• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.43-52
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• 2023

Rubber produced by rubber companies is subjected to quality suitability inspection through rheometer test, followed by secondary processing for automobile parts. However, rheometer test is being conducted by humans and has the disadvantage of being very dependent on experts. In order to solve this problem, this paper proposes a deep learning-based rheometer quality inspection system. The proposed system combines LSTM(Long Short-Term Memory) and CNN(Convolutional Neural Network) to take advantage of temporal and spatial characteristics from the rheometer. Next, combination materials of each rubber was used as an auxiliary input to enable quality conformity inspection of various rubber products in one model. The proposed method examined its performance with 30,000 validation datasets. As a result, an F1-score of 0.9940 was achieved on average, and its excellence was proved.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.121-130
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• 2024

In this paper, we propose an installation approach for Naval Combat Management System(CMS) software that identifies potential data anomalies during installation. With the popularization of wireless communication methods, such as Low Earth Orbit(LEO) satellite communications, various utilization methods using wireless networks are being discussed in CMS. One of these methods includes the use of wireless network communications for installation, which is expected to enhance the real-time performance of the CMS. However, wireless networks are relatively more vulnerable to security threats compared to wired networks, necessitating additional security measures. This paper presents a method where files are transmitted to multiple nodes using encryption, and after the installation of the files, a validity check is performed to determine if there has been any tampering or alteration during transmission, ensuring proper installation. The feasibility of applying the proposed method to Naval Combat Systems is demonstrated by evaluating transmission performance, security, and stability, and based on these evaluations, results sufficient for application to CMS have been derived.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.

• Convergence Security Journal
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• v.24 no.2
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• pp.79-87
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• 2024

This paper addresses the increasing cyber attacks exploiting security vulnerabilities in software due to the rise in web applications. CSRF (Cross-Site Request Forgery) attacks pose a serious threat to web users and developers and must be prevented in advance. CSRF involves performing malicious requests without the user's consent, making protection methods crucial for web applications. This study compares and verifies the CSRF defense performance of two frameworks, Spring Security and Apache Shiro, to propose an effectively applicable framework. The results show that both frameworks successfully defend against CSRF attacks; however, Spring Security processes requests faster, averaging 2.55 seconds compared to Apache Shiro's 5.1 seconds. This performance difference stems from variations in internal processing methods and optimization levels. Both frameworks showed no significant differences in resource usage. Therefore, Spring Security is more suitable for environments requiring high performance and efficient request processing, while Apache Shiro needs improvement. These findings are expected to serve as valuable references for designing web application security architectures

• Journal of Information Technology Services
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• v.23 no.1
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• pp.1-10
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• 2024

The IT environment is changing due to the acceleration of digital transformation in enterprises and organizations. This expansion of the digital space makes centralized cybersecurity controls more difficult. For this reason, cyberattacks are increasing in frequency and severity and are becoming more sophisticated, such as ransomware and digital supply chain attacks. Even in large organizations with numerous security personnel and systems, security incidents continue to occur due to unmanaged and unknown threats and vulnerabilities to IT assets. It's time to move beyond the current focus on detecting and responding to security threats to managing the full range of cyber risks. This requires the implementation of asset Inventory for comprehensive management by collecting and integrating all IT assets of the enterprise and organization in a wide range. IT Asset Management(ITAM) systems exist to identify and manage various assets from a financial and administrative perspective. However, the asset information managed in this way is not complete, and there are problems with duplication of data. Also, it is insufficient to update of data-set, including Network Infrastructure, Active Directory, Virtualization Management, and Cloud Platforms. In this study, we, the researcher group propose a new framework for automated 'Comprehensive IT Asset Management(CITAM)' required for security operations by designing a process to automatically collect asset data-set. Such as the Hostname, IP, MAC address, Serial, OS, installed software information, last seen time, those are already distributed and stored in operating IT security systems. CITAM framwork could classify them into unique device units through analysis processes in term of aggregation, normalization, deduplication, validation, and integration.

• Korean Journal of Agricultural Science
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• v.51 no.1
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• pp.79-86
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• 2024

As the frequency and intensity of heavy rains increase, the vulnerability of agriculture to disasters also increases. Consequently, there is a need to improve flood and inundation predictions. To enhance the accuracy of inundation predictions, it is essential to monitor water level and discharge data within agricultural areas. This study was conducted to monitor water levels and rainfall in the Cheongju Sindae area from 2022 to 2023, and the data was utilized as input and validation data for agricultural inundation modeling. Four irrigation drainage canals were installed to a square-shaped concrete structure where the water level gauge is. It was then confirmed that the water level rises with rainfall. The flow velocities were monitored during periods of heavy rainfall. The rating curve, which estimates water level and flow velocity based on observations, was estimated using the software K-HQ. The resulting curve was presented with the Coefficient of Determination (R²). K-HQ was also used to calculate the equation for the rating curve, taking outliers into account at each data point. Outliers were extracted and the rating curve was recalculated. As the coefficient of determination of three out of four stations exceeded 0.95, the estimated rating curve may be considered reliable for discharge estimation. This study provides critical data for enhancing agricultural inundation modeling accuracy and drainage improvement projects.

