• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.199-202
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• 2023

Carriage return (CR) and line feed (LF), also known as CRLF injection is a type of vulnerability that allows a hacker to enter special characters into a web application, altering its operation or confusing the administrator. Log poisoning and HTTP response splitting are two prominent harmful uses of this technique. Additionally, CRLF injection can be used by an attacker to exploit other vulnerabilities, such as cross-site scripting (XSS). Email injection, also known as email header injection, is another way that can be used to modify the behavior of emails. The Open Web Application Security Project (OWASP) is an organization that studies vulnerabilities and ranks them based on their level of risk. According to OWASP, CRLF vulnerabilities are among the top 10 vulnerabilities and are a type of injection attack. Automated testing can help to quickly identify CRLF vulnerabilities, and is particularly useful for companies to test their applications before releasing them. However, CRLF vulnerabilities can also lead to the discovery of other high-risk vulnerabilities, and it fosters a better approach to mitigate CRLF vulnerabilities in the early stage and help secure applications against known vulnerabilities. Although there has been a significant amount of research on other types of injection attacks, such as Structure Query Language Injection (SQL Injection). There has been less research on CRLF vulnerabilities and how to detect them with automated testing. There is room for further research to be done on this subject matter in order to develop creative solutions to problems. It will also help to reduce false positive alerts by checking the header response of each request. Security automation is an important issue for companies trying to protect themselves against security threats. Automated alerts from security systems can provide a quicker and more accurate understanding of potential vulnerabilities and can help to reduce false positive alerts. Despite the extensive research on various types of vulnerabilities in web applications, CRLF vulnerabilities have only recently been included in the research. Utilizing automated testing as a recurring task can assist companies in receiving consistent updates about their systems and enhance their security.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.77-84
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• 2021

The purpose of this study is to propose AI maker coding education as a way to improve computational thinking(CT), which is an essential competence for problem-solving capability in modern society, and to analyze the effectiveness of this education on improving CT in elementary school students. For the research, 5 students from 4th graders and 5 students from 6th graders were recruited, and AI maker coding education was planned in 8 sessions to form classes from basic block coding and maker education to real-life problem solving. To analyze the effectiveness of AI maker coding education, pre- and post-CT examinations were performed. The test results confirmed that AI maker coding education had a significant effect on "abstraction", "algorithm", and "data processing" in the five CT components, and confirmed that there was no correlation in "problem resolution" and "automation". Overall, the average score of all students increased, and the deviation between students decreased, confirming that AI maker coding education was effective in improving CT.

• Journal of KIBIM
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• v.13 no.3
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• pp.21-29
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• 2023

ChatGPT, a chatbot based on GPT large language models, has gained immense popularity among the general public as well as domain professionals. To assess its proficiency in specialized fields, ChatGPT was tested on mainstream exams like the bar exam and medical licensing tests. This study evaluated ChatGPT's ability to answer questions related to Building Information Modeling (BIM) by testing it on Korea's BIM expertise exam, focusing primarily on multiple-choice problems. Both GPT-3.5 and GPT-4 were tested by prompting them to provide the correct answers to three years' worth of exams, totaling 150 questions. The results showed that both versions passed the test with average scores of 68 and 85, respectively. GPT-4 performed particularly well in categories related to 'BIM software' and 'Smart Construction technology'. However, it did not fare well in 'BIM applications'. Both versions were more proficient with short-answer choices than with sentence-length answers. Additionally, GPT-4 struggled with questions related to BIM policies and regulations specific to the Korean industry. Such limitations might be addressed by using tools like LangChain, which allow for feeding domain-specific documents to customize ChatGPT's responses. These advancements are anticipated to enhance ChatGPT's utility as a virtual assistant for BIM education and modeling automation.

• Journal of Drive and Control
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• v.20 no.4
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• pp.27-34
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• 2023

Weeding in orchards is closely associated with productivity and quality. The customary weeding process is both labor-intensive and time-consuming. To solve the problems, there is need for automation of agricultural robots and machines in the agricultural field. On the other hand, orchards have complicated working areas due to narrow spaces between trees and amorphous terrain. Therefore, it is necessary to develop customized robot technology for unmanned weeding work within the department. This study developed a path generation and path control method for unmanned weeding according to the orchard environment. For this, the width of the weeding span, the number of operations, and the width of the weeding robot were used as input parameters for the orchard environment parameters. To generate a weeding path, a weeding robot was operated remotely to obtain GNSS-based location data along the superheated center line, and a driving performance test was performed based on the generated path. From the results of orchard field tests, the RMSE in weeding period sections was measured at 0.029 m, with a maximum error of 0.15 m. In the steering period within row and steering to the next row sections, the RMSE was 0.124 m, and 0.047 m, respectively.

