Automatic terminal guidance for small fixed-wing unmanned aerial vehicles

The research presented in this article focuses on expanding and deepening the prior research of a low-cost terminal guidance system in a previous paper entitled “Design, implementation and verification of a low-cost terminal guidance system for small fixed-wing UAVs.” An automatic terminal guidance workflow is specially designed for an individual in a small fixed-wing unmanned aerial vehicle (SUAV) swarm. The extended work around the proposed workflow primarily involves upgrading onboard hardware modules to improve sensor accuracy and environmental adaptability, the imaging performance of the seeker, as well as the computational capability of the image processor, applying object detection to replace the human-in-the-loop function and adopting the integral proportional guidance law in the vertical direction to reduce the required overload and obtain a larger impact angle. Furthermore, we conducted several field tests on two types of SUAV against a stationary target on the ground in a field scenario. The experiments have generated valuable onboard image data and SUAV status information, all of which are aligned in the time domain. The only remaining data sets that support the findings of this study are available from the corresponding author. Our study into automatic terminal guidance has yielded a solution of the automatic strap-down monocular terminal guidance problem of individual SUAVs. The field trials of a single SUAV demonstrate the robustness and efficiency of the proposed automatic terminal guidance methodology and lays a foundation for the future SUAVs' cooperative attack test.     

Exploring Temporal Structure of Trajectory Components for Action Recognition

Action recognition is one of the most important components for video analysis. In addition to objects and atomic actions, temporal relationships are important characteristics for many actions and are not fully exploited in many approaches. We model the temporal structures of midlevel actions (referred to as components) based on dense trajectory components, obtained by clustering individual trajectories. The trajectory components are a higher level and a more stable representation than raw individual trajectories. Based on the temporal ordering of trajectory components, we describe the temporal structure using Allen's temporal relationships in a discriminative manner and combine it with a generative model using bag of components. The main idea behind the model is to extract midlevel features from domain‐independent dense trajectories and classify the actions by exploring the temporal structure among these midlevel features based on a set of relationships. We evaluate the proposed approach on public data sets and compare it with a bag‐of‐words–based approach and state‐of‐the‐art application of the Markov logic network for action recognition. The results demonstrate that the proposed approach produces better recognition accuracy.     

Psychophysiological evaluation of seafarers to improve training in maritime virtual simulator

Over the years, safety in maritime industries has been reinforced by many state-of-the-art technologies. However, the accident rate hasn’t dropped significantly with the advanced technology onboard. The main cause of this phenomenon is human errors which drive researchers to study human factors in the maritime domain. One of the key factors that contribute to human performance is their mental states such as cognitive workload and stress. In this paper, we propose and implement an Electroencephalogram (EEG)-based psychophysiological evaluation system to be used in maritime virtual simulators for monitoring, training and assessing the seafarers. The system includes an EEG processing part, visualization part, and an evaluation part. By using the processing part of the system, different brain states including cognitive workload and stress can be identified from the raw EEG data recorded during maritime exercises in the simulator. By using the visualization part, the identified brain states, raw EEG signals, and videos recorded during the maritime exercises can be synchronized and displayed together. By using the evaluation part of the system, an indicative recommendation on “pass”, “retrain”, or “fail” of the seafarers’ performance can be obtained based on the EEG-based cognitive workload and stress recognition. Detailed analysis of the demanding events in the maritime tasks is provided by the system for each seafarer that could be used to improve their training. A case study is presented using the proposed system. EEG data from 4 pilots were recorded when they were performing maritime tasks in the simulator. The data are processed and evaluated. The results show that one pilot gets a “pass” recommendation, one pilot gets a “retrain” recommendation, and the other two get “fail” results regarding their performance in the simulator.     

Person recognition by fusing palmprint and palm vein images based on “Laplacianpalm” representation

Unimodal analysis of palmprint and palm vein has been investigated for person recognition. One of the problems with unimodality is that the unimodal biometric is less accurate and vulnerable to spoofing, as the data can be imitated or forged. In this paper, we present a multimodal personal identification system using palmprint and palm vein images with their fusion applied at the image level. The palmprint and palm vein images are fused by a new edge-preserving and contrast-enhancing wavelet fusion method in which the modified multiscale edges of the palmprint and palm vein images are combined. We developed a fusion rule that enhances the discriminatory information in the images. Here, a novel palm representation, called “Laplacianpalm” feature, is extracted from the fused images by the locality preserving projections (LPP). Unlike the Eigenpalm approach, the “Laplacianpalm” finds an embedding that preserves local information and yields a palm space that best detects the essential manifold structure. We compare the proposed “Laplacianpalm” approach with the Fisherpalm and Eigenpalm methods on a large data set. Experimental results show that the proposed “Laplacianpalm” approach provides a better representation and achieves lower error rates in palm recognition. Furthermore, the proposed multimodal method outperforms any of its individual modality.     

