• Journal of Information Processing Systems
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• 제20권3호
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• pp.285-295
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• 2024

Driven by the vague assessment big data, a product service system (PSS) evaluation method is developed based on a hybrid model of multi-weight combination and improved TOPSIS by relative entropy. The index values of PSS alternatives are solved by the integration of the stakeholders' vague assessment comments presented in the form of trapezoidal fuzzy numbers. Multi-weight combination method is proposed for index weight solving of PSS evaluation decision-making. An improved TOPSIS by relative entropy (RE) is presented to overcome the shortcomings of traditional TOPSIS and related modified TOPSIS and then PSS alternatives are evaluated. A PSS evaluation case in a printer company is given to test and verify the proposed model. The RE closeness of seven PSS alternatives are 0.3940, 0.5147, 0.7913, 0.3719, 0.2403, 0.4959, and 0.6332 and the one with the highest RE closeness is selected as the best alternative. The results of comparison examples show that the presented model can compensate for the shortcomings of existing traditional methods.

• 한국기계가공학회지
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• 제15권1호
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• pp.103-109
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• 2016

The performance of a multi-head, computerized combination scaling system to automatically identify a group of agricultural products having a total weight within the target range has been optimized to reduce the package cycle time of the merchandise. First, the structure of the scale was modified to enable faster measurement by enhancing the dynamic stability during the process. Second, the high frequency noise in the measured signal was eliminated by a high frequency filter to provide more accurate weight data. Finally, the algorithm to identify a group of products with a total weight within the target range was modified to enable a user to select an optimal number of scales. According to the experimental verifications, this modified system reduced the package cycle time significantly and also was accurate in measuring the total weight of the selected products.

• 한국기계가공학회지
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• 제14권6호
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• pp.77-83
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• 2015

This paper is a study of the functions required for the system to quantify the closest target weight by combining several random weights such as chips, snacks, fruits, and vegetables. The multi-head weigher is designed for high-performance applications requiring increased production rates and tight accuracy tolerances. This combination system has 12 heads considered in the form of a rectangular array of $2{\times}6$ or $3{\times}4$. Channel combination can usually occur between 1 and n, and the frequency was the highest with two or three combinations. Experimental result of a combination system for a total target weight was measured at the range from 100g to 500g by increments of 50g, and the average success rate was about 70%. The average elapsed time was about 1.7 seconds, which means it can be used for the packaging of agricultural products with a variety of items.

• 대기
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• 제28권3호
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• pp.247-261
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• 2018

The objective of this paper is to compare probabilistic temperature forecasts from different regional and global ensemble prediction systems over PyeongChang area. A statistical post-processing method is used to take into account combination and calibration of forecasts from different numerical prediction systems, laying greater weight on ensemble model that exhibits the best performance. Observations for temperature were obtained from the 30 stations in PyeongChang and three different ensemble forecasts derived from the European Centre for Medium-Range Weather Forecasts, Ensemble Prediction System for Global and Limited Area Ensemble Prediction System that were obtained between 1 May 2014 and 18 March 2017. Prior to applying to the post-processing methods, reliability analysis was conducted to identify the statistical consistency of ensemble forecasts and corresponding observations. Then, ensemble model output statistics and bias-corrected methods were applied to each raw ensemble model and then proposed weighted combination of ensembles. The results showed that the proposed methods provide improved performances than raw ensemble mean. In particular, multi-model forecast based on ensemble model output statistics was superior to the bias-corrected forecast in terms of deterministic prediction.

• Journal of Welding and Joining
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• 제34권5호
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• pp.19-25
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• 2016

Global warming is hot issue to keep the earth everlastingly. Despite the increase of the world population and the energy demand, the world oil supply and the oil price are hold the steady state. If we are not decrease the world population and the energy consumption, unforeseeable energy crisis will come in the immediate future. AMT acronym of Advanced Materials for Transportation is a non-profitable IEA-affiliated organization to mitigate the oil consumption and the environment contamination for the transportation. In recent, Annex X Multi-materials Joining was added to enhance the car body weight reduction cause the high fuel efficiency and the low emission of exhaust gas. Multi-materials are the advanced materials application technology to optimize the weight, the performance and the cost with the combination of different materials such as Al-alloy, Mg- alloy, AHSS and CFRP. In this study, the trends of AMT strategy and Al-alloy based multi-materials joining technology were review. Also several technologies for Al-alloy dissimilar joining were investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2720-2736
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• 2013

Multi-view face detection has become an active area for research in the last few years. In this paper, a novel multi-view human face detection algorithm based on improved real Adaboost is presented. Real Adaboost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. After that, we proved that the function of sample weight adjusting method and weak classifier training method is to guarantee the independence of weak classifiers. A coarse-to-fine hierarchical face detector combining the high efficiency of Haar feature with pose estimation phase based on our real Adaboost algorithm is proposed. This algorithm reduces training time cost greatly compared with classical real Adaboost algorithm. In addition, it speeds up strong classifier converging and reduces the number of weak classifiers. For frontal face detection, the experiments on MIT+CMU frontal face test set result a 96.4% correct rate with 528 false alarms; for multi-view face in real time test set result a 94.7 % correct rate. The experimental results verified the effectiveness of the proposed approach.

