Publications

In China, traditional buildings have begun to exhibit a range of issues, such as elevated levels of energy consumption and pollution. Consequently, these concerns have led to substantial resource inefficiency and environmental degradation. The evaluation and examination of green buildings are of utmost importance in promoting sustainable development. The current green building assessment framework is intricate and lacks sufficient development in terms of visual representation. Developing a green building strategy during the initial design phase is a multifaceted process that necessitates substantial allocation of human, material, financial, and temporal resources. In this study, we propose an assessment framework that incorporates a 15 s-level and 45 three-level green building indicator system, along with a 4-level classification standard. This framework is developed based on the most recent Chinese Assessment Standard for Green Building and the utilization of a Building Information Modeling (BIM) database. Furthermore, the integration of BIM with Pathfinder software is employed to assess the safety aspects of green buildings. On top of that, the combination of BIM with Ecotect software is utilized to evaluate the environmental aspects of green buildings. In this study, we performed a case study on a teaching building located at a university in central China, specifically focusing on the simulation of green building practices. The sequential calculation involves determining the duration of personnel evacuation, assessing the lighting conditions, evaluating the thermal conditions, analyzing the sound conditions, and examining the wind conditions. In addition, efforts were made to optimize the indicators requiring enhancement in order to enhance the efficacy of green buildings.

Abstract—In this paper, we introduce a new architecture of Information Granulation-based genetically optimized Fuzzy Relation-based Polynomial Neural Networks (IG_gFRPNN) that is based on a genetically optimized multilayer perceptions with fuzzy Relation-based polynomial neurons (FRPNs). The proposed IG_gFRPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNN. In addition, the fuzzy rules used in the networks rely directly upon the notion of information granules defined over system's variables and formed through the process of information granulation. This granulation is realized with the aid of the C-Means clustering algorithm. Through the consecutive process of such structural and parametric optimization, a flexible topology of the fuzzy neural network becomes generated in a dynamic fashion. To evaluate the performance of the IG_gFRPNN, the model is experimented with using time series data (Mackey-Glass time series). I.

Byproduct gaseous energy is crucial to the iron-steel manufacturing process, where the tendencies of its generation and consumption can be deemed as a significant reference for scheduling production and decision-making. Besides the requirements imposed on numeric prediction, practical applications also demand that the result be represented in terms of intervals expressing the reliability of prediction outcomes. Meanwhile, prediction intervals should cover a long period of time for delivering more information on future long-term trends. Bearing this in mind, in this study, a Granular Computing-based hybrid hierarchical method is proposed for constructing long-term Prediction Intervals (PIs), in which the horizontal modelling gives rise to long periods of prediction, and the vertical one extends them to the interval-valued format. Information granules are hierarchically distributed over single data and then on industrial features-based segments. Considering the criteria of coverage and specificity as sound performance indexes of the model, a suite of optimization problems is formulated and solved by involving Particle Swarm Optimization (PSO). Experimental studies demonstrate that the proposed approach exhibits better performance when compared with the performance reported for other commonly encountered methods.

This paper reports on the experience of the authors in quan-
\ntitatively assessing the development process of an Eastern
\nEuropean software SME (Small or Medium Size Enterprise).
\nThe company produces a very successful workflow and doc-
\numentation tool, features about 30 full time developers and
\nhas a customer base of about 40 major organizations. It has
\nhired the authors as consultants to address quality and pro-
\nductivity issues raised by the upper management and cus-
\ntomers. The adopted approach is based on systemic analysis,
\nand starts with a comprehensive GQM session with the top
\nmanagers of the company, to fully define the scope of work,
\nand progresses analysing the documentation, interviewing
\nthe manager and the lead developers, and quantitatively
\nanalysing the issue tracking system in place. Specific at-
\ntention is placed in identifying “schismogenesis”, situations
\nthat may lead to unresolvable conflicts. The approach has
\nbeen proven successful in providing a result in short fore-
\ncasted timeframe, and systemic analysis has been effective
\nin spotting the most critical situations present in the com-
\npany. The result has been a set of prioritized recommenda-
\ntions, centered first in eliminating the schismogenetic situ-
\nations and then ranging from adopting a more quantitative
\nprocess control, to streamline the activities, to organize a
\nline of product

In this study, we introduce a concept of granular agents and elaborate on various representation, communication and learning issues arising in this framework. A granular world, in which the granular agents interact, embodies a collection of information granules being regarded as generic conceptual entities used to represent knowledge and handle problem solving. On the other hand, granular computing is a paradigm supporting knowledge representation, coping with complexity, and facilitating interpretation of processing. In this sense, it is crucial to all man-machine pursuits, data mining and intelligent data analysis, in particular. There are three essential facets that are inherently associated with any agent, that is formalism used to describe and manipulate information granules and the granularity of the granules themselves as well as the internal structure of the agents. There are numerous formal models of granular worlds ranging from set-theoretic developments (including sets, fuzzy sets, and rough sets) to probabilistic counterparts (random sets, random variables and alike). In light of the evident diversity of granular world (occurring both in terms of the underlying formal settings as well as levels of granularity), we elaborate on their possible interaction and identify implications of such communication. More specifically, we have cast these in the form of the interoperability problem, that is associated with the representation of information granules. Moreover, we explore various internal models of agents including the concepts stemming from fuzzy state machines and discuss pertinent models of learning.

