We investigate in this paper the problem of misalignment-robust facial expression recognition. To the best of our knowledge, this problem has not been formally addressed in the literature. Most existing facial expression recognition methods, however, can only work well when face images are well-aligned. In many real world applications such as human robot interaction and visual surveillance, it is still very challenging to obtain well-aligned face images for expression recognition due to currently imperfect vision techniques, especially under uncontrolled conditions. Motivated by the fact that interclass facial images with small differences are more easily mis-classified than those with large differences, we propose a biased linear discriminant analysis (BLDA) method by imposing large penalties on interclass samples with small differences and small penalties on those samples with large differences simultaneously, such that more discriminative features can be extracted for recognition. Moreover, we generate more virtually misaligned facial expression samples and assign different weights to them according to their occurrence probabilities in the testing phase to learn a weighted BLDA (WBLDA) feature space to extract misalignment-robust discriminative features for recognition. Experimental results on two widely used face databases are presented to show the efficacy of the proposed method.

This paper investigates the problem of cross-dataset facial expression recognition. To the best of our knowledge, this problem has not been formally addressed in the literature. Conventional facial expression recognition methods assume expression images in the training and testing sets are collected under the same condition such that they are independent and identically distributed. In many real applications, this assumption may not hold as the testing data are usually collected online and generally more uncontrollable than the training data, and hence, they are likely different from the training data. This problem is referred to as cross-dataset facial expression recognition in this paper as the training and testing data are considered to be collected from different datasets due to different acquisition conditions. To address this, we propose a new transfer subspace learning approach to learn a feature space which transfers the knowledge gained from the training set to the target (testing) data to improve the recognition performance under cross-dataset scenarios. Experimental results for facial expression recognition tasks on different datasets are presented to demonstrate the efficacy of the proposed approach.

In this paper, we propose a new model called Augmented Linear Inverted Pendulum (ALIP) in which an augmented function F is added to the dynamic equation of the linear inverted pendulum. The purpose of adding the function F is to modify/adjust the inverted pendulum dynamics in such a way that disturbance caused by un-modeled dynamics (legs, arms, etc.) can be compensated or minimized. By changing the key parameters of the augmented function we can easily modify the inverted pendulum dynamics. The desired walking motion with maximized stability margin is achieved by optimizing the key parameters using genetic algorithm. The disturbance created by the un-modeled dynamics is minimized because full robot dynamics is considered in the optimization process. Simulations results show that the walking gait obtained using the proposed method is more stable than that obtained using the Linear Inverted Pendulum Mode (LIPM).

In this paper, we investigate stability of a nonlinear tracking control for a mobile robot in connection with the method of artificial potential trench. We first refine the concept of potential trench function and propose several approaches to construct potential trench functions. Then we synthesize a control law that stabilizes a robot while tracking a goal point moving on a given segment (smooth curve) and extend it to a simple case of formation control. Results from computer simulation verify and demonstrate the effectiveness of this novel control.

Blood loss is a concern in the liver resections. In line with the wide implementation of ablation process, bleeding during liver resections can be minimized. However, the division process has to be accurately monitored to provide efficient liver resection in terms of time and safety. The modeling of liver tissue and division tool interaction along with the deformation of the liver tissue is studied in this research. As liver tissue is made of complex viscoelastic material, we determined that it could be modeled using standard viscoelastic model based on biomechanics experiment on fresh porcine livers. This result is further extended to incorporate a modeled division tool. Simulation of the modeled interaction is discussed. A state control feedback system which can help monitor the combined ablation and division process is also proposed.

Due to the extensive utilization in engineering designs, various vibration controller designs have been investigated to meet the design specifications. However, not all of them exactly meet the design requirements. In this paper, mechatronic design quotient (MDQ) approach and genetic algorithm are coupled together to perform this evaluation and optimization task. MDQ is presented to formulate an evaluation function of passive vibration controller design for flexible beam structures, and GA is then used to optimize this function so as to achieve a design solution with the highest MDQ value. Experimental results from one damper and two dampers are presented and compared. It showed that the linear dampers design with the proposed method can achieve the desired performance.

In this paper, an approach using the Taylor series expansion is explored for error compensation in bi-axis CNC machines. The objective is to improve the contouring accuracy in these machines. The contour errors that will occur if the desired reference inputs are directly applied to the control loops are first predicted using the known axial dynamic models. The Taylor series expansion is then used to obtain an expression of the contour error around the current tool position. A robust numerical method is devised which is then used to compute the modified reference components along individual axes for cancellation of the predicted contour errors. Simulation results show that, with perfect knowledge of the machine's dynamic models, the proposed strategy can perfectly cancel out the contour errors for both linear and circular contours despite the mismatches in the axes dynamics.

