基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。     

An FCM–FAHP approach for managing readiness-relevant activities for ERP implementation

Any organization which plans to introduce a new enterprise resource planning (ERP) system will carry out a range of activities to improve its readiness for the new system. This paper develops a new approach for managing these interrelated activities using fuzzy cognitive maps (FCMs) and the fuzzy analytical hierarchy process (FAHP). This approach enables the organization to (1) identify the readiness-relevant activities, (2) determine how these activities influence each other, (3) assess how these activities will contribute to the overall readiness and (4) prioritize these activities according to their causal interrelationships to allocate management effort for the overall readiness improvement. The approach first uses FCMs and a fuzzy connection matrix to represent all possible causal relationships between activities. It then uses FAHP to determine the contribution weights and uses FCM inference to include the effects of feedback between the activities. Based on the contribution and interrelationships between activities, a management matrix is developed to categorize them into four management zones for effective allocation of limited management efforts. An empirical study is conducted to demonstrate how the approach works.     

Managing readiness-relevant activities for the organizational dimension of ERP implementation

An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted.     

供应链管理视角下装配式建筑可持续发展分析

为探究供应链管理对装配式建筑可持续发展的影响,基于供应链管理视角,构建装配式建筑可持续发展 FCM 模型。在 3 种供应链领导者关键管理行为的基础上,从装配式建筑可持续发展速度和效果两个方面进行情境仿真,分析 3 种关键供应链管理行为对装配式建筑可持续竞争优势的影响机理。结果表明,在生态设计、标准设立、利益共享契约 3 种不同管理行为刺激下,可持续发展速度均有不同,且 3 种管理行为对可持续发展效果具有不同方向的影响:生态设计、标准设立正向促进可持续发展效果,而利益共享契约产生负向影响。本研究为装配式建筑健康发展的管理实践提供了理论基础。     

Application of Fuzzy c-means algorithm for the estimation of Asphaltene precipitation

One of problematic topics in petroleum engineering is Asphaltene precipitation issue which causes problems such as tubing plugging and formation damage due to temperature, pressure and composition changes so the notability of this issue increases. In the present investigation a novel Fuzzy c-means (FCM) algorithm was developed to predict precipitated asphaltene as function of dilution ratio, carbon number of precipitants and temperature for solving the problem. The results showed that this novel approach has great ability to predict precipitated asphaltene in terms of aforementioned parameters. The coefficients of determination (R²) for training and testing steps are calculated as 0.9828 and 0.9387 respectively. This great degree of accuracy expresses that the predicting algorithm has potential to be utilized as software for prediction of asphaltene behavior.     

Coupling of an innovative small PWR and advanced sodium‐cooled fast reactor for incineration of TRU from once‐through PWRs

In the nuclear industry, safely managing spent fuels discharged from PWRs (pressurized water reactors) is an ongoing challenge. In this paper, a synergistic coupling of innovative small long‐cycle PWRs and advanced sodium‐cooled fast reactors is considered to reduce the accumulated TRUs (transuranics) by transmuting them with electricity production. In the coupling strategy, the innovative small PWRs employing UO₂–ThO₂ and fully ceramic micro‐encapsulated fuels are used to deeply burn TRUs from commercial PWRs, while advanced SFRs (sodium‐cooled fast reactors) with actinide recycling are designed to further transmute the TRUs discharged from innovative small PWRs. This work focuses on the core physics analysis of new SFR burner cores using different TRU feeds discharged from small PWRs. Additionally, quasi‐static reactivity balance analyses are performed to understand the safety of the SFR burner cores. The mass flows of TRUs in the nuclear park, which is composed of PWRs, small long‐cycle PWRs, and SFR burners, are analyzed to evaluate TRU inventory reduction. The results of this study show that the advanced SFR burners with all the TRU feed types discharged from the small long‐cycle PWRs have a high TRU consumption rate. They satisfy all of the conditions for self‐controllability under unprotected accidents with a reasonable number of control rods. This coupling strategy requires ~35% less power in conjunction with the advanced SFR burners in the nuclear park and increases the support ratio of SFR burners by ~42% than does the coupling of commercial PWRs and SFR burners. Copyright © 2015 John Wiley & Sons, Ltd.     

Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C‐means algorithm

This paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C‐means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM‐based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.     

H2-selective mixed matrix membranes modeling using ANFIS,PSO-ANFIS,GA-ANFIS

The novel contribution of the current study is to employ adaptive neuro-fuzzy inference system (ANFIS) for evaluation of H₂-selective mixed matrix membranes (MMMs) performance in various operational conditions. Initially, MMMs were prepared by incorporating zeolite 4A nanoparticles into polydimethylsiloxane (PDMS) and applied in gas permeation measurement. The gas permeability of CH₄, CO₂, C₃H₈ and H₂ was used for ANFIS modeling. In this manner, the H₂/gas selectivity as the output of the model was modeled to the variations of feed pressure, nanofiller contents and the kind of gas, which were defined as input (design) variables. The proposed method is based on the improvement of ANFIS with genetic algorithm (GA) and particle swarm optimization (PSO). The PSO and GA were applied to improve the ANFIS performance. To determine the efficiency of PSO-ANFIS, GA-ANFIS and ANFIS models, a statistical analysis was performed. The results revealed that the PSO-ANFIS model yields better prediction in comparison to two other methods so that root mean square error (RMSE) and coefficient of determination (R²) were obtained as 0.0135 and 0.9938, respectively. The RMSE and R² values for GA-ANFIS were 0.0320 and 0.9653, respectively, and for ANFIS model were 0.0256 and 0.9787, respectively.     

Fuzzy cognitive maps enabled root cause analysis in complex projects

This paper presents a Fuzzy Cognitive Maps (FCM) enabled Root Cause Analysis (RCA) approach to assessing the TBM performance in tunnel construction. Fuzzy logic is used to capture and utilize construction experience and knowledge from domain experts, and a cause-effect model consisting of nine concepts is established for simulating the TBM performance within the FCM framework. A tunnel case in the Wuhan metro system in China is used to demonstrate the applicability of the developed approach. Results indicate that (i) C₄ (Soil Density) displays a strongest negative correlation with the concept C_T (TBM Advance Rate); while C₈ (Grouting Speed) displays a strongest positive correlation with C_T; (ii) TBM performance is very sensitive to the change of operational conditions, where the values of operational parameters can be adjusted to go up (or down) in case the TBM performance negatively (or positively) reduces; and (iii) we can identify the magnitude of the adjustment scope of operational variables when the TBM operational performance suffers a reduction. The novelty of the proposed approach is that it is verified to be capable of modeling dynamics of system behaviors over time and performing many kinds of what-if scenario analysis, including predictive, diagnostic, and hybrid RCA, which turns out to be a more competitive solution that deals with uncertainty, dynamics, and interactions in the approximate reasoning process, compared to other traditional approximate methods (i.e. Fault Tree Analysis (FTA), Rule-Based Reasoning (RBR), and Case-Based Reasoning (CBR)). The proposed approach can be used as a decision support tool for ensuring the satisfactory performance of TBMs, and thus, increases the efficiency of tunnel construction projects.     

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.