The Error Correcting Output Codes (ECOC) algorithm has played more and more important role in the artificial intelligence (AI) study and research fields in recent years due to its special properties and error correcting abilities. Various methods have been reported to improve its performance and classification accuracy. In this study, we developed a simplified ECOC encoding method to reduce the complexity in encoding process to reduce its random in initializing the coding matrix. Furthermore, we also suggested a fuzzy mapping decoding (FMD) method used for ECOC decoding process to improve its classification accuracy. By using this FMD, the classification accuracy can be improved up to 84% compared with the decoding method without using the FMD, which is 77%.

Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible topologies. Image registration models on the other hand are able to warp known topologies to target images as a means of segmentation, but typically require large amounts of training data, and have not widely been benchmarked against pixel-wise segmentation models. We propose the Atlas Image-and-Spatial Transformer Network (Atlas-ISTN), a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process. Atlas-ISTN learns to segment multiple structures of interest and to register the constructed atlas labelmap to an intermediate pixel-wise segmentation. Additionally, Atlas-ISTN allows for test time refinement of the model's parameters to optimize the alignment of the atlas labelmap to an intermediate pixel-wise segmentation. This process both mitigates for noise in the target image that can result in spurious pixel-wise predictions, as well as improves upon the one-pass prediction of the model. Benefits of the Atlas-ISTN framework are demonstrated qualitatively and quantitatively on 2D synthetic data and 3D cardiac computed tomography and brain magnetic resonance image data, out-performing both segmentation and registration baseline models. Atlas-ISTN also provides inter-subject correspondence of the structures of interest.

In nature and human society, successive lag synchronization (SLS) is an important synchronization phenomenon. Compared with other synchronization patterns, the control theory of SLS is very lacking. To this end, we first introduce a complex dynamical network model with distributed delayed couplings, and design both the linear feedback pinning control and adaptive feedback pinning control to push SLS to the desired trajectories. Second, we obtain a series of sufficient conditions to achieve SLS to a desired trajectory with global stability. What is more, the control flow of SLS is given to show how to pick the pinned nodes accurately and set the feedback gains as well. Finally, since time-varying delay is common, we extend the constant time delay in SLS to be time varying. We find that the proposed pinning control schemes are still feasible if the coupling terms are appropriately adjusted. The theoretical results are verified on a neural network and the coupled Chua's circuits.

A comparison study among type 1 fuzzy interpolations (T1FI), interval type 2 fuzzy interpolations (IT2FI) and neural network fitting functions (NNFF) applied on modeless robots calibrations is proposed. Traditional robots calibration implements either model or modeless method. The compensation of position error in modeless method is to move the robot's end-effector to a target position in the robot workspace, and to find the target position error based on the measured neighboring 4-grid-point errors around the target position. A camera or other measurement device is attached on the robot's end-effector to find and measure the neighboring position errors, and compensate the target position with various error interpolation methods. By using the NNFF technique provided in this paper, the accuracy of the position error compensation can be greatly improved, which has been confirmed by the simulation results given in this paper. Compared with some other popular interpolation methods, this NNFF technique is a better choice. The simulation results show that more accurate compensation result can be achieved using this technique compared with the type-1 fuzzy and interval type 2 fuzzy interpolation methods.

This paper provides an effective method to evaluate the input quantization and multiplication roundoff errors for two-dimensional (2-D) separable-in-denominator recursive digital (SDRD) filters. This work is based on the partial fraction expansion of 2-D SDRD filters [1] and their modular implementation structures [2] proposed by the authors. Equations are derived for the calculation of roundoff errors for various implementations of 2-D SDRD filters. An example is given to demonstrate the application of the proposed method.

It is critically important to develop intelligent mobile robots to effectively reduce the effects of nuclear radiations to perform desired rescuing tasks in critical emergency situations. One of the good examples is the recent nuclear disposing damage to the Japanese nuclear power plants after the earth quick occurred in the northeast of Japan. In this study, a nested fuzzy logic control system is designed for mobile robots to effectively reduce the effects of those lower-frequency nuclear radiations on rescuing robots. A coarse and a fine fuzzy controller are combined together to realize this objective. The simulation and experimental results of this study confirmed the effectiveness of this nested fuzzy control system in reduction of the effects of lower-frequency nuclear radiations on mobile robots. This is the first paper related to this topic to be presented.

