Weight Assignment Method Research Articles

Synergetic assessment of water, energy and food nexus system

Water, energy and food are three important resources for human to face serious security risks. A systematic perspective, namely Water, Energy and Food Nexus (WEFN), was proposed to achieve the sustainable human development. In this study, a method of hierarchical weight assignment based on synergetic theory and Shannon information theory was applied in the Bayan Nur city, China. Through calculating order degree of order parameter components of resources, society, economic and environmental dimensions, the order degree of order parameters and sub-systems and the order entropy were obtained. It is found that water resources supply, energy supply, energy consumption and food price would dominate the corresponding trends in water, energy and food sub-systems respectively. The water and food sub-system may play a more dominant role in the harmonious assessment of WEFN system in the study area. Although the variance of the order degree of sub-systems and the order entropy were small, the WEFN system was still moving toward an unstable state, which could aggravate the inharmonious degree of WEFN system in the study area.

Fermentative production of chiral acetoin by wild-type Bacillus strains

In recent years, there have been many studies on producing acetoin by microbial fermentation, while only a few studies have focused on chiral acetoin biosynthesis. The weight assignment method was first applied to balance the chiral purity (expressed as the enantiomeric excess value) and the titer of acetoin. Bacillus sp. H-18W, a thermophile, was selected from seven Bacillus strains for chiral acetoin production. To lower the cost of the fermentation medium, soybean meal was used as a feedstock. Four kinds of frequently used commercial proteinases with different active sites were tested for the hydrolyzation of the soybean meal, and the combination of the acidic proteinase and the neutral proteinase showed the best results. In a fermentation medium containing 100 g L−1 glucose and 200 g L−1 hydrolysate, Bacillus sp. H-18W produced 21.84 g L−1 acetoin with an ee value of 96.25% at 60 h. This is the first report of using a thermophilic strain to produce chiral acetoin by microbial fermentation. Thermophilic fermentation can reduce the risk of bacterial contamination and can save cooling water. Using soybean meal hydrolysate and glucose as feedstocks, this work provides an economical and alternative method for the production of chiral pure acetoin.

Determining the relative importance of factors affecting the success of innovations in forest technology using AHP

AbstractA number of factors affect the success, or otherwise, of any technological innovation. In this paper, we consider factors that affect the success of innovations in the context of forest machinery manufacturing (FMM). We report the results of a study that seeks to determine the relative importance of 19 different factors affecting the success of innovations in the FMM sector. The first stage of the study makes use of the analytic hierarchy process to determine the relative importance (weights) of these factors, based on the responses of 15 decision makers (DMs) employed by different FMM companies. The study then considers three different ways of weighting the responses of DMs, based on different assumptions about how the responses of outliers—those DMs whose views differ significantly from the majority—should be handled. The first method assumes that the responses of all DMs are of equal value. The second assumes that the influence of outliers' responses should be reduced and makes use of a Euclidean distance metric to determine the weight that should be ascribed to each DM's responses. The third assumes that the influence of outliers' responses should be increased and makes use of hierarchical clustering techniques. Although the weight assignment methods were based on very different assumptions about how to treat the views of outliers, they produced similar rankings of factors. Results showed that for FMM companies, customer satisfaction, proportion of total sales by new products, influence on the overall profit of the company, and growth of demand for the company products were the most important factors for measuring the success of innovations.

Dynamic Multi-layer Ensemble Classification Framework for Social Venues Using Binary Particle Swarm Optimization

Multi-layer ensemble frameworks perform much better as compared to individual classifiers. However, selection of a classifier and its placement, impacts the overall performance of ensemble framework. This problem becomes very difficult, if there are more classifiers and layers. To address these problems in this paper, we design “Binary Particle Swarm Optimization” method for selection and placement of right classifiers in multi-layer ensemble model. Proposed classifier weight-assignment method is implemented to prioritize the selected classifiers. The model is simulated for the classification of social-user check-ins in Location-Based Social Network datasets. The experimental results show that the proposed ensemble model outperforms the state-of-the-art ensemble methods in the literature. It can be used by security firms, high level decision makers and various governmental organizations for tracking malicious users.

