Enabling multitasking by designing for situation awareness within the vehicle environment

In the driving environment, competition exists between Driving Related Activities (DRAs) and Non-Driving Related Activities (NDRAs). This is a source of inattention and human error. Continual proliferation of in-vehicle information systems (IVIS) presents drivers with opportunities for distraction. Drivers simultaneously manage DRAs alongside unrelated but cognitively demanding NDRAs. Vehicle designers need ways of understanding human capability in such situations to provide solutions that accommodate these conflicting demands. This paper proposes a framework intended to address such challenges, rooted in the widely accepted construct of Situation Awareness (SA). However, SA theory does not presently accommodate disparate unrelated goal-driven tasks performed in parallel. This framework reconciles the present reality of drivers simultaneously devoting cognitive resources to attain SA for multiple activities by proposing a separate body of knowledge for each active goal. Additionally, the process of achieving SA is expanded to incorporate this concurrent development of separate bodies of goal-directed knowledge. The advantage of reconceptualising SA for driving allows consideration of interface design which minimises the impact of competing activities. The aim is a framework facilitating creation of IVIS that help drivers succeed in multi-goal multitasking situations. Implications of the proposed framework for theory, design and industry-driven automotive safety efforts are discussed.     

A computational model for knowledge-driven monitoring of nuclear power plant operators based on information theory

To develop operator behavior models such as IDAC, quantitative models for the cognitive activities of nuclear power plant (NPP) operators in abnormal situations are essential. Among them, only few quantitative models for the monitoring and detection have been developed. In this paper, we propose a computational model for the knowledge-driven monitoring, which is also known as model-driven monitoring, of NPP operators in abnormal situations, based on the information theory. The basic assumption of the proposed model is that the probability that an operator shifts his or her attention to an information source is proportional to the expected information from the information source. A small experiment performed to evaluate the feasibility of the proposed model shows that the predictions made by the proposed model have high correlations with the experimental results. Even though it has been argued that heuristics might play an important role on human reasoning, we believe that the proposed model can provide part of the mathematical basis for developing quantitative models for knowledge-driven monitoring of NPP operators when NPP operators are assumed to behave very logically.     

Network Security Situation Awareness Framework based on Threat Intelligence

Network security situation awareness is an important foundation for network security management, which presents the target system security status by analyzing existing or potential cyber threats in the target system. In network offense and defense, the network security state of the target system will be affected by both offensive and defensive strategies. According to this feature, this paper proposes a network security situation awareness method using stochastic game in cloud computing environment, uses the utility of both sides of the game to quantify the network security situation value. This method analyzes the nodes based on the network security state of the target virtual machine and uses the virtual machine introspection mechanism to obtain the impact of network attacks on the target virtual machine, then dynamically evaluates the network security situation of the cloud environment based on the game process of both attack and defense. In attack prediction, cyber threat intelligence is used as an important basis for potential threat analysis. Cyber threat intelligence that is applicable to the current security state is screened through the system hierarchy fuzzy optimization method, and the potential threat of the target system is analyzed using the cyber threat intelligence obtained through screening. If there is no applicable cyber threat intelligence, using the Nash equilibrium to make predictions for the attack behavior. The experimental results show that the network security situation awareness method proposed in this paper can accurately reflect the changes in the network security situation and make predictions on the attack behavior.     

基于模糊神经网络的机载设备智能故障诊断系统

针对某型飞机机载设备故障多,且具有模糊性、复杂性的特点,本文将模糊逻辑和神经网络相结合,采用模糊隶属函数来描述这些故障的程度,建立了模糊神经网络故障诊断模型.采用图形化编程技术,开发了一种故障诊断推理流程图,方便了用户的开发.该系统依据专家知识和测试数据,可将故障隔离到内场可更换单元(SRU)或某个功能电路.实践证明该诊断系统是有效的,具有推广应用价值.     

