• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.8
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• pp.488-490
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• 2014

This paper deals with distributed time and frequency synchronization algorithms using the flocking technique for OFDMA-based wireless mesh networks. We propose a time and frequency synchronization model taking into account channel propagation delays existing in wireless mesh networks, and analyze the convergence condition of the proposed synchronization algorithm. Convergence performance of the proposed synchronization algorithm is analyzed via computer simulation in terms of synchronization parameters in the time and frequency synchronization model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2225-2239
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• 2021

To assist in data collection, the use of a mobile sink has been widely suggested in the literature. Due to the limited sensor node's storage capacity, this manner to collect data induces huge latencies and drop packets. Their buffers will be overloaded and lead to network congestion. Recently, a new bio-inspired optimization algorithm appeared. Researchers were inspired by the swarming mechanism of salps and thus creating what is called the Salp Swarm Algorithm (SSA). This paper improves the sink mobility to enhance energy dissipation, throughput, and convergence speed by imitating the salp's movement. The new approach, named the Mobile Sink based on Modified Salp Swarm Algorithm (MSSA), is approved in a heterogeneous Wireless Sensor Network (WSN) data collection. The performance of the MSSA protocol is assessed using several iterations. Results demonstrate that our proposal surpass other literature algorithms in terms of lifespan and throughput.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.596-601
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• 2011

Autonomous navigating algorithms for mobile robots have been proved to be a difficult task. Based on the excellent homing performance shown by many insects, bio-inspired navigation algorithms for robotic experiments have been widely researched and applied to the design of navigational strategies for mobile robots. In this paper, among them, we analyze two simple landmark navigation methods their strengths and limits. We investigate the effect of the occlusion problem mainly, which is an important yet tough problem in many landmark navigation algorithms. In the point of view of the error of homing vector and the performance of the homing paths in the environment with artificial occlusions, we investigate the effect of occlusion problem in both methods in order to further study on solutions.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.280-280
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• 2000

In this paper, we propose a method of constructing equation using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is. we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.107-110
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.224-227
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• 2000

In this Paper, we prepose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series, Sun spot data and KOSPI data.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.405-423
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• 2022

The distance vector-hop (DV-Hop) is one of the emblematic algorithms that use node connectivity for locating, which often accompanies by a large positioning error. To reduce positioning error, the bio-inspired algorithm and weight optimization model are introduced to address positioning. Most scholars argue that the weight value decreases as the hop counts increases. However, this point of view ignores the intrinsic relationship between the error and weight. To address this issue, this paper constructs the relationship model between error and hop counts based on actual communication characteristics of sensor nodes in wireless sensor network. Additionally, we prove that the error converges to 1/6CR when the hop count increase and tendency to infinity. Finally, this paper presents a modified error-oriented weight positioning model, and implements it with genetic algorithm. The experimental results demonstrate excellent robustness and error removal.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.27-29
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• 2007

패턴 인식 문제를 푸는데 있어 특징 선택을 해주는 것은 패턴 인식의 성능 향상을 위해 중요한 과정 중 하나이다. 본 연구에서는 대표적인 생태계 모방 알고리즘 2 가지를 선택하여 특징 선택 문제에 적용하여 보고, 그 성능을 비교 분석하였다. 데이터의 특징을 줄여주는 기능과 패턴 인식 성능의 향상 여부를 중심으로 평가하였으며, 이를 통해 생태계 모방 알고리즘이 특징 선택 문제에 효과적으로 사용될 수 있는지에 대해 논의해보고, 두 방법의 장단점과 특징에 대해 생각해 본다.