Search

In robotics, decoding complex human movements of multiple degrees of freedom (DOFs) from surface electromyography (sEMG) remains challenging. Recently, the rapid development of artificial intelligence (AI) technology provides a new solution to this problem. In this paper, we propose an AI-based framework that consists of a series of deep learning approaches, for achieving a precise and robust decoding on three-dimensional (3D) wrist movements. A previously developed device (WMD) was utilized to tag the myoelectric signals with 3D kinematic labels. A public dataset (HIT-SimCo) was established, wherein the sEMG signals were collected from diverse wrist movements and multiple subjects. A lightweight convolutional neural network (CNN) was constructed, which can extract the motion-related features directly from raw sEMG, and make motion predictions in an end-to-end manner. In addition, several data augmentation strategies were explored to improve the robustness of the model against environmental variations; and a fine-tuning policy was proposed to further improve the subject-specific accuracy. The decoding model was finally tested in three scenarios: a robotic arm/hand system performing daily-living tasks (pouring, screwing, etc.), a supernumerary robotic hand playing blocks-building, and a virtual prosthesis conducting motor rehabilitation training.

Pattern recognition (PR) methods are commonly utilized in the extraction of motion intentions from myoelectric signals, which is realized by relating several electromyogram (EMG) patterns to specific types of motion. Researchers have reported that the hand-engineering features widely used in PR-based methods can be significantly affected by external confounding factors that diminish their accuracy and robustness in clinical settings. Moreover, since only simple mapping is carried out from the feature space to the task space (involving, for the most part, discrete motion intentions), there is no opportunity to exploit fully the underlying mechanism of synergic neuromuscular control. Inspired by deep learning, we have proposed a novel convolutional neural network (CNN) structure based on the characteristics of raw EMG signals that can effectively decode complex wrist movements with three degrees of freedom (DOF) directly from raw EMG signals rather than relying on hand-engineering features. Our method has the potential to incorporate more information than other models by enlarging the training dataset. We demonstrate here that our method performs significantly better (in terms of R 2) than the current state-of-art regression method (i.e., support vector regression), especially when confounding factors are involved. We further found that this CNN-based decoding method can be generalized when multiple healthy subjects are taken into account. For a new subject, our method can provide an appropriate control over 3-DOF cursor movements on a screen even without a specific training.

We design novel graphyne/blue phosphorene van der Waals (vdW) heterostructures (G/BP) under the external electric fields (Efield) and strains. It is demonstrated that the G/BP is a type-I semiconductor with a direct band gap of approximately 0.378 eV. The external Efield and strains not only have important effects on the band structure which undergoes a fascinating direct-indirect and semiconductor-metal transitions, but also influence the band alignment which experiences transition from type-I to type-II. In brief, the features of tunable bandgap and controllable band alignment endow G/BP with potential for application in future optoelectronic devices.

Steady state numerical simulations were carried out with twisted tapes on shell side of a shell and tube heat exchanger. Thermal hydraulic performance of a heat exchanger with various twisted tape widths and twist ratios was conducted. The effects of structural modifications were investigated on the heat exchanger performance. Limited lab-scale experiments, using water as working fluid, were carried out to validate the numerical simulations. The entire model meshing method was adopted to mesh the geometry with Gambit 2.4 software while k- e realizable model was adopted. Combining effects of both the geometric and structural modifications, an improved design with optimum comprehensive performance was suggested.

For a multi-fingered robot hand, the individual control over single joints cannot guarantee their fine collaboration. For achieving a high-precision synchronization, a theory of synchronous control is introduced to multi-fingered robot hands. This paper introduced a new model-free and cross-coupling control strategy. It had been tested on the humanoid robot fingers and showed high positioning performance. For realizing the mutual influence between the control of all joints, we establish the synchronization error by the differential disposal of adjacent actuator errors, then position errors and synchronization errors are incorporated into a unified control frame. Meanwhile, considering the complex dynamic formulations of the dexterous hand and the characteristics of the control system, a model-free, cross-coupled trajectory tracking method is introduced and the explicit dynamic modeling parameters is not necessary. Finally, we tested our method on a multi-fingered hand platform HIT/DLR-II. The results prove that the new method has superior performance over traditional non-synchronous approaches.

Pultruded sandwich panel (PSP) usually has light weight, but its flexural performance is limited by the weak shear strength. Filling into expanded polystyrene (EPS) mortar can effectively improve the mechanical properties. The bending performance and failure mode of the EPS mortar-filled PSP were investigated by flexural testing. The ultimate bearing capacity improved from 49.562 kN for unfilled PSP to 72.065 kN for EPS mortar-filled PSP. The equivalent shear modulus is increased from 140.068 to 354.685 MPa. Increasing the EPS mortar’s density, the failure of EPS mortar-filled PSP would change from core shear failure, local indentation failure to overall shear failure. According to the thermal insulation tests, which shows EPS mortar-filled PSPs have excellent thermal insulation, the thermal insulation performance of different types of filler PSPs are obtained. The research on the mechanical and thermal insulation properties of EPS mortar-filled PSP can provide technical support for its application in engineering.

