• Journal of Integrative Natural Science
• /
• v.11 no.4
• /
• pp.204-208
• /
• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.41.1-41
• /
• 2001

Since the advanced control technique such as model predictive control has been introduced to industrial plant, there have been many progresses in the process control. As a way to improve the control performance, the on-line process optimizer was integrated with the advance controller. In this study, a control technique which improves the control. As the number of changes by the optimizer is increased, the control performance of the proposed algorithm is improved. Its control performance is shown via an numerical example.

• 열병합발전
• /
• s.24
• /
• pp.16-19
• /
• 2001

• Journal of Korea Multimedia Society
• /
• v.8 no.11
• /
• pp.1556-1564
• /
• 2005

Various optimization techniques are deployed in the compilation process of a source program for improving the program's execution speed and reducing the size of the source code. Of the optimization pattern matching techniques, the string pattern matching technique involves finding an optimal pattern that corresponds to the intermediate code. However, it is deemed inefficient due to excessive time required for optimized pattern search. The tree matching pattern technique can result in many redundant comparisons for pattern determination, and there is also the disadvantage of high cost involved in constructing a code tree. The objective of this paper is to propose a table-driven code optimizer using the DFA(Deterministic Finite Automata) optimization table to overcome the shortcomings of existing optimization techniques. Unlike other techniques, this is an efficient method of implementing an optimizer that is constructed with the deterministic automata, which determines the final pattern, refuting the pattern selection cost and expediting the pattern search process.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.9
• /
• pp.1250-1256
• /
• 2018

Recently, wireless networks are becoming an important infrastructure for the critical large-scale applications such as cyber-physical systems and next generation industrial automations. However, the fundamental performance uncertainty of wireless networks may incur the serious instability problem of the overall systems. This paper proposes the proactive network optimizer to guarantee the application demands without any real-time link monitoring information of the networks. In particularly, the proposed proactive optimizer is the cross-layer approach to jointly optimize the routing path and traffic distribution in order to guarantee the performance demand within a maximum k number of link faults. Through the simulations, the proposed proactive network optimizer provides better robustness than the traditional existing reactive networks. Furthermore, the proactive network does not expose to the major weakness of the reactive networks such as the performance degradation due to the erroneous link monitoring information and the network reconfiguration cost.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1043-1051
• /
• 2018

The coordination of directional overcurrent relay (DOCR) is employed in this work, considering gray wolf optimizer (GWO), a recently designed optimizer that employs the hunting and leadership attitude of gray wolves for searching a global optimum. In power system protection coordination problem, the objective function to be optimized is the sum of operating time of all the main relays. The coordination of directional overcurrent relays is formulated as a linear programming problem. The proposed optimization technique aims to minimize the time dial settings (TDS) of the relays. The calculation of the Time Dial Setting (TDS) setting of the relays is the core of the coordination study. In this article two case studies of IEEE 6-bus system and IEEE 30-bus system are utilized to see the efficiency of this algorithm and the results had been compared with the other algorithms available in the reference and it was observed that the proposed scheme is quite competent for dealing with such problems. From analyzing the obtained results, it has been found that the GWO approach provides the most globally optimum solution at a faster convergence speed. GWO has achieved a lot of relaxation due to its easy implementation, modesty and robustness. MATLAB computer programming has been applied to see the effectiveness of this algorithm.

• The Journal of Korean Society for Radiation Therapy
• /
• v.24 no.2
• /
• pp.137-147
• /
• 2012