• Computers and Concrete
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• v.34 no.1
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• pp.1-14
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• 2024

The design of disturbed regions in reinforced concrete structures usually applies the well known Strut and Tie Method (STM). As an alternative, the Stringer-Panel Method (SPM), an intermediate model between STM and the Finite Element Method (FEM), consists in dividing a structure into two distinct elements: the stringers (which carry axial forces) and panels (which carry shear forces). SPM has already showed good applicability in manual calculations and computer implementations, and its most known application was SPanCAD, an AutoCAD plugin for linear and nonlinear analysis by SPM. Unfortunately, SPanCAD was discontinued by the developers, and it's not compatible with the most recent versions of AutoCAD. So, this paper aims to present a computer program that was developed as an upgrade to the latter: the Stringer Panel Modelling Tool (SPMTool), which is intended to be an auxiliary design tool and it presents improvements, in comparison to SPanCAD. It is possible to execute linear and nonlinear analysis by three distinct formulations: Modified Compression Field Theory (MCFT), Disturbed Stress Field Model (DSFM) and Softened Membrane Model (SMM). The nonlinear results were compared to experimental data of reinforced concrete elements that were not designed by SPM; these elements were also analyzed in SPanCAD. On overall, SPMTool made more realistic predictions to the behavior of the analyzed structures than SPanCAD. Except for DSFM predictions for corbels (1.24), in overall average, the ultimate load predictions were conservative (0.85 to 0.98), which is a good aspect for a design tool. On the other hand, the cracking load predictions presented overestimations (1.06 to 1.47) and higher variations (25.59% to 34.25%) and the post-cracking behavior could not be accurately predicted; for this use case, a more robust finite element software is recommended.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.91-107
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• 2023

Lean startup is a concept that combines the words "lean," meaning an efficient way of running a business, and "startup," meaning a new business. It is often cited as a strategy for minimizing failure in early-stage businesses, especially in software-based startups. By scrutinizing the case of a startup L, this study suggests that lean startup methodology(LSM) can be useful for hardware and manufacturing companies and identifies ways for early startups to successfully implement LSM. To this end, the study explained the core of LSM including the concepts of hypothesis-driven approach, BML feedback loop, minimum viable product(MVP), and pivot. Five criteria to evaluate the successful implementation of LSM were derived from the core concepts and applied to evaluate the case of startup L . The early startup L pivoted its main business model from defecation alert system for patients with limited mobility to one for infants or toddlers, and finally to a smart bottle for infants. In developing the former two products, analyzed from LSM's perspective, company L neither established a specific customer value proposition for its startup idea and nor verified it through MVP experiment, thus failed to create a BML feedback loop. However, through two rounds of pivots, startup L discovered new target customers and customer needs, and was able to establish a successful business model by repeatedly experimenting with MVPs with minimal effort and time. In other words, Company L's case shows that it is essential to go through the customer-market validation stage at the beginning of the business, and that it should be done through an MVP method that does not waste the startup's time and resources. It also shows that it is necessary to abandon and pivot a product or service that customers do not want, even if it is technically superior and functionally complete. Lastly, the study proves that the lean startup methodology is not limited to the software industry, but can also be applied to technology-based hardware industry. The findings of this study can be used as guidelines and methodologies for early-stage companies to minimize failures and to accelerate the process of establishing a business model, scaling up, and going global.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of the Korean Society of Radiology
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• v.82 no.2
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• pp.406-416
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• 2021

Purpose To evaluate the association between magnetic resonance imaging (MRI)-based texture parameters and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in patients with non-mucinous rectal cancer. Materials and Methods Seventy-nine patients who had pathologically confirmed rectal non-mucinous adenocarcinoma with or without KRAS-mutation and had undergone rectal MRI were divided into a training (n = 46) and validation dataset (n = 33). A texture analysis was performed on the axial T2-weighted images. The association was statistically analyzed using the Mann-Whitney U test. To extract an optimal cut-off value for the prediction of KRAS mutation, a receiver operating characteristic curve analysis was performed. The cut-off value was verified using the validation dataset. Results In the training dataset, skewness in the mutant group (n = 22) was significantly higher than in the wild-type group (n = 24) (0.221 ± 0.283; -0.006 ± 0.178, respectively, p = 0.003). The area under the curve of the skewness was 0.757 (95% confidence interval, 0.606 to 0.872) with a maximum accuracy of 71%, a sensitivity of 64%, and a specificity of 78%. None of the other texture parameters were associated with KRAS mutation (p > 0.05). When a cut-off value of 0.078 was applied to the validation dataset, this had an accuracy of 76%, a sensitivity of 86%, and a specificity of 68%. Conclusion Skewness was associated with KRAS mutation in patients with non-mucinous rectal cancer.