Selection of key visual cues in real and virtual environments for assembly tasks

Previous research identified that learning assembly tasks in Virtual Environments (VEs) is more difficult than in Real Environments (REs). This work's objective is to identify the key visual areas for both environments when performing an assembly task for ten consecutive cycles, when following visual instructions and when having visual distractors. Using an eye-tracker, we identified the key visual areas required for an assembly task in both environments. Results indicate that practice allowed participants to reduce their assembly time in both environments. They also indicate that two areas, Assembly Area and Blocks, concentrated a higher proportion of eye-fixations; 59.98% for REs and 81.48% for VEs, with a statistically significant observation difference between environments (t-test value = −14.23, with p-value <0.00001 and Cohen's d = 6.36). We conclude that participants considered the same key visual areas for both environments and that VE interaction has a significant role in observation behavior.     

Knowledge representation for diagnosis of care problems through an expert system: Model of the auto-care deficit situations

The caregiver role strain is today an increasing problem because of the population aging; moreover, its diagnosis is highly difficult. In this article, we summarize the design of an Expert System for the diagnosis of this health problem. The Expert System Knowledge Base is composed by a set of production rules written in classic bi-valued logic and by a set of potential facts. In order to build this Knowledge Base it has been necessary to design previously a Model of the problem treatment. The Expert System Inference Engine uses Gröbner Bases and Normal Form to obtain the diagnosis from the information stored in the Knowledge Base. Furthermore, a Graphic User’s Interface has been implemented to make easier the access to the System by all kinds of users.     

Using semantic technologies and the OSU ontology for modelling context and activities in multi-sensory surveillance systems

Automatic systems that monitor human behaviour for detecting security problems are a challenge today. Previously, our group defined the Horus framework, which is a modular architecture for the integration of multi-sensor monitoring stages. In this work, structure and technologies required for high-level semantic stages of Horus are proposed, and the associated methodological principles established with the aim of recognising specific behaviours and situations.

Our methodology distinguishes three semantic levels of events: low level (compromised with sensors), medium level (compromised with context), and high level (target behaviours). The ontology for surveillance and ubiquitous computing has been used to integrate ontologies from specific domains and together with semantic technologies have facilitated the modelling and implementation of scenes and situations by reusing components.

A home context and a supermarket context were modelled following this approach, where three suspicious activities were monitored via different virtual sensors. The experiments demonstrate that our proposals facilitate the rapid prototyping of this kind of systems.     

数码监控系统磁盘阵列的扩展及数据优化管理

近年来,数码相机监控系统以其高分辨率、方便、快捷、处理软件丰富以及高性能比等优点,愈来愈受到人们的关注。但是该系统仍然存在着一系列有待继续研究的问题,比如存储数据量大、不易管理等。针对以上不足,在介绍数码相机监控系统软、硬件的设计和开发过程同时,着重讨论数码相机监控系统磁盘阵列存储系统的扩展和图像采集数据的管理等问题。     

A method for the analysis of the interaction between users and objects in 3D navigational space

Along with the improvement of eye-tracking technology, more and more distinct field of researches have introduced movements of the eye in relation to the head to understand user behavior. Most of current researches focus on the perception process of single 2-dimensional images by fixed eye-tracking devices or the head-mount devices. A method of applying eye-tracking on the analysis of the interaction between users and objects in 3D navigational space is proposed in this article. It aims to understand the visual stimulation of 3D objects and the user’s spatial navigational reactions while receiving the stimulation, and proposes the concept of 3D object attention heat map. It also proposes to construct a computational visual attention model for different geometric featured 3D objects by applying the method of feature curves. The VR results of this study also provide future assistance in the incoming immersive world. This study sets to promote eye-tracking from the mainstream of 2D field to 3D spaces and points to a deeper understanding between human and artificial product or natural objects. It would also serve an important role in the field of human-computer interaction, product usability, aids devices for cognition degenerative individuals, and even the field of visual recognition of daily human behavior.     

Evaluation of the safety and usability of touch gestures in operating in-vehicle information systems with visual occlusion

Nowadays, many automobile manufacturers are interested in applying the touch gestures that are used in smart phones to operate their in-vehicle information systems (IVISs). In this study, an experiment was performed to verify the applicability of touch gestures in the operation of IVISs from the viewpoints of both driving safety and usability. In the experiment, two devices were used: one was the Apple iPad, with which various touch gestures such as flicking, panning, and pinching were enabled; the other was the SK EnNavi, which only allowed tapping touch gestures. The participants performed the touch operations using the two devices under visually occluded situations, which is a well-known technique for estimating load of visual attention while driving.     

Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria

We describe an approach of automatic feature extraction for shape characterization of seven distinct species of Eimeria, a protozoan parasite of domestic fowl. We used digital images of oocysts, a round-shaped stage presenting inter-specific variability. Three groups of features were used: curvature characterization, size and symmetry, and internal structure quantification. Species discrimination was performed with a Bayesian classifier using Gaussian distribution. A database comprising 3891 micrographs was constructed and samples of each species were employed for the training process. The classifier presented an overall correct classification of 85.75%. Finally, we implemented a real-time diagnostic tool through a web interface, providing a remote diagnosis front-end.     