Purpose: Analyze the Effectiveness of Radiation Treatment Planning by dose calculation and optimization algorithm, apply consideration of actual treatment planning, and then suggest the best way to treatment planning protocol. Materials and Methods: The treatment planning system use Eclipse 10.0. (Varian, USA). PBC (Pencil Beam Convolution) and AAA (Anisotropic Analytical Algorithm) Apply to Dose calculation, DVO (Dose Volume Optimizer 10.0.28) used for optimized algorithm of Intensity Modulated Radiation Therapy (IMRT), PRO II (Progressive Resolution Optimizer V 8.9.17) and PRO III (Progressive Resolution Optimizer V 10.0.28) used for optimized algorithm of VAMT. A phantom for experiment virtually created at treatment planning system, $30{\times}30{\times}30$ cm sized, homogeneous density (HU: 0) and heterogeneous density that inserted air assumed material (HU: -1,000). Apply to clinical treatment planning on the basis of general treatment planning feature analyzed with Phantom planning. Results: In homogeneous density phantom, PBC and AAA show 65.2% PDD (6 MV, 10 cm) both, In heterogeneous density phantom, also show similar PDD value before meet with low density material, but they show different dose curve in air territory, PDD 10 cm showed 75%, 73% each after penetrate phantom. 3D treatment plan in same MU, AAA treatment planning shows low dose at Lung included area. 2D POP treatment plan with 15 MV of cervical vertebral region include trachea and lung area, Conformity Index (ICRU 62) is 0.95 in PBC calculation and 0.93 in AAA. DVO DVH and Dose calculation DVH are showed equal value in IMRT treatment plan. But AAA calculation shows lack of dose compared with DVO result which is satisfactory condition. Optimizing VMAT treatment plans using PRO II obtained results were satisfactory, but lower density area showed lack of dose in dose calculations. PRO III, but optimizing the dose calculation results were similar with optimized the same conditions once more. Conclusion: In this study, do not judge the rightness of the dose calculation algorithm. However, analyzing the characteristics of the dose distribution represented by each algorithm, especially, a method for the optimal treatment plan can be presented when make a treatment plan. by considering optimized algorithm factors of the IMRT or VMAT that needs to optimization make a treatment plan.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.4
• /
• pp.1693-1713
• /
• 2018

An important technique used by database administrators (DBAs) is to improve performance in decision-support workloads associated with a Star schema is multi-level partitioning. Queries will then benefit from performance improvements via partition elimination, due to constraints on queries expressed on the dimension tables. As the task of multi-level partitioning can be overwhelming for a DBA we are proposing a wizard that facilitates the task by calculating a partitioning scheme for a particular workload. The system resides completely on a client and interacts with the costing estimation subsystem of the query optimizer via an API over the network, thereby eliminating any need to make changes to the optimizer. In addition, since only cost estimates are needed the wizard overhead is very low. By using a greedy algorithm for search space enumeration over the query predicates in the workload the wizard is efficient with worst-case polynomial complexity. The technology proposed can be applied to any clustering or partitioning scheme in any database management system that provides an interface to the query optimizer. Applied to the Teradata database the technology provides recommendations that outperform a human expert's solution as measured by the total execution time of the workload. We also demonstrate the scalability of our approach when the fact table (and workload) size increases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.6
• /
• pp.2644-2657
• /
• 2018

To utilize the energy of sensor nodes efficiently and extend the network lifetime maximally is one of the primary goals in wireless sensor networks (WSNs). Thus, designing an energy-efficient protocol to optimize the determination of cluster heads (CHs) in WSNs has become increasingly important. In this paper, we propose a novel energy-efficient protocol based on an improved Grey Wolf Optimizer (GWO), which we refer to as Fitness value based Improved GWO (FIGWO). It considers a fitness value to improve the finding of the optimal solution in GWO, which ensures a better distribution of CHs and a more balanced cluster structure. According to the distance to the CHs and the BS, sensor nodes' transmission distance are recalculated to reduce the energy consumption. Simulation results demonstrate that the proposed approach can prolong the stability period of the network in comparison to other algorithms, namely by 31.5% in comparison to SEP, and even by 57.8% when compared with LEACH protocol. The results also show that the proposed protocol performs well over the above comparative protocols in terms of energy consumption and network throughput.

• Journal of the Society of Naval Architects of Korea
• /
• v.33 no.2
• /
• pp.44-55
• /
• 1996

Since the use of computer for the ship structural design around mid 1960``s, specially many researches on the midship section optimum design were carried out from 1980. For a rule-based optimum design case, there has been a problem of handling a discrete design variable such as plate thickness for a practical use. To deal with the discrete design variable problems and to develop an effective new method using artificial neural network for the ship structural design applications, Neuro-Optimizer combing Hopfield Neural Network and other Simulated Annealing is proposed as a new optimization method and then applied to the fundamental skeletal structures and Midship section of Tanker. From the numerical results, it is confirmed that Neuro-Optimizer could be used effectively as a new optimization method for the structural design.