A hybrid Kansei engineering design expert system based on grey system theory and support vector regression

Nowadays customers choose products strictly in terms of their specific demands. How to quickly and accurately catch customers’ feelings and transform them into design elements and vice versa becomes an important issue. This study explores the bi-directional relationship between customers’ demands or needs and product forms by using a novel integral approach. High-price machine tools are used as our demonstration target. This integral approach adopts the “grey system theory (GST)”, and the state-of-the-art machine learning based modeling formalism “support vector regression (SVR)” in the “Kansei engineering (KE)” process. The GST is used to effectively determine the influence weighting of form parameters on product images and the SVR is used to precisely establish the mapping relationship between product form elements and product images. Furthermore, for practical concerns, a user-friendly design hybrid design expert system was developed based on the proposed novel integral schemes.     

A robust constrained model predictive control scheme for norm‐bounded uncertain systems with partial state measurements

A robust model predictive control scheme for a class of constrained norm‐bounded uncertain discrete‐time linear systems is developed under the hypothesis that only partial state measurements are available for feedback. The proposed strategy involves a two‐phase procedure. Initialization phase is devoted to determining an admissible, though not optimal, linear memoryless controller capable to formally address the input rate constraint; then, during on‐line phase, predictive capabilities complement the designed controller by means of N steps free control actions in a receding horizon fashion. These additive control actions are obtained by solving semidefinite programming problems subject to linear matrix inequalities constraints. As computational burden grows linearly with the control horizon length, an example is developed to show the effectiveness of the proposed approach for realistic control problems: the design of a flight control law for a flexible unmanned over‐actuated aircraft, where the states of the flexibility dynamics are not measurable, is discussed, and a numerical implementation of the controller within a nonlinear simulation environment testifies the validity of the proposed approach and the possibility to implement the algorithm on an onboard computer.     

Virtual reality-based evaluation of indoor earthquake safety actions for occupants

Evaluations of indoor earthquake safety actions can provide effective guidance for occupants, and can be beneficial for reducing earthquake-induced casualties. Accordingly, in this study, a virtual reality (VR)-based evaluation method is proposed for indoor earthquake safety actions. Specifically, an indoor seismic damage scene is constructed based on the Federal Emergency Management Agency (FEMA) P-58 method and a physics engine, by which the damage and movements of indoor nonstructural components can be accurately calculated. Subsequently, a virtual body model is created, and a corresponding inverse kinematics (IK) algorithm is developed to accurately track the earthquake safety actions of occupants. Finally, a human safety model based on collision kinetic energy and fatal conditions is designed for quantitatively evaluating the effectiveness of earthquake safety actions. Taking an office room and living room as case studies, three earthquake safety actions (i.e., “drop, cover, and hold on,” “triangle of life,” and “run outside”) are evaluated using the proposed method. The results indicate that “drop, cover, and hold on” is the safest action in most earthquake scenarios. The outcomes of this study can be used to rate the effectiveness of various earthquake safety actions, and to support occupant safety decision-making.     

From Interface to Correspondence: Recovering Classical Representations in a Pragmatic Theory of Semantic Information

One major fault line in foundational theories of cognition is between the so-called “representational” and “non-representational” theories. Is it possible to formulate an intermediate approach for a foundational theory of cognition by defining a conception of representation that may bridge the fault line? Such an account of representation, as well as an account of correspondence semantics, is offered here. The account extends previously developed agent-based pragmatic theories of semantic information, where meaning of an information state is defined by its interface role, to a theory that accommodates a notion of representation and correspondence semantics. It is argued that the account can be used to develop an intermediate approach to cognition, by showing that the major sources of tension between “representational” and “non-representational” theories may be eased.     