A new weighted naive Bayes method based on information diffusion for software defect prediction

Software defect prediction (SDP) plays a significant part in identifying the most defect-prone modules before software testing and allocating limited testing resources. One of the most commonly used classifiers in SDP is naive Bayes (NB). Despite the simplicity of the NB classifier, it can often perform better than more complicated classification models. In NB, the features are assumed to be equally important, and the numeric features are assumed to have a normal distribution. However, the features often do not contribute equivalently to the classification, and they usually do not have a normal distribution after performing a Kolmogorov-Smirnov test; this may harm the performance of the NB classifier. Therefore, this paper proposes a new weighted naive Bayes method based on information diffusion (WNB-ID) for SDP. More specifically, for the equal importance assumption, we investigate six weight assignment methods for setting the feature weights and then choose the most suitable one based on the F-measure. For the normal distribution assumption, we apply the information diffusion model (IDM) to compute the probability density of each feature instead of the acquiescent probability density function of the normal distribution. We carry out experiments on 10 software defect data sets of three types of projects in three different programming languages provided by the PROMISE repository. Several well-known classifiers and ensemble methods are included for comparison. The final experimental results demonstrate the effectiveness and practicability of the proposed method.

Solving Intuitionistic Fuzzy Linear Programming Problem

Intuitionistic Fuzzy Set (IFS) can be used as a general tool for modeling problems of decision making under uncertainty where, the degree of rejection is defined simultaneously with the degree of acceptance of a piece of information in such a way that these degrees are not complement to each other. Accordingly, an attempt is made to solve intuitionistic fuzzy linear programming problems using a technique based on an earlier technique proposed by Zimmermann to solve fuzzy linear programming problem. Our proposed technique does not require the existing ranking of intuitionistic fuzzy numbers. This method is also different from the existing weight assignment method or the Angelov’s method. A comparative study is undertaken and interesting results have been presented.

Establishing a dynamic ergonomic evaluation index system for complex product designs based on the theory of product life cycle

Ergonomic design and evaluation for complex product (tractor as an example) in different lifecycle stages should be different and specific according to changing business strategies. Being key factors for multi-attribute evaluation, how to define indicators and assign weights to them for tractor's different lifecycle stages are discussed in this paper. Focus group has been used for brainstorming initial indicators as many as possible. Then, indicators and their relationship-structures for three lifecycle stages were assessed and optimized by two rounds of Delphi surveys involving 15 experts. After that, a synthetic indicator weight assignment method suitable for value evaluation and ranking evaluation was proposed. Indicators' weights for value evaluation was composed by experts' value judgment weight and experts' credibility weight, the former was assigned by Group Analytic Hierarchy Process method and the latter was assigned by calculating distances between individual weight and the average weight. To differentiate alternatives' ergonomic performances clearly, weight set of value evaluation has been modified by indicator's information entropy weight. The results show that there are some significant differences in three indicator systems (for three lifecycle stages) and weight sets developed by the proposed method, and this will have significant impacts on ergonomic evaluation results, which can provide useful feedback for tractor's design during its early design stage. The proposed method can be applied to ergonomic evaluation study of other complex products in their lifecycle.