The weapon identification and selection issue is an important and strategic component and has a significant impact on the efficiency of defense system in US. The main purpose of this research is to develop a universal model and system to effectively assess, evaluate and identify the optimal weapons from a large collection of available weapon systems that have multiple criteria based on a fuzzy multiple criteria decision making (FMCDM) model. A simple but effective weight estimation method is adopted in this paper to make this selection more objective and reliable. There are some different weight estimation methods reported by researchers, however, most methods need a lot of mathematical operations and make the process very time consuming and even more complicated. In this paper, we utilized a weight estimation method based on the paired comparison matrix to simplify this estimation process. The evaluation and selection process can be significantly simplified and improved by using this method.

The paper provides a comparison between a novel technique used for the pose error measurements and compensations of robots based on a fuzzy error interpolation method and some other popular interpolation methods. By using the proposed fuzzy error interpolation technique, the accuracy of the pose-error compensation can be improved, which has been confirmed by the simulation results given in this paper, compared with other interpolation methods. A comparison study among various interpolation methods, such as trilinear, cubic spline, and the fuzzy error interpolation technique is also made and discussed via simulation. The simulation results show that more accurate measurement and compensation results can be achieved using the fuzzy error interpolation technique compared with its trilinear and cubic spline counterparts.

In this paper, a number of algorithmic options for fuzzy logic control applications are presented. The emphasis is on the analyses of the computational load and memory requirements of all the processing stages of fuzzy logic control algorithms. Comparisons are made among commonly used techniques and recommendations are provided. The results could be used to guide the selection of a fuzzy logic algorithm for a specific application.

This paper provides a comparison between a novel technique used for the pose-error measurements and compensations of robots based on a fuzzy-error interpolation method and some other popular interpolation methods. A traditional robot calibration implements either model or modeless methods. The measurement and compensation of pose errors in a modeless method moves the robot's end-effector to the target poses in the robot workspace and measures the target position and orientation errors using some interpolation techniques in terms of the premeasured neighboring pose errors around the target pose. For the measurement purpose, a stereo camera or other measurement devices, such as a coordinate-measurement machine (CMM) or a laser-tracking system (LTS), can be used to measure the pose errors of the robot's end-effector at predened grid points on a cubic lattice. By the use of the proposed fuzzy-error interpolation technique, the accuracy of the pose-error compensation can be improved in comparison with other interpolation methods, which is conrmed by the simulation results given in this paper. A comparison study among most popular interpolation methods used in modeless robot calibrations, such as trilinear, cubic spline, and the fuzzy-error interpolation technique, is also made and discussed via simulations. The simulation results show that more accurate measurement and compensation results can be achieved using the fuzzy-error interpolation technique compared with its trilinear and cubic-spline counterparts.

Topology-preserving geometric deformable models (TGDMs) are used to segment objects that have a known topology. Their accuracy is inherently limited, however, by the resolution of the underlying computational grid. Although this can be overcome by using fine-resolution grids, both the computational cost and the size of the resulting contour increase dramatically. In order to maintain computational efficiency and to keep the contour size manageable, we have developed a new framework, termed QTGDMs, for topology-preserving geometric deformable models on balanced quadtree grids (BQGs). In order to do this, definitions and concepts from digital topology on regular grids were extended to BQGs so that characterization of simple points could be made. Other issues critical to the implementation of geometric deformable models are also addressed and a strategy for adapting a BQG during contour evolution is presented. We demonstrate the performance of the QTGDM method using both mathematical phantoms and real medical images.

Traditional robot calibration implements model and modeless methods. The compensation of position error in modeless method is to move the end-effector of robot to the target position in the workspace, and to find the position error of that target position by using a bilinear interpolation method based on the neighboring 4-point's errors around the target position. A camera or other measurement devices can be utilized to find or measure this position error, and compensate this error with the interpolation result. This paper provides a novel fuzzy interpolation method to improve the compensation accuracy obtained by using a bilinear interpolation method. A dynamic online fuzzy inference system is implemented to meet the needs of fast real-time control system and calibration environment. The simulated results show that the compensation accuracy can be greatly improved by using this fuzzy interpolation method compared with the bilinear interpolation method.

This paper describes a novel technique for the position error compensations of the robot and manipulator calibration process based on a fuzzy error interpolation method. Traditional robots calibration implements model and modeless methods. Bilinear and cubic splines are the two popular techniques for the compensation of position error in modeless method. By using the fuzzy error interpolation technique provided in this paper, the accuracy of the position error compensation can be greatly improved. The comparison between the two popular traditional interpolation methods and this fuzzy error interpolation technique is made via simulation for three commonly used error models. The simulation results show that more accurate compensation result can be achieved using the fuzzy error interpolation technique compared with the bilinear and cubic spline interpolation methods.