• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.11 no.6
• /
• pp.45-51
• /
• 2002

The existing methods to split overall gear ratio of the cylindrical multi-stage gear train have their own limitations to be used in practical design and are also problematic to be implemented in a formalized algerian. This paper proposes two types of new methods to find gear ratios best approximating the overall gear ratio. The proposed methods are quite general to be applied to the gear train having any number of stages, and offer a considerably good result in a very short time. The first method uses the random search method and the second one is based on the simulated annealing algorithm. The proposed algorithms are expected to be very useful not only as an independent program to split overall gear ratio, but also as a desist sub-module for the integrated desist system of multi-stage gear drives.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.377-381
• /
• 2003

The design of speed controller for induction motor using tabu search is studied. The proposed sensorless vector control for Induction Motor is composed of two parts. The first part is for optimizing the initial parameters of input-output. The second part is for real time changing parameters of input-output using tabu search. Proposed tabu search is improved by neighbor solution creation using Gaussian random distribution. In order to show the usefulness of the proposed method, we apply the proposed controller to the sensorless speed control of an actual AC induction Motor System. The performance of this approach is verified through simulation.

• Journal of Advanced Marine Engineering and Technology
• /
• v.33 no.3
• /
• pp.403-410
• /
• 2009

The aim of this research is to develop the Bees Algorithm named 'the dynamic allocated Bees Algorithm' for multi-objective problem, especially in order to be suit for Pareto optimality. In addition two new neighbourhood search methods have been developed to produce enhanced solutions for a multi-objective problem named 'random selection neighbourhood search' and 'weighted sum neighbourhood search' and they were compared with the basic neighbourhood search in the dynamic allocated Bees Algorithm. They were successfully applied to an Environmental/Economic (electric power) dispatch (EED) problem and simulation results presented for the standard IEEE 30-bus system and they were compared to those obtained using other approaches. The comparison shows the superiority of the proposed dynamic allocated Bees Algorithms and confirms its suitability for solving the multi-objective EED problem.

• Journal of the Korea Society of Computer and Information
• /
• v.9 no.1
• /
• pp.87-93
• /
• 2004

In the design of mobile wireless communication network, BSC(Base Station Location), BSC(Base Station Controller) and MSC(Mobile Switching Center) are the most important parameters. Designing base station location, the cost must be minimized by combining various, complex parameters. We can solve this Problem by combining optimization algorithm, such as Simulated Annealing, Tabu Search, Genetic Algorithm, Random Walk Algorithm that have been used extensively for global optimization. This paper shows the 4 kinds of algorithm to be applied to the optimization of base station location for communication system and then compares, analyzes the results and shows optimization process of algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.5
• /
• pp.1653-1660
• /
• 2010

The user information stored in database have been leaked frequently. To protect information against malevolent manager on the inside or outside aggressor, it is one of the most efficient way to encrypt information and store to database. It is better to destruct information than not to use encrypted information stored in database. The encrypted database search system is developed variously, and used widely in many fields. In this paper, we implemented the scheme that can search encrypted document without exposing user's information to the untrusted server in mobile device. We compared and analyzed the result embodied with DES, AES, and ARIA based on symmetric key by searching time.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.15 no.11 s.104
• /
• pp.1223-1231
• /
• 2005

Recently, the issue of ship nitration due to the large scale, high speed and lightweight of ship is emerging. For pleasantness in the cabin, shipbuilders are asked for strict vibration criteria and the degree of nitration level at a deckhouse became an important condition for taking order from customers. This study proposes a new optimization framework that is NASTRAN external call type optimization method (OptShip) and applies to an optimum design to decrease the nitration level of a deckhouse. The merits of this method are capable of using of global searching method and selecting of various objective function and design variables. The global optimization algorithms used here are random tabu search method which has fast converging speed and searches various size domains and genetic algorithm which searches multi-point solutions and has a good search capability in a complex space. By adapting OptShip to full-scale model, the validity of the suggested method was investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.2846-2866
• /
• 2022

Caenorhabditis elegans exhibits sophisticated chemotaxis behavior through two parallel strategies, klinokinesis and klinotaxis, executed entirely by a small nervous circuit. It is therefore suitable for inspiring fast and energy-efficient solutions for autonomous navigation. As a random search strategy, the Lévy walk is optimal for diverse animals when foraging without external chemical cues. In this study, by combining these biological strategies for the first time, we propose a spiking neural network model for search and contour tracking of specific concentrations of environmental variables. Specifically, we first design a klinotaxis module using spiking neurons. This module works in conjunction with a klinokinesis module, allowing rapid searches for the concentration setpoint and subsequent contour tracking with small deviations. Second, we build a random exploration module. It generates a Lévy walk in the absence of concentration gradients, increasing the chance of encountering gradients. Third, considering local extrema traps, we develop a termination module combined with an escape module to initiate or terminate the escape in a timely manner. Experimental results demonstrate that the proposed model integrating these modules can switch strategies autonomously according to the information from a single sensor and control steering through output spikes, enabling the model worm to efficiently navigate across various scenarios.

• International Journal of Computer Science & Network Security
• /
• v.23 no.7
• /
• pp.131-140
• /
• 2023

Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.4
• /
• pp.1-8
• /
• 2024

The traveling salesman problem(TSP) is one of the well known combinatorial optimization problems. Local search has been used as a method to solve TSP. Greedy Random Insertion(GRI) is known as an effective neighbor generation method for local search. GRI selects some cities from the current solution randomly and inserts them one by one into the best position of the current partial solution considering only one city at a time. We first propose another greedy neighbor generation method which is named Full Greedy Insertion(FGI). FGI determines insertion location one by one like GRI, but considers all remaining cities at once. And then we propose a method to combine GRI with FGI, in which GRI or FGI is randomly selected and executed at each iteration in simulated annealing. According to the experimental results, FGI alone does not necessarily perform very well. However, we confirmed that the combined method outperforms the existing local search methods including GRI.

• Korean Journal of Construction Engineering and Management
• /
• v.21 no.6
• /
• pp.38-45
• /
• 2020

This study presents a systematic procedure for developing a short-term prediction deep learning model of rebar price using bidirectional LSTM, Random Search, data combination, Dropout. In general, users intuitively determine these values, making it time-consuming and repetitive attempts to explore results with good predictive performance, and the results found by these attempts cannot be guaranteed to be excellent. With the proposed approach presented in this study, the average accuracy of short-term price forecasts is approximately 98.32%. In addition, this approach could be used as basic data to produce good predictive results in a study that predicts prices with time series data based on statistics, including building materials other than rebars.