An integrated approach to path analysis for weighted citation networks

Profuse growth of scientometrics as a research field owes a discernible attribution to the introduction of citation networks and other scientograms. Centrality analysis, path analysis and cluster analysis are three major network analysis tools. Hummon and Doreian’s introduction of path retrieval methods based on (1) traversal count as weight assignment (for arcs) method and (2) search methods such as local (forward) search and global search, marked the commencement of path analysis. Original Hummon–Doreian traversal count based weight assignment methods such as Search Path Link Count and Search Path Node Pair were computationally complex. Along with the computational improvement of these weights, Batagelj added another computationally efficient traversal count method to the path analysis literature known as Search Path Count. A major development in search methods was seen recently with the introduction of innovative search methods such as backward (local) search and key-route (local and global) search by Liu and Lu. They also powered the available and new local search methods with a parameter to control the search. Major advantage of Liu–Lu methods lies in the fact that these can reveal more paths or more papers that are usually missed out in classical methods. All these contributions considered unweighted citation networks as the object of analysis. Despite being a tool of tremendous potential, path analysis is much underexplored relative to other network analysis tools. Inspired by these, we generalise Liu–Lu integrated approach, the present state-of-art in path analysis to an integrated approach for weighted networks. We demonstrate a manifold improvement in analysis opportunities with the generalized integrated approach using FV gradient weights for weight assignment, on a case study of the field ‘IT for engineering’. Integrated approach for weighted networks do not need additional implementation effort in PAJEK and this will be beneficial for a multitude of analysts and decision makers.

Evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment

Multiple attribute decision making (MADM) problems often consists of quantitative and qualitative attributes which can be assessed by numerical values and subjective judgments. Subjective judgments can be evaluated by linguistic variables, and both numerical values and subjective judgments can be accurate or uncertain. The evidential reasoning (ER) approach provides a process for dealing with MADM problems of both a quantitative and qualitative nature under uncertainty. The existing ER approach considers both benefit and cost attributes in the evidence combination process. In this paper, deviated interval and fixed interval attributes are introduced into ER based MADM approach and the frames of discernment for representing these two kinds of attributes are given. The transformation rules from the assessment values of deviated interval attributes to belief degrees in the ER structure are then studied. An ave-entropy based weight assignment method considering the risk preference of decision maker is also shown to deal with uncertain assessment situation, such as belief distribution with qualitative attribute and uncertain utility function. Some programming models to generate interval weights and utilities are constructed. The rationality and efficiency of the methods in supporting MADM problems are discussed. Two case studies are provided to demonstrate the applicability and validity of the proposed approaches and the potential in supporting MADM under uncertainty.

Algorithm of Fuzzy Support Vector Machine based on a Piecewise Linear Fuzzy Weight Method

This article describes how fuzzy support vector machines (FSVMs) function well with good anti-noise performance, which receives the attention of many experts. However, the traditional center-distance fuzzy weight assignment method assigns support vectors with a small value of a membership degree and this weakens the role of support vectors in classification. In this article, a piecewise linear fuzzy weight computing method is proposed, in which boundary samples are assigned with a larger value of membership degree and samples far from the mean vector are assigned a smaller value of membership degree. The proposed method has a good classification performance, because the influence of noise samples is weakened and meanwhile the support vectors are paid much more attention. The experiments on the UCI database and MNIST data set fully verify the effectiveness of the proposed algorithm.

A comprehensive evaluation of regional water safety systems based on a similarity cloud model.

Regional water safety systems are affected by social, economic, ecological, hydrological and other factors, and their effects are complicated and variable. Studying water safety systems is crucial to promoting the coordinated development of regional water safety systems and anthropogenic processes. Thus, a similarity cloud model is developed to simulate the evolution mechanisms of fuzzy and complex regional systems of water security and overcome the uncertainty that is associated with the indices that are used in water safety index systems. This cloud generator is used to reciprocally transform a qualitative cloud image with a quantitative cloud characteristic value, and the stochastic weight assignment method is used to determine the weight of the evaluation indices. The results of case studies show that Jiansanjiang's water safety systems were in a safe state in 2002-2011, but the water safety systems in the arid area of Yinchuan City were in a dangerous state in 2006-2007 because of climate factors and a lack of effective water and soil resource protection. The experimental results are consistent with the research subjects' actual situations, and the proposed model provides a tool for decision makers to better understand the security issues that are associated with regional water safety systems.