The Fuzzy Logic Application in Volume Fractions Prediction of the Annular Three-Phase Flows

In this paper, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system consisting of \(^{152}\)Eu and \(^{137}\)Cs and one NaI detector, and then modeled by fuzzy logic. Since the summation of volume fractions are constant (equal to 100%), therefore the fuzzy network must predict only two volume fractions. In this study, three fuzzy networks are applied. The first network is utilized to predict the gas and water volume fractions. The next one is applied to predict the gas and oil volume fractions, and the last one to predict the water and oil volume fractions. In the next step, the numerically obtained data from MCNP-X code, must be imported to the fuzzy models. Then, the average errors of these three networks are computed and compared. The network which has the least error is selected as the best predictor model. According to the modeling results, the best fuzzy network, predicts the gas and water volume fractions with the mean relative error of less than 0.3%, which shows that the fuzzy logic can predict the results precisely.     

Knowledge verification for fuzzy expert systems

Abstract

The introduction and use of fuzzy logic has strengthened knowledge representation and reasoning capability in expert systems; nevertheless, it also increases the complexity and difficulty of knowledge verification, which is known to be an important issue for building reliable and high performance expert systems. In the past decade, knowledge verification problems, e.g., redundancy, conflict, circularity and incompleteness of knowledge, have been widely discussed from the viewpoint of using binary logic; nevertheless, the issue of verifying fuzzy knowledge is seldom discussed. In this paper, we attempt to detect potential structural errors among fuzzy rules by proposing a fuzzy verification algorithm. Moreover, a system for verifying fuzzy knowledge base has been developed based on the novel approach.     

Fuzzy logic controller in a packaging plant

This paper deals with the analysis of a controller used to synchronize two parallel belt conveyors in a packaging plant. A first conveyor carries the products, while the second delivers the packages. The insertion is obtained by a proper mechanical action. The control system is based on a ‘hybrid’ fuzzy logic controller, whose parameters are optimized by using an advanced ‘operational’ genetic algorithm. ‘Hybrid’ means that a conventional fuzzy logic controller is integrated with a set of special rules needed to solve particular situations characterizing the system. An important constraint is given, since the physical structure of the existing control system is to be kept unchanged. It is shown that the controller efficiently governs the belt conveyors when: (a) the distances between goods and the relative packages become higher than a certain value; (b) the performance of the electrical engine deteriorates during working time; and also (c) interference phenomena occur between consecutive good‐package couples. Copyright ©2003 John Wiley & Sons, Ltd.     

Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation

The well-known extended Kalman filter (EKF) has been widely applied to the Global Positioning System (GPS) navigation processing. The adaptive algorithm has been one of the approaches to prevent the divergence problem of the EKF when precise knowledge on the system models are not available. One of the adaptive methods is called the strong tracking Kalman filter (STKF), which is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. Traditional approach for selecting the softening factors heavily relies on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the adaptive fuzzy strong tracking Kalman filter (AFSTKF) is carried out. In the AFSTKF, the fuzzy logic reasoning system based on the Takagi-Sugeno (T-S) model is incorporated into the STKF. By monitoring the degree of divergence (DOD) parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the softening factor according to the change in vehicle dynamics. GPS navigation processing using the AFSTKF will be simulated to validate the effectiveness of the proposed strategy. The performance of the proposed scheme will be assessed and compared with those of conventional EKF and STKF     

Towards a conceptual model of motorcyclists’ Risk Awareness: A comparative study of riding experience effect on hazard detection and situational criticality assessment

