• The Journal of Information Systems
• /
• v.29 no.3
• /
• pp.35-52
• /
• 2020

Purpose Local governments in each region actively hold local festivals for the purpose of promoting the region and revitalizing the local economy. Existing studies related to local festivals have been actively conducted in tourism and related academic fields. Empirical studies to understand the effects of latent variables on local festivals and studies to analyze the regional economic impacts of festivals occupy a large proportion. Despite of practical need, since few researches have been conducted to predict the number of visitors, one of the criteria for evaluating the performance of local festivals, this study developed a model for predicting the number of visitors through various observed variables using a machine learning algorithm and derived its implications. Design/methodology/approach For a total of 593 festivals held in 2018, 6 variables related to the region considering population size, administrative division, and accessibility, and 15 variables related to the festival such as the degree of publicity and word of mouth, invitation singer, weather and budget were set for the training data in machine learning algorithm. Since the number of visitors is a continuous numerical data, random forest, Adaboost, and linear regression that can perform regression analysis among the machine learning algorithms were used. Findings This study confirmed that a prediction of the number of visitors to local festivals is possible using a machine learning algorithm, and the possibility of using machine learning in research in the tourism and related academic fields, including the study of local festivals, was captured. From a practical point of view, the model developed in this study is used to predict the number of visitors to the festival to be held in the future, so that the festival can be evaluated in advance and the demand for related facilities, etc. can be utilized. In addition, the RReliefF rank result can be used. Considering this, it will be possible to improve the existing local festivals or refer to the planning of a new festival.

• IE interfaces
• /
• v.2 no.2
• /
• pp.15-24
• /
• 1989

When we design, plan, and schedule for group technology, the limitation on the machine cells and cell size may occur. The purpose of this study is to find machine cells and part families to minimize the exceptional elements, constraining both the number of machine cells and the cell size. To solve this problem, the algorithm extending Kusiak's p-median method is proposed. In the proposed algorithm, the method finding initial solution and reducing the number of constraints is presented for computational efficiency. The proposed algorithm is evaluated and compared with well-known algorithms for machine-part group formation in terms of the exceptional elements. An example is shown to illustrate the proposed algorithm.

• East Asian mathematical journal
• /
• v.19 no.1
• /
• pp.17-26
• /
• 2003

This paper demonstrates the CAS technique of analyzing the nature and the structure of the numerical error for education and research purposes. This also illustrates the CAS approach in experimenting with the numerical operations in an arbitrary computer number system and also in doing error analysis in a visual manner.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.4
• /
• pp.360-367
• /
• 2002

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells. The purpose of this study is to develop a two-phase machine-part group formation algorithm based on Self-Organizing Maps (SOM). In phase I, it forms machine cells from the machine-part incidence matrix by means of SOM whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the input vectors. In phase II, it generates part families which are assigned to machine cells by means of machine ratio related with processing part and it gives machine-part group formation. The proposed algorithm performs remarkably well in comparison with many well-known algorithms for the machine-part group formation problems.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.455-456
• /
• 2006

Multi-tasking machines are widely used in machine tool industries nowadays. This study focuses on the effect of load on the structure stabilities of laser multi-tasking machine which is comprehensively combined turning center and laser machine. For design of the machine, simulation of structural analysis is carried out varying number of elements. The analysis is carried out by FEM simulation using the commercial software, CATIA V5. This method showed a proper number of elements can be selected to obtain good result by reduced computation time.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.7
• /
• pp.1324-1330
• /
• 2007

In this paper, we propose pattern recognition algorithm for activities of daily living by adopting extreme learning machine based on single layer feedforward networks(SLFNs) to the signal from bidirectional accelerometer. For activity classification, 20 persons are participated and we acquire 6, types of signals at standing, walking, running, sitting, lying, and falling. Then, we design input vector using reduced model for ELM input. In ELM classification results, we can find accuracy change by increasing the number of hidden neurons. As a result, we find the accuracy is increased by increasing the number of hidden neuron. ELM is able to classify more than 80 % accuracy for experimental data set when the number of hidden is more than 20.

• Korean Management Science Review
• /
• v.12 no.3
• /
• pp.177-185
• /
• 1995

This paper presents a Tabu Search method to minimize a number of tardy jobs in the nonidentical parallel machine scheduling. The Tabu Search method employs a restricted neighborhood for the reduction of computation time. In this paper, we use two different types of method for a single machine scheduling. One is Moore's algorithm and the other is insertion method. We discuss computational experiments on more than 1000 test problems.

• Korean Management Science Review
• /
• v.12 no.1
• /
• pp.1-17
• /
• 1995

The success of cell manufacturing applications in FMS rests on the effective cell formation to maintain the independent relations both between machine cells and between part families. This paper presents an integrated method for concurrent formation of cells and families with no E.E (Exceptional Element) in FMS with alternative routings. To determine the maximum number of cell and family with no E.E, mathematical conditions and properties are derived. New concept of nonsimilarity is introduced for each machine and part based on machine-operation incidence matrix and part-operation incidence matrix. To concurrently form the cells and families, integer programming based mathematical models are developed. For the predetermined number of cell or family, model I is used to identify whether E.E exists or not. Model II forms cells and families considering only nonsimilarity. But model III can consider nonsimilarity and processing times. The proposed method is tested and proved by using numerical examples.

• Earthquakes and Structures
• /
• v.16 no.6
• /
• pp.679-690
• /
• 2019

This paper presents machine learning methods using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) to analyze optimal damper distribution for structural vibration control. Regarding different building structures, a genetic algorithm based optimization method is used to determine optimal damper distributions that are further used as training samples. The structural features, the objective function, the number of dampers, etc. are used as input features, and the distribution of dampers is taken as an output result. In the case of a few number of damper distributions, multi-class prediction can be performed using SVM and MLP respectively. Moreover, MLP can be used for regression prediction in the case where the distribution scheme is uncountable. After suitable post-processing, good results can be obtained. Numerical results show that the proposed method can obtain the optimized damper distributions for different structures under different objective functions, which achieves better control effect than the traditional uniform distribution and greatly improves the optimization efficiency.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.1
• /
• pp.19-24
• /
• 2021

In this paper, we study performance factor of machine learning in the distributed environment using Apache Spark and presents an efficient distributed processing method through experiments. This work firstly presents performance factor when performing machine learning in a distributed cluster by classifying cluster performance, data size, and configuration of spark engine. In addition, performance study of regression analysis using Spark MLlib running on the Hadoop cluster is performed while changing the configuration of the node and the Spark Executor. As a result of the experiment, it was confirmed that the effective number of executors was affected by the number of data blocks, but depending on the cluster size, the maximum and minimum values were limited by the number of cores and the number of worker nodes, respectively.