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This paper aims to present a novel scheme of multiple vanishing points (VPs) estimation and corresponding lanes identification.

The scheme proposed here includes two main stages: VPs estimation and lane identification. VPs estimation based on vanishing direction hypothesis and Bayesian posterior probability estimation in the image Hough space is a foremost contribution, and then VPs are estimated through an optimal objective function. In lane identification stage, the selected linear samples supervised by estimated VPs are clustered based on the gradient direction of linear features to separate lanes, and finally all the lanes are identified through an identification function.

The scheme and algorithms are tested on real data sets collected from an intelligent vehicle. It is more efficient and more accurate than recent similar methods for structured road, and especially multiple VPs identification and estimation of branch road can be achieved and lanes of branch road can be identified for complex scenarios based on Bayesian posterior probability verification framework. Experimental results demonstrate VPs, and lanes are practical for challenging structured and semi-structured complex road scenarios.

A Bayesian posterior probability verification framework is proposed to estimate multiple VPs and corresponding lanes for road scene understanding of structured or semi-structured road monocular images on intelligent vehicles.

Positioning is a key task in most field robotics applications but can be very challenging in GPS‐denied or high‐slip environments. The purpose of this paper is to describe a visual odometry strategy using only one camera in country roads.

This monocular odometery system uses as input only those images provided by a single camera mounted on the roof of the vehicle and the framework is composed of three main parts: image motion estimation, ego‐motion computation and visual odometry. The image motion is estimated based on a hyper‐complex wavelet phase‐derived optical flow field. The ego‐motion of the vehicle is computed by a blocked RANdom SAmple Consensus algorithm and a maximum likelihood estimator based on a 4‐degrees of freedom motion model. These as instantaneous ego‐motion measurements are used to update the vehicle trajectory according to a dead‐reckoning model and unscented Kalman filter.

The authors' proposed framework and algorithms are validated on videos from a real automotive platform. Furthermore, the recovered trajectory is superimposed onto a digital map, and the localization results from this method are compared to the ground truth measured with a GPS/INS joint system. These experimental results indicate that the framework and the algorithms are effective.

The effective framework and algorithms for visual odometry using only one camera in country roads are introduced in this paper.

The current study aims to investigate the temporal mechanisms in HRM systems by focusing on how HRM systems evolve over time and how such changes affect organizational innovation.

This paper draws on organizational entrainment theory to examine how pace of change in employee involvement programs (EIPs) influences innovation via data from an eight-year longitudinal survey collected by Statistics Canada. The final sample includes 15,679 workplace–year observations.

This research shows that the effects of HRM programs on performance are more than just the mean effect – the pace of change by which changes are implemented in HRM programs matters in the long run. The optimal level of change pace occurs when the EIPs are changing at a pace that entrains (or synchronizes) with organizational rhythm of strategic changes. Results suggest that change pace in EIPs has an inverted-U-shaped relationship with both pace and quality of innovation. The curvilinear effect is more pronounced for organizations with relatively lower mean level of EIPs.

First, this study captures only key measures of the EIPs and may not be generalizable to other dimensions of the HR systems. Second, the results of this paper should be interpreted at the HR program level or bundles of HR practices – the findings may not be generalizable to lower levels of analysis. Third, as a result of annual measurement, this study cannot capture short-lived minor dynamic HR misfits where workplaces quickly adjust to regain alignment. Fourth, to attain meaningful and consistent measures of strategic HR change, this study only includes surviving workplaces with at least five years of observations.

This paper provides insights to managers and business leaders on how to implement strategic changes in HRM systems effectively to attain sustained innovation outcomes in the long run. To achieve an optimal level of innovation, organizations need to consider not only what and how many EIPs should be used but also how to strategically change EIPs to meet dynamic internal and external changes.

The current research introduces organizational entrainment theory to explain and empirically test the conflicting predictions of the universalist and contingency perspectives on the effects of strategic changes in HRM.

Commitment to change is vital for the success of any organizational change initiative. However, despite a sustained increase in research interest on employees’ commitment to change, there is still no consistency about the unidimensional or multi-dimensional construct of commitment to change, and previous research tends to ignore the impact vocational drivers may have on it. The paper aims to discuss these issues.

Drawing on prospect theory, the authors extended Herscovitch and Meyer’s (2002) commitment to change construct by developing and testing an additional dimension of commitment to change centered on employees’ vocational commitment across two studies, adopting a longitudinal design within a Chinese context. As organizational change often has implications that impacts individual decision making, vocational development and work adjustments and attitudes within the workplace, the authors presented the case for vocational commitment to change as an important extension to the commitment to change literature. The authors first provided evidence for the internal consistency, factor structure and the validity of the commitment to change in the Chinese context. Subsequently, the authors examined the changes of employees’ commitment to change across time, and demonstrated its predictive validity by exploring the relationship between commitment to change and change-related behaviors.