Objective. The purpose of this study was to observe the dental arch asymmetry in 12-year-olds with normal occlusion during the early permanent dentition stage. Methods. Ninety-two 12-year-old students (46 males and 46 females) who had normal occlusion during early permanent dentition were selected from a junior high school in Tangshan, China. Once per year for three consecutive years, a dental cast was obtained from each subject, and the cast was scanned with a 3D digital scanner (R700 3D). The median palatal plane (MPP) and transverse palatal plane (TPP) were used as the reference plane for the transverse and anteroposterior measurements, respectively. Results. Most of the dental arch asymmetry indicators decreased with age, but these differences were not statistically significant. The values of the midincisal edge of the upper central incisors (U1), midincisal edge of the upper lateral incisors (U2), upper canine cusp tip (U3), upper first premolar buccal cusp tip (U4), upper second premolar buccal cusp tip (U5), upper first molar mesiobuccal cusp tip (U6MB), and upper first molar distobuccal cusp tip (U6DB) to the TPP were 0.019 mm, 0.279 mm, 0.017 mm, 0.016 mm, 0.016 mm, 0.027 mm, and 0.200 mm, respectively; these values were larger in males than in females ( P < 0.05 ). The values of 2–5, 6MB, and 6DB-TPP were 0.154 mm, 0.102 mm, 0.119 mm, 0.259 mm, 0.206 mm, and 0.123 mm, respectively, larger in the mandibular than in the maxillary dental arch ( P < 0.05 ). The values of the midincisal edge of the lower central incisors (L1), midincisal edge of the lower lateral incisors (L2), lower canine cusp tip (L3), lower first premolar buccal cusp tip (L4), lower second premolar buccal cusp tip (L5), lower first molar mesiobuccal cusp tip (L6MB), and lower first molar distobuccal cusp tip (L6DB) to the MPP were 0.399 mm, 0.197 mm, 0.258 mm, 0.248 mm, 0.214 mm, 0.575 mm, and 0.531 mm, respectively, larger than L1-5, L6MB, and L6DB-TPP ( P < 0.05 ). Conclusion. The asymmetry of the dental arch in 12-to-15-year-olds with normal occlusion did not change significantly with age. The anteroposterior asymmetry of the maxillary dental arch is larger in males than in females. With the exception of the central incisor, the anteroposterior asymmetry of the mandibular dental arch is larger than that of the maxillary dental arch. The transverse asymmetry of the mandibular dental arch is larger than the anteroposterior asymmetry.

Artificial intelligence provides new feasibilities to the control of dexterous prostheses. To achieve suitable grasps over various objects, a novel computer vision-based classification method assorting objects into different grasp patterns is proposed in this paper. This method can be applied in the autonomous control of the multi-fingered prosthetic hand, as it can help users rapidly complete “reach-and-pick up” tasks on various daily objects with low demand on the myoelectric control. Firstly, an RGB-D image database (121 objects) was established according to four important grasp patterns (cylindrical, spherical, tripod, and lateral). The image samples in the RGB-D dataset were acquired on a large variety of daily objects of different sizes, shapes, postures (16), as well as different illumination conditions (4) and camera positions (4). Then, different inputs and structures of the discrimination model (multilayer CNN) were tested in terms of the classification success rate through cross-validation. Our results showed that depth data play an important role in grasp pattern recognition. The bimodal data (Gray-D) integrating both grayscale and depth information about the objects can improve the classification accuracy acquired from the RGB images (> 10%) effectively. Within the database, the network could achieve the classification with high accuracy (98%); it also has a strong generalization capability on novel samples (93.9 ± 3.0%). We finally applied the method on a dexterous prosthetic hand and tested the whole system on performing the “reach-and-pick up” tasks. The experiments showed that the proposed computer vision-based myoelectric control method (Vision-EMG) could significantly improve the control effectiveness (6.4 s) , with comparison to the traditional coding-based myoelectric control method (Coding-EMG, 13 ${s}$ ).

Large-aperture space telescope is the key equipment for studying the origin and evolution of the universe and various celestial bodies. Developing large-aperture space telescope is challenging and has become a priority in the field of optical engineering. Due to the mass and volume constraints of launch vehicles, it has to be designed and launched in modules and then assembled on-orbit by space robots. In this paper, we present a conceptual of 10-meter diameter modular space telescope together with robotic assembly strategy. To fulfill on-orbit assembly and maintenance of the space telescope, a novel assembly robot with a ring-shaped mobile base and a redundant stretchable manipulator is proposed. The time-optimal trajectory planning based on genetic algorithm is utilized to achieve efficient assembly. The preliminary ground experiment of assembling submirror modules of space telescope using the KUKA LWR iiwa-7 is carried to verify the feasibility of the proposed method, and the results show that the assembly task of telescope modules can be accomplished successfully with appropriate forces and torques.

