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The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

Lead users are essential participants in crowdsourcing innovation events; their continuance intention significantly affects the success of the crowdsourcing innovation community (CIC). Although researchers have acknowledged the influences of network externalities on users' sustained participation in general information systems, limited work has been conducted to probe these relationships in the CIC context; particularly, the predictors of lead users' continued usage intention in such context are still unclear. Hence, this paper aims to explore the precursors of lead users' continuance intention from a network externalities perspective in CIC.

This work ranked users' leading-edge status to recognize lead users in the CIC. And then, the authors proposed a research model based on the network externalities theory, which was examined utilizing the partial least squares (PLS) technique. The research data were collected from an online survey of lead users (n = 229) of a CIC hosted by a China handset manufacturer.

Results revealed that the number of peers, perceived complementarity and perceived compatibility significantly influence lead users' continuance intention through identification and perceived usefulness.

This work contributes to the crowdsourcing innovation research and provides views regarding how lead users' sustained participation can be developed in the CICs. This work also offers an alternative theoretical framework for further research on users' continued intention in open innovation activities.

This study aims at investigating the differential impact of different CSR communication strategies on consumers' brand trust and consequent attitudes and behaviors in the credence goods market.

An experimental laboratory study was conducted to collect data from graduate students (n = 414) from two Chinese universities. Structural equation modeling was used to test the hypothesized model.

The results show that a dialogical communication strategy, compared to a monological communication strategy, is significantly more effective in increasing consumers' brand trust for credence goods as well as their willingness-to-buy and positive word-of-mouth. The results also confirm the mediating effect of CSR knowledge and the moderating effect of broad-scope trust on the relationship between CSR communication strategies and brand trust.

This study implies that in order to promote consumer trust and supportive behaviors toward brands, managers of companies in credence goods market should consider the increased use of various dialogical CSR communication strategies.

The study is one of the first to verify the impact of dialogical vs monological CSR communication strategies on consumers of credence goods.

The purpose of this study is to reveal the effect of nano-Al₂O₃ particle addition on the nucleation/growth kinetics, microhardness, wear resistance and corrosion resistance of Co–P–xAl₂O₃ nanocomposite plating.

The kinetics and properties of Co–P–xAl₂O₃ nanocomposite plating prepared by electroplating were investigated by electrochemical measurements, scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, Vickers microhardness measurement, SRV5 friction and wear tester and atomic force microscopy.

A 12 g/L nano-Al₂O₃ addition in the plating solution can transform the nucleation/growth kinetics of the plating from the 3D progressive model to the 3D instantaneous model. The microhardness of the plating increased with the increase of nano-Al₂O₃ content in plating. The wear resistance of the plating did not adhere strictly to Archard’s law. An even and denser corrosion product film was generated due to the finer grains, with a high corrosion resistance.

The effect of different nano-Al₂O₃ addition on the nucleation/growth kinetics and properties of Co–P–xAl₂O₃ nanocomposite plating was investigated, and an anticorrosion mechanism of Co–P–xAl₂O₃ nanocomposite plating was proposed.

This paper aims to investigate the stress corrosion cracking (SCC) process of sensitized 304 stainless steel during the slow strain rate test by using the electrochemical noise (EN) technique.

EN data are interpreted based on chaos and wavelet analyses, and correlation dimension and wavelet energy distribution are used as indicators for SCC process identification.

Experimental results reveal that the corrosion potential abruptly decreases from 180 to 100 mV at 6,300 s and the current increases from 10 to 100 nA accordingly, which is attributed to passive film breakdown and crack initiation. Chaos and wavelet analyses results reveal that, as crack initiates, the correlation dimensions increase from 1.2 to 1.9, and the corresponding distribution frequencies of maximum relative wavelet energy change from high frequency to low frequency.

SCC is monitored in lab, and crack initiation and propagation are identified by chaos and wavelet analyses. This work lays the foundation for SCC detection in field using EN.

This paper aims to study the effect of chloride concentration on the properties of passive film formed on Q235 steel in simulated concrete pore solutions.

Mott–Schottky analysis and electrochemical impedance spectroscopy were used to study the passive film of Q235 steel in simulated concrete pore solution. X-ray photoelectron spectroscopy was used to analyze the composition of passive film on Q235 steel.

When the chloride concentration is below the chloride threshold value, open circuit potential (OCP) and Rct gradually increases and donor concentration (N_D) remains unchanged with the increasing immersion time. When the chloride concentration exceeds chloride threshold value, OCP and Rct decreases after a temporary increase and N_D increases. The linear region of the Mott–Schottky curve lost its linearity. The electrochemical process control step is changed from charge transfer control to oxygen diffusion control. As the chloride concentration increases, the FeO content in the passive film increases and the Fe₂O₃ content decreases. Chloride can destroy the outer layer of passive film and introduce impurities.

