• Korean Journal of Clinical Laboratory Science
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• v.49 no.4
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• pp.350-358
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• 2017

The hemolysis index (HI) is semi-quantitative marker for hemolysis. Because the characteristics of the HI vary from one commercial platform to another, no standardization or harmonization of the HI is currently available. Specimens (N=40) randomly selected from clinical patients were artificially hemolyzed in vitro. The serum of the specimens was then diluted with a 20 mg/dL difference between 0~300 mg/dL based on serum hemoglobin measured using the XE-2100 hematology automation equipment (Sysmex, Japan). Diluted serum was measured using the Hitachi-7600 biochemical automation equipment (Hitachi, Japan) to differentiate between HI and serum hemoglobin. The data showed linearity between HI and serum hemoglobin and that HI 1 contained approximately 20 mg/dL of serum hemoglobin. To determine the blood rejection threshold, the HI was divided into three groups: HI 0~1, HI 4~6, HI 9~15. After another batch of clinical specimens (N=40) was measured using a Hitachi-7600 (Hitachi, Japan), each specimen was moved forward and backward with the piston of the syringe to induce an artificial in vitro hemolysis, then measured again with a Hitachi-7600 (Hitachi, Japan). The percentage difference between the three groups was analyzed by ANOVA or the Kruskal-Wallis test. In the post-test, there were significant differences between the HI 0~1 and the HI 5~6: Glucose, creatinine, total protein, AST, direct bilirubin, uric acid, phosphorus, triglyceride, LDH, CPK, Magnesium, and potassium levels. Because many clinical tests differed significantly, the threshold for hemolysis could be appropriate for HI 5 (serum hemoglobin 100 mg/dL).

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.219-234
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• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.194-203
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• 2021

With the recent increase in maintenance works on water and sewer pipes as well as district heating supply pipes, pavement cutting work using pavement cutter is on the rise. The pavement cutting operation generates considerable dust (cutting sludge) as well as noise; therefore, it is necessary to apply eco-friendly technologies that have low noise and dust recovery capability. Thus far, various equipment for recovering dust have been developed; however, there is a limitation in that the environmental friendliness is not quantified. Therefore, in this study, we developed a low-noise, dust-recovery type pavement cutter that can fundamentally remove the causes of environmental hazards such as noise and dust and evaluated the eco-friendliness of the pavement cutting process performed by this cutter. To this end, an integrated water cooling-sludge recovery system composed of a vacuum device and a sludge suction unit was developed, and the developed system was applied to a pavement cutter. Subsequently, the developed equipment was applied to the test bed, and data related to its eco-friendliness were collected and evaluated. The results showed that the cutting sludge recovery rate of the developed equipment was greater than 83%, the noise level was approximately 82 - 83 dB, and the sound power level was 115 dB. The results of this study will be used as basic data to develop improved pavement cutters in the future with improved cutting sludge recovery performance and lower noise.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.483-490
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• 2021

Due to the recent increase in various problems from underground development in urbanized areas, accurate underground facility information management is highly needed. Therefore, in this study, in order to utilize the Integration Map of Underground Goespatial Information in real time on-site, the function of comparing the mutual location of the GPR (Ground Penetration Radar) sensing data and the Integration Map of Underground Goespatial Information, and function of analyze underground facilities, and function of converting surveying data into a shape file through position correction & attribute editing in a 3D space, and the function of submitting the shape file to the Integration Map of Underground Goespatial Information mobile center was defined and developed as a program. In addition, for the on-site application test of the development program, scenarios used at the underground facility real-time survey site and GPR exploration site were derived, and four sites in Seoul were tested to confirm that the use scenario worked properly. Through this, the on-site utilization of the program developed in this study could be confirmed, and it would contribute to the confirmation of the quality of Shape-file and the "update automation" of "Integration Map of Underground Goespatial Information". In addition, it is expected that the development program will be further applied to the Underground Facility Map's Accuracy Improvement Diffusion Project' promoted by the MOLIT (Ministry of Land, Infrastructure, and Transport).