Holistic context models for visual recognition

A novel framework to context modeling based on the probability of co-occurrence of objects and scenes is proposed. The modeling is quite simple, and builds upon the availability of robust appearance classifiers. Images are represented by their posterior probabilities with respect to a set of contextual models, built upon the bag-of-features image representation, through two layers of probabilistic modeling. The first layer represents the image in a semantic space, where each dimension encodes an appearance-based posterior probability with respect to a concept. Due to the inherent ambiguity of classifying image patches, this representation suffers from a certain amount of contextual noise. The second layer enables robust inference in the presence of this noise by modeling the distribution of each concept in the semantic space. A thorough and systematic experimental evaluation of the proposed context modeling is presented. It is shown that it captures the contextual “gist” of natural images. Scene classification experiments show that contextual classifiers outperform their appearance-based counterparts, irrespective of the precise choice and accuracy of the latter. The effectiveness of the proposed approach to context modeling is further demonstrated through a comparison to existing approaches on scene classification and image retrieval, on benchmark data sets. In all cases, the proposed approach achieves superior results.     

Streaming association rule (SAR) mining with a weighted order-dependent representation of Web navigation patterns

This paper considers a problem of finding predictive and useful association rules with a new Web mining algorithm, a streaming association rule (SAR) model. We first adopt a weighted order-dependent scheme (assigning more weights for early visited pages) rather than taking a traditional Boolean scheme (assigning 1 for visited and 0 for non-visited pages). This way, we intend to improve the limited representation of navigation patterns in previous association rule mining (ARM) algorithms. We also note that most traditional association rule models are not scalable because they require multiple scans of all records to re-calibrate a predictive model when there are new updates in original databases. The proposed SAR model takes a “divide-and-conquer” approach and requires only single scan of data sets to avoid the curse of dimensionality. Through comparative experiments on a real-world data set, we show that prediction models based on a weighted order-dependent representation are more accurate in predicting the next moves of Web navigators than models based on a Boolean representation. In particular, when combined with several heuristics developed to eliminate redundant association rules, SAR models show a very comparable prediction accuracy while maintaining a small fraction of association rules compared to traditional ARM models. Finally, we quantify and graphically show the significance or contribution of each pages to forming unique rule sets in each database segments.     

Universal Remote Console-based next-generation accessible television

This paper presents a new approach to make current and future television universally accessible. The proposed approach provides a means of universal accessibility both for remotely operating the TV set and for interacting with online services delivered through the TV. This proposal is based on the ISO/IEC 24752 “Universal Remote Console” (URC) standard. This standard defines an abstract user interface layer called the “user interface socket” and allows the development of pluggable (plug-in) user interfaces for any type of user and any control device. The proposed approach lays the foundation for the development of advanced user interfaces that can be interacted within various modalities. Different prototypes have been developed based on this approach and tested with end users. The user tests have shown this approach to be a viable option for the proposed scenarios. Based on the experience gathered with the prototypes, recommendations and implementation options are suggested for commercial adoption.     

Solving the fuzzy shortest path problem using multi-criteria decision method based on vague similarity measure

Many researchers have focused on the fuzzy shortest path problem in a network with non-deterministic information due to its importance to various applications. The goal of this paper is to select the shortest path in multi-constrained network using multi-criteria decision method based on vague similarity measure. In our approach, each arc length represents multiple metrics. The multi-constraints are equivalent to the concept of multi-criteria based on vague sets. We propose a similarity measure of vague sets in which the positive constraints and the negative constraints are defined. Furthermore, the procedures are developed to obtain the “best” and “worst” ideal paths. We evaluate similarity degrees between all candidate paths and two ideal paths with the proposed similarity measure. Through comparing the relative degrees of paths, it is shown that the path with the largest relative degree is the shortest path. Finally, we conduct two sets of numerical experiments—using Matlab to verify the feasibility and correctness of the proposed algorithm and developing a routing decision simulation system (RDSS) to demonstrate that the proposed approach is reasonable and effective.     

Basic onboard real-time advisory system for a duel situation of distant air fight

The structure of the knowledge base of an onboard real-time advisory system for distant air fight 1 × 1 in which current information on the external situation is used for activation of one of the problem subsituations chosen from the total set of these subsituations is presented. Based on the analysis of the subject domain of distant air fight 1 × 1, four problem subsituations are formed: the problem subsituation no. 1 “taking the tactically advantageous position”; the problem subsituation no. 2 “attack”, the problem subsituation no. 3 “defense with attack”, and the problem subsituation no. 4 “defense”. For each of these subsituations on the basis of virtual simulation of the proposed development of the air fight and the corresponding set of production rules, the recommendations to the crew on rational method of its resolution are developed. The results of mathematical simulation of the operation of the onboard real-time advisory system for distant air fight 1 × 1 in the problem subsituation no. 2 are presented.     