Traditional electromyogram (EMG) pattern recognition does not take into account the effect of confounding factors, preventing its effective clinical application. In this paper, we investigated EMG pattern recognition of compound confounding factors including electrode shift, force variation, limb posture and temporary drift. We started with feature extraction, to identify suitable representations to resolve classification degradation caused by electrode doffing. Then, we proceeded to examine a variety of classifiers, to achieve a favorable classification accuracy (CA) when switching the control arms (dominant and nondominant) without additional calibration. Lastly, we investigated adaptive learning strategies, to mitigate the decline in CA over a relatively long duration (one day) of use. Two data-collection protocols (electrode doffing and donning, and one-day-long collection) were applied, and EMG datasets collected in these two protocols were arranged into three scenarios for progressive validation of the features, classifiers, and adaptive learning methods. Our results showed that the features extracted from the frequency domain improved the CA by 30% in comparison with their original time-domain representations (14 motions, 67.5 ± 9.8%, p

For describing the state of the wrist, either the force or movement of wrist can be measured as the training target in the simultaneous electromyography control. However, the relationship between the force and movement is so complex that only the force or movement is not precise enough to describe its actual situations. In this paper, we propose a novel platform that can acquire three degrees of freedom (DOF) wrist motion/force synchronously with multi-channel electromyography signals in a hemi-constraint way. The self-made wrist force-movement mapping device establishes a stable relationship between the wrist movement and force. Meanwhile, the elicited wrist movement can be directly fed back to the subjects via laser cursor. The information of the cursor can directly reflect the 3-DOF movement of the wrist without any decoupling algorithms. Through this platform, the support vector regression model learned from the training data can well predict the arbitrary combinations of 3-DOF wrist movements. The cross-validation result indicates that the regression accuracy of free 3-DOF movements can reach a similar performance to that of 2-DOF regular movements (in terms of R2, regular movement vs. free movement, p > 0.1).

A new type of Ag/AgCl electrode as a marine electric field sensor is prepared using electrospray. The surface of the electrode is porous, and the particle size of AgCl is small and uniform with an average particle size of 1.43 µm, which accelerated the speed of the oxidation-reduction reactions. Therefore, the electrode with large specific surface area has high stability and low noise. The impedance, sensitivity, self-noise, and stability of the electrode are measured to study the electrochemical performance of the electrode. The impedance of the electrode is 7.9 Ω, and the electrode shows resistance characteristics, meaning that the electrode can well receive the weak ocean electric field signals with low signal distortion. The sensitivity experiment result shows that the electrode can well restore the sinusoidal electric field signal of 1 Hz (10 mV). The voltage drift is less than 5 µV/100 h, the self-potential is between -51 and 56 µV, and the self-noise of the electrode is 2.48 nV @ 1 Hz. The AgCl layer on the surface of the electrode is porous and thick, and the particle size of AgCl is small and uniform. This makes the electrode have excellent electrochemical performance. All the experimental results show that the electrode has ultra-low noise and excellent response to low frequency weak electric field signals. The electrode is of great significance to the exploitation of marine resources as the marine electric field sensor.

As one of the key technologies of large-scale machinery construction, welding technology has been attached great importance to by shipbuilding enterprises all over the world. With the general progress and development of welding intelligent automation technology, most of the welding work in simple operating environment is gradually transformed from manual operation to welding robot. Through the integration of automation to operate it, so the detection of welding quality is also an important part. Because of the complex physical and chemical reactions in the welding process, a variety of information sources can be extracted for analysis and identification. In the process of robot welding, the accurate extraction and analysis of the effective feature information in the dynamic process of welding are an effective guarantee for the automation and intelligence of welding. In this part, the detection of weld defects can help the welding quality from another aspect. Through the real-time monitoring and analysis of defects, the corresponding features can be extracted to the penetration of the dynamic process of welding. Status provides reference significance.

The state-of-art dexterous prosthetic hand with multi-degree-of-freedom urgently requires a control method to match its dexterity. The purpose of this paper is to develop a method on rapid grasp control of prosthetic hand based on eye-tracking and verify its effectiveness by experiments. Firstly, an interactive experiment platform was established. Based on the platform, the operator can select the necessary grasping pattern from a graphic interface through eye movement and trigger the selected pattern by gazing the target for a short time (~300ms). The selected grasp pattern will be fed back to the operator during the hand prosthesis performing reach-and-grasp tasks. Finally, to verify the effectiveness of this method, experiments were conducted by an able-bodied participant wearing the prosthesis to grasp some daily-used objects. Our result shows that, by adopting the eye tracking technology in the human visual channel, the grasp control of the prosthesis can be supervised and regulated in real-time and thus an effective interaction between the dexterous prosthesis and the operator can be achieved.

Recently, the Supernumerary Robotic Limbs (SRL) have emerged in the field of robotics for enhancing the user's ability without replacing natural limbs. Lots of researches have contributed to the mechanical design and control of SRL. However, building an artificial sensory nerve pathway of SRL has been largely ignored. In this paper, we built a multi-modal sensory feedback pathway for SRL. Three kinds of sensory pathway contained 1) fingertip pressure, 2) slippage and 3) proprioception information are built under the consideration of SRL platform through electrical stimulation. According to the experiment results of event-related potential (ERP) amplitude, establish the relationship between the physical parameters of the stimulus and the sensation. In addition, enhancement potentials of human sensation based on the brain-computer interface (BCI) is found in two aspects contained: 1) improvement of subjective perception resolution; 2) Shorten the time of sensory recognition. This paper can be used to objectively benchmark the human subjective and individualized sensation, and guide researchers to build the SRL sensory feedback pathways.

