Particle filter with one-step randomly delayed measurements and unknown latency probability

In this paper, a new particle filter is proposed to solve the nonlinear and non-Gaussian filtering problem when measurements are randomly delayed by one sampling time and the latency probability of the delay is unknown. In the proposed method, particles and their weights are updated in Bayesian filtering framework by considering the randomly delayed measurement model, and the latency probability is identified by maximum likelihood criterion. The superior performance of the proposed particle filter as compared with existing methods and the effectiveness of the proposed identification method of latency probability are both illustrated in two numerical examples concerning univariate non-stationary growth model and bearing only tracking.     

Can eye-tracking data be measured to assess product design?: Visual attention mechanism should be considered

Eye tracking probes user's perception of real-time reaction to products, while conventional methods (i.e. interviews, focus group, questionnaires and so on) have generally failed because they depend on users' willingness and competency to describe how they feel when they are exposed to a product. Two tasks were designed to explore the indexes of eye movement that can reflect user experience of product, and analyse the attention captured by product attributes and goal-oriented. In task one, participants just browsed two smart phone pictures and evaluated the whole user experience. Binary choices were used in task two to ask participants to select the smart phone picture with higher user experience and then click the mouse. The results showed that in the browsing task, participants had shorter time to first fixation for the smart phone picture with higher level of user experience than the lower. And pupil dilated significantly when participants browse smart phone picture with lower level of user experience. In goal-oriented task, participants' attentions were dominated by visual perception of task driven, mainly reflected on longer fixation time and larger pupil diameter when looking at the smart phone with higher level of user experience. These results support the notion that we cannot assess product design just by several eye-movement indexes without considering the effects of visual attention mechanism.Relevance to industryThe appearance of product plays an important role to attract user's attention and stimulate their intention to experience. And vision is the main channel for users to obtain product information. Hence a thorough research on the inherent mechanism of vision perception can provide technical support for product designers, which in turn can attract more consumers to experience the product, even buy it. Moreover, the seller can find out the real buyers and predict their desired products by tracking user's eyes.     

Integrated multi-layer representation and ant colony search for product selective disassembly planning

Selective disassembly plays an important role in product life cycle to meet requirements of the product repairing, reusing and recycling. An efficient disassembly plan is essential to minimize processing time in product maintenance for cost saving. This paper introduces a method for integration of the multi-layer product representation and the optimal search in product selective disassembly planning. The multi-layer representation is based on the product structure formed in product design. The method enables an efficient search for the disassembly sequence. Unlike the existing product representation methods, the multi-layer representation is a dynamic product data model integrated with an ant colony search process for a near optimal solution. Industrial applications have proven the method effectiveness.     

A study of bicycle dynamics via system identification approaches

This study investigates bicycle dynamic properties by using system identification approaches. The non-linear bicycle model with configuration parameters from a previously developed benchmark model is studied. The roll angle of the bicycle is controlled at different speeds to generate input–output data including steering torque, roll, and steering angles. The collected data are then used to identify the one-input two-output linear model by a prediction-error identification method using parameterization in canonical state-space form derived as Whipple's model. Simulations are used to verify the accuracy of the generated linear model. Numerous properties for various speed ranges are discussed from the pole and zero locations of the identified linear model. The system stability, limit-cycle phase portraits of the roll and steering angles, and the non-minimum phase property of the non-linear system are further investigated and compared with the corresponding linearized results from previous studies in the literature.     

Modular product platform configuration and co-planning of assembly lines using assembly and disassembly

Formation of products platforms is carried out during the planning stage and very often separately from the planning of corresponding assembly lines. There is a dearth of literature which considers the different aspects of fully integrating platform design, product family formation, assembly line design, delayed product differentiation, and new concepts of mass customization. A Modular Product Platform Configuration model which uses assembly and disassembly for configuring product variants and Co-Planning of products platforms (MPCC) and their assembly Lines is presented. It is used to co-plan the common platform components and the associated product families simultaneously with the planning of its corresponding mixed-model assembly line. Using both assembly and disassembly to customize the product family platform in order to generate product variants is not commonly discussed in literature. It is defined as the formation of platforms for use to derive multiple products by including many components not shared by every product. The platform is then customized by assembling or disassembling components to form different product variants. The model is formulated using mixed integer mathematical programming to minimize the number of assembly stations and cycle time. Two case studies are used for verification and demonstration. They illustrated the ability of the MPCC model to integrate the planning of product platform, product families and the number of assembly stations required to assemble and disassemble components from mass-assembled product platforms to derive new product variants.     

