• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.259-261
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• 2001

An accurate digital distance relaying algorithm which is immune to the combined reactance effect of the fault resistance and the load current is proposed. The algorithm can estimate adaptively the impedance to a fault point independent of the fault resistance. To compensate the apparent impedance, this algorithm uses iteratively the angle of an impedance deviation vector improved step by step. The impedance correction algorithm for ground faults uses a current distribution factor to compensate mutual coupling effect.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.269-276
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• 2014

In recent, autonomous navigation techniques to avoid obstacles have been studied by using unmanned aircraft vehicles(UAVs) since the increment of UAV's interest and utilization. Particularly, autonomous navigation based UAVs are utilized in several areas such as military, police, media, and so on. However, there are still some problems to avoid obstacle when UVAs perform autonomous navigation. For instance, the UAV can not forward in the corner of corridors even though it utilizes the improved vanish point algorithm that makes an autonomous navigation system. Therefore, in this paper, we propose an obstacle avoidance technique based on immune algorithm for autonomous navigation of Quadrotor. The proposed algorithm is consisted of two steps such as 1) single color discrimination and 2) multiple color discrimination. According to the result of experiments, we can solve the previous problem of the improved vanish point algorithm and improve the performance of autonomous navigation of Quadrotor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1273-1293
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• 2010

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc. However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes). In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems. First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation. In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given. Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor. Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance. Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1286-1302
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• 2012

In this paper, the resource allocation problem with proportional fairness rate in cognitive OFDM-based wireless network is studied. It aims to maximize the total system throughput subject to constraints that include total transmit power for secondary users, maximum tolerable interferences of primary users, bit error rate, and proportional fairness rate among secondary users. It is a nonlinear optimization problem, for which obtaining the optimal solution is known to be NP-hard. An efficient bio-inspired suboptimal algorithm called immune clonal optimization is proposed to solve the resource allocation problem in two steps. That is, subcarriers are firstly allocated to secondary users assuming equal power assignment and then the power allocation is performed with an improved immune clonal algorithm. Suitable immune operators such as matrix encoding and adaptive mutation are designed for resource allocation problem. Simulation results show that the proposed algorithm achieves near-optimal throughput and more satisfying proportional fairness rate among secondary users with lower computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.735-748
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• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1178-1191
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• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

• ETRI Journal
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• v.38 no.6
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• pp.1153-1162
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• 2016

This paper addresses the resource allocation (RA) problem in multi-cell cognitive radio networks. Besides the interference power threshold to limit the interference on primary users PUs caused by cognitive users CUs, a proportional fairness constraint is used to guarantee fairness among multiple cognitive cells and the impact of imperfect spectrum sensing is taken into account. Additional constraints in typical real communication scenarios are also considered-such as a transmission power constraint of the cognitive base stations, unique subcarrier allocation to at most one CU, and others. The resulting RA problem belongs to the class of NP-hard problems. A computationally efficient optimal algorithm cannot therefore be found. Consequently, we propose a suboptimal RA algorithm composed of two modules: a subcarrier allocation module implemented by the immune algorithm, and a power control module using an improved sub-gradient method. To further enhance algorithm performance, these two modules are executed successively, and the sequence is repeated twice. We conduct extensive simulation experiments, which demonstrate that our proposed algorithm outperforms existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5574-5591
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• 2017

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.161-166
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• 2021

Errors in the semiconductor process are generated by a change in the state of the equipment, and errors usually arise when the state of the equipment changes or when parts that make up the equipment have flaws. In this investigation, we anticipated that aging of the mass flow controller in the plasma enhanced chemical vapor deposition SiO2 thin film deposition method caused a minute flow rate shift. In seven cases, fourier transformation infrared film quality analysis of the deposited thin film was used to characterize normal and pathological processes. The plasma condition was monitored using optical emission spectrometry data as the flow rate changed during the procedure. Preprocessing was used to apply the collected OES data to the artificial immune system algorithm, which was then used to process diagnosis. Through comparisons between datasets, the learning algorithm compared classification accuracy and improved the method. It has been confirmed that data characterized as a normal process and abnormal processes with differing flow rates may be discriminated by themselves using the artificial immune system data mining method.