One-and-only item recommendation with fuzzy logic techniques

Recommender systems anticipate users’ needs by suggesting items that are likely to interest them. Most existing systems employ collaborative filtering (CF) techniques, searching for regularities in the way users have rated items. While in general a successful approach, CF cannot cope well with so-called one-and-only items, that is: items of which there is only one single instance (like an event), and which as such cannot be repetitively “sold”. Typically such items are evaluated only after they have ceased being available, thereby thwarting the classical CF strategy. In this paper, we develop a conceptual framework for recommending one-and-only items. It uses fuzzy logic, which allows to reflect the graded/uncertain information in the domain, and to extend the CF paradigm, overcoming limitations of existing techniques. A possible application in the context of trade exhibition recommendation for e-government is discussed to illustrate the proposed conceptual framework.     

Fuzzy logic techniques for mobile robot obstacle avoidance

This paper is concerned with the problem of reactive navigation for a mobile robot in an unknown clustered environment. We will define reactive navigation as a mapping between sensory data and commands. Building a reactive navigation system means providing such a mapping. It can come from a family of predefined functions (like potential fields methods) or it can be built using ‘universal’ approximators (like neural networks). In this paper, we will consider another ‘universal’ approximator: fuzzy logic. We will explain how to choose the rules using a behaviour decomposition approach. It is possible to build a controller working quite well but the classical problems are still there: oscillations and local minima. Finally, we will conclude that learning is necessary for a robust navigation system and fuzzy logic is an easy way to put some initial knowledge in the system to avoid learning from zero.     

Fuzzy cognitive map architectures for medical decision support systems

Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.     

Fuzzy logic based energy and throughput aware design space exploration for MPSoCs

Multicore architectures were introduced to mitigate the issue of increase in power dissipation with clock frequency. Introduction of deeper pipelines, speculative threading etc. for single core systems were not able to bring much increase in performance as compared to their associated power overhead. However for multicore architectures performance scaling with number of cores has always been a challenge. The Amdahl’s law shows that the theoretical maximum speedup of a multicore architecture is not even close to the multiple of number of cores. With less amount of code in parallel having more number of cores for an application might just contribute in greater power dissipation instead of bringing some performance advantage. Therefore there is a need of an adaptive multicore architecture that can be tailored for the application in use for higher energy efficiency. In this paper a fuzzy logic based design space exploration technique is presented that is targeted to optimize a multicore architecture according to the workload requirements in order to achieve optimum balance between throughput and energy of the system.     

Multi‐adjoint Relation Equations: A Decision Support System for Fuzzy Logic

Fuzzy relation equations (FRE) are an important decision support system (DSS), for example, in fuzzy logic. FRE have recently been extended to a more general framework, called multiadjoint relation equations (MARE). This paper shows MARE as a fundamental DSS in multi‐adjoint logic programming. For that purpose, multi‐adjoint logic programs will be interpreted as a MARE, and the solvability of them will be given in terms of concept lattice theory. Furthermore, two approximations (optimistic and pessimistic approximations) of unsolvable equations will be obtained from a multiadjoint object‐oriented concept lattice. Finally, a real‐life example will be studied.     

Quantified risk assessment for plants producing and storing explosives

This paper presents a methodology for risk assessment of plants producing and storing explosives. The major procedural steps for quantified risk assessment (QRA) in explosive plants are the following: hazard identification, accident sequence modeling, data acquisition, accident sequence quantification, consequence assessment and integration of results. This methodology is demonstrated and applied in an explosive plant consisting of four separate units, which produce detonating cord, nitroglycol, dynamites and ammonium nitrate fuel oil (ANFO). A GIS platform is used for depicting individual risk from explosions in this plant. Total individual risk is equal to 1.0 × 10-4/y in a distance of 340m from the center of the plant, and 1.0 × 10-6/y in a distance of 390m from the center of the plant.     

