• Title/Summary/Keyword: multi-dimensional evaluation

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(Design and Implementation of Multi-dimensional Evaluation Result Analyzing System) (다차원 평가결과 분석 시스템의 설계 및 구현)

  • 백장현;장세희;김도윤;김영식
    • Journal of the Korea Computer Industry Society
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    • v.3 no.8
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    • pp.1007-1018
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    • 2002
  • A systematic and multi-dimensional analysis in evaluation results may play a role of providing both learners and instructors with essential information. Conventional types of evaluation research have a tendency of partiality for developing evaluation tools, and analyzing the evaluation results has mostly been fragmentary and a one-dimensional analysis. In this study, through analyzing evaluation results in a multi-dimensional way, a multi-dimensional evaluation result analyzing system was developed for the purpose of providing various information for both learners and instructors to accomplish quality learning teaching. Dimensions are classified into four dimensions including period, student, difficulty degree, and evaluation domain. Analysis results are presented in various types of Dcube, graphs, and spreadsheets.

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Multi-Dimensional Reinforcement Learning Using a Vector Q-Net - Application to Mobile Robots

  • Kiguchi, Kazuo;Nanayakkara, Thrishantha;Watanabe, Keigo;Fukuda, Toshio
    • International Journal of Control, Automation, and Systems
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    • v.1 no.1
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    • pp.142-148
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    • 2003
  • Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

Effect of Product Involvement and Brand Preference on Consumers' Evaluation Effort for Multi-Dimensional Prices (소비자의 다차원가격 평가노력에 대한 제품관여도와 브랜드선호도의 영향)

  • Kim, Jae-Yeong
    • Journal of Distribution Science
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    • v.13 no.4
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    • pp.55-64
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    • 2015
  • Purpose - Multi-dimensional prices comprise multiple components such as monthly payments and a number of payments rather than a single lump-sum amount. According to previous studies, an increase in the number of price dimensions leads to a massive amount of cognitive stress resulting in incorrect calculation, and deterioration in the consistency of the price judgment. However, an increase only in the level of complexity of calculating multi-dimensional prices does not always result in a corresponding decrease in the accuracy of price evaluation. Since diverse variables could affect consumers' purchase-decision-making process, the results of price evaluation would be different. In this study, an empirical analysis was performed to determine how the accuracy of price evaluation varies depending on the extent of the complexity of price dimensions using product involvement and brand preference as moderating variables. Research design, data, and methodology - A survey was conducted on 260 students, and 252 effective responses were used for analysis. The data was analyzed using t-test, one-way ANOVA, and two-way ANOVA. In this study, six hypotheses were developed to examine the effect of product involvement and brand preference on consumers' evaluation effort of multi-dimensional prices. Results - As the number of price dimensions increased, accuracy of price evaluation appeared to be low in high involvement, as expected. However, it showed no differences in price evaluation effort when the level of complexity of calculating multi-dimensional prices is low. When a small number of price dimensions are presented in both cases of high and low involvement, accuracy of price evaluation is much higher in a weak brand preference. On the contrary, a strong brand preference enhances an accuracy of price evaluation only in case of low involvement when the number of price dimensions is increased. An interaction effect of product involvement and brand preference on consumers' evaluation of multi-dimensional prices did not exist irrespective of the level of complexity of calculating prices being high or low. Conclusions - When the number of price dimensions is small, consumers' effort for price evaluation shows almost no difference without the moderating effect of involvement, and a weak brand preference leads to a higher accuracy of price evaluation in an effort to make the best selection. No interaction effect of product involvement and brand preference was found except for a main effect of brand preference. When a price is composed of multiple dimensions rendering it more difficult to calculate the final price, the effort for price evaluation was expected to decrease only slightly in case of combination of high involvement and strong brand preference. This is because people have a higher purchase intentions and trust for that particular brand. However, the accuracy of price evaluation was much lower in cases of high involvement, and there was no interaction effect between product involvement and brand preference except for a main effect of involvement and brand preference, respectively.

An Improvement of FSDD for Evaluating Multi-Dimensional Data (다차원 데이터 평가가 가능한 개선된 FSDD 연구)

  • Oh, Se-jong
    • Journal of Digital Convergence
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    • v.15 no.1
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    • pp.247-253
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    • 2017
  • Feature selection or variable selection is a data mining scheme for selecting highly relevant features with target concept from high dimensional data. It decreases dimensionality of data, and makes it easy to analyze clusters or classification. A feature selection scheme requires an evaluation function. Most of current evaluation functions are based on statistics or information theory, and they can evaluate only for single feature (one-dimensional data). However, features have interactions between them, and require evaluation function for multi-dimensional data for efficient feature selection. In this study, we propose modification of FSDD evaluation function for utilizing evaluation of multiple features using extended distance function. Original FSDD is just possible for single feature evaluation. Proposed approach may be expected to be applied on other single feature evaluation method.

