• 한국통신학회논문지
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• 제40권11호
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• pp.2230-2237
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• 2015

본 논문에서는 자연계에 존재하는 동기화 현상에 착안하여 무선 네트워크를 위한 자율적 부하균형 기법을 제안한다. 이를 위해 본 논문에서는 무선 접속 서비스를 제공하는 셀 사이의 부하균형 문제를 자연계 동기화 현상을 이용하여 모델링 한 후 각 셀들이 이웃 셀과의 부하 차이에 따라 자율적으로 부하를 분배하기 위한 알고리즘을 설계한다. 모의실험을 통해 제안 기법을 이용하여 각 셀들이 자신의 지역적 정보만을 이용하여 자율적으로 부하 분배 여부를 결정하더라도 셀 간 부하균형을 이룰 수 있다는 것을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.702-723
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• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1317-1341
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• 2021

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2011년도 제41회 하계 정기 학술대회 초록집
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• pp.202-202
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• 2011

The research of 3-dimensional (3-D) scaffold for tissue engineering has been widely investigated as the importance of the 3-D scaffold increased. 3-D scaffold is needed to support for cells to proliferate and maintain their biological functions. Furthermore, its architecture defines the shape of the new bone and cartilage growth. Polycaprolactone (PCL) has been one of the most promising materials for fabricating 3-D scaffold owing to its excellent mechanical property and biocompatibility. However, there are practical problems for using it, in vitro and in vivo; extracellular matrix components and nutrients cannot penetrate into the inner space of scaffold, due to its hydrophobic property, and thus cell seeding and attachment onto the inner surface remain as a challenge. Thus, the surface modification strategy of 3-D PCL scaffold is prerequisite for successful tissue engineering. Herein, we utilized a mussel-inspired approach for surface modification of 3-D PCL scaffold. Modification of 3-D PCL scaffolds was carried out by simple immersion of scaffolds into the dopamine solution and stimulated body fluid, and as a result, hydroxyapatite-immobilized 3-D PCL scaffolds were obtained. After surface modification, the wettability of 3-D PCL scaffold was considerably changed, and infiltration of the pre-osteoblastic cells into the 3-D scaffold followed by the attachment onto the surface was successfully achieved.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• The Journal of Asian Finance, Economics and Business
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• 제7권8호
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• pp.197-204
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• 2020

Nowadays, many construction engineering and technology enterprises are evolving to find that prosperity is driven and inspired by an open economy with dynamic markets and fierce multifaceted competition. Besides brand and product uniqueness, the ability to quickly provide customers with quotes are matters of concern. Such a requirement for prompt cost estimation of construction investment projects with the use of a construction price index poses a significant challenge to contractors. This is because the nature of the construction industry is shaped by changes in domestic and foreign economic factors, socio-financial issues, and is under the influence of various micro and macro factors. This paper presents a fuzzy decision-making approach for calculating critical factors that affect the construction price index. A qualitative approach was implemented based on in-depth interviews of experts in the construction industry in Vietnam. A synthetic comparison matrix was calculated using Buckley approach. The CoA approach was applied to defuzzified the fuzzy weights of factors that affect the construction price index. The research results show that the top five critical factors affecting the construction price index in Vietnam are (1) consumer price index, (2) gross domestic product, (3) basic interest rate, (4) foreign exchange rate, and (5) total export and import.

• Steel and Composite Structures
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• 제32권4호
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1020-1024
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• 2005

In this paper, an approach to dynamic systems control is addressed based on exploiting the potential features of the new nonlinear neural oscillator. Neural oscillators have recently enabled robots to exhibit natural dynamics using their robustness and entrainment properties. To technically accomplish this objective, the neural oscillator should be connected to the robot joints under the sensory feedback. This also requires the neural oscillator to adapt to the non-periodic nature of arbitrary input patterns. However, even in the most widely-used Matsuoka oscillator, when an unknown quasi-periodic or non-periodic signal is applied, its output signal is not always closely entrained. Therefore, current neural oscillators may not be applied to the precise control of the dynamic systems response. We illustrate the enhanced entrainment properties of the new neural oscillator by numerical simulation and show the possibility for implementation to control a variety of dynamic systems. It is verified that the oscillator can produce rhythmic signals for generating actuator signals which can be naturally modified by incorporating sensory feedback to adapt to outer circumstances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1142-1166
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• 2020

Salp Swarm Algorithm (SSA) is a new nature-inspired swarm optimization algorithm that mimics the swarming behavior of salps navigating and foraging in the oceans. SSA has been proved to enable to avoid local optima and enhance convergence speed benefiting from the adaptive nonlinear mechanism and salp chains. In this paper, visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. A novel SSA-based tracking framework is proposed and the analysis and adjustment of parameters are discussed experimentally. Besides, the qualitative analysis and quantitative analysis are performed to demonstrate the tracking effect of our proposed approach by comparing with ten classical tracking algorithms. Extensive comparative experimental results show that our algorithm has good performance in visual tracking, especially for abrupt motion tracking.

• 한국통신학회논문지
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• 제42권1호
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• pp.64-76
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• 2017

MANET (Mobile Ad hoc Network)에서 라우팅은 네트워크 토폴로지의 동적인 변화에 큰 영향을 받는다. AntHocNet은 집단 개미가 최적 경로를 통해 먹이를 찾아가는 원리를 모방한 집단생태 특성 기반 MANET 라우팅 프로토콜이다. 하지만, AntHocNet은 다른 MANET 라우팅 프로토콜과 달리 단방향 경로만을 지원하여 양방향 통신이 요구되는 다양한 응용 환경에서 사용하기에 많은 제약이 따른다. 또한, AntHocNet은 다중 경로를 통한 확률적 라우팅으로 인해 루핑 문제 (looping problems)를 빈번히 발생시킨다. 본 논문에서는 AntHocNet에서 양방향 경로 수립을 위한 향상된 경로 수립 방안을 제안한다. 또한, 다양한 시나리오별 루핑 문제의 발생 원인을 분석하고 루프 방지를 위한 해결 방안을 제시한다. NS-2 시뮬레이션을 통해 기존 AntHocNet과의 성능을 비교하였으며, 제안 방안이 라우팅 오버헤드, 종단간 지연 시간, 패킷 전달률 측면에서 기존 방안에 비해 우수한 성능을 보임을 확인하였다.