In the context of robust optimization with information granules for distributional parameters, this paper investigates a two-stage waste-to-energy feedstock flow planning problem with uncertain capacity expansion costs. The objective is to minimize the worst-case overall loss in a mean-risk criterion where the risk is measured by a conditional value-at-risk operator. As a salient feature, an integrated uncertainty is considered which consists of not only the uncertainty in distribution shapes of the uncertain variables, but also the manifold uncertainties of the mean parameters. To tackle the robust optimization under such integrated uncertainty, we first discuss a distributional robust two-stage feedstock flow planning model with precise mean parameters that handles the uncertainty in distribution shape, and the model can be equivalently transformed into a linear program (LP). Furthermore, the precise-mean-based robust model is extended into the case of multifaceted uncertainty for mean-parameters that are allowed to assume intervals, historical-data-based probabilistic estimates, and/or human-knowledge-centric fuzzy set estimates, under different circumstances. These multifaceted uncertain mean-parameters are uniformly represented by using information granules, and a granular robust optimization model is then developed which maximizes the robustness of the solution within a shortfall tolerance, and realizes a tradeoff between the solution conservativeness and robustness. It is showed that the granular robust model is equivalent to solving a series of LPs and can be efficiently handled by a nested binary search algorithm. Finally, the computational study illustrates the model performance, solution analysis, and underlines a much higher scalability of the developed robust model compared to the stochastic programming approach.

The paper deals with the design problem of control algorithms in fuzzy systems described by means of fuzzy relational equations, which can be implemented in the framework of fuzzy controllers applied to control of ill-defined systems. Some ways leading to assigning varying grades of importance to goals and constraints imposed in control process and their influence on the relation of the fuzzy controller is discussed in detail.

Due to system complexity, research on fuzzy fractional-order, singular perturbation, multi-agent systems (FOSPMASs) remains limited in control theory. This article focuses on the leader-following consensus of fuzzy FOSPMASs with orders in the range of 0, 2. By employing the T-S fuzzy modeling approach, a fuzzy FOSPMAS is constructed. In order to achieve the consensus of a FOSPMAS with multiple time-scale characteristics, a fuzzy observer-based controller is designed, and the error system corresponding to each agent is derived. Through a series of equivalent transformations, the error system is decomposed into fuzzy singular fractional-order systems (SFOSs). The consensus conditions of the fuzzy FOSPMASs are obtained based on linear matrix inequalities (LMIs) without an equality constraint. The theorems provide a way to tackle the uncertainty and nonlinearity in FOSPMASs with orders in the range of 0, 2. Finally, the effectiveness of the theorems is verified through an RLC circuit model and a numerical example.

In this paper, the problem of output feedback model predictive control (MPC) for interval Type-2 Takagi-Sugeno fuzzy systems with bounded disturbance is investigated. The output feedback MPC approach includes an offline design of the state observer to estimate true states and predict bounds of future estimation error sets, and an online problem that optimizes the controller gains to stabilize the closed-loop observer system. The dynamics of the estimation error system is determined by the offline designed observer gain, and bounds of which are online refreshed by scaling a minimal robust positively invariant (RPI) set via a scalar. The optimized controller gains steer the current estimated state from an RPI set into another one such that future estimated states are invariant in the subsequent RPI set. Convergence of the estimation error system and stability condition on the closed-loop observer system in terms of linear matrix inequalities are derived using the technique of S-procedure. The estimation error and estimated state converge within the corresponding time-varying RPI sets, and therefore, recursive feasibility of the optimization problem and input-to-state stability of the closed-loop observer system with respect to the estimation error and bounded disturbance are ensured. For reducing the online computational burden, a lookup table that stores the offline calculated controller gains with corresponding regions of attraction is offline constructed for online searching real-time controller gains. A simulation example is given to show the effectiveness of the approach.

In this article, we introduce a design methodology of reinforced fuzzy models based both on univariate analysis and multivariable analysis to cope with high-dimensional problems. This approach is aimed at reducing the design process and curbing computing overhead inherently associated with the increasing volume of data in terms of both their number and the dimensionality of the feature space. The critical features of the proposed fuzzy models are highlighted as follows: First, the essential input variables of the model are selected by running the univariable and multivariable analyses. In univariate analysis, input variables with a strong linear relationship with the output variable are selected through correlation analysis completed for each input space and output space. On the contrary, in multivariable analysis, input variables are chosen by comparing the determination coefficients obtained from the subsets of input variables. Second, according to the analysis of the input variable, we construct two different kinds of fuzzy models. The first fuzzy model comprises the design of the univariable-based fuzzy model (UFM) and its aggregation. The UFMs are made by the individual input variables selected from the univariable analysis using a correlation coefficient. The subspaces formed by correlation analysis are applied for determining the centers of the membership function (MF). The results produced by individual fuzzy models are aggregated through some <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -conorms. The second fuzzy model is with the fuzzy clustering for improving the form of fuzzy space in the premise part of a fuzzy rule. To curb the dramatic increase in size of fuzzy rule in high-dimensional problems, the clustering space is employed as the fuzzy space, and the partition matrix produced by fuzzy provided the required degrees of the MF. Experimental studies include a suite of synthetic and publicly available data. The superiority of the proposed design methodology was demonstrated by using 34 publicly available datasets and also compared with the conventional models associated with the fuzzy rule-based models as well as the state-of-the-art models reported in the literature.