熵在随机系统故障诊断和容错控制中的应用

长期以来,关于随机动态系统的故障诊断和容错控制的研究一直是控制理论和应用的重要领域之一.随机控制系统故障诊断的目标是建立有效的故障估计算法以使残差信号方差最小这种方法仅适用于高斯型残差或者具有对称分布的概率密度函数的残差.然而,对非高斯残差而言,仅使用残差信号的方差不能够全面表示残差的不确定性.针对非高斯随机动态控制系...     

Using Signal Detection Theory and Time Window-based Human-In-The-Loop simulation as a tool for assessing the effectiveness of different qualitative shapes in continuous monitoring tasks

This paper provides a case study of Signal Detection Theory (SDT) as applied to a continuous monitoring dual-task environment. Specifically, SDT was used to evaluate the independent contributions of sensitivity and bias to different qualitative gauges used in process control. To assess detection performance in monitoring the gauges, we developed a Time Window-based Human-In-The-Loop (TWHITL) simulation bed. Through this test bed, we were able to generate a display similar to those monitored by console operators in oil and gas refinery plants. By using SDT and TWHITL, we evaluated the sensitivity, operator bias, and response time of flow, level, pressure, and temperature gauge shapes developed by Abnormal Situation Management^® (ASM^®) Consortium (www.asmconsortium.org). Our findings suggest that display density influences the effectiveness of participants in detecting abnormal shapes. Furthermore, results suggest that some shapes elicit better detection performance than others.     

Soft computing agents for e-Health in application to the research and control of unknown diseases

This paper presents an ontology-based holonic diagnostic system (OHDS) that combines the holonic paradigm with multi-agent system technology and ontology design, for the organization of unstructured biomedical research into structured disease information. We use ontologies as ‘brain’ for the holonic diagnostic system to enhance its ability to structure information in a meaningful way and share information fast. To integrate distributed heterogeneous knowledge available on the Web we use intelligent agents augmented by the mechanisms of the technology of fuzzy sets, which automatically structures the information in the adequate ontology template. Our vision of how this system implementation should be supported by a solid security shield that ensures the privacy and safety of medical information concludes the paper.     

Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery

A new object-oriented segmentation approach with special focus on shape analysis was developed for the extraction of large, man-made objects, especially agricultural fields, in high-resolution panchromatic satellite imagery. The approach, a combination of region- and edge-based techniques, includes new methods for the evaluation of straight edges, edge preserving degradation, and edge-guided region growing.     

On the equivalence between global recurrence and the existence of a smooth Lyapunov function for hybrid systems

We study a weak stability property called recurrence for a class of hybrid systems. An open set is recurrent if there are no finite escape times and every complete trajectory eventually reaches the set. Under sufficient regularity properties for the hybrid system we establish that the existence of a smooth, radially unbounded Lyapunov function that decreases along solutions outside an open, bounded set is a necessary and sufficient condition for recurrence of that set. Recurrence of open, bounded sets is robust to sufficiently small state dependent perturbations and this robustness property is crucial for establishing the existence of a Lyapunov function that is smooth. We also highlight some connections between recurrence and other well studied properties like asymptotic stability and ultimate boundedness.     

均衡能耗和时延的无线传感器网络组内融合机制研究

主要研究实时监测无线传感器网络系统中的数据传输时延及网络的能量效率的均衡优化,提出了一种基于博弈论的无线传感器网络组内数据融合(GA-G)模型.该机制采用逐级分层的融合架构,可以最小化网络的原始数据输出量并通过建立博弈模型均衡了节点的能耗及数据传输的时延.仿真结果表明该融合机制能提高网络数据融合的实时可靠性.     

Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors

This study aimed to evaluate the value of using 3-D breast MRI morphologic features to differentiate benign and malignant breast lesions. The 3-D morphological features extracted from breast MRI were used to analyze the malignant likelihood of tumor from ninety-five solid breast masses (44 benign and 51 malignant) of 82 patients. Each mass-like lesion was examined with regards to three categories of morphologic features, including texture-based gray-level co-occurrence matrix (GLCM) feature, shape, and ellipsoid fitting features. For obtaining a robust combination of features from different categories, the biserial correlation coefficient (|r_pb|) ≧ 0.4 was used as the feature selection criterion. Receiver operating characteristic (ROC) curve was used to evaluate performance and Student's t-test to verify the classification accuracy. The combination of the selected 3-D morphological features, including conventional compactness, radius, spiculation, surface ratio, volume covering ratio, number of inside angular regions, sum of number of inside and outside angular regions, showed an accuracy of 88.42% (84/95), sensitivity of 88.24% (45/51), and specificity of 88.64% (39/44), respectively. The A_Z value was 0.8926 for these seven combined morphological features. In conclusion, 3-D MR morphological features specified by GLCM, tumor shape and ellipsoid fitting were useful for differentiating benign and malignant breast masses.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

基于模糊神经网络和证据理论的列车智能控制系统多信息融合故障诊断研究

针对列车智能控制系统故障诊断中的多故障特征信息输入时的时变、冗余、不确定性和空间分布性,给出了一种列车智能控制系统多信息融合故障诊断的系统结构。讨论了采用模糊神经网络进行特征层融合和证据理论进行决策层融合相结合的列车智能控制系统多信息融合故障诊断方法。故障诊断实例的结果表明:该方法能够有效地提高诊断的可信度,减小诊断的不确定性。