• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.116-123
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• 2018

The sperm quality is determined by the kinetic characteristics and acrosome integrity of the sperm. In the previous studies, analysis of semen quality had large errors because those experiments by using microscope had been conducted by people. In recent years, the molecular biological methods have been newly developed to complement the previous techniques. The ZAR1 gene is known to be a gene that affects early embryonic development in vertebrates, but there is no study of the association with semen. In this study, we analyzed the association between the kinetic characteristics and ZAR1 single nucleotide polymorphism (SNP) genotype. To detect the SNPs, we performed sequencing using genomic DNA from the whole bloods of Duroc pigs. We identified an SNP in the ZAR1 gene g.2540T>C. ZAR1 SNP genotypeing in 105 pigs revealed that the major and minor alleles were T and C, respectively. After we analyzed the association between the kinetic characteristics of sperm and the ZAR1 SNP genotype, we found a significant association in MOT (p<0.01), VSL (p<0.05) of the kinetic characteristics in the Duroc boars. It was confirmed that the boars with T allele were lower in MOT and VSL than C allele. Therefore, pigs with C allele are judged to be better at the MOT and VSL of semen. Based on these results, ZAR1 SNP genotyping may be a useful molecular biomarker to improve semen quality by applying molecular breeding technology.

• The Korean Journal of Nuclear Medicine
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• v.32 no.4
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• pp.365-373
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• 1998

Purpose: The conventional gamma camera is not ideal for scintimammography because of its large detector size (${\sim}500mm$ in width) causing high cost and low image quality. We are developing a small gamma camera dedicated for breast imaging. Materials and Methods: The small gamma camera system consists of a NaI (T1) crystal ($60 mm{\times}60 mm{\times}6 mm$) coupled with a Hamamatsu R3941 Position Sensitive Photomultiplier Tube (PSPMT), a resister chain circuit, preamplifiers, nuclear instrument modules, an analog to digital converter and a personal computer for control and display. The PSPMT was read out using a standard resistive charge division which multiplexes the 34 cross wire anode channels into 4 signals ($X^+,\;X^-,\;Y^+,\;Y^-$). Those signals were individually amplified by four preamplifiers and then, shaped and amplified by amplifiers. The signals were discriminated ana digitized via triggering signal and used to localize the position of an event by applying the Anger logic. Results: The intrinsic sensitivity of the system was approximately 8,000 counts/sec/${\mu}Ci$. High quality flood and hole mask images were obtained. Breast phantom containing $2{\sim}7 mm$ diameter spheres was successfully imaged with a parallel hole collimator The image displayed accurate size and activity distribution over the imaging field of view Conclusion: We have succesfully developed a small gamma camera using NaI(T1)-PSPMT and nuclear Instrument modules. The small gamma camera developed in this study might improve the diagnostic accuracy of scintimammography by optimally imaging the breast.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.29-40
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• 2005

According as base of digital hologram has been magnified, discussion of compression technology is expected as a international standard which defines the compression technique of 3D image and video has been progressed in form of 3DAV which is a part of MPEG. As we can identify in case of 3DAV, the coding technique has high possibility to be formed into the hybrid type which is a merged, refined, or mixid with the various previous technique. Therefore, we wish to present the relationship between various image/video coding techniques and digital hologram In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video and image. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. Finally the proposed hybrid compression algorithm is all of these methods. The tool for still image coding is JPEG2000, and the toots for video coding include various international compression algorithm such as MPEG-2, MPEG-4, and H.264 and various lossless compression algorithm. The proposed algorithm illustrated that it have better properties for reconstruction than the previous researches on far greater compression rate above from four times to eight times as much. Therefore we expect that the proposed technique for digital hologram coding is to be a good preceding research.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Korean Journal of Soil Science and Fertilizer
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• v.22 no.4
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• pp.257-264
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• 1989

