catalyst to be active at relatively low temperatures ($250-500^{\circ}C$) and selectivity towards methane. In this study, the catalytic performance test was conducted using a pressurized bubbling fluidized bed reactor (Diameter: 0.025 m and Height: 0.35 m) with $Ni/{\gamma}-Al_2O_3$ (Ni70%, and ${\gamma}-Al_2O_3$30%) catalyst. The range of the reaction conditions were $H_2/CO_2$ mole ratio range of 4.0-6.0, temperature of $300-420^{\circ}C$, pressure of 1-9 bar, and gas velocity ($U_0/U_{mf}$) of 1-5. As the $H_2/CO_2$ mole ratio, temperature and pressure increased, $CO_2$ conversion increases at the experimental temperature range. However, $CO_2$ conversion decreases with increasing gas velocity due to poor mixing characteristics in the fluidized bed. The maximum $CO_2$ conversion of 99.6% was obtained with the operating condition as follows; $H_2/CO_2$ ratio of 5, temperature of $400^{\circ}C$, pressure of 9 bar, and $U_0/U_{mf}$ of 1.4-3.

• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.737-743
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• 2018

Patient exposure dose exposure test, which is one of the items of accuracy control of Computed Tomography, conducts measurements every year based on the installation and operation of special medical equipment under Article 38 of the Medical Law, And keep records. The CT-Dose phantom used for dosimetry can accurately measure doses, but has the disadvantage of high price. Therefore, through this research, the existing CT - Dose phantom was similarly manufactured with a 3D printer and compared with the existing phantom to examine the usefulness. In order to produce the same phantom as the conventional CT-Dose phantom, a 3D printer of the FFF method is used by using a PLA filament, and in order to calculate the CTDIw value, Ion chambers were inserted into the central part and the central part, and measurements were made ten times each. Measurement results The CT-Dose phantom was measured at $30.44{\pm}0.31mGy$ in the periphery, $29.55{\pm}0.34mGy$ CTDIw value was measured at $30.14{\pm}0.30mGy$ in the center, and the phantom fabricated using the 3D printer was measured at the periphery $30.59{\pm}0.18mGy$, the central part was $29.01{\pm}0.04mGy$, and the CTDIw value was measured at $30.06{\pm}0.13mGy$. Analysis using the Mann - Whiteney U-test of the SPSS statistical program showed that there was a statistically significant difference in the result values in the central part, but statistically significant differences were observed between the peripheral part and CTDIw results I did not show. In conclusion, even in the CT-Dose phantom made with a 3D printer, we showed dose measurement performance like existing CT-Dose phantom and confirmed the possibility of low-cost phantom production using 3D printer through this research did it.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.3
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• pp.1-7
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• 2021

In this paper, by embedding a bare-die chip-type drive amplifier into the PCB composed of ABF and FR-4, it implements an embedded active device that can be applied in 28 GHz band modules. The ABF has a dielectric constant of 3.2 and a dielectric loss of 0.016. The FR-4 where the drive amplifier is embedded has a dielectric constant of 3.5 and a dielectric loss of 0.02. The proposed embedded module is processed into two structures, and S-parameter properties are confirmed with measurements. The two process structures are an embedding structure of face-up and an embedding structure of face-down. The fabricated module is measured on a designed test board using Taconic's TLY-5A(dielectric constant : 2.17, dielectric loss : 0.0002). The PCB which embedded into the face-down expected better gain performance due to shorter interconnection-line from the RF pad of the Bear-die chip to the pattern of formed layer. But it is verified that the ground at the bottom of the bear-die chip is grounded Through via, resulting in an oscillation. On the other hand, the face-up structure has a stable gain characteristic of more than 10 dB from 25 GHz to 30 GHz, with a gain of 12.32 dB at the center frequency of 28 GHz. The output characteristics of module embedded into the face-up structure are measured using signal generator and spectrum analyzer. When the input power (P_in) of the signal generator was applied from -10 dBm to 20 dBm, the gain compression point (P_1dB) of the embedded module was 20.38 dB. Ultimately, the bare-die chip used in this paper was verified through measurement that the oscillation is improved according to the grounding methods when embedding in a PCB. Thus, the module embedded into the face-up structure will be able to be properly used for communication modules in millimeter wave bands.

