Purpose: The main theme of this study is to derive a specific quality loss function with multiple characteristics according to the same analytical structure as the single characteristic quality loss function of Taguchi. In other words, it presents an analytical framework for measuring quality costs that can be controlled in practice. Methods: This study followed the analytical methodology through geometric, linear algebraic, and statistical approaches
Results: The function suggested by this study is as follows; □□□□□
Conclusion: This paper derived the quality loss function with multiple quality characteristics to expand the usefulness of the Taguchi quality loss function. The function derived in this paper would be more meaningful to estimate quality costs under the practical situation and general structure with multiple quality characteristics than the function by linear algebraic approach in response surface analysis.

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network.
Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.

Purpose: The purpose of this study is to present a preferred parts list, establish a search tool for common parts, and propose a development plan for parts management.
Methods: When selecting the preferred parts list, the weight of the selection of the military parts and the online site of the civilian parts were taken into account. By establishing a database of selected preferred parts list, we have established a system for searching common parts.
Results: The results of this study are as follows; In order to establish parts management work in Korea's defense, it is necessary to improve the Defense Acquisition Program Act, Enforcement Decree (Rules), and the Regulations for Defense Acquisition Program Administration. Development of performance indicators for parts management is required, and establishment of procedures and standards for selection of preferred parts lists is required. Considerations for the selection of preferred parts list include whether they are used a lot in civilian products, whether they are used a lot, whether they are certified suppliers, whether they are national and industrial standard products, whether they perform well considering reliability, durability, and quality, and whether they can be supplied continuously.
Conclusion: It is necessary to establish policies and systems related to parts management and to establish criteria for selecting preferred parts list for the development of domestic parts management tasks.

Purpose: purpose of this study was to propose the web application platform which can be to detect and discriminate various diseases and pest of tomato plant based on the large amount of disease image data observed in the facility or the open field.
Methods: The deep learning algorithms uesed at the web applivation platform are consisted as the combining form of Faster R-CNN with the pre-trained convolution neural network (CNN) models such as SSD_mobilenet v1, Inception v2, Resnet50 and Resnet101 models. To evaluate the superiority of the newly proposed web application platform, we collected 850 images of four diseases such as Bacterial cankers, Late blight, Leaf miners, and Powdery mildew that occur the most frequent in tomato plants. Of these, 750 were used to learn the algorithm, and the remaining 100 images were used to evaluate the algorithm.
Results: From the experiments, the deep learning algorithm combining Faster R-CNN with SSD_mobilnet v1, Inception v2, Resnet50, and Restnet101 showed detection accuracy of 31.0%, 87.7%, 84.4%, and 90.8% respectively. Finally, we constructed a web application platform that can detect and discriminate various tomato deseases using best deep learning algorithm. If farmers uploaded image captured by their digital cameras such as smart phone camera or DSLR (Digital Single Lens Reflex) camera, then they can receive an information for detection, identification and disease control about captured tomato disease through the proposed web application platform.
Conclusion: Incheon Port needs to act actively paying

Purpose: The purpose of this paper is to present a probability distribution of the burst pressure of API 5L X65 pipes for the reliability assessment of corroded gas pipelines.
Methods: Corrosion is a major cause of weakening the residual strength of the pipe. The mean residual strength on the corrosion defect can be obtained using the burst pressure code. However, in order to obtain the pipe reliability, a probability distribution of the burst pressure should be provided. This study is concerned with estimating the burst pressure distribution using Monte Carlo simulation. A response surface method is employed to represent the distribution parameter as a model of the corrosion defect size.
Results: The experimental results suggest that the normal or Weibull distribution should be suitable as the probability distribution of the burst pressure. In particular, it was shown that the probability distribution parameters can be well predicted by using the depth and length of the corrosion defect.
Conclusion: Given a corrosion defect on the pipe, its corresponding burst pressure distribution can be provided at instant. Subsequently, a reliability assessment of the pipe is conducted as well.