This research investigates risk awareness abilities among different populations of motorcyclists. Risk awareness is defined here as an extension of the Situational Awareness theory applied to critical driving situations. This study is more particularly focused on two main cognitive abilities supporting risk awareness: hazard detection, corresponding to riders’ skill to perceive critical event occurring in the road environment and to identify it as a threat, and situational criticality assessment, corresponding to a subjective assessment of the accident risk. From this theoretical framework, the aim is to compare motorcyclists’ performances in risk awareness according to their experience in motorcycling. Four populations of motorcyclists are investigated: Professional (Policemen), Experienced riders, Novices, and Beginners. Method implemented is based of a set of 25 video sequences of driving situations presenting a risk of collision. Participants’ task was firstly to stop the video film if they detect a hazard. Then, at the end of each sequence, they have also to assess the criticality of the driving situation as a whole, with a Likert scale (from 0 to 100% of criticality). Results obtained show that cognitive abilities in both (i) hazard detection and (ii) situational criticality assessment depend of the riding experience, and are learnt from two different timing. On one side, Professional and Experienced riders obtained better results than Novices and Beginners for hazard perception (i.e. shortest reaction time). In terms of situational criticality assessment, Beginners underestimate the situational risk and seem overconfident in their abilities to manage the situational risk, against Novices, Professional and Experienced riders, who have better competences in criticality assessment. From these empirical results, a conceptual model of motorcyclists’ Risk Awareness is proposed.     

User strategies for the interaction with in-vehicle devices while driving

In this study, the efficiency of different strategies for the interaction with in-vehicle devices was analysed. Twenty-four drivers completed a test course in a motion-based driving simulator containing different critical situations. At predetermined points of the route, an additional menu navigation task was offered to the driver. The driver could decide whether the actual situation was suitable to execute a task and when to interrupt it. The results show that drivers are able to adapt their secondary task behaviour to the situational demands. The anticipation of potential conflicts could be shown in secondary task behaviour (complete task rejection or task delay in critical situations). These strategies were successful to maintain driving safety. Adequate situational assessment prior to the start of the task and adequate monitoring of situational development during secondary task execution are identified as relevant processes for situation awareness in this context.     

An approach of fuzzy logic evaluation and control in SPC

Quality control plays an important part in most industrial systems. Its role in providing relevant and timely data to management for decision‐making purposes is vital. A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. Engineers or managers can evaluate an abnormal process by using SPC zone rules in control charts. In the conventional use of the zone rules the user is only able to determine whether or not the process is out of control. What action should be taken to adjust the process is uncertain and is evaluated based on knowledge of the system and past experiences. This paper explores the integration of fuzzy logic and control charts to create and design a fuzzy–SPC evaluation and control (FSEC) method based on the application of fuzzy logic to the SPC zone rules. A simulation program implementing FSEC was written in Borland C++ 5.0 and simulation results were obtained and analysed. The abnormal processes simulated were automatically adjusted for each of the zone rules tested and showed an improved performance after the control action, thus confirming the merit of the technique as a special method with the specific numerical control action based on a quality evaluation criterion. Copyright © 2000 John Wiley & Sons, Ltd.     

Predicting the strength development of cements produced with different pozzolans by neural network and fuzzy logic

This study is based on the determination of optimum usage of pozzolans as supplementary cementing material for blended cement production. Blended cements were produced with natural zeolite (clinoptilolite), Eskişehir trass, Nevşehir trass, fly ash and ground granulated blast furnace slag at 10, 20, 30, 35, 40 and 45% replacement ratios. Experimental results were also obtained by building models according to artificial neural network and fuzzy logic systems. It is concluded that the strength development of cement mortars containing different pozzolans can be obtained according to neural network and fuzzy logic model test results without any experimental study.     