• Journal of Bio-Environment Control
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• v.28 no.4
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• pp.293-301
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• 2019

The experiment was conducted to determine the performance of DME combustion gas when used as a fuel for DME burner for raising temperature and $CO_2$ concentration in greenhouse and also to examine its effects on chlorophyll content, and fresh and dry weight of lettuce and Chinese cabbage. DME-1 and DME-2 treatments consisted of average DME flow quantity in duct were $17.4m^3min^{-1}$ and $10.2m^3min^{-1}$ respectively to greenhouse-1 and greenhouse-2 and no DME gas was supplied to greenhouse-3 which was left as control (DME-3). DME supply times were $0.5hr\;day^{-1}$, $1hr\;day^{-1}$, $1:30hrs\;day^{-1}$ and $2hrs\;day^{-1}$ on week 1, 2, 3, and 4 respectively. Chlorophyll content and fresh and dry weight of lettuce and Chinese cabbage were measured for each treatment and analyzed through analysis of variance with a significance level of P<0.05. The result of the study showed that $CO_2$ concentration increased up to 265% and 174% and the level of temperature elevated $4.8^{\circ}C$ and $3.1^{\circ}C$ in greenhouse-1 and 2, respectively as compared to greenhouse-3 due to application of DME combustion gas. Although, the same crop management practices were provided in greenhouse-1, 2 and 3 at a same rate, the highest change (p<0.05) of chlorophyll content, fresh weight and dry weight were found from the DME-1 treatment, followed by DME-2. As a result, DME combustion gas that raised the level of temperature and $CO_2$ concentration in the greenhouse-1 and greenhouse-2, might have an effect on growth of lettuce and Chinese cabbage. At end of experiment, the highest fresh and dry weight of lettuce and Chinese cabbage were measured in greenhouse-1 and followed by greenhouse-2. Similarly chlorophyll content of greenhouse-1 and greenhouse-2 were more compared to greenhouse-3. In general, DME was not producing any harmful gas during its combustion period, therefore it can be used as an alternative to conventional fuel such as diesel and liquefied petroleum gas (LPG) for both heating and $CO_2$ supply in winter season. Moreover, endorsed quantify of DME combustion gas for a specified crop can be applied to greenhouse to improve the plant growth and enhance yield.

• Journal of Korea Water Resources Association
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• v.56 no.3
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• pp.195-210
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• 2023

Additional deposition and erosion in large rivers in South Korea have continued to occur toward morphological stabilization after massive dredging through the four major river restoration project, subsequently requiring precise bathymetry monitoring. Hyperspectral bathymetry method has increasingly been highlighted as an alternative way to estimate bathymetry with high spatial resolution in shallow depth for replacing classical intrusive direct measurement techniques. This study introduced the conventional Optimal Band Ratio Analysis (OBRA) of hyperspectral bathymetry method, and evaluated the performance in a domestic large river in normal turbid and flow condition. Maximum measurable depth was estimated by applying correlation coefficient and root mean square error (RMSE) produced during OBRA with cascadedly applying cut-off depth, where the consequent hyperspectral bathymetry map excluded the region over the derived maximum measurable depth. Also non-linearity was considered in building relation between optimal band and depth. We applied the method to the Nakdong and Hwang River confluence as a large river case and obtained the following features. First, the hyperspectal method showed acceptable performance in morphological mapping for shallow regions, where the maximum measurable depth was 2.5 m and 1.25 m in the Nakdong and Hwang river, respectively. Second, RMSE was more feasible to derive the maximum measurable depth rather than the conventional correlation coefficient whereby considering various scenario of excluding range of in situ depths for OBRA. Third, highly turbid region in Hwang River did not allow hyperspectral bathymetry mapping compared with the case of adjacent Nakdong River, where maximum measurable depth was down to half in Hwang River.

• Knowledge Management Research
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• v.24 no.1
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• pp.245-271
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• 2023

The change to the data economy requires a new analysis beyond ordinary research in the management field. Data matching refers to a technique or processing method that combines data sets collected from different samples with the same population. In this study, statistical matching was performed using random hotdeck and Mahalanobis distance functions using 2020 Survey of Korea Venture Firms and 2020 Korea Innovation Survey datas. Among the variables used for statistical matching simulation, the industry and the number of workers were set to be completely consistent, and region, business power, listed market, and sales were set as common variables. Simulation verification was confirmed by mean test and kernel density. As a result of the analysis, it was confirmed that statistical matching was appropriate because there was a difference in the average test, but a similar pattern was shown in the kernel density. This result attempted to expand the spectrum of the research method by experimenting with a data matching research methodology that has not been sufficiently attempted in the management field, and suggests implications in terms of data utilization and diversity.