A general dissipativity constraint for feedback system design,with emphasis on MPC

A “general dissipativity constraint” (GDC) is introduced to facilitate the design of stable feedback systems. A primary application is to MPC controllers when it is preferred to avoid the use of “stabilising ingredients” such as terminal constraint sets or long prediction horizons. Some very general convergence results are proved under mild conditions. The use of quadratic functions, replacing GDC by “quadratic dissipativity constraint” (QDC), is introduced to allow implementation using linear matrix inequalities. The use of QDC is illustrated for several scenarios: state feedback for a linear time‐invariant system, MPC of a linear system, MPC of an input‐affine system, and MPC with persistent disturbances. The stability that is guaranteed by GDC is weaker than Lyapunov stability, being “Lagrange stability plus convergence.” Input‐to‐state stability is obtained if the control law is continuous in the state. An example involving an open‐loop unstable helicopter illustrates the efficacy of the approach in practice.     

Iterative classification for multiple target attributes

Many real-world applications require the simultaneous prediction of multiple target attributes. The techniques currently available for these problems either employ a global model that simultaneously predicts all target attributes or rely on the aggregation of individual models, each predicting one target. This paper introduces a novel solution. Our approach employs an iterative classification strategy to exploit the relationships among multiple target attributes to achieve higher accuracy. The computation scheme is developed as a wrapper in which many standard single-target classification algorithms can be simply “plugged-in” to simultaneously predict multiple targets. An empirical evaluation using eight data sets shows that the proposed method outperforms (1) an approach that constructs independent classifiers for each target, (2) a multitask neural network method, and (3) ensembles of multi-objective decision trees in terms of simultaneously predicting all target attributes correctly.     

A soft image representation approach by exploiting local neighborhood structure of self-organizing map (SOM)

When images are described with visual words based on vector quantization of low-level color, texture, and edge-related visual features of image regions, it is usually referred as “bag-of-visual words (BoVW)”-based presentation. Although it has proved to be effective for image representation similar to document representation in text retrieval, the hard image encoding approach based on one-to-one mapping of regions to visual words is not expressive enough to characterize the image contents with higher level semantics and prone to quantization error. Each word is considered independent of all the words in this model. However, it is found that the words are related and their similarity of occurrence in documents can reflect the underlying semantic relations between them. To consider this, a soft image representation scheme is proposed by spreading each region’s membership values through a local fuzzy membership function in a neighborhood to all the words in a codebook generated by self-organizing map (SOM). The topology preserving property of the SOM map is exploited to generate a local membership function. A systematic evaluation of retrieval results of the proposed soft representation on two different image (natural photographic and medical) collections has shown significant improvement in precision at different recall levels when compared to different low-level and “BoVW”-based feature that consider only probability of occurrence (or presence/absence) of a word.     

A new integrated approach for engineering characteristic prioritization in quality function deployment

As a customer-driven quality improvement tool, quality function deployment (QFD) can convert customer requirements (CRs) into appropriate engineering characteristics (ECs) in product design and development. However, the conventional QFD method has been criticized for a variety of drawbacks, which limit its efficiency and potential applications. In this study, a new QFD approach integrating picture fuzzy linguistic sets (PFLSs) and the evaluation based on distance from average solution (EDAS) method is proposed for the determination of ranking order of ECs. The PFLSs are utilized to express the judgements of experts on the relationships among CRs and ECs. Then, the EDAS method is extended under picture fuzzy linguistic environment for the prioritization of the ECs identified in QFD. Moreover, a combined weighing method based on technique for order of preference by similarity to ideal solution (TOPSIS) and maximum entropy theory is established to calculate the weights of experts objectively. Finally, a product-service system design is provided to illustrate the effectiveness of the proposed QFD approach. The result shows that the manufacturer should pay more attention to “Meantime before failure”, “Warning feature” and “Quality of product manual”. Feedback from domain experts indicates that the integrated approach being proposed in this paper is more suitable for assessing and prioritizing ECs in QFD.     

Control of attendance applied in higher education through mobile NFC technologies

This paper presents an improved attendance control system, required for continuous evaluation which has become compulsory following the Bologna Process. It provides a solution based on NFC technology and is based on a real project developed and pilot tested at the “Universidad Pontificia de Salamanca, Campus Madrid”.