• 한국측량학회지
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• 제37권5호
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• pp.293-302
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• 2019

In mountain accident events, it is important for the search team commander to determine the search area in order to secure the Golden Time. Within this period, assistance and treatment to the concerned individual will most likely prevent further injuries and harm. This paper proposes a method to determine the search priority area based on missing persons behavior and missing persons incidents statistics. GIS (Geographic Information System) and MCDM (Multi Criteria Decision Making) are integrated by applying WLC (Weighted Linear Combination) techniques. Missing persons were classified into five types, and their behavioral characteristics were analyzed to extract seven geographic analysis factors. Next, index values were set up for each missing person and element according to the behavioral characteristics, and the raster data generated by multiplying the weight of each element are superimposed to define models to select search priority areas, where each weight is calculated from the AHP (Analytical Hierarchy Process) through a pairwise comparison method obtained from search operation experts. Finally, the model generated in this study was applied to a missing person case through a virtual missing scenario, the priority area was selected, and the behavioral characteristics and topographical characteristics of the missing persons were compared with the selected area. The resulting analysis results were verified by mountain rescue experts as 'appropriate' in terms of the behavior analysis, analysis factor extraction, experimental process, and results for the missing persons.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• 전자공학회논문지
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• 제50권9호
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• pp.127-135
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• 2013

저조도 환경에서 카메라로 영상을 획득하기 위해 일반적으로 가시광 플래시를 사용하거나 장노출 기법을 사용하게 된다. 그러나 가시광 플래시를 사용할 때 플래시 광에 의한 색 왜곡이나 적목 현상, 눈부심에 의한 거부감을 발생시킨다. 또한 장노출을 사용하게 되면 물체의 움직임에 의한 흔들림 현상이 발생하게 된다. 따라서 최근에는 이러한 단점을 극복하고, 저조도 환경에서 고화질의 영상을 획득하기 위하여 멀티 스팩트럴 플래시(Multi-spectral flash image)를 이용하여 영상을 획득하는 방법이 소개되었다. 이 방법은 가시광과 UV/IR스펙트럼의 다섯 채널을 이용하여 가시광영상의 색 정보와 UV/IR 스팩트럼 영상의 세부정보를 최적화하여 영상을 획득하는 방법이다. 하지만, 픽셀 기반의 최적화 과정에 있어 색 왜곡과 다른 잡음을 발생시키게 된다. 따라서 본 논문에서는 이러한 색 왜곡과 잡음을 개선하기 위해 영역 기반의 가중치 맵을 최적화 방법에 적용하여 색 왜곡을 개선하는 알고리즘을 제안한다. 먼저, 영상에 대하여 Canny 에지 검출 방법을 사용하여 영상의 윤곽을 검출하였다. 이를 가중치 맵으로 최적화방법에 적용함으로, 세부 영역에 대하여 UV/IR 플래시 영상의 정보에 가중치를 부여하고, 평탄한 영역에 대하여 가시광 영상의 색 정보를 가중치를 부여하여 색 왜곡을 개선하였다. 제안한 방법을 평가하기 위하여 실험을 통하여 제안한 방법과 이전방법을 비교하였고, 객관적 평가와 주관적 평가 모두 제안한 방법이 우수한 성능을 나타내었다.

• Advances in concrete construction
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• 제11권2호
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• pp.163-171
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• 2021

This paper studies the effect of using multi-scale reinforcement additives on mechanical strengths, damage performance, microstructure, and water absorption of cementitious composites. Small dosages of carbon nanotubes (CNTs) or polypropylene (PP) microfibers; 0.05%, 0.1%, and 0.2% by weight of cement; were added either separately or simultaneously into cement mortar. The experimental results show the ability of these additives to enhance the mechanical behavior of the mortar. The best improvement in compressive and flexural strengths of cement mortar reaches 28% in the case of adding a combination of 0.1% CNTs and 0.2% PP fibers for compression, and a combination of 0.2% CNTs and 0.2% PP fibers for flexure. Adding CNTs does not change the brittle mode of failure of plain mortar whereas the presence of PP fibers changes it into ductile failure and clearly enhances the fracture energy of the specimens. Scanning electron microscopic (SEM) images of the fracture surfaces highlights the role of CNTs in improving the adhesion between the PP fibers and the hydration products and thus enhance the ability of the fibers to mitigate cracks propagation and to enhance the mechanical performance of the mortar.