In order to study spatial variability of soil chemical properties across the land-partitioned boundary on Hwadong silt clay loam soil (Fine clayey, mixed, mesic family of Aquic Hapludalfs) in the experimental fie ld of the wheat and Barley Research Institute in Suwon, all measured data were analyzed by means of kriging, fractile diagram, smooth frequency distribution, and autocorrelation. Sampling for soil chemical property analysis was made at 225 intersections of 15x 15 grid with 10m interval from three soil depths (0-10cm, 25-35cm, 50-60cm) in the seven patitioned fields. 1. The coefficient of variance (CV) of various chemical properties varied from 5.4 to 72.7%. Soil pH was classified into the low variation group with CV smaller than 10%, while the other chemical properties belonged to the medium variation group with C.V. between 10 and 100% 2. The approximate number of soil samples for the determination of various chemical properties with error smaller than 10% were two for pH, ten for CEC, 15 for exchangeable Ca, 32 for total nitrogen content, 39 for exchangeable Mg, 40 for exchangeable K, 61 for exchangeable Na, 82 for organic matter content, 212 for available phosphate,. 3. Smooth frequency distribution and fractile diagram showed that available phosphate was in log-normal distribution while others were in normal distribution. 4. Serial correlation analysis revaled that the soil chemical properties had spatial dependence between two nearest neighbouring grid points. Autocorrelation analysis of chemcial properties measured between the serial grid points in the direction of south to north following land-partitioned boundary showed that the zone of influence showing stationarity ranged from 20 to 50m. In the direction of east to west accross land-partitioned boundary, the autocorrelogram of many chemical properies showed peaks with the periodic interval of 30m, which were similar to the partitioned land width. This reveals that the land-partitioned boundary causes soil variability.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.177-186
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• 2021

For disaster management and mitigation of earthquakes in Korea Peninsula, active fault investigation has been conducted for the past 5 years. In particular, investigation of sediment-covered active faults integrates geomorphological analysis on airborne LiDAR data, surface geological survey, and geophysical exploration, and unearths subsurface active faults by trench survey. However, the fault traces revealed by trench surveys are only available for investigation during a limited time and restored to the previous condition. Thus, the geological data describing the fault trench sites remain as the qualitative data in terms of research articles and reports. To extend the limitations due to temporal nature of geological studies, we utilized a terrestrial LiDAR to produce 3D point clouds for the fault trench sites and restored them in a digital space. The terrestrial LiDAR scanning was conducted at two trench sites located near the Yangsan Fault and acquired amplitude and reflectance from the surveyed area as well as color information by combining photogrammetry with the LiDAR system. The scanned data were merged to form the 3D point clouds having the average geometric error of 0.003 m, which exhibited the sufficient accuracy to restore the details of the surveyed trench sites. However, we found more post-processing on the scanned data would be necessary because the amplitudes and reflectances of the point clouds varied depending on the scan positions and the colors of the trench surfaces were captured differently depending on the light exposures available at the time. Such point clouds are pretty large in size and visualized through a limited set of softwares, which limits data sharing among researchers. As an alternative, we suggested Potree, an open-source web-based platform, to visualize the point clouds of the trench sites. In this study, as a result, we identified that terrestrial LiDAR data can be practical to increase reproducibility of geological field studies and easily accessible by researchers and students in Earth Sciences.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.6 no.1
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• pp.77-87
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• 1996

