Influencing factors of employee innovation and satisfaction in fast growing medical and internet service companies

This thesis studies the factors that affect the innovation and satisfaction of employees in fast-growing medical and Internet service companies. More and more enterprises, especially the managers of medical and Internet enterprises, are constantly aware that employees are really the core competitiveness of modern enterprises and can attract and retain excellent employees, even to encourage employees' continuous innovation is the key for medical and Internet enterprises to grow and win the market competition. This thesis mainly studies three problems: (1) The influences of incentive mechanisms and dynamic capability on innovation and employee satisfaction? (2) Team factors that affect incentive mechanisms and dynamic capabilities? (3) The mediating roles of incentive mechanisms and dynamic capabilities between team factors and innovation/employee satisfaction? The two research methods of this thesis are qualitative case study based on action and quantitative study based on questionnaire survey data. The research model in this thesis mainly comes from literature and case studies, and the measurement model also verifies whether the indicators in the literature adapt to the current enterprise environment through case studies. In the case study, by company started to build in June 2015, located in Beijing. It is an Internet finance company established with regulatory approval. By interviewing company leaders and related employees, the thesis studies the changes and evolution of the variables in the three different growth stages of the enterprise. In the questionnaire survey, 774 questionnaires were sent out to 12 Internet companies and medical service companies, and 523 valid survey samples were collected to quantitatively test the research models and hypotheses in the thesis. The results show that team diversity, team leadership, team culture, career development and dynamic ability have a significant positive impact on employee innovation, while team leadership, team culture, financial incentive, career development and dynamic ability have a significant positive impact on employee satisfaction. My research results show that in order to improve employee satisfaction and innovation ability, we need to effectively manage team factors and incentive mechanism to improve the management mechanism.Esta tese estuda os fatores que afetam a inovação e a satisfação dos funcionários em empresas de serviços médicos e de Internet em rápido crescimento. Cada vez mais empresas, especialmente gerentes de empresas médicas e de Internet, estão constantemente percebendo que os funcionários são realmente o núcleo da competitividade das empresas modernas. Atrair e ser capaz de reter funcionários excepcionais, e até mesmo incentivar os funcionários a continuar a inovar, é a chave para o desenvolvimento e crescimento contínuos das empresas médicas e de Internet e, em última instância, a capacidade de vencer a concorrência no mercado. Esta tese estuda principalmente três questões: (1) O impacto dos mecanismos de incentivo e capacidades dinâmicas na inovação e na satisfação dos funcionários? (2) Fatores da equipe que afetam o mecanismo de incentivo e as capacidades dinâmicas? (3) O papel mediador do mecanismo de incentivo e as capacidades dinâmicas entre os fatores da equipe e a inovação / satisfação do funcionário? Os dois métodos de pesquisa nesta tese são um estudo de caso qualitativo baseado em ação e o outro é uma pesquisa quantitativa baseada em dados de pesquisa por questionário. O modelo de pesquisa na tese é derivado principalmente da literatura e estudos de caso, e o modelo de medição também usa estudos de caso para verificar se os indicadores da literatura são adequados para o ambiente corporativo atual. No estudo de caso, a empresa BY iniciou os preparativos em junho de 2015 e está localizada em Pequim. É uma empresa financeira de Internet estabelecida com aprovação regulamentar. Por meio de entrevistas com os diretores da empresa e funcionários relacionados, estudamos as mudanças e a evolução das variáveis do papel nas três diferentes fases de crescimento da empresa. Na pesquisa de pesquisa por questionário, 774 questionários foram distribuídos a um total de 12 empresas de Internet e empresas de serviços médicos, e um total de 523 amostras de pesquisa válidas foram recuperadas para testar quantitativamente os modelos de pesquisa e hipóteses no artigo. Os resultados da pesquisa mostram que a diversidade da equipe, liderança da equipe, cultura da equipe, desenvolvimento de carreira e capacidades dinâmicas têm um impacto positivo significativo na inovação do funcionário, e liderança da equipe, cultura da equipe, incentivos financeiros, desenvolvimento de carreira e capacidades dinâmicas têm efeitos significativos na satisfação do funcionário Impacto positivo. Os resultados da minha pesquisa mostram que, para melhorar a satisfação dos funcionários e as capacidades de inovação de uma empresa, é necessário aprimorar o seu mecanismo de gestão por meio da gestão eficaz dos fatores da equipe e dos mecanismos de incentivo

Parametric Reshaping of Portraits in Videos

Sharing short personalized videos to various social media networks has become quite popular in recent years. This raises the need for digital retouching of portraits in videos. However, applying portrait image editing directly on portrait video frames cannot generate smooth and stable video sequences. To this end, we present a robust and easy-to-use parametric method to reshape the portrait in a video to produce smooth retouched results. Given an input portrait video, our method consists of two main stages: stabilized face reconstruction, and continuous video reshaping. In the first stage, we start by estimating face rigid pose transformations across video frames. Then we jointly optimize multiple frames to reconstruct an accurate face identity, followed by recovering face expressions over the entire video. In the second stage, we first reshape the reconstructed 3D face using a parametric reshaping model reflecting the weight change of the face, and then utilize the reshaped 3D face to guide the warping of video frames. We develop a novel signed distance function based dense mapping method for the warping between face contours before and after reshaping, resulting in stable warped video frames with minimum distortions. In addition, we use the 3D structure of the face to correct the dense mapping to achieve temporal consistency. We generate the final result by minimizing the background distortion through optimizing a content-aware warping mesh. Extensive experiments show that our method is able to create visually pleasing results by adjusting a simple reshaping parameter, which facilitates portrait video editing for social media and visual effects

