Linguistic summarization of video for fall detection using voxel person and fuzzy logic

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person’s states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.     

iReminder: An Intuitive Location-Based Reminder That Knows Where You Are Going

This article presents the design of iReminder, an intuitive location-based reminder that delivers reminding messages based on users' future routes. iReminder is implemented on mobile phones, and it can predict users' future routes by collecting their daily trajectory data. Then it delivers a reminding message via the mobile phone when it senses that the user is going to the task location. A field study was conducted on how iReminder extends its potential to help users perform everyday tasks and compared the method adopted by iReminder with the method used by traditional location-based reminders. The experimental results show that iReminder outperforms traditional location-based reminder because it delivers reminding messages more appropriately. A detailed discussion is also given to investigate the ideal message delivery point, and the discussion results show that a location-based reminding message is more useful and more likely to be accepted by the user if it is triggered by considering his or her future route.     

Type‐1 OWA Unbalanced Fuzzy Linguistic Aggregation Methodology: Application to Eurobonds Credit Risk Evaluation

In decision making, a widely used methodology to manage unbalanced fuzzy linguistic information is the linguistic hierarchy (LH), which relies on a linguistic symbolic computational model based on ordinal 2‐tuple linguistic representation. However, the ordinal 2‐tuple linguistic approach does not exploit all advantages of Zadeh's fuzzy linguistic approach to model uncertainty because the membership function shapes are ignored. Furthermore, the LH methodology is an indirect approach that relies on the uniform distribution of symmetric linguistic assessments. These drawbacks are overcome by applying a fuzzy methodology based on the implementation of the type‐1 ordered weighted average (T1OWA) operator. The T1OWA operator is not a symbolic operator and it allows to directly aggregate membership functions, which in practice means that the T1OWA methodology is suitable for both balanced and unbalanced linguistic contexts and with heterogeneous membership functions. Furthermore, the final output of the T1OWA methodology is always fuzzy and defined in the same domain of the original unbalanced fuzzy linguistic labels, which facilitates its interpretation via a visual joint representation. A case study is presented where the T1OWA operator methodology is used to assess the creditworthiness of European bonds based on real credit risk ratings of individual Eurozone member states modeled as unbalanced fuzzy linguistic labels.     

A new version of 2-tuple fuzzy linguistic representation model for computing with words

In this paper, we provide a new (proportional) 2-tuple fuzzy linguistic representation model for computing with words (CW), which is based on the concept of "symbolic proportion." This concept motivates us to represent the linguistic information by means of 2-tuples, which are composed by two proportional linguistic terms. For clarity and generality, we first study proportional 2-tuples under ordinal contexts. Then, under linguistic contexts and based on canonical characteristic values (CCVs) of linguistic labels, we define many aggregation operators to handle proportional 2-tuple linguistic information in a computational stage for CW without any loss of information. Our approach for this proportional 2-tuple fuzzy linguistic representation model deals with linguistic labels, which do not have to be symmetrically distributed around a medium label and without the traditional requirement of having "equal distance" between them. Moreover, this new model not only provides a space to allow a "continuous" interpolation of a sequence of ordered linguistic labels, but also provides an opportunity to describe the initial linguistic information by members of a "continuous" linguistic scale domain which does not necessarily require the ordered linguistic terms of a linguistic variable being equidistant. Meanwhile, under the assumption of equally informative (which is defined by a condition based on the concept of CCV), we show that our model reduces to Herrera and Mart/spl inodot//spl acute/nez's (translational) 2-tuple fuzzy linguistic representation model.     

基于模糊语言方法的信息检索系统的研究

该文提出了一个基于模糊语言方法的信息检索系统模型。该系统分为查询界面子系统、数据库子系统和检索子系统三大部分。在查询界面子系统,用布尔表达式表示用户的查询请求,并对每个查询关键词赋予了两种不同语义的语言值权重,该权重表达了用户的模糊检索要求;在数据库子系统,用索引词一文档模糊矩阵表示待检索的文档,对每个索引词。根据其在文档中的出现频率大小。引入了数值权重;在检索子系统,运用模糊语言方法,对用户输入的布尔查询表达式与索引词一文档模糊矩阵进行自底向上的模糊匹配,最后返回满足用户要求的检索结果。相对于传统的基于查询关键词精确匹配的检索系统而言,该系统能较好地满足用户查询要求中的灵活性。     

Understanding user commands by evaluating fuzzy linguistic information based on visual attention

This article proposes a method for understanding user commands based on visual attention. Normally, fuzzy linguistic terms such as “very little” are commonly included in voice commands. Therefore, a robot’s capacity to understand such information is vital for effective human-robot interaction. However, the quantitative meaning of such information strongly depends on the spatial arrangement of the surrounding environment. Therefore, a visual attention system (VAS) is introduced to evaluate fuzzy linguistic information based on the environmental conditions. It is assumed that the corresponding distance value for a particular fuzzy linguistic command depends on the spatial arrangement of the surrounding objects. Therefore, a fuzzy-logic-based voice command evaluation system (VCES) is proposed to assess the uncertain information in user commands based on the average distance to the surrounding objects. A situation of object manipulation to rearrange the user’s working space is simulated to illustrate the system. This is demonstrated with a PA-10 robot manipulator.     