The effects of chloride and immersion time on the change process of passive films on Q235 steel in simulated concrete pore solution were studied using electrochemical methods. The mechanism of chloride destroying passive film was analyzed.

This paper aims to present a control method to realize the constant force grinding of automobile wheel hub.

A constant force control strategy combined by extended state observer (ESO) and backstepping control is proposed. ESO is used to estimate the total disturbance to improve the anti-interference and stability of the system and Backstepping control is used to improve the response speed of the system.

The simulation and grinding experimental results show that, compared with the proportional integral differential control and active disturbance rejection control, the designed controller can improve the dynamic response performance and anti-interference ability of the system and can quickly track the expected force and improve the grinding quality of the hub surface.

The main contribution of this paper lies in the proposed of a new constant force control strategy, which significantly improved the stability and precision of grinding force.

Assembly Sequence Planning integrating assembly Resources (ASPR) is a trend in industry. Because of the introduction of resource, the complexity of ASPR for complex product increases drastically; besides, the dynamic property of resource and the co‐existence of assembly sequence and disassembly sequence (ASDS) make the problems in ASPR more difficult. The dynamic assembly model (DAM) based on polychromatic sets (PS) theory was proposed to address these issues.

First, a strategy was presented to simplify ASPR, taking advantage of assembly sequence generated in the phase of assembly design which considers no resource. Secondly, the concept of DAM was discussed, and some principles/criteria for DAM modeling were generalized from experience. Then, the DAM was modeled by formalizing its incidence relations as PS matrix, and refined based on the formalized criteria, which were expressed as PS locating and collision relation models. Finally, an application case was studied to demonstrate the effectiveness of the method.

The approach could reduce the complexity of ASPR significantly, and was able to identify dynamic resource, model DAM and handle the co‐existence of ASDS effectively.

The method may change the manual pattern of ASPR in simulation environment, and become a potential tool to change the pattern of traditional ASP which comes to work from scratch, by utilizing the upstream information of product design.

Different from traditional assembly model, DAM was a local model which consists of partial components of product and resource, and the DAM‐based ASPR approach would make the computational complexity of product assembly become more linear than exponential.

This study aims to investigate the corrosion inhibitor effect of migrating corrosion inhibitor (MCI) on Q235 steel in high alkaline environment under cathodic polarization.

This study investigated the electrochemical characteristics of Q235 steel with and without MCI by polarization curve and electrochemical impedance spectroscopy. Besides, the surface composition of Q235 steel under different environments was analyzed by X-ray photoelectron spectroscopy. In addition, the migration characteristic of MCI and the adsorption behavior of MCI under cathodic polarization were studied using Raman spectroscopy.

Diethanolamine (DEA) and N, N-dimethylethanolamine (DMEA) can inhibit the increase of Fe(II) in the oxide film of Q235 steel under cathodic polarization. The adsorption stability of DMEA film was higher under cathodic polarization potential, showing a higher corrosion inhibition ability. The corrosion inhibition mechanism of DEA and DMEA under cathodic polarization potential was proposed.

The MCI has a broad application prospect in the repair of damaged reinforced concrete due to its unique migratory characteristics. The interaction between MCIs, rebar and concrete with different compositions has been studied, but the passivation behavior of the steel interface in the presence of both the migrating electric field and corrosion inhibitors has been neglected. And it was investigated in this paper.

Unsupervised domain adaptation object detection not only mitigates model terrible performance resulting from domain gap, but also has the ability to apply knowledge trained on a definite domain to a distinct domain. However, aligning the whole feature may confuse the object and background information, making it challenging to extract discriminative features. This paper aims to propose an improved approach which is called intrinsic feature extraction domain adaptation (IFEDA) to extract discriminative features effectively.

IFEDA consists of the intrinsic feature extraction (IFE) module and object consistency constraint (OCC). The IFE module, designed on the instance level, mainly solves the issue of the difficult extraction of discriminative object features. Specifically, the discriminative region of the objects can be paid more attention to. Meanwhile, the OCC is deployed to determine whether category prediction in the target domain brings into correspondence with it in the source domain.

Experimental results demonstrate the validity of our approach and achieve good outcomes on challenging data sets.

Limitations to this research are that only one target domain is applied, and it may change the ability of model generalization when the problem of insufficient data sets or unseen domain appeared.

This paper solves the issue of critical information defects by tackling the difficulty of extracting discriminative features. And the categories in both domains are compelled to be consistent for better object detection.