Robust fuzzy stabilization of dithered chaotic systems using island-based random optimization algorithm

Applying dither to highly nonlinear systems may suppress chaotic phenomena, but dynamic performance, such as convergence rate and disturbance attenuation, is usually not guaranteed. This paper presents a dithered H_∞ robust fuzzy control scheme to stabilize chaotic systems that ensures disturbance attenuation bounds. In the proposed scheme, Takagi-Sugeno (T-S) fuzzy linear models are used to describe the relaxed models of the dithered chaotic system, and fuzzy controllers are designed based on an extension to the concept of parallel distributed compensation (PDC). Sufficient condition for the existence of the H_∞ robust fuzzy controllers is presented in terms of a novel linear matrix inequalities (LMI) form which takes full consideration of modeling error and disturbances, but cannot be solved by the standard procedures. In order to solve the LMI problem and to identify the chaotic systems as T-S fuzzy modes, we propose a compound optimization strategy called the island-based random-walk algorithm (IRA). The algorithm is composed of a set of communicating random-walk optimization procedures concatenated with the down-hill simplex method. The design procedure and validity of the proposed scheme is demonstrated via numerical simulation of the dithered fuzzy control of a chaotic system.     

An ontological engineering approach for automating inspection and quarantine at airports

Customs and quarantine departments are applying information systems to automate their inspection processes and improve their inspection efficiency and accuracy. The product codes from the Harmonized System (HS codes) are the essential elements of the system's integration, automation and intelligence. The identified HS codes are well-accepted and precise product references used by customs authorities, to match applicable policies to the products being inspected and taxed. Domain ontology for importing and exporting industry can be used to acquire HS codes for given products, and is a prerequisite for an integrated and intelligent automated inspection system. The authors have proposed and implemented an importing and exporting domain ontology. The ontology is composed of an integrated and comprehensive knowledge base derived from static dictionaries and the HS specification, and dynamic processing data. Based on this ontology, a reasoning engine is developed to generate HS codes intelligently for the given product names. Information systems can use the engine to get HS codes for submitted products and find applicable policies automatically. The ontology and the engine have been implemented in a Java-based platform and published as a HS Web service. In this paper, knowledge structure, reasoning mechanism and implementation details for the domain ontology and reasoning engine are presented. A test bed in the application environment has been conducted and experimental results have been obtained. The ontology and the service have the potential to be widely used by authorities and international traders of importing and exporting industry around the world.     

Automatic image authentication and recovery using fractal code embedding and image inpainting

In this paper, we propose a novel scheme of automatic image authentication and recovery, in which the altered region of the image can be detected and recovered in a fully automated fashion. To achieve high-quality image recovery, we choose the region of importance (ROI) in the image and perform fractal encoding for the blocks of ROI. And then we insert the fractal codes of ROI and the watermark into the original image to obtain the watermarked image. By watermark extraction, we can determine whether the watermarked image has been tampered or not. If the watermarked image has been tampered, we can automatically localize the altered region, and apply either fractal decoding or image inpainting for image recovery. Finally, experimental results are given to show the effectiveness of the proposed scheme.     

A high quality steganographic method with pixel-value differencing and modulus function

In this paper, we shall propose a new image steganographic technique capable of producing a secret-embedded image that is totally indistinguishable from the original image by the human eye. In addition, our new method avoids the falling-off-boundary problem by using pixel-value differencing and the modulus function. First, we derive a difference value from two consecutive pixels by utilizing the pixel-value differencing technique (PVD). The hiding capacity of the two consecutive pixels depends on the difference value. In other words, the smoother area is, the less secret data can be hidden; on the contrary, the more edges an area has, the more secret data can be embedded. This way, the stego-image quality degradation is more imperceptible to the human eye. Second, the remainder of the two consecutive pixels can be computed by using the modulus operation, and then secret data can be embedded into the two pixels by modifying their remainder. In our scheme, there is an optimal approach to alter the remainder so as to greatly reduce the image distortion caused by the hiding of the secret data. The values of the two consecutive pixels are scarcely changed after the embedding of the secret message by the proposed optimal alteration algorithm. Experimental results have also demonstrated that the proposed scheme is secure against the RS detection attack.     