Due to poor robustness, instability, and the heavy burden of use, the traditional myoelectric control method is still powerless in the face of the control of the dexterous prosthetic hand. To solve this problem, a new method (CVEEI), that combines computer vision, eye tracking, electromyogram (EMG) and IMU was proposed in this paper. Firstly, through gazing (eye-tracking) in front of the screen, the grasping pattern of the objects can be fed back to the prosthetic hand controller; Then, the prosthetic hand can be controlled to transport the object to the position expected, on collaboration of both EMG and IMU. In this process, the grasping pattern of all objects can be recognized by computer vision in real-time. Importantly, through comparing the traditional EMG control method (co-contraction to switch) in the transport experiment of the objects, the superiority of this new method in operating the dexterous prosthetic hand was further verified (fast > 1s/single object) in this paper.

It is a question of great import for the dexterous prosthesis to realize its continuous, simultaneous and proportional control of multi-DOFs so that its rehabilitation function can be well achieved and its user acceptance rate can be improved. In this paper, we propose a new method, CNMF-HP, which integrates the constrained non-negative matrix factorization (NMF) and the Hadamard product, for estimating 2-DOF wrist movements (DOF-1, flexion/extension; DOF-2, adduction/abduction) from myoelectric signals. Based on this method, the simultaneous control of a multi-fingered prosthesis is verified in this paper. Through establishing the mapping relationship between the wrist movements and the finger movements of the dexterous prosthesis, two grasp control strategies (synchronous and asynchronous) are proposed and successfully applied in operating the dexterous prosthesis to grasp four kinds of objects of different grasping patterns (cylindrical, spherical, tripod and lateral). Then, the performance of different strategies (synchronous/asynchronous control) in the grasping process is compared in terms of the completion time and completion rate of the tasks, in this way the superiority of EMG control proposed in this paper is further verified.

Transferring versatile skills of human behavior to teleoperate manipulators to execute tasks with large uncertainties is challenging in robotics. This paper proposes a hybrid mapping method with position and stiffness for manipulator teleoperation through the exoskeleton device combining with the surface electromyography (sEMG) sensors. Firstly, according to the redefinition of robot workspace, the fixed scale mapping in free space and virtual impedance mapping in fine space are presented for position teleoperation. Secondly, the stiffness at the human arm endpoint is predicted and classified into three levels based on the K nearest neighbor (KNN) and XGBoost, and the stiffness mapping method is utilized to regulate the stiffness behavior of manipulator. Finally, the proposed method is demonstrated in three complementary experiments, namely the trajectory tracking in free space, the obstacle avoidance in fine space and the human robot interaction in contact space, which illustrate the effectiveness of the method.

Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia.

In this paper, we investigated the emission spectra of plasmas produced from femtosecond and nanosecond laser ablations at different target temperatures in air. A brass was selected as ablated target of the experiment. The results indicated that spectral emission intensity and plasma temperature showed similar trend for femtosecond and nanosecond lasers, and the two parameters were improved by increasing the sample temperature in both cases. Moreover, the temperature of nanosecond laser-excited plasma was higher compared with that of femtosecond laser-excited plasma, and the increase of the plasma temperature in the case of nanosecond laser was more evident. In addition, there was a significant difference in electron density between femtosecond and nanosecond laser-induced plasmas. The electron density for femtosecond laser decreased with increasing the target temperature, while for nanosecond laser, the electron density was almost unchanged at different sample temperatures.

To better restore human hand function, advanced hand prostheses should be able to deal with a variety of daily living conditions. In this paper, we addressed myoelectric signal variations introduced by different muscle contractions, dynamic arm movements, and outer interfering forces in the practice of pattern recognition-based myoelectric control schemes. We examined four different training paradigms (data-collection protocols) and quantified their effectiveness for obtaining a robust classification. We further depicted the classification accuracy according to different arm/wrist motion primitives. Our results indicate the training paradigm that collects myoelectric signals on dynamic arm postures and varying muscular contractions (DPDE) can largely mitigate the motions' misclassification rate. The misclassification rate of finger motions seems to highly correlate to wrist pronation and supination, rather than different arm positions. Combining proprioceptive information, such as the hand's orientation, with myoelectric signals for classification only slightly alleviates the misclassification rate.

In order to promote the development of individualized, accurate and intelligent physical education teaching, combined with artificial intelligence technology, the current physical education teaching mode has been improved. Through the establishment of an artificial intelligence educational robot based on voice interaction, a hybrid physical education teaching mode is constructed to realize personalized education for students. First, the speech recognition system is designed from three aspects of speech recognition, interaction management and speech synthesis, and the accuracy of recognition is improved by algorithm. Second, a new mode of hybrid physical education teaching is constructed. Through intelligent information technology, the advantages of traditional physical education teaching are combined to improve the classroom efficiency of physical education teaching and personalized education ability for students. Finally, the relevant experimental scheme and questionnaire are designed, and the actual situation of an educational robot introduced into physical education teaching is investigated and evaluated. The results show that the recognition accuracy of the artificial intelligence speech recognition system can reach more than 90%. It can communicate well with students and answer students’ questions. An educational robot is introduced into physical education teaching, and students’ learning attitude and interest are evaluated. The results show that before and after the introduction of an educational robot in physical education teaching, the average score of students’ learning interest increases by 21 points, and the average score of learning attitude increases by 9.8 points. Therefore, the introduction of an artificial intelligence educational robot based on voice interaction in physical education teaching can help to improve the classroom efficiency of physical education teaching and students’ interest. This study provides a reference for the development of artificial intelligence teaching and promoting the development of artificial intelligence.