An integrated DEMATEL and Fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company

In recent years, strategy aspects related to core competency, risk analysis and organizational flexibility especially have been growing. This trend has led researchers and industries to become more interested in the multi-criteria decision making (MCDM) models for selecting outsourcing providers. The efficiency of decision-making mostly depends on the ability of decision-makers analyzing the complex cause and effect relationship between criteria and taking effective actions based on the analysis. Using an analytical method to select the most eligible outsourcing provider is significant for a company which desires to improve its competitiveness. In this study, a fuzzy integrated multi-criteria decision making method for evaluation and determination of an outsourcing provider for a telecommunication company is analyzed by using DEMATEL and Fuzzy ANP multi-criteria decision making techniques. First, DEMATEL method is used in order to put forward the interrelationship among the main criteria which are determined in the study for outsourcing selection process. Then, local weights of the sub-criteria and sub-subcriteria are calculated by Fuzzy ANP approach on the basis of cause-effect relationships that are exposed through DEMATEL method. The local weights are put into ANP supermatrix, and calculations are implemented to select out the most eligible outsourcing provider.     

Fuzzy decision support software for crisis management in gas transmission networks

In this work, a new software for fuzzy decision support system, with a new decision making structure, to control the crisis conditions in the gas transmission network is developed. This system after receiving both functional variables of the gas transmission network and faults signals makes right decisions to eliminate and repair the conditions of the transmission network according to its database established through experience gathered from experts. These decisions are expressed in the form of some scenarios with different desirability degrees. Desirability degrees measure the outcomes of the decisions taken for the aforementioned conditions; this in turn will assist the managers in choosing the best ones. The user interface properly developed is graphical and provides the manager a good facility to easily use the system without any hesitation at the occurrence time of crisis.     

Readiness assessment model for institutionalization of SMEs using fuzzy hybrid MCDM techniques

The process of institutionalization encompasses the creation of formal structure, emergence of informal norms, and the development of impersonal/objective procedures, including administrative rituals, ideologies, legalization, and a constant focus on legitimization. Forming the basis of institutionalization, institutional theory traces the “emergence of distinctive forms, processes, strategies, outlooks, and competences” from patterns of organizational interaction and adaptation in response to internal and external environments. In this context, the institutionalization readiness level of organizations is a focus of investigation for developing models to evaluate organizations using precise indicators. In this study, the fuzzy hybrid multi-criteria decision-making approach is used in order to measure institutionalization readiness of small and medium sized enterprises (SMEs). Firstly, factors indicating the institutionalization readiness level of SMEs are determined. Then, the weights of the factors are calculated by obtaining expert opinions, along with fuzzy DEMATEL and fuzzy analytic network process (ANP) methods. Several SMEs are evaluated according to these predefined factors, after which the TOPSIS method is implemented for measuring the level of institutionalization of the SMEs.     

Fuzzy logic based decision support for mass evacuations of cities prone to coastal or river floods

Facing climate change and more frequent extreme weather conditions, coastal floods and inundations will become more severe. Evacuation can be an efficient solution to secure people's safety in a major disaster. The main difficulty in making an evacuation decision is the imprecise, incomplete and spatially varying nature of the crisis information. In this paper, a fuzzy-logic based method combined with Geographic Information System is proposed to analyze evacuation decision making scenarios. The method can handle qualitative and quantitative data at the same time, avoid sudden changes of decisions affected by uncertainties, and evaluate the spatial necessity to evacuate to support evacuation decision making. The method has been tested at the city of Bordeaux in France. The maps produced representing the need to evacuate can help decision makers better understand evacuation decision situation in terms of local impacts and crisis management anticipation.     

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

Nowadays healthcare becoming more important aspect for everybody. Healthcare institutions now giving more attention to their patients’ safety by reducing the frequency of medical errors and trying to provide all kinds of best facilities to them. Clinical processes can be understood as a series of interactions between patients, providers, and technologies. Therefore, there are some chances exist for medical errors due to the involvement of human beings and machines. A number of tools exist to prospectively analyze processes in healthcare which generally needs precise numerical data. In general, available or extracted data is not precise and sufficient to assess the clinical processes upto a desired degree of accuracy due to various practical and economic reasons. Thus, collected data may have some sort of uncertainties and quantification of these uncertainties should be done very carefully before analysing further. In this paper, a new fuzzy fault tree approach has been presented for patient safety risk modeling in healthcare. This approach applies fault-tree, trapezoidal fuzzy numbers, α-cut set, and the weakest-t-norm (T_ω) based approximate arithmetic operations to obtain fuzzy failure probability of the system. The effectiveness of the developed approach is illustrated with two different kinds of problems taken from literature related to healthcare. Also, Tanaka et al.'s approach has been used to rank the critical basic events of the considered problems. Computed results have been compared with results obtained from other existing techniques.     