Modeling and optimization of stencil printing operations: A comparison study

This paper presents a comparison study for the optimization of stencil printing operations using hybrid intelligence technique and response surface methodology (RSM). An average 60% of soldering defects are attributed to solder paste stencil printing process in surface mount assembly (SMA). The manufacturing costs decrease with increasing first-pass yield in the stencil printing process. This study compares two hybrid intelligence approaches with RSM as methods of solving the stencil printing optimization problem that involves multiple performance characteristics. The optimization process is threefold. A data set obtained from an experimental design following data preprocessing process provides an accurate data source for RSM study and training neural networks to formulate the nonlinear model of the stencil printing process with/without combining multiple performance characteristics into a single desirability value, followed by a genetic algorithm searching the trained neural networks for obtaining the optimal parameter sets. The empirical defect-per-million-opportunities (DPMO) measurements demonstrate that the two hybrid intelligence methods can provide satisfactory performance for stencil printing optimization problem.     

Investigating university student preferences and beliefs about learning in the web-based context

Psychological studies have shown that personal beliefs about learning and environmental preferences affect learning behaviors. However, these learner characteristics have not been widely discussed in the web-based context. By developing questionnaires, this study attempted to detect learners’ web-based learning environmental preferences (WLEP) and beliefs about web-based learning (BWL). The scope of WLEP focused on the pedagogical dimension of the web-based learning environment, while BWL concerned the attributes and control factors of the web-based learning. There were about five hundreds of Taiwan university students participating in the study. Through factor analysis, the scales discussed in the study revealed a satisfactory validity and reliability in assessing students’ preferences and beliefs. Further analyses showed that university students preferred more of individual and structured instructional configurations while expected the outward mode of interaction. In general, students held a rather contextual belief about web-based learning, which was found to be correlated with their environmental preferences.     

Distributed reformation of core-based group-shared multicast trees in mobile ad hoc networks

This paper proposes a method to reduce the cost of a core-based group-shared multicast tree, where the cost is evaluated by the total bandwidth consumption of multicasting packets among all group members. Due to the broadcast nature of radio transmissions, we find that the challenge of determining minimum cost multicast tree can be approximated by finding the multicast tree with a minimum number of non-leaves (the minimum non-leaf multicast tree problem). However, we also find that the minimum non-leaf multicast tree problem is NP-complete. Thus, a method is proposed to dynamically reduce the number of non-leaves in an existing multicast tree. Experimental results show that our method reduces the cost of the multicast tree in both geometrically and randomly distributed network models and the random waypoint mobility model.     

The preferences toward constructivist Internet-based learning environments among university students in Taiwan

Numerous educators have proposed the development of constructivist Internet-based learning environments for students. When creating the constructivist Internet-based learning environments, it is important for researchers to be aware of students’ preferences toward these environments. Through gathering data from 659 university students in Taiwan, this study developed a questionnaire to assess students’ preferences toward constructivist Internet-based learning environments. The questionnaire, with adequate validity and reliability, included 34 items on the following seven scales: relevance, multiple sources (and interpretations), challenge, student negotiation, cognitive apprenticeship, reflective thinking and epistemological awareness. The questionnaire responses revealed that male students tended to prefer the Internet-based learning environments where they could solve challenging problems, acquire cognitive apprenticeship and guidance from experts, and promote epistemological development than did female students. The findings also suggested that, if educators intend to develop Internet-based learning environments for more academically advanced students, such as graduate students, care should be taken to create more opportunities for them to negotiate ideas, obtain proper guidance, reflect their own thoughts, and explore epistemological issues. Finally, students with more Internet experiences tended to demand more on many features of the constructivist Internet-based learning environments than those with less Internet experiences.     

Adaptive Robust Control of an Omnidirectional Mobile Platform for Autonomous Service Robots in Polar Coordinates

This paper presents an adaptive robust control method for trajectory tracking and path following of an omni-directional wheeled mobile platform with actuators’ uncertainties. The polar-space kinematic model of the platform with three independent driving omnidirectional wheels equally spaced at 120 from one another is briefly introduced, and the dynamic models of the three uncertain servomotors mounted on the driving wheels are also described. With the platform’s kinematic model and the motors’ dynamic model associated two unknown parameters, the adaptive robust controller is synthesized via the integral backstepping approach. Computer simulations and experimental results are conducted to show the effectiveness and merits of the proposed control method in comparison with a conventional PI feedback control method.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.