Towards true linguistic modelling through optimal numerical solutions

This paper is concerned with both the problems of quantitative and qualitative modelling of complex systems by using fuzzy techniques. A unified approach for the identification and subsequent extraction of linguistic knowledge of systems using fuzzy relational models is addressed. This approach deals with the identification problem by means of optimal numerical solutions based on weighted least squares and quadratic programming formulations. The linguistic knowledge is extracted in the form of consistent fuzzy rules that describe linguistically the behaviour of the identified system. A new methodology for the simplification of the extracted rules is derived by using a pruning criterion based on the representability matrix concept introduced in previous work. Several numerical aspects concerning the proposed optimization schemes and a covering discussion about the linguistic interpretation of the resulting models are also included together with illustrative examples in the contexts of pattern classification and dynamic systems identification. The paper also provides an overview of fuzzy modelling techniques that intends to situate the relational models among other fuzzy model architectures typically adopted in the literature, highlighting their main advantages and drawbacks.     

A linguistic approach for the control of information flow in abattlefield environment

We developed three linguistic statements to describe user information desires in a battlefield information environment. These rules are based on end-user interest in each track report generated from radars across the battlefield. Along with these rules of user interest, a linguistic statement describing communications systems capabilities at each node was created. These linguistic statements were converted to fuzzy variables and these variables were used as network control devices in a simulation model. The model results show that effective communications control can be exercised by these simple rules     

基于隐性反馈的自适应推荐系统研究

介绍个性化自适应推荐系统的整体架构与设计方法。阐述用户兴趣模型的建立,包括对用户个性化信息的收集、精炼处理、模糊语意处理、解模糊化及满意度计算。引入模糊自适应共振理论网络进行项目聚类分析,并进行推荐处理,实现自适应推荐服务。实验结果表明,系统对用户兴趣判断比较准确,能及时掌握用户兴趣偏移,推荐效果良好,且基本稳定。     

Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries

As in the Web, the growing of information is the main problem of the academic digital libraries. Thus, similar tools could be applied in university digital libraries to facilitate the information access by the students and teachers. In [46] we presented a fuzzy linguistic recommender system to advice research resources in university digital libraries. The problem of this system is that the user profiles are provided directly by the own users and the process for acquiring user preferences is quite difficult because it requires too much user effort. In this paper we present a new fuzzy linguistic recommender system that facilitates the acquisition of the user preferences to characterize the user profiles. We allow users to provide their preferences by means of incomplete fuzzy linguistic preference relation. We include tools to manage incomplete information when the users express their preferences, and, in such a way, we show that the acquisition of the user profiles is improved.     

Fuzzy logic-based formulation of the organizer of intelligent robotic systems

A fuzzy logic-based methodology is proposed to model the organization level of an intelligent robotic system. The user input commands to the system organizer are linguistic in nature and the primitive events-tasks from the task domain of the system are, in general, interpreted via fuzzy sets. Fuzzy relations are introduced to connect every event with a specific user input command. Approximate reasoning is accomplished via a modifier and the compositional rule of inference, whereas the application of the conjunction rule generates those fuzzy sets with elements all possible (crisp) plans. Themost possible plan among all those generated, that is optimal under an application dependent criterion, is chosen and communicated to the coordination level. Off-line feedback information from the lower levels is considered asa-priori known and is used to update all organization level information. An example demonstrates the applicability of the proposed algorithm to intelligent robotic systems.     

A multigranular hierarchical linguistic model for design evaluation based on safety and cost analysis

Before implementing a design of a large engineering system different design proposals are evaluated. The information used by experts to evaluate different options may be vague and/or incomplete. Although different probabilistic tools and techniques have been used to deal with these kinds of problems, it seems better to use the fuzzy linguistic approach to model vagueness and the Dempster‐Shafter theory of evidence for modeling incompleteness and ignorance. In the evaluation of alternative designs, different criteria can be considered. In this article an evaluation process is developed in terms of Safety and Cost analysis. Both criteria involve uncertainty, vagueness, and ignorance due to their nature. Therefore, we propose an evaluation process defined in a linguistic framework where both criteria will be conducted in different utility spaces, i.e., in a multigranular linguistic domain. Once the evaluation framework has been defined, we present an evaluation process based on a Multi‐Expert Multi‐Criteria decision model that will be able to deal with multigranular linguistic information without loss of information in order to evaluate different design options for an engineering system in a precise manner. Accordingly, we propose the use of a multigranular linguistic model based on the Linguistic Hierarchies presented by Herrera and Martínez (“A model based on linguistic 2‐tuples for dealing with multigranularity hierarchical linguistic contexts in multi‐expert decision‐making.” IEEE Trans Syst Man Cybern B 2001;31(2):227–234). © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1161–1194, 2005.     