Multi-Dimensional Selection Method of Port Logistics Location Based on Entropy Weight Method

  • Ruiwei Guo
    • Journal of Information Processing Systems
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    • v.19 no.4
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    • pp.407-416
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    • 2023
  • In order to effectively relieve the traffic pressure of the city, ensure the smooth flow of freight and promote the development of the logistics industry, the selection of appropriate port logistics location is the basis of giving full play to the port logistics function. In order to better realize the selection of port logistics, this paper adopts the entropy weight method to set up a multi-dimensional evaluation index, and constructs the evaluation model of port logistics location. Then through the actual case, from the environmental dimension and economic competition dimension to make choices and analysis. The results show that port d has the largest logistics competitiveness and the highest relative proximity among the three indicators of hinterland city economic activity, hinterland economic structure, and port operation capacity of different port logistics locations, which has absolute advantages. It is hoped that the research results can provide a reference for the multi-dimensional selection of port logistics site selections.

Multi-Detector Row CT of the Central Airway Disease (Multi-Detector Row CT를 이용한 중심부 기도 질환의 평가)

  • Kang, Eun-Young
    • Tuberculosis and Respiratory Diseases
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    • v.55 no.3
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    • pp.239-249
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    • 2003
  • Multi-detector row CT (MDCT) provides faster speed, longer coverage in conjunction with thin slices, improved spatial resolution, and ability to produce high quality muliplanar and three-dimensional (3D) images. MDCT has revolutionized the non-invasive evaluation of the central airways. Simultaneous display of axial, multiplanar, and 3D images raises precision and accuracy of the radiologic diagnosis of central airway disease. This article introduces central airway imaging with MDCT emphasizing on the emerging role of multiplanar and 3D reconstruction.

Evaluation of dynamical performance of 3 dimensional multi-arm robot (3차원 다중 로봇의 동적 성능 평가)

  • 김기갑;김충영
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.756-759
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    • 1997
  • Multi-arm cooperation robot system is required for more specific and dextrous jobs such as transferring very large or heavy objects, or grasping work piece while processing on it. There is little research on 3-dimensional multi-arm robot. Here we propose two performance indices presenting isotropy of end-effector's acceleration and velocity capabilities with constraints of joint torques, that is Isotropic Acceleration Radius [IAR] and Isotropic Velocity Radius [IVRI. Also the procedure to find 3-dimensional IAR, IVR is proposed, where available acceleration set concept is used. The case of 3-dimensional two 3 joint robot system was simulated and the distributions of IAR, IVR was studied.

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Evaluation and Optimal Arrangement of Multi-Dimensional Consecutive System

  • 안해일
    • Proceedings of the Korean Reliability Society Conference
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    • 2000.11a
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    • pp.397-397
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    • 2000
  • There exists an increasing need of study for generalized consecutive k-out-of-n systems. This paper demonstrates that a recursive formula for multi-dimensional consecutive k-out-of-n systems can be systematically developed by means of conventional structure function analysis. By taking advantage of notational convenience, the formulae expressed in the same recursive fashion just as the one dimensional consecutive k-out-of-n system. With the aids of the recursive formulae, not only the exact reliability of the system, but also the optimal arrangement of components is obtainable in a straightforward way.

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The application of GIS in analyzing acoustical and multidimensional data related to artificial reefs ground (인공어초 어장에서 수록한 음향학적 다차원 데이터 해석을 위한 GIS의 응용)

  • Kang, Myoung-Hee;Nakamura, Takeshi;Hamano, Akira
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.47 no.3
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    • pp.222-233
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    • 2011
  • This study is for the multi-dimensional analysis of diverse data sets for artificial reefs off the coast of Shimonoseki, Yamaguchi prefecture, Japan. Various data sets recorded in artificial reefs ground were integrated in new GIS software: to reveal the relationships between water temperature and fish schools; to visualize the quantitative connection between the reefs and the fish schools; and to compare the seabed types derived from two different data sources. The results obtained suggest that the application of GIS in analyzing multi-dimensional data is a better way to understand the characteristics of fish schools and environmental information around artificial reefs and particularly in the evaluation of the effectiveness of artificial reefs.