• ETRI Journal
• /
• v.40 no.6
• /
• pp.714-725
• /
• 2018

Destination-assisted jamming (DAJ) is usually used to protect confidential information against untrusted relays and eavesdroppers in wireless networks. In this paper, a DAJ-based untrusted relay network with multiple antennas installed is presented. To increase the secrecy, a joint optimization of beamforming and power allocation at the source and destination is studied. A matched-filter precoder is introduced to maximize the cooperative jamming signal by directing cooperative jamming signals toward untrusted relays. Then, based on generalized singular-value decomposition (GSVD), a novel transmitted precoder for confidential signals is devised to align the signal into the subspace corresponding to the confidential transmission channel. To decouple the precoder design and optimal power allocation, an iterative algorithm is proposed to jointly optimize the above parameters. Numerical results validate the effectiveness of the proposed scheme. Compared with other schemes, the proposed scheme shows significant improvement in terms of security performance.

• Journal of Drive and Control
• /
• v.20 no.4
• /
• pp.54-63
• /
• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.16-24
• /
• 2024

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.12
• /
• pp.1056-1060
• /
• 2002

Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2004.04a
• /
• pp.103-110
• /
• 2004

Recently, performance based seismic design (PBSD) methods in numerous forms have been suggested and widely studied as a new concept of seismic design. The PBDSs are far from being practical due to complexity of algorithms resided in the design philosophy In this paper, optimal seismic design method based on displacement coefficient method (DCM) described in FEMA 273 is developed. As an optimizer simple genetic algorithms are used for implementations. In the optimization problem formulated in this paper, strength design criteria, stiffness design criteria, and nonlinear response criteria specified in DCM are included in design constraints. The optimal performance based design(OPBD) method is applied to seismic design of a 9-story two-dimensional steel frame structures.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2005.04a
• /
• pp.636-643
• /
• 2005

Recently, performance based seismic design (PBSD) methods have been suggested in numerous forms and widely studied as a new concept of seismic design. The PBDSs are far from being practical method due to complexity of algorithms resided in the design philosophy. In this paper, optimal seismic design method based on displacement coefficient method (DCM) described in FEMA 273 is developed. As an optimizer simple genetic algorithms are used for implementations. In the optimization problem formulated in this Paper, strength design criteria stiffness design criteria, and nonlinear response criteria specified in DCM are included in design constraints. The optimal performance based design(OPBD) method is applied to seismic design of a 3-story two-dimensional steel frame structures.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.11 no.3
• /
• pp.135-145
• /
• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• The KIPS Transactions:PartB
• /
• v.17B no.2
• /
• pp.115-124
• /
• 2010

Stereo matching techniques are categorized in two major schemes, local and global matching techniques. In global matching schemes, several investigations are introduced, where cost accumulation is performed in multiple matching lines. In this paper, we introduce a new multi-line stereo matching techniques which expands a conventional single-line matching scheme to multiple one. Matching cost is based on simple normalized cross correlation. We expand the scan-line optimization technique to a multi-line scan-line optimization technique. The proposed technique first generates a reliability image, which is iteratively updated based on the previous reliability measure. After some number of iterations, the reliability image is completed by a hole-filling algorithm. The hole-filling algorithm introduces a disparity score table which records the disparity score of the current pixel. The disparity of an empty pixel is determined by comparing the scores of the neighboring pixels. The proposed technique is tested using the Middlebury and CMU stereo images. The error analysis shows that the proposed matching technique yields better performance than using conventional global matching algorithm.

• Journal of the Korea Society for Simulation
• /
• v.18 no.2
• /
• pp.49-55
• /
• 2009

In this paper, we investigate an optimal allocation of constant service resources in stochastic system to optimize the expected performance of interest. For this purpose, we use the control variates to estimate the gradients of expected performance with respect to given resource parameters, and apply these estimated gradients in stochastic optimization algorithm to find the optimal allocation of resources. The proposed gradient estimation method is advantageous in that it uses simulation results of a single design point without increasing the number of design points in simulation experiments and does not need to describe the logical relationship among realized performance of interest and perturbations in input parameters. We consider the applications of this research to various models and extension of input parameter space as the future research.

• Smart Structures and Systems
• /
• v.31 no.3
• /
• pp.247-257
• /
• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.