• Journal of Veterinary Science
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• v.25 no.3
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• pp.38.1-38.16
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• 2024

Importance: Deaths due to neonatal calf diarrhea are still one of the most critical problems of cattle breeding worldwide. Determining the parameters that can predict diarrhea-related deaths in calves is especially important in terms of prognosis and treatment strategies for the disease. Objective: The primary purpose of this study was to determine mortality rates and durations, survival status, and predictive prognosis parameters based on vital signs, hematology, and blood gas analyses in neonatal diarrheic calves. Methods: The hospital automation system retrospectively obtained data from 89 neonatal diarrheic calves. Results: It was found that 42.7% (38/89) of the calves brought with the complaint of diarrhea died during hospitalization or after discharge. Short-term and long-term fatalities were a median of 9.25 hours and a median of 51.50 hours, respectively. When the data obtained from this study is evaluated, body temperature (℃), pH, base excess (mmol/L), and sodium bicarbonate (mmol/L) parameters were found to be lower, and hemoglobin (g/dL), hematocrit (%), lactate (mmol/L), chloride (mmol/L), sodium (mmol/L) and anion gap (mmol/L) parameters were found to be higher in dead calves compared to survivors. Accordingly, hypothermia, metabolic acidosis, and dehydration findings were seen as clinical conditions that should be considered. Logistic regression analysis showed that lactate (odds ratio, 1.429) and CI^- (odds ratio, 1.232) concentration were significant risk factors associated with death in calves with diarrhea. Conclusions and Relevance: According to the findings obtained from this study, the determination of lactate and Cl^- levels can be used as an adjunctive supplementary test in distinguishing calves with diarrhea with a good prognosis.

• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.675-688
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• 2007

Memory access errors are frequently occurred in C programs. A number of tools and research works have been trying to detect the errors automatically. However, they have one or more of the following problems: inability to detect all memory errors, changing the memory allocation mechanism, incompatibility with libraries, and excessive performance overhead. In this paper, we suggest a new method to solve these problems, and then present a result of comparison to the previous research works through the experiments. Our approach consists of two phases. First is to transform source code at compile time through inserting instrumentation into the source code. And second is to detect memory errors at run time with a bitmap that maintains information about memory allocation. Our approach has improved the error detection abilities against the binary code analysis based ones by using the source code analysis technique, and enhanced performance in terms of both space and time, too. In addition, our approach has no problem with respect to compatibility with shared libraries as well as does not need to modify memory allocation mechanism.

• Journal of The Korean Association For Science Education
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• v.22 no.3
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• pp.604-616
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• 2002

The purpose of this study was to analyze the development of the compensational thinking by the compensation activities of 'Thinking Science' program. The 138 students were sampled in elementary schools and were divided into two groups, the experimental group of 74 students and the control group of 64 students. Both the compensation activities of the 'Thinking Science' program and a regular science curriculum were implemented to the experimental group, while only a regular science curriculum to the control group. Both experimental and control group were pre-tested with Science Reasoning Task II and compensational thinking test I and were post-tested with compensational thinking test II. This study revealed that the types of strategies used in compensation problem solving were categorized as illogical explanation, rule automation, proportionality, explanation in qualitative terms, additive quantification, inverse proportionality and were related to the context of the items. It was found that compensation activities of the 'Thinking Science' program were effective on the development of the compensational thinking.

• 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 Institute of Gas
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• v.25 no.2
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• pp.64-71
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• 2021

Research on polymer matrix composites with excellent molding processability and mechanical properties in the automotive field including hydrogen fuel cell electric vehicles is expanding to Computer-Aided Engineering (CAE) to support the design of materials with specific mechanical properties. CAE automation requires the prediction of the mechanical properties and behavior of materials. Unlike single materials, the mechanical properties prediction of polymer matrix composites is difficult to explain with formulas because the mechanical behavior is complicated to be explained only by the relationship between the matrix and the filler. In this study, the stress-strain curve according to the composition of polymer matrix composites, which was difficult to predict due to its sensitivity to large plastic deformation and composition, was predicted based on machine learning of the test data. The developed model finds a complex correlation between matrix and filler types and compositions, and predicts the total stress-strain curve meaningfully even in the absence of learned test data. It is expected that the material design AI system can be completed in the future based on the developed model that predicts the mechanical properties of polymer matrix composites even for the combination and composition that have not been learned.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.530-536
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• 2021

This paper presents the performance measurement results of a hybrid sheet with both shielding and heat dissipation functions developed by laminating copper mesh sheets and natural graphite sheets, which are used widely as electromagnetic shielding and heat-dissipating materials in electronic devices, without a pressure-sensitive adhesive (PSA). The results were compared by measuring the vertical and horizontal thermal conductivity with two other products to confirm the heat dissipation performance. A radiation emission test confirmed the electromagnetic shielding performance using a 3m electromagnetic anechoic chamber according to the CISPR 11 standard. In the case of vertical thermal conductivity, the proposed hybrid sheet was approximately 8.63 times higher than that of an aluminum sheet with heat dissipation coating and 18.7 times higher than that of a copper sheet laminated with artificial graphite with PSA. The proposed hybrid sheet was approximately 0.64 times that of the sheet, and approximately 1.76 times that of the heat-dissipated aluminum sheet in case of horizontal thermal conductivity. Measurements after applying each sheet in the same heat source revealed the proposed hybrid sheet to have the best heat dissipation performance. The radiation emission test showed that significantly radiation noise had been removed.