The current research represents improvements in commitment to change measurement, provides construct clarification in the Asia context, and sheds light on theoretical and empirical evidence for how to support change in the Chinese context. Limitations, implications and directions for future research are further discussed.

The current study responds to a call for research to further investigate the mechanisms of commitment to change within non-Western contexts, specifically within the Chinese context. Through a rigorous scale development process, the authors clarified Herscovitch and Meyer’s (2002) commitment to change model and present an augmented model with a fourth dimension –vocational commitment to change. Furthermore, through a longitudinal study, the current study also demonstrates that the cultivation of commitment to change has great importance to improving employees’ change-supportive behavior and reducing their resistance to change. This is consistent with cross-cultural research, which shows that Chinese individuals are more likely to possess inconsistent attitudes toward an object, including themselves, compared to Western individuals (Spencer-Rodgers et al., 2004). The study also explained the change of commitment to change over time, showing the significant relationships among the commitment to change and change-related behaviors.

Transformational leadership and service performance of civil servants greatly affect the government’s administrative effectiveness. However, there are few studies on the influence mechanism of transformational leadership on service performance in the context of public organizations. Based on the social exchange theory, this study aims to construct and examine the dual path mediating process of affective trust and cognitive trust for the effects of transformational leadership on service performance.

Drawing from 268 supervisor–subordinate dyads civil servants at the municipal level in China across three waves.

Both affective trust and cognitive trust partly mediated the relationships between transformational leadership and service performance, which supported the underlying theoretical mechanism of social exchange theory and transformational leadership theory in explaining the dual relationship between leaders and subordinates. This study innovatively and empirically examined the effects of transformational leadership on service performance through dual trust in civil servants in China, thus bridging the gap in this knowledge.

This study innovatively and empirically examined the effects of transformational leadership on service performance through dual trust in civil servants in China, thus bridging the gap in this knowledge.

Based on dual organizational theory, the purpose of this paper is to examine the relationship between transformational leadership and innovative behavior in groups. The authors proposed that group innovative behavior was influenced by transformational leadership as a group-level construct which was moderated by dual organizational change that represent organization-level resources. Furthermore, the authors identified two organizational change-related situational variables-radical change and incremental change and examined their effects on group innovative behavior.

The authors collected data from full-time employees working in groups in 43 companies, located in five cities in China including Beijing, Yantai, Chengdu, Xi’an, and Chengde. These enterprises were from a wide range of industries, including manufacturing, financing, information technology, and geological exploration. The authors chose a middle- or senior-level manager from each company to act as chief survey respondent, who were asked to contact managers and employees from a list they had provided and invite them to participate in a web-based survey (via an e-mailed link) or a paper-and-pencil survey. A total of 192 managers and 756 direct subordinates from 112 groups completed the survey.

Results found that transformational leadership was positively related to group innovative behavior, and this relationship was moderated by radical change, but not incremental change; radical change and incremental change were also positively related to group innovative behavior.

This study adopts a cross-sectional study design, which is insufficient for deriving causal inferences. Future research may adopt a longitudinal study design to investigate causal impacts. Besides, some unmeasured variables could be related to transformational leadership and innovative behavior.

The paper includes implications for adopting appropriate leadership style to motivate innovative behavior, promoting dual organizational change to boost innovative behavior, and generating greater innovative behavior for transformational leaders in times of radical change.

This cross-level study contributes to the relationship between transformational leadership and group innovative behavior in the context of dual organizational change.

The purpose of this paper was to perform an experimental investigation to analyze vibration and noise of unloaded gearbox with different oil quality. All motor-driven machinery used in the modern world can develop faults. The maintenance plans include analyzing the external relevant information of critical components, in order to evaluate its internal state. From the beginning of the twentieth century, different technologies have been used to process signals of dynamical systems.

A proposed neural network (NN) is also employed to predict vibration parameters of the experimental test rig. Moreover, four types of oils are used for gearbox to predict reliable oil. Vibration signals extracted from rotating parts of machineries carry lot many information within them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or the assembly under study. The experimental stand for testing an unloaded gearbox is composed by actuated direct current (DC) driving system.

This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gearbox using two types of artificial neural networks (ANNs) and stress analyzed with computer-based software ANNs. The results improved that the proposed NN has superior performance to adapt experimental results.

This paper is one such attempt to apply machine learning methods like ANN. This work deals with extraction of wavelet features from the vibration data of a gearbox system and classification of gear faults using ANNs.

These kind of NN-based approaches are novel approaches to predict real-time vibration and acceleration parameters of unloaded gearbox with five types of oils. Also, the investigation contains new information about studied process, containing elements of novelty.