Financial flow is an important part of supply chain management (SCM) and increasingly playing a crucial role as the amount of global trade increases. Reasonable and scientific financial operation is necessary in closed-loop supply chain management, especially when customer demand is uncertain. However, financial flow, which may lead to an increase in effectiveness, has rarely been considered in the literature. In this paper, we present a closed-loop supply chain design with financial management problem, which is tackled as a stochastic programming model with ambiguity demand set. The main contributions of this work include: (i) A joint chance constrained programming model is proposed to maximize the total profit, and (ii) financial flow and uncertain demand are both taken into consideration. According to the characteristic of the problem, we chose four approaches, namely sample average approximation (SAA), enhanced sample average approximation (ESAA), Markov approximation (MA), and mixed integer second-order conic program (MI-SOCP). Computational experiments were conducted to compare the adopted methods, and 10,000 scenarios were generated to examine the reliability of the methods. Numerical results revealed that the Markov approximation approach can achieve more reliable solutions.

Objective. The purpose of this study was to observe the three-dimensional growth and development of the maxillary arch in 10-year-olds with normal occlusion during the late mixed dentition stage. Methods. Forty-four 10-year-old students (22 males and 22 females) who had normal occlusion during late mixed dentition were selected from an elementary school in Beijing, China. Once per year for three consecutive years, a dental cast was obtained from each subject, and the cast was scanned with a 3D digital scanner (R700 3D). The three-dimensional measurements of the maxillary dental arch and the inclination of the bilateral maxillary first molars were obtained from the digital model. Results. The upper anterior arch length (UAAL), upper total arch length (UTAL), upper inter primary or permanent canine width (UICW), upper intermolar width (UIMW), and upper dental arch length (UDAL) increased by 0.959 mm, 0.583 mm, 0.955 mm, 1.462 mm, and 2.46 mm, respectively, over the two years (P<0.001). UR6BL and UL6BL decreased by 4.416° and 7.133°, respectively, over the two years (P<0.001). The values of the UICW and UIMW were 1.67 mm and 1.86 mm, respectively, larger in males than in females at 12 years old (P<0.01). The change in the UTAL was 0.431 mm greater in males than in females over the 2 years (P<0.05). Conclusion. The UAAL, UTAL, UICW, UIMW, and UDAL in 10- to 12-year-olds with normal occlusion increased with age. The buccolingual inclination of the bilateral maxillary first molars inclined to the palatal side with age. The UICW and UIMW were larger in males than in females at 12 years old. The male UTAL increased more than the female UTAL over the 2 years.

Benefiting from the powerful learning capacity of deep learning (DP), a general model for predicting 3-DOF wrist movements could achieve good prediction accuracy for those subjects even not involved in the training. This paper tends to verify this assumption. Since the quantity of training dataset in DP largely influences the model's performance, a limited dataset collected from a number of subjects should be extended first through some data-augmentation approaches. In this paper, we first summarized the possible mistakes happen in the EMG data-collection procedures. Then, according to these mistakes, we designed six data-augmentation approaches to expend our EMG dataset. Through experiments, we found the prediction accuracy can be improved when some approaches are introduced during training. As well, the model robustness could also be improved when the same mistakes occur in the predicting process. With regards to those approaches, placing all electrodes in opposite direction and random switching two channels have significant positive effect on both accuracy and robustness. These two data-augmentation approaches are highly advocated in the pre-processing of the training data for DP-based prediction models.

This paper presents a new high precision binocular camera calibration method. In the classical calibration method, the camera parameters are usually obtained by the optimization calculation of the re-projection error. However, for the system used for three-dimensional optical measurement, this error does not represent the result of three-dimensional reconstruction. In this method, a specially designed calibration board is photographed at multiple angles. (there is no strict requirement in the calibration process.), the intrinsic parameters of the two cameras are obtained by the image. Then, the rotation matrix and translation matrix between the two cameras are computed by the centroid distance increment matrix method. Thus the degree of coupling between the parameters is reduced. At the same time, the space intersection method is used to reconstruct the 3D coordinates of calibration points. Finally, the reconstruction error is calculated and minimized to optimize the calibration parameters. This error directly reflects the efficiency of 3D reconstruction and is more suitable for the quality evaluation of binocular camera calibration. Experiments show that this method is simple, accurate and significantly improves the accuracy.

The development of advanced and effective human-machine interfaces, especially for amputees to control their prostheses, is very high priority and a very active area of research. An intuitive control method should retain an adequate level of functionality for dexterous operation, provide robustness against confounding factors, and supply adaptability for diverse long-term usage, all of which are current problems being tackled by researchers. This paper reviews the state-of-the-art, as well as, the limitations of current myoelectric signal control (MSC) methods. To address the research topic on functionality, we review different approaches to prosthetic hand control (DOF configuration, discrete or simultaneous, etc.), and how well the control is performed (accuracy, response, intuitiveness, etc.). To address the research on robustness, we review the confounding factors (limb positions, electrode shift, force variance, and inadvertent activity) that affect the stability of the control performance. Lastly, to address adaptability, we review the strategies that can automatically adjust the classifier for different individuals and for long-term usage. This review provides a thorough overview of the current MSC methods and helps highlight the current areas of research focus and resulting clinic usability for the MSC methods for upper-limb prostheses.