This paper presents a novel technique to fabricate scaffold/cell constructs for tissue engineering by robotic assembly of microscopic building blocks (of volume 0.50.50.2 and 60 thickness). In this way, it becomes possible to build scaffolds with freedom in the design of architecture, surface morphology, and chemistry. Biocompatible microparts with complex 3-D shapes were first designed and mass produced using MEMS techniques. Semi-automatic assembly was then realized using a robotic workstation with four degrees of freedom integrating a dedicated microgripper and two optical microscopes. Coarse movement of the gripper is determined by pattern matching in the microscopes images, while the operator controls fine positioning and accurate insertion of the microparts. Successful microassembly was demonstrated using SU-8 and acrylic resin microparts. Taking advantage of parts distortion and adhesion forces, which dominate at micro-level, the parts cleave together after assembly. In contrast to many current scaffold fabrication techniques, no heat, pressure, electrical effect, or toxic chemical reaction is involved, a critical condition for creating scaffolds with biological agents.

This paper presented a method of bipedal walking pace supervision by the adjustment of stride-frequency and step-length simultaneously. A reinforcement learning algorithm is designed to learn the walking stride-frequency; A transition plan aims to adjust the step-length or update motion phases according to the dynamic feedback; A momentum based estimation gives another layer of stride-frequency adjustment when the learning agent has not gained enough experiences. Simulation experiments showed this learning based motion supervision is effective for maintaining stable walking under perturbations with a balanced performance of energy consumption and robustness.

This paper presents a method for optimally generating stable bipedal walking gaits, based on a truncated Fourier series formulation with coefficients tuned by genetic algorithm. It also provides a way to adjust the stride-frequency, step-length or walking pattern in real-time. The proposed approach to gait synthesis is not limited by the robot kinematic structure and can be used to satisfy various motion assumptions. It is also easy to generate optimal gaits on terrains of different slopes or on stairs under different motion requirements. Dynamic simulation results show the validity and robustness of the approach. The gaits generated resulted in human-like motions optimized for stability, even walking speed and lower leg-strike velocity of the swing foot

This paper presents a monolithic shape memory alloy (SMA) microgripper, which was developed to assemble microscopic building blocks of width 60 /spl mu/m into tissue engineering scaffolds. It consists of two small fingers for grasping, an actuator which changes its shape upon heating by Joule effect and a parallel elastic structure to provide a pullback force on cooling as well as to guide the finger movement. All the elements are laser-cut from the same Ni-Ti-Cu sheet but have different mechanical properties and are used for different functions. Using local laser annealing, a local shape memory effect is introduced on the actuator while leaving other areas in a cold-worked state, i.e. no shape memory effect occurs. The material has nonlinear mechanics and the actuator undergoes large deflections. A numerical method is introduced to compute the nonlinear dynamics of the coupled system formed by the actuator and pullback spring. The predicted deflection and force are in good agreement with the measured ones, and this model can be used to optimize the design.

In this paper, a dynamic scheduling algorithm is developed to optimize the performance digital control systems by allocating CPU time efficiently. The periods of the control tasks are regarded as variables with dependency on the task current importance and scheduling constraints. The task current importance can be measured by the current cutoff frequency of the control signal. It indicates the task current requirement on sampling frequency. CPU time is allocated under the consideration of task current requirement and the performance index change-rate to optimize the overall system performance. The simulation results verify that this scheduling algorithm can improve the overall system performance under limited computing resource.

Describes an integrated approach to design and fabricate scaffold/cell constructs for tissue engineering. With this approach it becomes possible to produce scaffolds with controlled distribution of living cells and growth factors, a critical condition for successful grafting. Our idea consists of building a scaffold/cell construct by robotic micro-assembly of microscopic polymer building blocks. The paper introduces the rationale and concept of this interdisciplinary project and presents some realized steps. A 3D contact FEM simulation has been carried out to study the forces involved on the scaffold elements and micro-gripper during assembly. An error analysis has been performed to evaluate the accumulated error when building a scaffold/cell construct. A dedicated monolithic shape memory alloy micro-gripper has been realized and tested, which is able to handle parts in the range of 50-100 microns.

Different autofocusing methods exist for many cameras today. While not ignoring commercially available methods requiring specialized hardware, this paper focuses mainly on pixel based autofocusing algorithms as applied to CCD camera systems. Different measures of image sharpness are compared. For each of these, different algorithms for searching the best lens setting are assessed in terms of performance as well as their applicability to various situations. In addition, several other factors potentially affecting camera focusing are also discussed. Based on the information obtained, this research attempts to formulate a robust autofocusing algorithm.

This paper proposes an enhanced sliding mode controller for the set point tracking control, which combine the pre-shape sliding mode control method with motion auxiliary design technique. Using the proposed pre-shape sliding mode controller, not only the robust property of sliding mode controller can be improved, but also the system trajectory can be designed. When comparing with the traditional control method, the proposed algorithm can give a better response with improved robust property. Simulation results for a single link robot manipulator also verify that the proposed controller gives a more attractive performance than the traditional PD computed torque controller.

The nonlinear and cross-coupled dynamics of motor-manipulator systems is studied, and a control strategy is developed for achieving ease of controller design and high control performance. By using the algorithm developed in this paper, not only can the highly complicated motor-manipulator dynamics be effectively controlled, but the motors are also made "sensible" for sensing and compensating for the uncertain dynamics acting at manipulator joints. Furthermore, as a significant result, the modeling difficulty and control complexity of the manipulator dynamics can be significantly reduced.