In order to compare the difference of the measurement of delta aminolevulinic acid(${\delta}$-ALA) in urine between HPLC method(HALA) and colorimetric method(CALA), and also to provide useful information for the new diagnostic criteria of ${\delta}$-ALA in urine in lead poisoning, if at all possible in the future, authors studied 234 male lead workers who were selected from 7 storage battery factories, 3 secondary smelting industries, and 2 litharge making industries. Study subjects were selected on the basis of blood Zinc protoporphyrin(ZPP) level from low to high concentration to cover wide range of lead exposure. Study variables for this study were ${\delta}$-ALA measured by two different methods, blood lead(PbB), and blood ZPP. The results were as follows: 1. There was very high correlation between ${\delta}$-ALA measured by two method(r = 0.989 : HALA = -0.8194 + 0.8110 ${\times}$ CALA), but the value of CALA was measured about 2mg/L greater than HALA. 2. While the correlations of ${\delta}$-ALA by two method with blood lead and blood ZPP were 0.46 and 0.37 respectively, they were increased to 0.63 and 0.57 if ${\delta}$-ALA values were log-transformed. 3. Simple linear regression of ${\delta}$-ALA measured by two method on ZPP were as follows: CALA = 2.0421 + 0.0341 ${\times}$ ZPP ($R^2=0.1385$ p = 0.0001) HALA = 0.8006 + 0.0280 ${\times}$ ZPP ($R^2=0.1389$ p = 0.0001) 4. Simple linear regression of ${\delta}$-ALA measured by two method on PbB were as follows: CALA = - 0.4134 + 0.1545 ${\times}$ PbB ($R^2=0.2085$ p = 0.0001) HALA = -1.2893 + 0.1287 PbB ($R^2=0.2154$ p = 0.0001), 5. Simple linear regression of log-transformed ${\delta}$-ALA by two method on ZPP and PbB were as follows: logHALA = 0.3078 + 0.0060 ZPP ($R^2=0.3329$ p = 0.0001) logCALA = 1.0189 + 0.0044 ZPP ($R^2=0.3290$ p = 0.0001) logHALA = -0.0221 + 0.0246 PbB ($R^2=0.4046$ p = 0.0001) logCALA = 0.7662 + 0.0184 PbB ($R^2=0.4108$ p = 0.0001) 6. The cumulative percent of colorimetric method to detect lead workers whose value of PbS and ZPP were over screening level such as $40{\mu}/dl$ and $100{\mu}/dl$ respectively was higher than HPLC method if cut-off level of ${\delta}$-ALA for screening of lead poisoning was 5 mg/L. But if cut-off level of ${\delta}$-ALA measured by HPLC was reduced to 3 mg/L which is compatible to 5 mg/L of ${\delta}$-ALA measured by colorimetric method, there were good agreement between two methods and showed dose-response relationship with other lead exposure indices such as PbB and ZPP.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.47-50
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• 2009

Purpose: Presently, any exact standard of radiopharmaceutical doses in pediatric nuclear medicine doesn't exist in the universe. So hospitals are following by manual of vial kit or guidelines of America and Europe based on recommended adult doses adjusted for body mass (MBq/kg) or body surface area (MBq/$m^2$). However, especially for children younger than 1 year and heavier than 50 kg, it's hard to estimate exact dosage for those children. Materials and Methods: In order to obtain objective data of multipliers for pediatric studies, we surveyed 4 major hospitals in Korea. After receiving feedbacks, we changed dosage to multiplier. And we compared multipliers of Korea to America's and Europe's. Results: Most hospitals in Korea are following by body mass formula (MBq/kg). On the other hand, standards don't include proper factors for a child younger than 1 year and heavier than 50 kg. Multipliers for 3 kg children who are injected lower doses than needed are America:0.12, Europe:0.09, Korea:0.05, multipliers for 30 kg children who are injected proper doses are America:0.58, Europe:0.51, Korea:0.45 and multipliers for 60 kg children who are injected more doses than needed are America:0.95, Europe:0.95, Korea:0.91. Conclusions : Through the survey, when calculating doses for children, usually output doses are based on adult doses adjusted for body mass (MBq/kg) but research has shown that standards of all of the compared standards don't reflect exact multipliers for children younger than 1 year and heavier than 50 kg. Therefore, we should give an effort to reduce needless radiation exposure in children by establishing a proper doses standard and also developing better image reconstruction software.

• Applied Biological Chemistry
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• v.24 no.1
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• pp.50-58
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• 1981

The influence of high temperature storage on the chemical composition and color intensity of the concentrated red ginseng extract(RGE) was investigated. The concentrated RGE was prepared by extraction of red ginseng tails with water and concentrated under reduced pressure. Changes in free sugars, saponin patterns and brown color intensity were measured during 96 hours of heat treatment at various temperature. A decrease in the contents of glucose, fructose and sucrose was resulted as the brown color intensity increased during the storage. The sugar contents and color intensity showed rapid initial change followed by slowing down at higher temperature. A significant relationship was found between sugar content and browning rate. The saponin pattern measured by high performance liquid chromatography, particularly in the region of protopanaxtriol, was also affected significantly. The peak heights of ginsenoside -Re and $-Rg_1$ were decreased while those of ginsenoside $-Rg_2$ and -Rh group were increased.