Combined numerical and linguistic knowledge representation and its application to medical diagnosis

In this paper, we propose a novel hybrid intelligent system (HIS) which provides a unified integration of numerical and linguistic knowledge representations. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a linguistic model, i.e., fuzzy expert system (FES), optimized via the genetic algorithm (GA). The ILFN is a self-organizing network. The linguistic model is constructed based on knowledge embedded in the trained ILFN or provided by the domain expert. The knowledge captured from the low-level ILFN can be mapped to the higher level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy, comprehensibility, completeness, compactness, and consistency. The resulted HIS is capable of dealing with low-level numerical computation and higher level linguistic computation. After the system is completely constructed, it can incrementally learn new information in both numerical and linguistic forms. To evaluate the system's performance, the well-known benchmark Wisconsin breast cancer data set was studied for an application to medical diagnosis. The simulation results have shown that the proposed HIS performs better than the individual standalone systems. The comparison results show that the linguistic rules extracted are competitive with or even superior to some well-known methods. Our interest is not only on improving the accuracy of the system, but also enhancing the comprehensibility of the resulted knowledge representation.     

基于模糊模拟的加权偏爱浏览模式的挖掘

每个网页由不同的专家给出语义上的重要性评估,这些语义评估再被刻画成相应的模糊语言变量,通过模糊模拟的方法,这些模糊语言变量被转化成表示网页重要性的权重。此外,简单地认为用户的访问频度反映了用户的访问兴趣是不准确的,因此在提出的加权支持度和偏爱度概念的基础上,从建立的包含了所有用户浏览信息的FLaAT(Frequent Link and Access Tree)上,挖掘用户偏爱的加权浏览模式。试验证明该算法是行之有效的。     

AN APPROACH TO THE LINGUISTIC SYNTHESIS OF FUZZY CONTROLLERS

This paper presents a linguistic approach to synthesizing fuzzy controllers based on the fuzzy model of processes. The main procedure of the method is to solve the linguistic relation equation of a closed loop system. The fuzzy controller is obtained from a given fuzzy model of process and a desired fuzzy model of the closed loop system. The usefulness of the method provided is demonstrated by means of two examples.     

An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems

Since organizational tacit knowledge such as know-how and experiences usually resides in the owner’s brain, consulting the expert is an effective and efficient way to utilize this type of knowledge. However, users are no longer able to effectively find the appropriate experts in the knowledge management system due to the complexity and diversity of the expertise and the knowledge needs. In this paper, an approach to expert recommendation is proposed to assist the user to find the required experts. The method adopts the fuzzy linguistic method to construct the expert profile, that is, to model expert’s expertise. In addition, the fuzzy text classifier is used to get the relevant degree of the document to each knowledge area when the document is registered, which is the base of the following user profile construction. Then, the user profile consisting of the time and the relevance factors of the rated documents is constructed to derive the overall knowledge needs level of the user. Consequently, the expert that fulfills the knowledge needs most is recommended based on the similarity between the derived expert profile and the user profile. The developed prototype system, “knowledge management system in aircraft industry company”, is introduced and the experimental results show the proposed approach is feasible and effective.     

语言变量模糊本体的表示与构建

语言变量模糊本体是语言变量在语义Web中的明确的规范化说明,有利于模糊系统与语义Web的结合,使得语义web更加方便地处理模糊信息。通过引入语言变量模糊本体的概念,研究使用RDF表示模糊本体的方法,将本体与模糊概念表示为“资源”。进而以工业洗衣机的模糊控制为例,提出从模糊系统构造语言变量模糊本体的过程。     

Discovering fuzzy personal moving profiles in wireless networks

Wireless networks and mobile applications have grown very rapidly and have made a significant impact on computer systems. Especially, the usage of mobile phones and PDA is increased very rapidly. Added functions and values with these devices are thus greatly developed. If some regularity can be known from the user mobility behavior, then these functions and values can be further expanded and used intelligently. This paper thus attempts to discover fuzzy personal mobility patterns for helping systems provide personalized service in a wireless network. The arrival time and the duration time of each location area visited by a mobile user are used as important attributes in representing the results. Since both the arrival time and the duration time are numeric, fuzzy concepts are used to process them and to form linguistic terms. A fuzzy mining algorithm has then been proposed, which is based on the AprioriAll algorithm, but different from it in several ways. The difference causes a delicate consideration in the design of the algorithm. An example is also given to demonstrate the algorithm. The linguistic representation of personal mobility patterns will be more natural and understandable for the system managers to provide better personalized service in a wireless network.     

针对特殊人群用药提醒的医联网系统设计

针对现有用药提醒系统功能单一、操作复杂、使用地点受限等缺点,设计了一种基于特殊人群的用药提醒系统。本系统结合 RFID标签、蓝牙模块以及智能手机,通过STM32单片机将模块整合,并设计医院处方服务器、手机端的用药数据库以及语音提醒模块,实现了病人刷药品上的 RFID标签后,智能手机会以语音形式提醒正确用药的功能。测试结果表明,本系统功能稳定、性能可靠,具有一定的实用价值。