How to design a robotic hand for reproducing the move characteristics of human hand joints is a big challenge in robotics. In this paper, we present an approach to determine the actuation configuration based on the statistical results of hand joint angle in different grasps. A relationship between the basic statistical metrics and actuation configuration strategies is built. In this case, a novel actuation configuration is proposed and the joints of four fingers are arranged into five actuation modules. For the mechanical implementation, the motion of human four finger joints is decomposed to proportion motion, differential motion and chain proportion motion, mechanically implemented by pulley, planetary gear differential module and gear transmission chain. Finally, the implemented mechanism is embedded in palm, and the mechanical implementation to the human hand move characteristics is verified by the measured joint angles of the robotic hand when actuators separately move along PC1 and PC2. Meanwhile, the robotic hand can grasp different objects with a versatile grasp function.

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).

Attributed to its superior dexterity, the human’s thumb plays a significant role in the activities of daily living (ADLs). In order to promote the operational capability of the prosthetic hand, state-of-the-art designs tend to integrate multiple degrees of freedom (DOFs) in thumb’s trapeziometacarpal (TM) joint for achieving versatile movements. However, without proper control methods, this fine dexterity of multi-DOF prosthetic hand is hard to be demonstrated. This problem becomes more serious when the control signals, electromyography (EMG), are highly limited on the residual stump of the amputee. Through experiments, the paper examines the feasibility of recognizing 4 thumb motions (flexion/extension and abduction/adduction) together with the other 12 finger- and wrist-related motions under a general surface EMG configuration. After surveying the classification accuracy of several selective motion groups (thumb motions, thumb-involved finger motions, thumb-involved wrist motions, etc.), we report here ...

Purpose – The purpose of this paper is to present a five-fingered, multisensory prosthetic hand integrating both intuitive myoelectric control and sensory feedback. Design/methodology/approach – The artificial hand’s palm has a three-arcuate configuration and the thumb can move along a cone surface, improving the resemblance with the biological hand. By using a coupling linkage mechanism, each finger is independently actuated by a direct current motor. Both torque and position sensors are embedded in the finger to sense the hand’s status and its interaction with the outer environment. The proposed human-in-the-loop control system consists of an internal motion control scheme and an external human–machine interface. The pattern recognition-based electromyography (EMG) control scheme is adopted to control the motion of the hand, and the transcutaneous electrical nerve stimulation (TENS) is utilized to feedback the hand’s sensory information to its user. Findings – The hand prototype shows that it has an anthropomorphic appearance (85 per cent to an average human hand), low weight (420 g), great power (10 N on the fingertip) and eligible dexterity. Clinical evaluation of the prosthetic hand on transradial amputees also approves the hand design. Originality/value – From a systematic view, the paper details the design concepts of the HIT–DLR prosthetic hand IV, especially on its appearance, mechanism, myoelectric control and sensory feedback.

In this paper, we attempted to evaluate the performance of an electromyography (EMG)-controlled 5-DOF prosthetic hand on ten transradial amputees. The proposed prosthesis is composed of a five-fingered hand, a passive wrist, and a customized socket for each subject. The EMG control methods included both a commonly used pattern recognition-based scheme (DD-SVM) and a novel digital encoding strategy (double-channel template matching (DCTM)). A virtual 3D hand platform was developed for training the subjects and rapidly testing the control methods. For each subject, the performance of the EMG control methods was firstly measured by off-line classification accuracy; then, according to the accuracy, a particular control method was selected and embedded in the EMG controller for further validation on ordinary daily life activities. Our experiments were conducted to test not only the hand's grasp ability but also other multifinger cooperation skills. The result indicated that the subjects of rich control experience can accomplish several intuitive motion control over their hands. However, the kinds of the motions and their relative recognition accuracy may depend on some individual differences, such as the amputation level, the activity of the residual nerve-muscle system, and the richness of control experience. Meanwhile, the proposed digital encoding method, DCTM, which only utilized two channels of EMG, was necessary for those amputees with few available control signals. This paper suggested that the EMG control method should be differently considered according to the particular condition of each subject.

Deep learning (DL) has made tremendous contributions to image processing. Recently, the DL has also attracted attention in the specialized field of neural decoding from raw myoelectric signals (electromyograms, EMGs). However, to our knowledge, most existing methods require some measure of preprocessing of the raw EMGs. Moreover, research to date has not accounted for the variability in the signal during time sequences. In this paper, we propose a new convolutional neural network (CNN) structure that can directly process raw EMG signals for hand gesture classification. More specifically, we assess the effects of various window sizes and of two different EMG representations (time sequence and frequency spectra) on open EMG datasets. We found that the frequency spectra derived from raw EMGs is more suitable as the model input in the task of gesture classification. Meanwhile, the combination use of long window could improve the classification accuracy (CA) and the window of 1024 ms achieved the best results on two open datasets ([Formula: see text]% and [Formula: see text]%). Further, our model requires no feature extraction procedures and is comparable with the optimal combination of features and classifier used by the traditional methods in the performance of specific tasks.

This paper considers the distributed attitude tracking problem of multiple spacecraft with a leader whose control input is possibly nonzero, bounded, and not available to any follower. Based on the relative attitudes and angular velocities of neighboring spacecraft, we design a distributed discontinuous adaptive controller to each follower to guarantee that the attitude errors between the followers and the leader converge to zero for any communication graph containing a directed spanning tree with the leader as the root. To tackle the chattering effect caused by the discontinuous controller, we further propose a distributed continuous adaptive controller, under which both the attitude tracking errors and the adaptive gains are ultimately bounded.

