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Nhu-Ty Nguyen 대한산업공학회 2019 Industrial Engineeering & Management Systems Vol.18 No.4
Manufacturing activities, in general, consume nearly 35% of total global production of electricity and are responsiblefor nearly 20% of total global carbon emissions (Graedel et al., 2011). According to Industrial Development Report,the manufacturing sector contributes one in six jobs globally. Since the financial crisis of 2008, there has been increased debate on maintaining sustained growth (Dubey et al., 2015). Food manufacturers have been shifted into theposition whereby they have to deal with recent trends of high and volatile commodity prices, transportation and energy cost. For example, “transportation systems are essential to sustenance of human life and business growth. At thesame time, they are also source of several negative impacts on human life and their environment. Therefore, theyshould be effectively controlled to achieve the socio-economic environmental objectives of sustainability” (Sayyadiand Awasthi, 2018a). Consequently, the urge to gain competitive advantages while staying commited to the qualityand healthy margins is getting stronger. Demand forecasting can be an advantage or a drawback to a company. Especially, when the products have short-life cycle, it is complicated for transportation, storage and quality management;therefore, an accuracy forecasting would schedule for production planning to avoid later obstacles. Based on the assessment of the financial and logistics information of the Puratos Grand-Place Indochina, five different forecastingtechniques including ARIMA, Exponential smoothing, GM(1,1), DGM(1,1) and Verhulst are employed, and theirresults are evaluated. The results indicate that DGM(1,1) has the best performance with the smallest error. The secondbest methods were the GM(1,1) and Verhulst. This result strongly supports the claim that Grey Forecasting Modelscan deal with small, limited and violated sequences of data input. In addition, since the forecast values show smalldifferences from the actual values; if proper investigation can be done on this matter, it would create a huge impact onthe company performance for having an accurate prediction of future events.
Nhu-Ty Nguyen 대한산업공학회 2020 Industrial Engineeering & Management Systems Vol.19 No.2
In the plastic industry in Vietnam, an important issue becomes increasing productivity performance and sustainable development. This research builds upon a current Data Development Analysis method which helps companies identify the efficiency and seek out potential strategic alliances the performance evaluation. Grey predicting - GM (1,1) is manipulated to foresee future statistics based on the successive past data gathered from the audited financial statements that give further insight into the potential trend. This can improve business performance and create a sustainable development strategy for decision-making units (DMUs). The study was carried out by 13 plastic companies which published their full information on Vietstock’s website. In addition, the super-SBM method also supplements the DEA in the ranking efficiency to indicate the best DMU with the highest level of performance. The targeted company will then cooperate in terms of strategic alliances with the efficient company. The results of this study have shown that the number of effective firms and the ranking order change every year. Some suggestions and discussion on how to improve the performance of the DEA and Grey model theory more precisely in the future are mentioned.
Criteria for Supplier Selection in Textile and Apparel Industry : A Case Study in Vietnam
Nhu-Mai Thi NONG,Phong Thanh HO 한국유통과학회 2019 The Journal of Asian Finance, Economics and Busine Vol.6 No.2
The study aims to investigate some criteria of supplier selection in the textile and apparel (T&A) sector in Vietnam. Most research on supplier selection criteria for T&A sector was mainly conducted based on the review of literature. Therefore, the purpose of this study is to explore these criteria based on a framework in which an integrated approach of qualitative and quantitative was employed. First, an in-depth interview was used to explore what supplier selection criteria T&A companies were utilized after the literature on supplier selection criteria had been reviewed. Next, a prequestionnaire was built and sent to some practitioners and experts for their revision. Then, a pilot survey of 31 T&A companies with numerous statistical tests was conducted to validate the questionnaire. Finally, an official study of 282 respondents was conducted to determine supplier selection criteria which are best suited for T&A companies through exploratory factor analysis. The findings of the study suggest that there are eight supplier selection criteria including Quality, Cost, Delivery, Service, Capability, Company’s image, Relationship, and Sourcing country. Each criterion comprises certain sub-criteria to make the supplier selection criteria set more comprehensive. The findings will be a contribution to the selection process of T&A companies as they can utilize these criteria to select capable suppliers.
Nhu-Tai Do(도누따이),Sung-Taek Jung(정성택),Hyung-Jeong Yang(양형정),Soo-Hyung Kim(김수형) 한국정보과학회 2020 정보과학회논문지 Vol.47 No.2
무릎 골종양 검출은 의료진단 보조 시스템 구현에 있어서 중요한 역할을 담당한다. 지금까지 제시된 방법 중 입력 X-ray 영상에서 종양을 검출하고 이를 분류하는 기능이 모두 포함된 end-to-end 시스템은 없다. 본 논문에서는 다중 딥러닝에 기반한 end-to-end 시스템을 제안한다. 이를 위해 우리는 영상내 종양부분에 대한 거리변환으로부터 다단계 마스크를 생성하고, 이를 해당 종양의 의미론적 정보를 추출하는 신경망의 guided filter로 활용한다. 또한, 제안된 신경망 구조는 종양의 분할과 분류 과정을 학습하는 과정에서 정규화하는 효과를 포함하고 있다. 제안된 신경망 모델이 전남대학교병원에서 구축한 데이터셋에 대해 다른 기법들보다 우수한 성능을 보임을 입증하였다. Knee bone tumor detection plays an essential role in assisting the clinical diagnosis process. To the best of our knowledge, there is no method to integrate end-to-end segmentation and classification for this problem. In this paper, we propose a multi-task deep learning architecture for classification and segmentation of the tumor regions in the knee bone. Also, we introduce multi-level distance masks from the distance transform of tumor region, and these multi-level distance masks have a role as a guided filter in enabling the network to capture semantic data around tumor regions. Besides, the architecture has a regularizing effect on the learning process between segmentation and classification. Our model was evaluated on the Chonnam National University Hospital dataset and achieved good performance compared to other methods.
Repetitively Enhanced Neural Networks Method for Complex Engineering Design Optimization Problems
Nhu Van Nguyen,Maxim Tyan,Jae-Woo Lee,Sangho Kim 한국전산유체공학회 2014 한국전산유체공학회 학술대회논문집 Vol.2014 No.5
Repetitively Enhanced Neural Networks (RENN) method is developed and presented for complex and implicit engineering design problems. Enhance neural networks module constructs an accurate surrogate models and ensures for avoiding over-fitting during neural networks training from supervised learning data. The optimizer is executed by the enhanced neural networks models to seek for a tentative optimum point. It is repetitively added into the supervised learning data set to refine surfaces till the RENN tolerance reaches. The RENN method demonstrates the effectiveness and feasibility for 2D highly non-linear numerical example and the structure design of two-member frame reaching convergent solution at 10 and 14 iterations respectively at the maximum error of 1% when compared with the exact solution. Then, the RENN method is applied for a long endurance unmanned aerial vehicle (UAV) airfoil design optimization. Class/Shape function transformation (CST) geometry parameterization method represents an accurate UAV airfoil with 10 geometry design variables. The high-fidelity analysis solvers with structured mesh for airfoil is used for UAV airfoil design problem. The total 88 experiment points are required to obtain an optimal UAV airfoil configuration after 13 RENN iterations and 75 initial experiments by Latin Hypercube method in reasonable turnaround time. The optimal UAV airfoil shows 10.8% in drag reduction in cruise condition and improvement in the maximum lift coefficient and stall angle of attack.