• Geomechanics and Engineering
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• 제28권4호
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• pp.397-404
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• 2022

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Structural Engineering and Mechanics
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• 제84권2호
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• 기술혁신학회지
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• 제19권1호
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• pp.80-104
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• 2016

기존 유망 신기술에 대한 기술기획의 방법론들은 대상 기술 자체에 초점을 두어 해당 기술이 영향을 미치는 사회 환경적 맥락 이해가 부족한 현실이다. 이에 본 연구는 과학 커뮤니케이션 분야에서 널리 쓰이고 있는 뉴스 내용분석 방법론이 유망 신기술 대한 기술기획에서 사회적 환경의 맥락적 이해를 위한 보완적 방법론으로 활용될 수 있음을 살펴보고자 한다. 기술-사회 공진화 환경에서 유망 신기술은 사회와의 관계에서 하이프(hype) 현상을 나타낸다. 이에 착안하여 뉴스 내용분석을 수행하여 해당 분석 결과가 하이프 사이클 궤적과 부합하는 지를 탐색적으로 살피고, 뉴스 프레임 분석을 통해서는 유망 신기술에 따른 사회적 가시성의 실체적 내용을 이해하고자 하였다. 이를 위해 대표적인 유망 신기술로 클라우드 컴퓨팅을 대상으로 뉴스 내용분석을 수행하였다. 종합지, 경제지 및 IT전문지를 대상으로 한 뉴스 내용분석 결과는 뉴스 보도빈도가 가트너가 제시한 하이프 사이클 궤적과 부합하였으며, 특히 보도 태도 및 뉴스 프레임 분석은 유망 신기술에 대한 거시 환경요인별 맥락적 내용을 이해할 수 있는 유용한 정보를 제공함을 확인하였다. 본 연구의 결과는 뉴스 내용분석이 기술기획에서 주로 활용되는 논문 특허를 중심으로 한 기술정보 분석의 한계점을 극복하고 거시환경 요인별 맥락 이해를 위한 보완적 방법론으로 활용될 수 있음을 시사한다. 결론적으로 기술기획의 거시환경 분석 단계에서 뉴스 내용분석 방법론의 활용은 기술-사회 공진화 관점의 상호 균형된 시각을 확보함에 기여할 수 있을 것이다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권12호
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• pp.1217-1228
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• 2005

최근 들어 DNA 컴퓨팅이 활발하게 연구되면서, DNA 컴퓨팅에서 가장 기본적이고도 중요한 DNA 서열 디자인 문제가 부각되고 있다. 기존의 연구에서 DNA 서열 디자인 문제를 다중목적 최적화 문제로 정의하고, elitist non-dominated sorting genetic algorithm(NSGA-II)를 이용하여 성공적으로 DNA 서열을 디자인하였다. 그런데, NSGA-II는 계산속도가 느리다는 단점이 있어서, 이를 극복하기 위해 본 논문에서는 $\varepsilon$-다중목적함수 진화알고리즘(r-Multiobjective evolutionary algorithm, $\varepsilon$-MOEA)을 DNA 서열 디자인에 이용하였다. 우선, 두 알고리즘의 성능을 보다 자세히 비교하기 위해서 DTLZ2 벤치 마크 문제에 대해서 적용한 결과, 목적함수의 개수가 작은 경우에는 큰 차이가 없으나, 목적함수의 개수가 많을 경우에는 $\varepsilon$-MOEA가 NSGA-II에 대해서 최적해를 찾는 정도(Convergence)와 다양한 해를 찾는 정도 (diversity)에 있어서 각각 $70\%,\;73\%$ 향상된 성능을 보여주었고, 또한 최적해를 찾는 속도도 비약적으로 개선되었다. 이러한 결과를 바탕으로 기존의 DNA 서열 디자인 방법론으로 디자인된 DNA 서열들과 7-순환외판원 문제 해결에 필요한 DNA 서열을 NSGA-II와 $\varepsilon$-MOEA로 재디자인하였다. 대부분의 경우 $\varepsilon$-MOEA가 우수한 결과를 보였고, 특히 7-순환외판원 문제에 대해서 NSGA-II와 비교하여 convergence와 diversity의 측면에서 유사한 결과를 2배 이상 빨리 발견하였고, 동일한 계산 시간을 이용해서는 $22\%$ 정도 보다 다양하게 해를 발견하였으며, $92\%$ 우수한 최적해를 발견하는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• 대한기계학회논문집A
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• 제27권6호
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• pp.1020-1027
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• 2003

A traditional mold design has been conducted by an experience-based trial and error, whereby the mold designer would decide the gate locations and processing conditions based on the caring characteristics and its functional requirements. The paper suggests an optimal gate location and processing conditions in the injection molding using a global search method referred to as micro genetic algorithm( ${\mu}$ GA). ${\mu}$ GA yields the optimal solution with a small size of population without respect to design variables for saving time that is needed to calculate the fitness of many individuals. Due to the reason, the paper uses a commercial analysis package of injection molding(CAPA) to analysis a state of flux. In addition to that, axiomatic approach .is applied in the beginning of design. It is a useful method to draw a well-organized and reasonable idea to handle a problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• 한국인터넷방송통신학회논문지
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• 제23권4호
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• pp.47-52
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• 2023

기계학습의 데이터 크기 및 컴퓨팅 부하 증가로 사물인터넷 시스템에서 에너지 효율적인 자원 플래닝이 중요해지고 있다. 본 논문에서는 사물인터넷 시스템의 실시간 워크로드 변화를 지원하는 자원 플래닝 정책을 제안한다. 이를 위해 본 논문은 실시간 태스크를 고정 태스크와 가변 태스크로 나누고 다양한 워크로드 상황에 대한 자원 플래닝 최적화를 수행한다. 이를 바탕으로 사물인터넷 시스템의 자원 설정을 고정 태스크 기반으로 시작한 후, 가변 태스크가 활성화될 경우 상황에 맞는 자원 플래닝을 즉시 적용할 수 있도록 한다. 시뮬레이션을 통해 제안한 정책이 사물인터넷 시스템의 프로세서 및 메모리 소모 에너지를 크게 줄일 수 있음을 보인다.