Traditional electromyography (EMG) pattern recognition did not take into account confounding factors such as electrode shifting, force variation, limb posture, etc., which lead to a great gap between academic research and clinical practice. In this paper, we investigated the robustness of EMG pattern recognition under conditions of electrode shifting, force varying, limb posture changing, and dominant/non-dominant hand switching. In feature extraction, we proposed a method for threshold optimization based on Particle Swarm Optimization (PSO). Compared with the traditional trail & error method, it can largely increase the classification accuracy (CA) by 10.2%. In addition, the hybrid features integrated with discrete Fourier transform (DFT), wavelet transform (WT), and wavelet packet transform (WPT) were proposed, which increased the CA by 30.5%, 25.4%, 22.9%, respectively. We introduced probabilistic neural network (PNN) as a new classifier for EMG pattern recognition, and reported the CA’s obtained by a large variety of features and classifiers. The results showed that the combination of DFT_MAV2 (a novel feature based on DFT) and PNN reached the best CA (45.5%, 14 motions, validated on different hands without re-training).

The gold-coated metal thin films are widely used in modern engineering applications. In this paper, the ultrafast electron dynamics of gold-coated two-layer thin films has been investigated by ultrafast time-resolved pump–probe experiment. The dependence of the surface electron temperature on the film structure was considered based on the two-temperature model at the different two-layer film structure. The effect of laser fluence (3, 6 and 17 mJ/cm 2 ), and two-layer film thickness (the thickness of 50 nm and 100 nm gold layer) is considered. The theoretical predictions are compared with experimental data, which agree well with both thermal model and transient reflectivity.

It is known that the muscles actuate human hand joints with a coordinated manner and the hand postures can be reconstructed by a few (usually two) inputs through postural synergies, which has been applied in robotic hand design for reproducing the human versatile grasp function. As for the mechanical implementation, in terms of the joints implemented postural synergies, due to the excessive number of the output and the high contribution ratio of two inputs to one output, the mechanical implementation size is usually too bulky. In this paper, to make the implemented mechanism size smaller and possible to be integrated in the palm, we divided the joints of four fingers into five modules according to the statistical analysis results of each hand joint angle recorded from the human grasp experiment. In order to meet the three needs of prosthetic hand design: versatile grasp function, anthropomorphic physical property and easy-control, three different actuation configurations of postural synergies were evaluated. The actuation configuration of inter-module coordination shows an acceptable reconstruction error, a smaller implementation size and a keeping of the clear corresponding relation between principle components (PCs) and joints.

Due to the hand biomechanical constrains and coordinate manner of motor control, single one joint is difficult to move independently. The aim of this paper is to have an analysis on the joint independence of hand and wrist. At first, a grasp experiment under the tolerance grasping was completed, and the hand and wrist joint angles in different grasping postures were recorded for representing the functionality of human hand. Moreover, based on the recorded postures, the characteristics of each joint were analyzed from correlation and adaptability, for representing the joint inter-joint characteristic and self-characteristic, respectively. In this case, the joint independence was quantified through the joint independence-correlation and independence-adaptability. The quantified joint independence results show that the thumb, index finger and pinky had the highest independence from the view of finger, while from the joint view, the joint independence from high to low were: wrist, metacarpo-phalangeal (MCP), proximal interphalangeal (PIP) and abduction/adduction (ABD). Finally, based on the quantified joint independence, a preliminary actuation configuration was proposed.

Emergency service decision makers are facing the following problems: where to locate the ambulance facility and how many ambulance vehicles should allocate to the station in order to meet the patient needs effectively. In this paper, we study the problem of joint planning of emergency medical services management, which typically consist of emergency location planning and patients assignment to hospitals. Due to ambulance station response time and the shortest traveling time for correct hospital means life to the patient, especially for the patients with various severity of injuries. We have formulated a mixed integer program for the problem at hand, which tries to minimize the total cost for construction of ambulance facility location, ambulance vehicles, and the traveling time cost including the ambulance from station to patient point and from demand point to hospital. The problem is solved using some real-world data sets which have been collected through the Songjiang EMS system at Shanghai General Hospital. Finally, we compare the performance of the obtained results which corresponding to a series of location and allocation hospital with the existing emergency system in Songjiang district, the optimal ambulance station and vehicles allocation plan can cut down the government expenditures at least 3,100,000 RMB.

For controlling dexterous prosthetic hand with a high number of active Degrees of Freedom (DOF), it is necessary to reliably extract control volitions of finger motions from the human body. In this study, a large variety of finger motions are discriminated based on the diversities of the pressure distribution produced by the mechanical actions of muscles on the forearm. The pressure distribution patterns corresponding to the motions were measured by sensor array which is composed of 32 Force Sensitive Resistor (FSR) sensors. In order to map the pressure patterns with different finger motions, a multiclass classifier was designed based on the Support Vector Machine (SVM) algorithm. The multi-subject experiments show that it is possible to identify as many as seventeen different finger motions, including individual finger motions and multi-finger grasping motions, with the accuracy above 99% in the in-session validation. Further, the cross-session validation demonstrates that the performance of the proposed method is robust for use if the FSR array is not reset. The results suggest that the proposed method has great application prospects for the control of multi-DOF dexterous hand prosthesis.