Multinational vehicle license plate detection in complex backgrounds

Many methods for multinational License Plate Detection (LPD) have been proposed in recent times but most of them are not sophisticated enough to handle complex backgrounds. Moreover, their ability to handle various environmental and illumination conditions has been limited and still needs improvement. In this paper, we propose a novel technique to detect license plates of vehicles regardless of their color, size, and content. As the rear vehicle lights are an essential part of any vehicle, we reduce the image processing area to eliminate the complex background by detecting the rear-lights as the license plates are in a certain range of these lights. Heuristic Energy Map (HEM) of the vertical edge information in the Region of Interest (ROI) is calculated and area with the dense edges is selected using a unique histogram approach which is considered to be the license plate. The proposed algorithm is tested on 855 images from various countries including China, Pakistan, Serbia, Italy and various states of America. Experimental results show that the proposed method is able to detect license plates 90.4% of times despite of complex backgrounds in 0.25 s on average that can achieve real time performance.     

RF fingerprint measurements for the identification of devices in wireless communication networks based on feature reduction and subspace transformation

This document proposes a radiofrequency (RF) fingerprinting strategy for the proper identification of wireless devices in mobile and wireless networks. The proposed identification methods are based on the extraction of the preamble RF fingerprint of a device and its comparison with a set of already known device RF fingerprints. The identification method combines techniques for feature reduction such as Principal Component Analysis (PCA) and Partial Least Squares regression (PLS), both based on subspace transformation, along with a similarity-based analysis. In this work, a complete procedure for RF fingerprint data extraction and analysis is provided. In addition, some experimentation with commercial Wi-Fi devices is carried out for the methods validation.     

Just-about-right and ideal scaling provide similar insights into the influence of sensory attributes on liking

Just-about-right (JAR) scaling is criticized for measuring attribute intensity and acceptability simultaneously. Using JAR scaling, an attribute is evaluated for its appropriateness relative to one’s hypothetical ideal level that is pre-defined at the middle of a continuum. Alternatively, ideal scaling measures these two constructs separately. Ideal scaling allows participants to rate their ideal freely on the scale (i.e., without assuming the “Too Little” and “Too Much” regions are equal in size). We hypothesized that constraining participants’ ideal to the center point, as is done in the JAR scale, may cause a scaling bias and, thereby, influence the magnitude of “Too Little” and “Too Much”. Furthermore, we hypothesized that the magnitude of “Too Little” and “Too Much” would influence liking to different extents.Coffee-flavored dairy beverages (n = 20) were formulated using a fractional, constrained-mixture design that varied the ratio of water, milk, coffee extract, and sucrose. Participants tasted 4 of 20 prototypes that were served in a monadic sequential order using a balanced incomplete block design. Data reported here are for participants randomly assigned to one of two research conditions: ideal scaling (n = 129) or JAR scaling (n = 132). For both conditions, participants rated overall liking using a 9-point hedonic scale. Four attributes (sweetness, milk flavor, coffee flavor and thickness) were evaluated. The reliability of an individual participant’s ideal rating for an attribute was evaluated using the standard deviation of their ideal ratings (n = 4). All data from a participant were eliminated from further analyses when his/her standard deviation of the ideal ratings for any of the four rated attributes was identified as a statistical outlier. This resulted in the elimination of 15 of 129 (12%) of participants in the ideal scaling group. Multiple linear regression was employed to model liking as a function of “Too Little” or “Too Much” attribute intensities.Mean ideal ratings (averaged across participants) for all four attributes were significantly different from the central point of the scale (i.e., 50). However, coffee flavor was the only attribute for which the mean ideal rating (57.2) fell outside the central 10% (45.0–55.0). Even so, the magnitude of “Too Little” and “Too Much” was not affected by the scaling method. The influence of the magnitude of “Too Little” and “Too Much” on liking was asymmetrical. Both scaling methods agreed that sweetness and coffee flavor were the main sensory attributes affecting liking. Overall, JAR scaling and ideal scaling were comparable for measuring “Too Little” and “Too Much”, and identifying the main factors affecting liking.     

Identification of tuna species by a real-time polymerase chain reaction technique

Tuna are highly priced fishes that are often used in processed products. For effective fishery management and protection of consumers’ rights, it is important to develop a molecular method to identify the species of the tuna products. In this study we have developed a molecular method based on real-time polymerase chain reaction (real-time PCR) technology for the rapid identification of four tuna species. Four species-specific TaqMan probes were designed to identify bigeye tuna (Thunnus obesus), Pacific bluefin tuna (Thunnus orientalis), southern bluefin tuna (Thunnus maccoyii), and yellowfin tuna (Thunnus albacares). A SYBR green system was also designed to enhance the authentication of T. obesus. Both systems can distinguish target species from others in an efficient and high-throughput manner and can be applied to species identification of tuna products.