The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

Anticipation of welding deformation with finite element method is a very interested topic in the industries, adequate heat source model is essential for concluding reasonable results. This study is related to estimate the parameters of Goldak heat source model, and global optimization algorithm is applied to this research. The heat affected zone (HAZ) boundary line of bead on plate (BOP) welding is used as the target, parameters of heat sources are used as the variables. Adaptive simulated annealing is applied and the optimal result is obtained out of 1,000 candidates. The convergence of finite element method and the global optimization is meaningful for estimation of welding deformation, which could enhance to reduce the resources and time for experiments.

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

In this study, we propose a new method for generating candidate solutions based on both the Cauchy and the Gaussian probability distributions in order to use the merit of the solutions generated by these distributions. The Cauchy probability distribution

In this study, we propose a new method for generating candidate solutions based on both the Cauchy and the Gaussian probability distributions in order to use the merit of the solutions generated by these distributions. The Cauchy probability distribution has larger probability in the tail region than the Gaussian distribution. Thus, the Cauchy distribution can yield higher probabilities of generating candidate solutions of large-varied variables, which in turn has an advantage of searching wider area of variable space. On the contrary, the Gaussian distribution can yield higher probabilities of generating candidate solutions of small-varied variables, which in turn has an advantage of searching deeply smaller area of variable space. In order to compare and analyze the performance of the proposed method against the conventional method, we carried out experiments using benchmarking problems of real valued functions. From the result of the experiment, we found that the proposed method based on the Cauchy and the Gaussian distributions outperformed the conventional one for most of benchmarking problems, and verified its superiority by the statistical hypothesis test.

  Forming central warehouses for a number of stores can save costs in the continuous review inventory model due to economy of scale and information sharing. In this paper, transportation costs are included in this inventory model. Hence, the tradeoff betw

A number of stores can save costs by forming central warehouses. In this paper, transportation cost is included in the inventory model with the continuous review policy, namely, (Q,R) policy. The objective of this paper is to develop an efficient simulated annealing algorithm and heuristic for large-scale problems of determining the location and the number of central warehouses by minimizing total costs. Some computational results and suggestions for future research are also discussed.

수정 시뮬레이티드어닐링은 Simulated Annealing(SA)가 확률 탐색 방법을 사용하기 때문에 수렴시간이 오래 걸리는 단점를 개선한 방법이다. 따라서 본 논문에서는 RSA와 SA을 트러스구조물과 인공위성구조물의 최적화에 적용하여 서로 비교하여 보았다. 최적화 예제로 10부재 트러스, 실제 응용예제로 인공위성구조물은 위성 상단 플랫폼과 추진모듈의 최적화를 수행하였다. 인공위성구조물의 최적화에서 응력과 고유진동수, 전단응력 등을 제한조건으로 고려하여 최적화를 수행하였다. 인공위성구조물의 최적화를 수행한 결과 RSA을 이용하여 다양한 인공위성 구조물의 최적화에 적용될 수 있음을 확인하였으며, 인공위성 구조물의 최적화에서 RSA가 SA보다 수렴속도가 향상되었음을 확인하였다.

In this paper a modified simulated annealing approach for solving single-machine mean tardiness scheduling problems is proposed. The results of the simulation indicate that the proposed method provides more stable solutions than those of previous studies. The proposed method also provides better quality solutions for large-size problems.

구조최적화는 최근 CAD와 컴퓨터 기술이 발전하면서 구조설계부분에 널리 이용되고 있다. 본 연구에서는 30층의 강구조물을 대상으로 유한요소해석 및 어댑티브 시뮬레이티드 어닐링 알고리즘을 이용하여 최적중량설계를 구현하였다. 최적설계는 모든 설계상수와 설계하중들이 주어졌을 때, 목적함수가 최소로 됨과 동시에 모든 설계제약조건을 만족시키는 설계변수를 결정하는 설계법이라고 정의할 수 있다. 최적설계 구현을 통해 건설 측면에 있어 성능 향상과 신뢰도 향상 효과를 가져 올 수 있을 것이다.