Electronic structure and optical properties of CuGaS2 are calculated using the full potential linearized augmented plane wave plus local orbitals method. The calculated equilibrium lattice is in reasonable agreement with the experimental data. The electronic structures indicate that CuGaS2 is a semiconductor with a direct bandgap of 0.81802 eV. Furthermore, other experiments and theory also show that this material has a direct bandgap. It is noted that there is quite strong hybridization between Ga 3d and S 3s orbitals, which belongs to the (GaS2)−. The complex dielectric functions are calculated, which are in good agreement with the available experimental results.

A five-fingered, multi-sensory and biomimetic hand is presented. The cambered palm is specially designed by utilizing the knowledge of anatomy. The position of each finger is arranged according to human hand skeleton. Each finger with three phalanges could fulfill flexion-extension movement independently. All the fingers are designed using coupling link mechanism. The location of the thumb is designed by maximizing interaction area between the thumb and other fingers. The opposite thumb could grasp along a cone surface, while maintaining its function. The hardware architecture is divided into control system and human-machine interaction system. The mechanical parts, sensors and motion control systems are integrated in the hand structure. In addition, a skin-like glove is designed, which makes the hand more humanlike. According to our primary experimental results, the hand can accomplish several grasp modes by applying an intuitive surface electromyography control scheme. The success rate of 10 modes is up to 90%.

When developing a humanoid myo-control hand, not only the mechanical structure should be considered to afford a high dexterity, but also the myoelectric (electromyography, EMG) control capability should be taken into account to fully accomplish the actuation tasks. This paper presents a novel humanoid robotic myocontrol hand (AR hand III) which adopted an underactuated mechanism and a forearm myocontrol EMG method. The AR hand III has five fingers and 15 joints, and actuated by three embedded motors. Underactuation can be found within each finger and between the rest three fingers (the middle finger, the ring finger and the little finger) when the hand is grasping objects. For the EMG control, two specific methods are proposed: the three-fingered hand gesture configuration of the AR hand III and a pattern classification method of EMG signals based on a statistical learning algorithm – Support Vector Machine (SVM). Eighteen active hand gestures of a testee are recognized effectively, which can be directly mapped into the motions of AR hand III. An on-line EMG control scheme is established based on two different decision functions: one is for the discrimination between the idle and active modes, the other is for the recognition of the active modes. As a result, the AR hand III can swiftly follow the gesture instructions of the testee with a time delay less than 100 ms.

Cultural security is an important part of the non-traditional security. It is a problem that the national security needs to be considered. The cultural security is the 'basic value' and the 'cultural identity' of the 'national - nation' which is considered as a safe feeling. Endogenous security is always a difficult problem, in the cultural unity and the development of cultural diversity on the road, to build a new pattern through the perspective of cultural security.

As an emerging field of research, strategic entrepreneurship has gain lots of attention. However, the specific process of strategic entrepreneurship remains vague. This study try to unravel the process of strategic entrepreneurship through the view of ambidexterity and dynamic capability, a new framework of strategic entrepreneurship process combing the development of dynamic capability is raised.

We consider the modes of surface waves with the diffusion mechanism by the band transport model of the photorefractive effect and the nonlinear equation for the first time. We demonstrate the relationship between the modes of surface waves and the angles of incidence. The conditions for the realization of surface modes at the interface between photorefractive crystal and a metal, a linear dielectric with a lower refractive index, or another photorefractive crystal are discussed.

Photorefractive surface waves on the surface of Sr0.6Ba0.4NbO3 with a diffusion mechanism of nonlinearity have been observed. The formation time of the photorefractive surface waves is related to the intensity of the incident beam and the background illumination. The formation time of the photorefractive surface waves is about 40 min with a beam intensity of 3 mW, 25 min with 10 mW, and 30 s with 10 mW when an additional background illumination is applied. The experimental results showed that an appropriate background illumination will shorten the photorefractive response time.

In order to improve the prosthetic hand's design and usability, this paper presents a synthetic framework for evaluating the current prosthetic hands' anthropomorphic features with 12 quantified prosthetic hand anthropomorphism evaluation indexes (QPHAEIs). Our primary results show that a global anthropomorphic score (GAS) of the current commercial prosthetic hands is only 45.2%. The compliance, coupling speed ratio and configuration of the degrees of freedom (DOF) are found to be the lowest three anthropomorphism evaluation indexes in all QPHAEIs. Besides these significant findings, a consideration priority order in prosthetic hand's anthropomorphic index design is also proposed. Further correlation analysis indicates that evaluation methods using rotation axis distribution, rather than experimental grasp gesture, are more reliable to measure a prosthetic hand's GAS.

The distance between tip and sample can be regulated using piezoelectric quartz fork glued with micro optic fiber probe. A biquadrate vibration equation for the fork–probe–sample system is established to theoretically analyze the relations of the electric current flow through fork versus tip–sample (T–S) distance (I–d). The I–d curve and the action distance for shear force are influenced by environmental humidity. The results reinforce the opinions in the earlier works by other researches that the physical origin of the shear force is due to a material filling the tip–sample gap. Furthermore, the intrinsic reasons for shear force damping between tip and sample have been confirmed and developed, i.e. the water and hydrocarbon coupled between optic fiber probe and sample due to the capillary cohesion force.