• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.360-363
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• 2018

Human activity is influenced by various factors, from individual physical features such as vertebral flexion and pelvic distortion to feelings such as joy, anger, and sadness. However, the nature of these behaviors changes over time, and behavioral characteristics do not change much in the short term. The activity data of a person has a time series characteristic that changes with time and a certain regularity for each action. In this study, we applied LSTM, a kind of cyclic neural network to deal with time - series characteristics, to the technique of recognizing activity type and improved recognition rate of activity type by measuring time and parameter optimization of components of LSTM model.

• Smart Structures and Systems
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• v.26 no.3
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• pp.263-275
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• 2020

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

• ETRI Journal
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• v.36 no.4
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• pp.599-608
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• 2014

This paper studies a parallel relay network where the relays employ an amplify-and-forward (AF) relaying scheme and are subjected to individual power constraints. We consider correlated effective relay noise arising from practical scenarios when the relays are exposed to common interferers. Assuming that the noise covariance and the full channel state information are available, we investigate the problem of finding the optimal AF scheme in terms of maximum end-to-end transmission rate. It is shown that the maximization problem can be equivalently transformed to a convex semi-definite program, which can be efficiently solved. Then an upper bound on the maximum achievable AF rate of this network is provided to further evaluate the performance of the optimal AF scheme. It is proved that the upper bound can be asymptotically achieved in two special regimes when the transmit power of the source node or the relays is sufficiently large. Finally, both theoretical and numerical results are given to show that, on average, noise correlation is beneficial to the transmission rate - whether the relays know the noise covariance matrix or not.

• Journal of Communications and Networks
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• v.15 no.1
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• pp.102-110
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• 2013

This paper considers a cooperative orthogonal frequency division multiple access (OFDMA)-based cognitive radio network where the primary system leases some of its subchannels to the secondary system for a fraction of time in exchange for the secondary users (SUs) assisting the transmission of primary users (PUs) as relays. Our aim is to determine the cooperation strategies among the primary and secondary systems so as to maximize the sum-rate of SUs while maintaining quality-of-service (QoS) requirements of PUs. We formulate a joint optimization problem of PU transmission mode selection, SU (or relay) selection, subcarrier assignment, power control, and time allocation. By applying dual method, this mixed integer programming problem is decomposed into parallel per-subcarrier subproblems, with each determining the cooperation strategy between one PU and one SU. We show that, on each leased subcarrier, the optimal strategy is to let a SU exclusively act as a relay or transmit for itself. This result is fundamentally different from the conventional spectrum leasing in single-channel systems where a SU must transmit a fraction of time for itself if it helps the PU's transmission. We then propose a subgradient-based algorithm to find the asymptotically optimal solution to the primal problem in polynomial time. Simulation results demonstrate that the proposed algorithm can significantly enhance the network performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.399-406
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• 2020

In this paper, we propose a time-varying proportional navigation guidance law that determines the proportional navigation gain in real-time according to the operating situation. When intercepting a target, an unidentified evasion strategy causes a loss of optimality. To compensate for this problem, proper proportional navigation gain is derived at every time step by solving an optimal control problem with the inferred evader's strategy. Recently, deep reinforcement learning algorithms are introduced to deal with complex optimal control problem efficiently. We adapt the actor-critic method to build a proportional navigation gain network and the network is trained by the Proximal Policy Optimization(PPO) algorithm to learn an evasion strategy of the target. Numerical experiments show the effectiveness and optimality of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.953-959
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• 2012

There are a number of idle nodes in sensor networks, these can act as detector nodes for anomaly detection in the network. For detecting node selection problem modeled as optimization equation, the conventional method using centralized genetic algorithm was evaluated. In this paper, a method to improve the convergence of the optimal value, while improving energy efficiency as a method of considering the characteristics of the network topology using parallel genetic algorithm is proposed. Through simulation, the proposed method compared with the conventional approaches to the convergence of the optimal value was improved and was found to be energy efficient.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.546-553
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• 2012

Compact routing intends to achieve good tradeoff between the routing path length and the memory overhead, and is recently considered as a main alternative to overcome the fundamental scaling problems of the Internet routing system. Plenty of studies have been conducted on compact routing, and quite a few universal compact routing schemes have been designed for arbitrary network topologies. However, it is generally believed that specialized compact routing schemes for peculiar network topologies can have better performance than universal ones. Studies on complex networks have uncovered that most real-world networks exhibit power-law degree distributions, i.e., a few nodes have very high degrees while many other nodes have low degrees. High-degree nodes play the crucial role of hubs in communication and inter-networking. Based on this fact, we propose two highest-degree landmark based compact routing schemes, namely HDLR and $HDLR^+$. Theoretical analysis on random power-law graphs shows that the two schemes can achieve better space-stretch trade-offs than previous compact routing schemes. Simulations conducted on random power-law graphs and real-world AS-level Internet graph validate the effectiveness of our schemes.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.248-254
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• 2009

The use of evolutionary algorithm is limited in the field of aerodynamics, mainly because the population-based search algorithm requires excessive CPU time. In this paper a coupling method with adaptive range genetic algorithm for floating point and back-propagation neural network is proposed to efficiently obtain a converged solution. As a result, it is shown that a reduction of 14% and 33% respectively in wave drag and its consumed time can be achieved by the new method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1032-1037
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• 2006

The channel is one of the precious and limited resources in wireless networks. There are many researches on the channel management. Recently, the optimization problem of guard channels has been an important issue. In this paper, we propose an intelligent channel management technique based on the neural networks. An SRN channel allocation model is developed to generate the learning data for the neural networks and the performance analysis of system. In the proposed technique, the neural network is trained to generate optimal guard channel number g, using backpropagation supervised learning algorithm. The optimal g is computed using the neural network and compared to the g computed by the SRM model. The numerical results show that the difference between the value of 8 by backpropagation and that value by SRM model is ignorable.

• Journal of Soil and Groundwater Environment
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• v.6 no.3
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• pp.43-52
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• 2001

It is important that the gas collected from wells completed in waste landfill should be continuously and stably transported to pre-treatment stage through pipelines. The transport is generally affected by fluid flow characteristics of landfill, gas reserves, leachate moisture holdup in pipeline, structures and dimensions of pipeline network, etc. This paper analyzes the pipeline transport and collection mechanism for gas generated in a durable waste landfill. From the results, the optimal controlled scheme of blower inlet pressure is proposed for the prevention of trapped gas pocket zones.