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.474-480
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• 2018

FRP is a new material that is lightweight, has high strength and high durability, and is emerging as a third construction material in many countries. The composite material panel targeted in this study was a curved member and is the most frequently used arch-shaped member of a structures, such as tunnels. Composite curved panels can be produced in high quality and large quantities through automation operations. On the other hand, the frequency of application is low, and the design criteria and experimental data are lacking. Therefore, this study examined the mechanical performance of the member unit first to verify its performance as structural members of the FRP curved panel. For this purpose, tensile, compression, and connection performance tests were carried out. The tensile tests showed greater tensile strength of specimens with larger curvature, and the compression tests showed that the composite section of a composite material has greater compressive strength than the concrete section. Finally, the test of the performance of the connection showed that the attachment performance of the connection was more than equal to that of the FRP composite material panel.

• Journal of Pharmaceutical Investigation
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• v.30 no.3
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• pp.191-199
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• 2000

Genomics is providing targets faster than we can validate them and combinatorial chemistry is providing new chemical entities faster than we can screen them. Historically, the drug discovery cascade has been established as a sequential process initiated with a potency screening against a selected biological target. In this sequential process, pharmacokinetics was often regarded as a low-throughput activity. Typically, limited pharmacokinetics studies would be conducted prior to acceptance of a compound for safety evaluation and, as a result, compounds often failed to reach a clinical testing due to unfavorable pharmacokinetic characteristics. A new paradigm in drug discovery has emerged in which the entire sample collection is rapidly screened using robotized high-throughput assays at the outset of the program. Higher-throughput pharmacokinetics (HTPK) is being achieved through introduction of new techniques, including automation for sample preparation and new experimental approaches. A number of in vitro and in vivo methods are being developed for the HTPK. In vitro studies, in which many cell lines are used to screen absorption and metabolism, are generally faster than in vivo screening, and, in this sense, in vitro screening is often considered as a real HTPK. Despite the elegance of the in vitro models, however, in vivo screenings are always essential for the final confirmation. Among these in vivo methods, cassette dosing technique, is believed the methods that is applicable in the screening of pharmacokinetics of many compounds at a time. The widespread use of liquid chromatography (LC) interfaced to mass spectrometry (MS) or tandem mass spectrometry (MS/MS) allowed the feasibility of the cassette dosing technique. Another approach to increase the throughput of in vivo screening of pharmacokinetics is to reduce the number of sample analysis. Two common approaches are used for this purpose. First, samples from identical study designs but that contain different drug candidate can be pooled to produce single set of samples, thus, reducing sample to be analyzed. Second, for a single test compound, serial plasma samples can be pooled to produce a single composite sample for analysis. In this review, we validated the issue whether the second method can be applied to practical screening of in vivo pharmacokinetics using data from seven of our previous bioequivalence studies. For a given drug, equally spaced serial plasma samples were pooled to achieve a 'Pooled Concentration' for the drug. An area under the plasma drug concentration-time curve (AUC) was then calculated theoretically using the pooled concentration and the predicted AUC value was statistically compared with the traditionally calculated AUC value. The comparison revealed that the sample pooling method generated reasonably accurate AUC values when compared with those obtained by the traditional approach. It is especially noteworthy that the accuracy was obtained by the analysis of only one sample instead of analyses of a number of samples that necessitates a significant man-power and time. Thus, we propose the sample pooling method as an alternative to in vivo pharmacokinetic approach in the selection potential lead(s) from combinatorial libraries.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.319-326
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• 2016

Given the attributes of mobile apps that shorten the time to make purchase decisions while enabling easy purchase cancellations, usability can be regarded to be a highly prioritized quality attribute among the diverse quality attributes that must be provided by mobile apps. With that backdrop, mobile app developers have been making great effort to minimize usability hampering elements that degrade the merchantability of apps in many ways. Most elements that hamper the convenience in use of mobile apps stem from those potential errors that occur when GUIs are designed. In our previous study, we have proposed a technique to analyze the usability of mobile apps using user behavior logs. We proposes a technique to detect usability hampering elements lying dormant in mobile apps' GUI models by expressing user behavior logs with finite state models, combining user behavior models extracted from multiple users, and comparing the combined user behavior model with the expected behavior model on which the designer's intention is reflected. In addition, to reduce the burden of the repeated test operations that have been conducted by existing developers to detect usability errors, the present paper also proposes a mobile usability error detection automation tool that enables automatic application of the proposed technique. The utility of the proposed technique and tool is being discussed through comparison between the GUI issue reports presented by actual open source app developers and the symptoms detected by the proposed technique.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.