The spatial distribution of threats to plant species with extremely small populations

Many biological conservationists take actions to conserve plant species with extremely small populations (PSESP) in China; however, there have been few studies on the spatial distribution of threats to PSESP. Hence, we selected distribution data of PSESP and made a map of the spatial distribution of threats to PSESP in China. First, we used the weight assignment method to evaluate the threat risk to PSESP at both country and county scales. Second, we used a geographic information system to map the spatial distribution of threats to PSESP, and explored the threat factors based on linear regression analysis. Finally, we suggested some effective conservation options. We found that the PSESP with high values of protection, such as the plants with high scientific research values and ornamental plants, were threatened by over-exploitation and utilization, habitat fragmentation, and a small sized wild population in broad-leaved forests and bush fallows. We also identified some risk hotspots for PSESP in China. Regions with low elevation should be given priority for ex- and in-situ conservation. Moreover, climate change should be considered for conservation of PSESP. To avoid intensive over-exploitation or utilization and habitat fragmentation, in-situ conservation should be practiced in regions with high temperatures and low temperature seasonality, particularly in the high risk hotspots for PSESP that we proposed. Ex-situ conservation should be applied in these same regions, and over-exploitation and utilization of natural resources should be prevented. It is our goal to apply the concept of PSESP to the global scale in the future.

Hybrid similarity measure for retrieval in case-based reasoning systems and its applications for computer numerical control turret design

Analyzing the history of design is helpful in creating the computer numerical control turret design. Knowledge acquired from reasonably organizing and reusing designs may contribute to establishing computer numerical control turret design tasks. This article presents the results of our study on the representation and reuse of computer numerical control turret design knowledge and highlights the application of a case-based reasoning method in the structure design of a computer numerical control turret. The primary step in case-based reasoning systems is case retrieval where the similarity measure plays a significant role. The objective of this study is to develop a new method for a hybrid similarity measure with five formats of attribute values: crisp symbols, crisp numbers, fuzzy numbers, fuzzy linguistic variables, and fuzzy intervals. First, a hybrid similarity measure for a mixture of crisp and fuzzy sets is proposed for retrieving cases. Subsequently, a synthesis weight is formed through the combination of the subjective weight and objective weight. The calculation formula of the global similarity, which can retrieve the proper historical case, can be established by combining the hybrid similarity measure and the synthesis weight measure accordingly. Finally, the hybrid similarity measure and weight assignment method were applied in a computer numerical control turret conceptual design case-based reasoning system. The results showed that the global similarity of these five attribute types and the distribution of weight coefficients could improve the accuracy of case retrieval, which would help designers achieve the goal of rapid design.

Supporting Business Intelligence Usage: An Integrated Framework with Automatic Weighting

ABSTRACTThis paper presents an integrated framework that comprises an automatic weighting method for assessing data quality (DQ) of the framework so as to better support the business intelligence (BI) usage. Specifically, we utilize business process modeling (BPM) notation and information product map and frame them into a hierarchical mapping structure. Furthermore, we develop and demonstrate an automatic weight-assignment method for evaluating critical dimensions (i.e., completeness and accuracy) of DQ of the integrated framework. Through a design science paradigm, the effectiveness of the framework and the associated DQ weighting method has been rigorously validated by faculty management users of a university. The framework together with the DQ weighting method builds user confidence by enhancing the traceability of a BI product. The automatic DQ weight assignment also provides better time efficiency because the weight of each data attribute is determined automatically based on its usage on the BI dashboard.

A Route Weight Assignment Method for Predictive Congestion Control in WSN

A Route Weight Assignment Method for Predictive Congestion Control in WSN

Extended symmetric and asymmetric weight assignment methods in data envelopment analysis

Dual weight restrictions are commonly suggested as a remedy to the problem of low discriminatory power and absurd marginal prices in conventional Data Envelopment Analysis (DEA) models. However, weight restriction models also suffer from potential problems of infeasibility, lack of exogenous determination and ambiguous interpretations. The Symmetric Weight Assignment Technique (SWAT) addresses these concerns through a symmetric endogenous weight selection process. In this paper, we extend the SWAT method by proposing four new DEA models. Symmetric and asymmetric weights are rewarded and penalized, respectively, in the proposed models. The first model takes into account the symmetrical weights assigned to the outputs in the input-oriented model. The second model takes into account the symmetrical weights assigned to the inputs in the output-oriented model. The third and fourth models simultaneously take into account symmetric input–output weights in both the input and output orientations. We demonstrate the applicability of the proposed models and the efficacy of the procedures and algorithms with an application to Danish district heating plants.

Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network

Purpose – To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights. Design/methodology/approach – Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established. Findings – Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods. Originality/value – SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

基于物理指标的烟支质量评价方法及应用

根据红河卷烟厂在2014年10月至11月份卷烟质量抽检过程中的实验数据，在烟支卷制环节的六个物理指标即重量、圆周、吸阻、长度、总通风率和硬度的基础上，采用功效系数法和主客观相结合的权重赋值法设计出了烟支质量的综合评价方法。从评价结果可以看出，质量评价合格的抽检烟支的物理指标得分分布在区间[60, 100]，以及超标烟支的物理指标得分分布在区间[0, 60]。另外，本文设计的综合评价方法所得的结果能很直观地从整体上反映出烟支质量抽检的综合情况，并且采用该方法评扩展到评价机台质量水平的结果也比较契合CPK值这一卷烟厂评价机台质量控制水平常用工具的结果。 According to the experiment data of sampling observation of cigarette quality, happening in the Honghe Cigarette Factory from October to November 2014, based on six physical indicators of cig-arette making part: Weight, Circumference, Draw Resistance, Length, Total ventilation rate and Hardness, I have used the efficacy coefficient method and the combination of subjective and objec-tive weight assignment method to design a comprehensive evaluation method of cigarette quality. From the evaluation results, we can find that the physical index score of qualified sampling ciga-rette distributed from 60 to 100, and it also shows that the physical index score of unqualified sampling cigarette distributed from 0 to 60. In addition, the results of this paper can intuitively re-flect the overall situation of comprehensive quality of the sampling cigarette, and this evaluation method can be extended to evaluate the quality level of the machine, whose results relatively fit the CPK value which is a common tool for the cigarette factory evaluating the quality control level of machine.

基于AIRS的遥感图像检索特征权值调整方法

针对当前图像检索中存在的有效特征提取问题，提出了一种基于人工免疫识别系统(AIRS)的特征权值调整方法。利用人工免疫识别系统的泛化学习及记忆的特点，对训练样本的特征值进行学习，从而确定各特征之间的权值分配。实验结果表明，与传统权值调整法相比，本方法能够为各特征提供较好的权值，提高图像检索的准确率。 In view of the effective feature extraction issue in current image retrieval, a feature weight as-signment method based on Artificial Immune Recognition System (AIRS) is promoted in this paper. The generalized learning and memory character of AIRS is used to learn the character of training samples, in order to determine the weight between the image features. Experimental results show that comparing to the traditional feature weight assignment method, this method can provide better feature weight, and improve the image retrieval accuracy.

Probability Distributions for Stochastic Gene Expression with Multiple Binding Sites

While regulation of intrinsic stochasticity in gene expression is essential for many cellular functions, there is considerable interest in understanding how different molecular mechanisms of gene expression impact variations in protein levels across a population of cells. Stochastic properties of the corresponding gene systems are usually examined by directly finding the probability distributions of mRNA or protein governed by chemical master equations. Here, we analyze four stochastic models of gene expression, each describing a representative topology of binding sites. The total mRNA or protein probability distributions are derived by the weight assignment method that we propose here. These distributions indicate that the total mRNA or protein number obeys a beta distribution or a linear superposition of two beta distributions in spite of differences in the local mRNA or protein numbers distributed in individual binding sites among the models. Our results reveal essential mechanisms of gene expression in terms of the probability distribution rather than finite-order moments, as was done in previous studies.