3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses

3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-quarter headshot 3D portraits with complete head, neck, and shoulder geometry. Two reasons account for this issue: First, existing facial recognition methods struggle with extracting facial data captured from large camera angles or back views. Second, it is challenging to learn a distribution of 3D portraits covering the one-quarter headshot region from single-view data due to significant geometric deformation caused by diverse body poses. To this end, we first create the dataset 360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-quality single-view real portraits annotated with a variety of camera parameters (the yaw angles span the entire 360{\deg} range) and body poses. We then propose 3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with body pose self-learning. Our model can generate view-consistent portrait images from all camera angles with a canonical one-quarter headshot 3D representation. Our experiments show that the proposed framework can accurately predict portrait body poses and generate view-consistent, realistic portrait images with complete geometry from all camera angles

Machine Vision based Grabbing Objects with Manipulator System Design

In recent years, machine vision technology and robot control technology have attracted lots attention of the researchers. They provide people with fast and efficient services in many fields, which have an increasingly important impact on the modern manufacturing industry and the inspection industry. In this paper, a mechanical vision-based grab control system based on machine vision is developed and analyzed accordingly. This design employs industrial cameras with Gigabit Ethernet ports, six-degree-of-freedom servo drive robots. The Host computer control software is designed on the development platform provided by Microsoft and processed in machine vision image processing. The software has implemented an image processing algorithm. It aims to combine machine vision, robot control and other technologies to achieve precise positioning, recognition and capture of targets. In the end, the proposed method is displayed in the upper computer accordingly

Exploring personalised autonomous vehicles to influence user trust

Trust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of appropriate trust could prevent drivers and society in general from taking advantage of such technology. This paper makes a new attempt to explore the effects of personalised AVs as a novel approach to the cognitive underpinnings of drivers’ trust in AVs. The personalised AV system is able to identify the driving behaviours of users and thus adapt the driving style of the AV accordingly. A prototype of a personalised AV was designed and evaluated in a lab-based experimental study of 36 human drivers, which investigated the impact of the personalised AV on user trust when compared with manual human driving and non-personalised AVs. The findings show that a personalised AV appears to be significantly more reliable through accepting and understanding each driver’s behaviour, which could thereby increase a user’s willingness to trust the system. Furthermore, a personalised AV brings a sense of familiarity by making the system more recognisable and easier for users to estimate the quality of the automated system. Personalisation parameters were also explored and discussed to support the design of AV systems to be more socially acceptable and trustworthy

Accelerated Transport through Sliding Dynamics of Rodlike Particles in Macromolecular Networks

Transport of rodlike particles in macromolecular networks is critical for many important biological processes and technological applications. Here, we report that speeding-up dynamics occurs once the rod length L reaches around integral multiple of the network mesh size ax. We find that such a fast diffusion follows the sliding dynamics and demonstrate it to be anomalous yet Brownian. The good agreement between theoretical analysis and simulations corroborates that sliding dynamics is an intermediate regime between hopping and Brownian dynamics, and suggests a mechanistic interpretation based on the rod-length dependent entropic free energy barrier. These theoretical findings are captured by the experimental observations of rods in synthetic networks, and bring new insight into the physics of the transport dynamics in confined media of networks

Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm

Accurate road extraction and recognition of roadside fruit in complex orchard environments are essential prerequisites for robotic fruit picking and walking behavioral decisions. In this study, a novel algorithm was proposed for unstructured road extraction and roadside fruit synchronous recognition, with wine grapes and nonstructural orchards as research objects. Initially, a preprocessing method tailored to field orchards was proposed to reduce the interference of adverse factors in the operating environment. The preprocessing method contained 4 parts: interception of regions of interest, bilateral filter, logarithmic space transformation and image enhancement based on the MSRCR algorithm. Subsequently, the analysis of the enhanced image enabled the optimization of the gray factor, and a road region extraction method based on dual-space fusion was proposed by color channel enhancement and gray factor optimization. Furthermore, the YOLO model suitable for grape cluster recognition in the wild environment was selected, and its parameters were optimized to enhance the recognition performance of the model for randomly distributed grapes. Finally, a fusion recognition framework was innovatively established, wherein the road extraction result was taken as input, and the optimized parameter YOLO model was utilized to identify roadside fruits, thus realizing synchronous road extraction and roadside fruit detection. Experimental results demonstrated that the proposed method based on the pretreatment could reduce the impact of interfering factors in complex orchard environments and enhance the quality of road extraction. Using the optimized YOLOv7 model, the precision, recall, mAP, and F1-score for roadside fruit cluster detection were 88.9%, 89.7%, 93.4%, and 89.3%, respectively, all of which were higher than those of the YOLOv5 model and were more suitable for roadside grape recognition. Compared to the identification results obtained by the grape detection algorithm alone, the proposed synchronous algorithm increased the number of fruit identifications by 23.84% and the detection speed by 14.33%. This research enhanced the perception ability of robots and provided a solid support for behavioral decision systems