• 한국컴퓨터정보학회논문지
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• 제22권3호
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• pp.69-79
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• 2017

In this paper, we propose our four-phase life cycle of P2P botnet with corresponding detection methods and the future direction for more effective P2P botnet detection. Our proposals are based on the intensive analysis that compares existing P2P botnet detection schemes in different points of view such as life cycle of P2P botnet, machine learning methods for data mining based detection, composition of data sets, and performance matrix. Our proposed life cycle model composed of linear sequence stages suggests to utilize features in the vulnerable phase rather than the entire life cycle. In addition, we suggest the hybrid detection scheme with data mining based method and our proposed life cycle, and present the improved composition of experimental data sets through analysing the limitations of previous works.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4742-4770
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• 2019

Cloud computing provides a broad range of services like operating systems, hardware, software and resources. Availability of these services encourages data owners to outsource their intensive computations and massive data to the cloud. However, considering the untrusted nature of cloud server, it is essential to encrypt the data before outsourcing it to the cloud. Unfortunately, this leads to a challenge when it comes to providing search functionality for encrypted data located in the cloud. To address this challenge, this paper presents a public key encryption with equality test for heterogeneous systems (PKE-ET-HS). The PKE-ET-HS scheme simulates certificateless public encryption with equality test (CLE-ET) with the identity-based encryption with equality test (IBE-ET). This scheme provides the authorized cloud server the right to actuate the equivalence of two messages having their encryptions performed under heterogeneous systems. Basing on the random oracle model, we construct the security of our proposed scheme under the bilinear Diffie-Hellman (BDH) assumption. Eventually, we evaluate the size of storage, computation complexities, and properties with other related works and illustrations indicate good performance from our scheme.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2008년도 추계학술대회 논문집
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• pp.325-328
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• 2008

With intensive research and development to mass particular nanostructure of 10nm, Nanoimprint lithography will soon be put to practical use. This paper reviews latest research and application trend and also covers technical articles about Nanoimprint lithography technology Published since 1998, including statistical analysis of collected data(Web of Science DB) and related technical trend.

• 한국환경농학회지
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• 제31권1호
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• pp.16-23
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• 2012

BACKGROUND: To improve the Daechung reservoir water quality, a quantitative estimation of the delivery load from upper watershed need to be conducted prior to others. To do so, an intensive monitoring is necessary because of the complexity and uncertainty of the delivery load from uppper watershed. However, intensive monitoring need to invest much time, cost, and effort. So, many researcher have developed an equation to estimate the delivery loads. But, relatively little research has been conducted on the applicability of pre-developed equation using other sites. Therefore, the objective of this study was to evaluate application of the equation for BOD, T-N and T-P delivery load. METHODS AND RESULTS: To verify the applicability of the equation, the following equation was used; Delivery loads(kg/day)=generated pollutant loads${\times}(1-{\alpha}){\times}$(daily outflow/${\beta})^{\gamma}$. The equations could be calculated the daily delivery loads of streams without any data of water quality, only with the data of daily runoff of study sites. The equations were applied to Youngdogcheon, Chogangcheon, Bocheongcheon, Sookcheon to examine its applicability using monitoring data. The results showed that the estimated delivery loads were in a good agreement with the observed data and indicated reasonable applicability of the equations. CONCLUSION(s): Overall, the equations were satisfactory in estimation of delivery loads at upper watershed of the Daechung reservoir. Therefore, the equations could be contributed to better water quality management in the Daechung reservoir.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
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• pp.290-293
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• 2000

Knowledge discovery refers to the overall process of discovering useful knowledge from data. The linear programming model is a special form of useful knowledge that is embedded in a database. Since formulating models from scratch requires knowledge-intensive efforts, knowledge-based formulation support systems have been proposed in the DSS area. However, they rely on the strict assumption that sufficient domain knowledge should already be captured as a specific knowledge representation form. Hence, the purpose of this paper is to propose a methodology that finds useful knowledge on building linear programming models from a database. The methodology consists of two parts. The first part is to find s first-cut model based on a data dictionary. To do so, we applied the GPS algorithm. The second part is to discover a second-cut model by applying neural network technique. An illustrative example is described to show the feasibility of the proposed methodology.

• 유통과학연구
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• 제13권3호
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• pp.21-29
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• 2015

Purpose - This paper addresses the role of designers in design-oriented firms and how they should work together with other organization members. The aim of this paper is to investigate how designers generate ideas and cooperate with others as well as how their participation in decision-making reflects on corporate design strategies. Research design, data, and methodology - An in-depth exploratory study examined how designers actually perform their roles in enterprises; in addition, information, knowledge, communication among designers, and sources of creativity were examined. Hanssem and Ikea grew as design-intensive businesses in a declining industry. Data were obtained from interviews with the design staff of each company as well as secondary sources. Results - Designers were found to use their designs to communicate with customers as well as with communities outside of the enterprise; they also participated in overall decision-making in relation to important design strategies. Conclusions - This study emphasized the increasing importance of the innovative and creative role of designers; thus, it might substantially help companies to develop their own design capabilities and deploy design strategies.

• International journal of advanced smart convergence
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• 제7권3호
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• pp.15-22
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• 2018

Nowadays multiple control methods are used in robot control systems. A model, predictor or error estimator is often used as feedback controller to control a robot. While robots have become more and more intensive with algorithms capable to acquiring independent knowledge from raw data. This paper represents experimental results of real time machine learning control that does not require explicit knowledge about the plant. The controller can be applied on a broad range of tasks with different dynamic characteristics. We tested our controller on the balancing problem of a single propeller pendulum. Experimental results show that the use of a supervised machine learning algorithm in a single propeller pendulum allows the stable swing of a given angle.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.319-325
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• 2022

By using the Blackboard (BB) system in the education sector, the educational process for both academics and students is facilitated. Two data resources were used to evaluate the use of the BB system by students of Umm Al-Qura University: statistical reports issued by the university and an online questionnaire. A total of 989 students from all colleges and different programmes provided by the university responded to the questionnaire survey. According to our findings, most students did not use the BB before the pandemic. Therefore, the sudden conversion to the BB system required intensive training courses. After the data analysis, the relationship between the use of the BB system before the pandemic and the problems students faced during the lockdown was revealed. The most critical issues raised by the respondents were: (1) "The voice of the lecturer went on and off during BB collaborate class", (2) "internet connection of the lecturer went on and off during BB collaborate class" and (3) "High possibility of IT problems during exams".