Springer Science and Business Media LLC

Controlling human-like robots with musculoskeletal structure has been a challenging problem in engineering. In biological studies, motor synergy hypothesis has been proposed as a solution in order to control high degree-of-freedom and complex human body. In this paper, we focus on exploring the applicability of motor synergies in generating goal-directed movements by optimal control in a human-like robotic arm. We focus on three problems: 1) Can motor synergies facilitate the solving of optimal control problem? 2) What properties should motor synergies have in order to achieve tasks? 3) How should motor synergies be utilized better? For the first problem we show that goal-directed movements can be achieved by utilizing motor synergies which have properties of achieving the goals. For the second problem, we testify motor synergies which have different properties and discover that energy efficiency is an important aspect in motor synergies which can also be utilized to achieve goal-directed movements. This discovery also implies that we can obtain motor synergies by other ways rather than from goal-directed optimal control signals. For the third problem, we show that the control complexity can be further reduced by utilizing a subset of motor synergies which are effective to achieve goals.

A novel approach to estimate reliability properties of systems or components individually during operation is presented. It is distinguished between slow and fast reliability states based on an equivalent system representation. Conditions for their observability and control are given and objectives for optimal reliability-based control are discussed in general.

Purcell’s swimmer was proposed by E. M. Purcell to explain bacterial swimming motions. It has been proved experimentally that a swimmer of this kind is possible under inertial-less and high viscous environment. But we could not investigate all the aspects of this mechanism through experiments due to practical difficulties. The computational fluid dynamics (CFD) provides complementary methods to experimental fluid dynamics. In particular, these methods offer the means of testing theoretical advances for conditions unavailable experimentally. Using such methodology, we have investigated the fluid dynamics of force production associated with the Purcell’s swimmer. By employing dynamic mesh and user-defined functions, we have computed the transient flow around the swimmer for various stroke angles. Our simulations capture the bidirectional swimming property successfully and are in agreement with existing theoretical and experimental results. To our knowledge, this is the first CFD study which shows the fact that swimming direction depends on stroke angle. We also prove that for small flapping frequencies, swimming direction can also be altered by changing frequency-showing breakdown of Stokes law with inertia.

This paper studies an adaptive regulation problem for the modified FitzHugh-Nagumo neuron model under external electrical stimulation. We first present the solution of the global robust output regulation problem for output feedback system with an uncertain exosystem which models the external electrical stimulation with unknown frequency and amplitude. Then, we show that the robust control problem for the modified FitzHugh-Nagumo neuron model can be formulated as the global robust output regulation problem and the solvability conditions for the output regulation problem for the modified FitzHugh-Nagumo neuron model are all satisfied. Then, we apply the obtained output regulation result to constructing an output feedback control law for the modified FitzHugh-Nagumo neuron model to achieve global stability of the closed-loop system in the presence of uncertain parameters and external stimulus. An example is given to show that the proposed algorithm can completely reject the external electrical stimulation.

Cloud computing can provide a great capacity for massive computing, storage as well as processing. The capacity comes from the cloud computing system itself, which can be likened to a virtualized resource pool that supports virtualization applications as well as load migration. Based on the existing technologies, the paper proposes a resource virtualization model (RVM) utilizing a hybrid-graph structure. The hybrid-graph structure can formally represent the critical entities such as private clouds, nodes within the private clouds, and resource including its type and quantity. It also provides a clear description of the logical relationship and thedynamic expansion among them as well. Moreover, based on the RVM, a resource converging algorithm and a maintaining algorithm of the resource pool which can timely reflect the dynamic variation of the private cloud and resource are presented. The algorithms collect resources and put them into the private cloud resource pools and global resource pools, and enable a real-time maintenance for the dynamic variation of resource to ensure the continuity and reliability. Both of the algorithms use a queue structure to accomplish functions of resource converging. Finally, a simulation platform of cloud computing is designed to test the algorithms proposed in the paper. The results show the correctness and the reliability of the algorithms.

Face recognition (FR) is a practical application of pattern recognition (PR) and remains a compelling topic in the study of computer vision. However, in real-world FR systems, interferences in images, including illumination condition, occlusion, facial expression and pose variation, make the recognition task challenging. This study explored the impact of those interferences on FR performance and attempted to alleviate it by taking face symmetry into account. A novel and robust FR method was proposed by combining multi-mirror symmetry with local binary pattern (LBP), namely multi-mirror local binary pattern (MMLBP). To enhance FR performance with various interferences, the MMLBP can 1) adaptively compensate lighting under heterogeneous lighting conditions, and 2) generate extracted image features that are much closer to those under well-controlled conditions (i.e., frontal facial images without expression). Therefore, in contrast with the later variations of LBP, the symmetrical singular value decomposition representation (SSVDR) algorithm utilizing the facial symmetry and a state-of-art non-LBP method, the MMLBP method is shown to successfully handle various image interferences that are common in FR applications without preprocessing operation and a large number of training images. The proposed method was validated with four public data sets. According to our analysis, the MMLBP method was demonstrated to achieve robust performance regardless of image interferences.

Contract is a common and effective mechanism for supply chain coordination, which has been studied extensively in recent years. For a supply chain network model, contracts can be used to coordinate it because it is too ideal to obtain the network equilibrium state in practical market competition. In order to achieve equilibrium, we introduce revenue sharing contract into a supply chain network equilibrium model with random demand in this paper. Then, we investigate the influence on this network equilibrium state from demand disruptions caused by unexpected emergencies. When demand disruptions happen, the supply chain network equilibrium state will be broken and change to a new one, so the decision makers need to adjust the contract parameters to achieve the new coordinated state through bargaining. Finally, a numerical example with a sudden demand increase as a result of emergent event is provided for illustrative purposes.

Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life (RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network (LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure. In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.