• 한국과학교육학회지
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• 제14권2호
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• pp.143-158
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• 1994

This study aims to identify the characteristics of gas phase problem solving of college freshmen. Four students were participated in this study and solved the problem by using think-aloud method. The thinking processes were recorded and transferred into protocols. Problem solving stage, the ratio spended in each solving stage, solving strategy, misconceptions, and errors were identified and discussed. The relationships between students' belief system about chemistry problem solving and problem solving characteristics were also investigated. The results were as follows: 1. Students felt that chemical equation problem was easier than word problem or pictorial problem. 2. When students had declarative knowledge and procedural knowledge required by given problem, their confidence level and formula selection were not changed by redundunt information in the problem. 3. When the problem seemed to be difficult, students tended to use the Means-End or Random strategy. 4. In complicated problems, students spent longer time for problem apprehension and planning. In familiar problems, students spent rather short time for planning. 5. Students spent more time for overall problem solving process in case of using Means-End or Random strategy than using Knowledge-Development strategy.

• 한국구조물진단유지관리공학회 논문집
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• 제8권3호
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• pp.225-233
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• 2004

본 연구에서는 응답면기법을 이용하여 격납건물의 내진안전성 평가를 하였다. ABAQUS를 이용하여 하중, 저항과 해석에서의 랜덤변수를 고려한 구조해석을 수행하였고 이로부터 변수의 다항식으로 표현되는 구조물의 응답을 얻었다. 그리고 Level II에 의해 신뢰성해석을 하였다. 한계상태함수로는 콘크리트의 2축응력 상태를 고려하기 위해 Drucker-Prager 파괴기준을 이용하였다. 구조물의 수명, 지진의 연발생율과 조건부 파괴확률을 고려하여 격납건물의 파괴확률을 계산하였다. 또한 응답면기법의 안정적인 결과를 얻기 위해 표본점 선정에 대한 민감도해석을 수행하였다.

• 산업경영시스템학회지
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• 제29권1호
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• pp.1-8
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• 2006

This paper proposes a real-time resource allocation algorithm for minimizing the completion time of overall workflow process. The jobs in a workflow process are interrelated through the precedence graph including Sequence, AND, OR and Loop control structure. A resource should be allocated for the processing of each job, and the required processing time of the job can be varied by the resource allocation decision. Each resource has several inherent restrictions such as the functional, geographical, positional and other operational characteristics. The algorithm suggested in this paper selects an effective resource for each job by considering the precedence constraint and the resource characteristics such as processing time and the inherent restrictions. To investigate the performance of the proposed algorithm, several numerical tests are performed for four different workflow graphs including standard, parallel and two series-parallel structures. In the tests, the solutions by the proposed algorithm are compared with random and optimal solutions which are obtained by a random selection rule and a full enumeration method respectively.

• Journal of Information Processing Systems
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• 제11권3호
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• pp.465-482
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• 2015

IEEE 802.11p is a standard MAC protocol for wireless access in vehicular environments (WAVEs). If a packet collision happens when a safety message is sent out, IEEE 802.11p chooses a random back-off counter value in a fixed-size contention window. However, depending on the random choice of back-off counter value, it is still possible that less important messages are sent out first while more important messages are delayed longer until sent out. In this paper, we present a new scheme for safety message scheduling, called the enhanced message priority mechanism (EMPM). It consists of the following two components: the benefit-value algorithm, which calculates the priority of the messages depending on the speed, deceleration, and message lifetime; and the back-off counter selection algorithm, which chooses the non-uniform back-off counter value in order to reduce the collision probability and to enhance the throughput of the highly beneficial messages. Numerical results show that the EMPM can significantly improve the throughput and delay of messages with high benefits when compared with existing MAC protocols. Consequently, the EMPM can provide better QoS support for the more important and urgent messages.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.212-222
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• 2021

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

• 아태비즈니스연구
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• 제13권2호
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• pp.107-122
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• 2022

Purpose - This study aims to develop machine learning models to predict administrative issue designation in KOSDAQ Market using financial data. Design/methodology/approach - Employing four classification techniques including logistic regression, support vector machine, random forest, and gradient boosting to a matched sample of five hundred and thirty-six firms over an eight-year period, the authors develop prediction models and explore the practicality of the models. Findings - The resulting four binary selection models reveal overall satisfactory classification performance in terms of various measures including AUC (area under the receiver operating characteristic curve), accuracy, F1-score, and top quartile lift, while the ensemble models (random forest and gradienct boosting) outperform the others in terms of most measures. Research implications or Originality - Although the assessment of administrative issue potential of firms is critical information to investors and financial institutions, detailed empirical investigation has lagged behind. The current research fills this gap in the literature by proposing parsimonious prediction models based on a few financial variables and validating the applicability of the models.

• 정보관리학회지
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• 제40권3호
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• pp.99-118
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• 2023

이 연구의 목적은 기록물의 맥락정보를 담고 있는 메타데이터를 활용하여 기록물 자동분류 과정에서의 성능요소를 파악하는데 있다. 연구를 위해 2022년 중앙행정기관 원문정보 약 97,064건을 수집하였다.수집한 데이터를 대상으로 다양한 분류 알고리즘과 데이터선정방법, 문헌표현기법을 적용하고 그 결과를 비교하여 기록물 자동 분류를 위한 최적의 성능요소를 파악하고자 하였다. 연구 결과 분류 알고리즘으로는 Random Forest가, 문헌표현기법으로는 TF 기법이 가장 높은 성능을 보였으며, 단위과제의 최소데이터 수량은 성능에 미치는 영향이 미미하였고 자질은 성능변화에 명확한 영향을 미친다는 것이 확인되었다.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3163-3181
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• 2023

Eavesdropping attacks have seriously threatened network security. Attackers could eavesdrop on target nodes and link to steal confidential data. In the traditional network architecture, the static routing path and the important nodes determined by the nature of network topology provide a great convenience for eavesdropping attacks. To resist monitoring attacks, this paper proposes a random routing mutation defense method based on dynamic path weight (DPW-RRM). It utilizes network centrality indicators to determine important nodes in the network topology and reduces the probability of important nodes in path selection, thereby distributing traffic to multiple communication paths, achieving the purpose of increasing the difficulty and cost of eavesdropping attacks. In addition, it dynamically adjusts the weight of the routing path through network state constraints to avoid link congestion and improve the availability of routing mutation. Experimental data shows that DPW-RRM could not only guarantee the normal algorithmic overhead, communication delay, and CPU load of the network, but also effectively resist eavesdropping attacks.

• Biotechnology and Bioprocess Engineering:BBE
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• 제5권2호
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• pp.71-78
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• 2000

The recent progress od DNA technologies including DNA fingerprinting (DFP) and random amplified DNA polymorphism (RAPD) analysis make it possible to identify the specific genetic trits of animals and to analyze the genetic diversity and relatedness between or withinspecies or populations. Using those techniquse, some efforts to identify and develop the specific DNA markers based on DNA polymorphism, which are related with economic traits for Korean native animals, Hanwoo(Korean native cattle),Korean native pig and Korean native chicken, have been made in Korea for recent a few years. The developed specific DNA markers successfully characterize the Korean native animals as the unique Korean genetic sources, distinctively from other imported breeds. Some of these DNA markers have been related to some important economic traits for domestic animals, for example, growth rate and marbling for Honwoo, growth rate and back fat thinkness fornative pig, and growth rate, agg weight and agg productivity for native chicken. This means that those markers can be used in important marker-assised selection (MAS) of Korean native domestic animals and further contribute to genetically improve and breed them.