In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy.

It is necessary to innovatively design the dexterity and applicability of end-effector to solve the last centimeter problem of automated production. This paper presents a scissor-shaped deployable metamorphic hand (SDMH). First, a scissor-shaped sub-mechanism module mechanism was designed with three metamorphic configurations. A novel connection mechanism was introduced. It can realize the passively adaptive grasping motion and avoid the bifurcation position problem in the clamping process. Subsequently, the three configurations of SDMH were analyzed by the sub-constraint function. Then, the conversion relationship between the various configurations was established through the adjacency matrix, which fundamentally explains the metamorphic mechanism of SDMH. Furthermore, the kinematics analysis of the SDMH was described by the D–H method. Kinematic equations of the single finger can be established in the different three grasping configurations using the matrix transformation method. The working space of SDMH can be obtained by numerical simulation. Finally, the physical prototype of SDMH was manufactured to verify the deployable and grasping performance of SDMH.

The temperature of coke oven is an important process parameter, but it is difficult to obtain the temperature of the vertical flue in real-time. The establishment based on the case-based reasoning (CBR) and radial basis function neural network (RBFNN) of coke oven flue temperature intelligent prediction model, realise the real-time prediction of the temperature, and help to realise the coke oven production process of intelligent optimisation control. The real-time forecast under different conditions is realised by the selective intelligent forecasting model of the coke oven, and the forecasting performance of system model is simulated. The results show that the forecasting model is faster and more reliable than the traditional artificial forecast. Finally, combining with the actual data of a steel enterprise to verify, the results show that the model meet the actual working condition, it can provide relevant processing methods for the soft measurement of complex industrial production control process, and it has some practical significance for intelligent optimisation control.

Active hand prostheses controlled using surface electromyography (SEMG) signals have been used for decades to restore the grasping function. Amputees with myoelectric hands wish to control the prostheses according to their own will and act like human hands as much as possible. Therefore, substantial research efforts have been put forth to advance the control of myoelectric hands. However, the tactile sensing and feedback of the myoelectric hands are still missing, thus limit hand grasp capabilities. In fact, integration of tactile sensing and feedback with hand prostheses plays an important role in improving the manipulation performance and enhancing perceptual embodiment for users. This paper reviews current state-of-the-art of tactile sensing technologies, including tactile sensor types and integration methods. Then, it introduces the basic theory of SEMG signals and presents an overview of the sensory feedback employed to prosthetic hand. The paper concludes with a detailed discussion of challenging issues and future developments.