大系统模糊决策研究及其智能化决策支持系统IDSS

研究了模糊数学原理应用于大系统决策的理论和方法。设计了相应的多用途智能化决策支持系统ＩＤＳＳ。     

Integrating gray system theory and logistic regression into case-based reasoning for safety assessment of thermal power plants

Safety assessment of thermal power plants (TPPs) is one of the important means to guarantee the safety of production in thermal power production enterprises. Due to various technical limitations, existing assessment approaches, such as analytic hierarchy process (AHP), Monte Carlo methods, artificial neural network (ANN), etc., are unable to meet the requirements of the complex security assessment of TPPs. Currently, most of the security assessments of TPP are completed by the means of experts’ evaluations. Accordingly, the assessment conclusions are greatly affected by the subjectivity of the experts. Essentially, the evaluation of power plant systems relies to a large extent on the knowledge and length of experience of the experts. Therefore in this domain case-based reasoning (CBR) is introduced for the security assessment of TPPs since this methodology models expertise through experience management. Taking the management system of TPPs as breakthrough point, this paper presents a case-based approach for the Safety assessment decision support of TPPs (SATPP). First, this paper reviews commonly used approaches for TPPs security assessment and the current general evaluation process of TPPs security assessment. Then a framework for the Management System Safety Assessment of Thermal Power Plants (MSSATPP) is constructed and an intelligent decision support system for MSSATPP (IDSS-MSSATPP) is functionally designed. IDSS-MSSATPP involves several key technologies and methods such as knowledge representation and case matching. A novel case matching method named Improved Gray CBR (IGCBR) has been developed in which a statistical approach (logistic regression) and Gray System theory are integrated. Instead of applying Gray System theory directly, it has been improved to integrate it better into CBR. In addition this paper describes an experimental prototype system of IDSS-MSSATPP (CBRsys-TPP) in which IGCBR is integrated. The experimental results based on a MSSATPP data set show that CBRsys-TPP has high accuracy and systematically good performance. Further comparative studies with several other common classification approaches also show its competitive power in terms of accuracy and the synergistic effects of the integrated components.     

Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management

Safety management (SM) is a very important element within an effective manufacturing organization. One of the most important components of SM is to maintain the safety of work systems in the workplace. Safety of work systems is a function of many factors which affect the system, and these factors affect the safety of work systems simultaneously. For this reason, measuring work system safety needs a holistic approach. In this study, the work safety issue is studied through the analytic hierarchy process (AHP) approach which allows both multi-criteria and simultaneous evaluation. Another limitation faced in SM is the inability to measure the variables exactly and objectively. Generally, the factors affecting work system safety have non-physical structures. Therefore, the real problem can be represented in a better way by using fuzzy numbers instead of numbers to evaluate these factors. In this study, a fuzzy AHP approach is proposed to determine the level of faulty behavior risk (FBR) in work systems. The proposed method is applied in a real manufacturing company. In the application, factors causing faulty behavior are weighted with triangular fuzzy numbers in pairwise comparisons. These factors are evaluated based on the work system by using these weights and fuzzy linguistic variables. As a result of this evaluation FBR levels of work systems are determined and different studies are planned for work systems according to the FBR levels. In this way, faulty behavior is prevented before occurrence and work system safety is improved.     

火电厂综合安全评价模型的构建与应用

将模糊层次分析法、集对分析和系统功能论集于一体,构建了一个适用于火电企业的综合安全评价模型,不但考虑了影响火电厂安全的关键因素,而且可对火电厂安全因素之间的相互作用的协调性做量化分析,并将其结果构造系统优势函数,以解决系统安全功能大于各子系统安全功能之和的问题。实例表明该综合安全评价模型能够整合火电厂中各安全因素内在联系和非线性影响,使安全评价趋于合理,为企业安全生产管理提供科学的依据。     

Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection

Uncomplicated urinary tract infection (uUTI) is a bacterial infection that affects individuals with normal urinary tracts from both structural and functional perspective. The appropriate antibiotics and treatment suggestions to individuals suffer of uUTI is an important and complex task that demands a special attention. How to decrease the unsafely use of antibiotics and their consumption is an important issue in medical treatment. Aiming to model medical decision making for uUTI treatment, an innovative and flexible approach called fuzzy cognitive maps (FCMs) is proposed to handle with uncertainty and missing information. The FCM is a promising technique for modeling knowledge and/or medical guidelines/treatment suggestions and reasoning with it. A software tool, namely FCM-uUTI DSS, is investigated in this work to produce a decision support module for uUTI treatment management. The software tool was tested (evaluated) in a number of 38 patient cases, showing its functionality and demonstrating that the use of the FCMs as dynamic models is reliable and good. The results have shown that the suggested FCM-uUTI tool gives a front-end decision on antibiotics’ suggestion for uUTI treatment and are considered as helpful references for physicians and patients. Due to its easy graphical representation and simulation process the proposed FCM formalization could be used to make the medical knowledge widely available through computer consultation systems.     

基于强化学习和模糊逻辑的移动机器人导航

自主导航是移动机器人的一项关键技术。该文采用强化学习结合模糊逻辑的方法实现了未知环境下自主式移动机机器人的导航控制。文中首先介绍了强化学习原理,然后设计了一种未知环境下机器人导航框架。该框架由避碰模块、寻找目标模块和行为选择模块组成。针对该框架,提出了一种基于强化学习和模糊逻辑的学习、规划算法：在对避碰和寻找目标行为进行独立学习后,利用超声波传感器得到的环境信息进行行为选择,使机器人在成功避碰的同时到达目标点。最后通过大量的仿真实验,证明了算法的有效性。     

Higher-Order Syntax and Saturation Algorithms for Hybrid Logic

We present modal logic on the basis of the simply typed lambda calculus with a system of equational deduction. Combining first-order quantification and higher-order syntax, we can maintain modal reasoning in terms of classical logic by remarkably simple means. Such an approach has been broadly uninvestigated, even though it has notable advantages, especially in the case of Hybrid Logic.We develop a tableau-like semi-decision procedure and subsequently a decision procedure for an alternative characterization of , a well-studied fragment of Hybrid Logic.With regards to deduction, our calculus simplifies in particular the treatment of identities. Moreover, labeling and access information are both internal and explicit, while in contrast traditional modal tableau calculi either rely on external labeling mechanisms or have to maintain an implicit accessibility relation by equivalent formulas.With regards to computational complexity, our saturation algorithm is optimal. In particular, this proves the satisfiability problem for to be in PSPACE, a result that was previously not achieved by the saturation approach.     

Geometric and form feature recognition tools applied to a design for assembly methodology

The paper presents geometric tools for an automated Design for Assembly (DFA) assessment system. For each component in an assembly a two step features search is performed: firstly (using the minimal bounding box) mass, dimensions and symmetries are identified allowing the part to be classified, according to DFA convention, as either rotational or prismatic; secondly form features are extracted allowing an effective method of mechanised orientation to be determined. Together these algorithms support the fuzzy decision support system, of an assembly-orientated CAD system known as FuzzyDFA.     

Fuzzy logic-based decision support system for automating ergonomics risk assessments

Workers in the modular construction industry are frequently exposed to ergonomic risks, which may lead to injuries and lower productivity. In light of this, researchers have proposed a number of ergonomics risk assessment methods to identify design flaws in work systems, thereby reducing ergonomic discomfort and boosting workplace productivity. However, organizations often disregard ergonomics risk assessments due to a lack of convenient tools and knowledge. Therefore, this study proposes a fuzzy logic-based decision support system to help practitioners to automatically and comprehensively assess the ergonomic performance of work systems. For comprehensive assessment of ergonomic risk, the proposed decision support system considers physical, environmental, and sensory factors. Specifically, the decision support system comprises eight fuzzy expert systems that output a composite risk score, called an “ergonomic risk indicator”, that indicates the overall level of ergonomic risk present in a given work system. The performance of the proposed decision support system is then evaluated using a real-world case study in a modular construction facility by comparing the results of the decision support system with the facility's occupational injury reports. The results prove the effectiveness of the decision support system. Overall, the decision support system is capable of generating a composite risk score, the ergonomic risk indicator, and the proposed high-level architecture and design represent significant contributions for the enhancement of health and safety in the modular construction industry.