面向设计过程的情境建模与研究

目的 解决产品设计过程中知识获取不完善及设计过程表征不能满足用户需求的问题。方法 针对设计过程,从知识获取、知识物化和特征验证等方面,剖析用户知识在设计过程情境中的行为逻辑,提出一种面向设计过程的情境建模方法。基于可拓基元概念,知识获取情境将用户信息转化为需求基元;知识物化情境将需求基元与设计思维进行融合,形成概念方案。结果 利用验证情境模型判断特征元素是否满足设计需求,从而使产品设计方案达到效能的最大化。结论 基于物流分拣设备设计过程的研究,对设计情境模型进行实例验证,其产品的特征得到了高效的表达,完善了产品设计的方案,验证了该方案的可行性。     

Using fuzzy signal detection theory to determine why experienced and trained drivers respond faster than novices in a hazard perception test

Drivers’ hazard perception ability, as measured in video-based simulations, correlates with crash involvement, improves with experience and can be trained. We propose two alternative signal detection models that could describe individual differences in this skill. The first model states that novice drivers are poorer at discriminating more hazardous from less hazardous situations than experienced drivers. The second model proposes that novice drivers require a higher threshold of danger to be present before they notice a situation is hazardous or before they are willing to classify a situation as hazardous. We applied a technique involving fuzzy signal detection analysis to differentiate between these two models when comparing novice and experienced drivers, and trained and untrained drivers, in various video-based hazard perception measures. The data favored the second model.     

The situational reflection of reality in activity theory and the concept of situation awareness in cognitive psychology

This article presents new data from the implementation of Activity Theory and integrates it with some concepts of cognitive psychology. The main focus is on orienting activity, which is directed to comprehending and interpreting reality as well as predicting future events' significance for the performance of present events. The concept of orienting activity is directly related to what is known as Situation Awareness (SA) in the US. However, the concept of orienting activity is broader than that of SA. Orientation and comprehension of a situation includes not only conscious and verbally logical components, but also unconscious components. The imaginative elements of the activity and the non-verbalized meaning of the situation are important in the unconscious reflection of reality. This paper describes SA as one of the important function mechanisms involved in the dynamic reflection of the situation. Other important elements of this dynamic reflection are its emotional and motivational components.     

随机模糊信息系统的知识约简

利用模糊逻辑中的R-型蕴涵算子定义随机模糊信息系统对象集上的模糊等价关系,进而实现对随机模糊信息系统知识的近似表示。讨论随机模糊下近似、上近似集的模糊概率与模糊信任测度、模糊似然测度之间的关系。给出基于模糊信任测度和模糊似然测度的随机模糊信息系统知识约简的方法。     

Randomized Complete Block Designs in Industrial Studies

Product quality is a function of several performance characteristics. If it is desired to optimize a single performance characteristic of the product, traditional mathematical models may be used. But, when all the optimum performance characteristics need to be integrated into the product then the problem has to be formulated as a multi-objective optimization problem. In most of the real life situations, the customers' requirements are fuzzy in nature, e.g., minimum surface roughness or maximum strength. This inexact information creates a problem, i.e., where to draw a line between what is required and what is not. This inexactness in the nature of the goals can be studied well through the use of fuzzy set logics.

In the present work, a multi-objective optimization model using the concept of fuzzy set logic is developed. A case study “Obtaining an optimal set of V-process parameters that yields the optimum composite quality features to Al-11% Si alloy castings” is also included in this paper.     

结构选型中基于模糊推理的评判系统

本文在基于自组织竞争神经网络检索机制与模糊逻辑评判的实例推理系统基础上，讨论了该系统用于结构选型过程中的模糊推理过程；建立了高层建筑结构高度适应性的模糊评价标准；并将其应用于模糊推理系统中；以剪力墙结构高度适应性评价为例展示了推理过程。证明采用模糊推理系统作为设计型结构选型决策支持系统的评价模块是理想的解决方案。     

A mathematical structure relating situation knowledge to performance

The linguistic conjecture that a person's knowledge regarding a situation affects some aspects of that person's performance at a task is translated into a mathematical structure. The structure contains a symbolic representation of the conjecture, along with a symbolic definition of what type of knowledge constitutes ‘situation’ knowledge. The structure is applied to a short example to demonstrate its use. The translation of the conjecture into a mathematical structure enables researchers to use a consistent, objective framework for testing hypothesised relationships between one's situation knowledge and performance, as well as enables mathematical tools to be applied to the study of this conjecture.