Research on internal service based CTS team performance management

JEL: M54; O15Along with the economy globalizing, the fierce competition between enterprises is sparking in international markets. As a sharp and strong tool to obtain competitive advantage, the performance management thoughts and system have been highlighted in theory and practice circles (like what mentioned in Build to Last, Taking People with You). Meanwhile, the internal services, as a new theory, havebeen a new turning point to promote the performance of enterprises. However, the existing managerial theory on performance management combined with the internal services is quite few. Considering the existing situations and based on the researches had been done within China and oversea, the internal services and performance management of a team had been systematically studied with theatrically and practically in this dissertation. The main contents are summarized as following: First, in order to study the effect of internalservices in the performance management of STX CTS team in China, build up a cubic 3-dimension performance management model. The three dimensions are "Sales sense, Factory operation sense, and Customer sense”. Secondly, deploy an action research method to analyze the internal services effects on the performance management and set up a model of performance management process for STX CTS team in China, which is in consist of4 steps: plan, align and develop, control and improvement, and reward (namely incentive). And analyze the different traits and effects of internal services in the differentperformance management process. The empirical study is conducted by utilizing the model within STX China CTS team. The result of the cubic 3-dimension model for team performance improvement is positive, which means the mechanism works well and can beleveraged in Hard Disk Drive firms as well as other IT firms. In the end, the dissertation summarizes the limitations, and proposes the direction for further study.A globalização da economia tem conduzido, cada vez mais, a uma forte competição entre as empresas nos mercados internacionais. Os sistemas de gestão de desempenho têm vindo a ser propostos, quer ao nível teórico quer empírico, como ferramentas relevantes para as empresas obterem vantagens competitiva nos mercados (tal como foi mencionado no Build to Last, Taking People with You). No mesmo sentido, os serviços internos podem ser relevantes para promover o desempenho das empresas. Contudo, as propostas teóricas que combinam a gestão do desempenho organizacional com os serviços internos são ainda reduzidas. Tendo como ponto de partida a situação exsitente e a investigação conduzida na China e internacionalmente, procurou-se neste trabalhoestudar de forma sistemática os serviços internos e a gestão do desempenho de uma equipa específica. Apresenta-se de seguinda as principais etapas e conclusões deste trabalho: Em primeiro lugar, com o objectivo de estudar os efeitos dos serviços internos na gestão do desempenho da equipa CTS da STX na China, foi elaborado um modelo cúbico de 3 dimensões de gestão do desempenho. As três dimensões são: Sentido de Vendas, Sentido de Operações na Fábrica e Sentido no Cliente. Em segundo lugar, foi utilizado o método de pesquisa-ação para analisar os efeitos dos serviços internos na gestão do desempenho e desenvolver um modelo de processo de gestão de desempenho para a equipa CTS da STX na China, que consiste em quatro etapas: planear, alinhar e desenvolver, controlar e melhorar e recompensar (nomeadamente incentivos). Foram ainda analisados os efeitos das diferentes características dos serviços internos nas diferentes etapas do processo de gestão de desempenho. O estudo empírico teve por base a utilizaçãodo modelo descrito na equipa de apoio técnico ao cliente (CTS) da STX. O modelo cúbico das 3 dimensões para a melhoria do desempenho mostrou-se adequado, o que significa que os mecanismos funcionaram de acordo com o previsto e podem utilizados em empresas que produzem unidades de disco rígidos ou outras empresas de TI. No final da dissertação, são apresentadas asprincipais limitações deste trabalho e propõe-se direcções para estudos futuros

Identification of second-order kernels in aerodynamics

Volterra series is one of the powerful system identification methods for representing the nonlinear dynamic system behavior. The methods of step response and impulse response are commonly applied to a discrete aerodynamic Computational Fluid Dynamic (CFD) to identify the first- and second-order Volterra kernels. A critical problem, however, is the difficulty of identifying the second-order Volterra kernels correctly in CFD-based method. In this paper the second-order Volterra kernel function is expanded in terms of Chebyshev functions to reduce the size of the problem and the accuracy of the identification is also improved based on a third-order reduced model of Volterra series

SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering

Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample efficient than model-free RL. How an accurate model can be developed automatically and efficiently from raw sensory inputs (such as images), especially for complex environments and tasks, is a challenging problem that hinders the broad application of MBRL in the real world. In this work, we propose a sensing-aware model-based reinforcement learning system called SAM-RL. Leveraging the differentiable physics-based simulation and rendering, SAM-RL automatically updates the model by comparing rendered images with real raw images and produces the policy efficiently. With the sensing-aware learning pipeline, SAM-RL allows a robot to select an informative viewpoint to monitor the task process. We apply our framework to real-world experiments for accomplishing three manipulation tasks: robotic assembly, tool manipulation, and deformable object manipulation. We demonstrate the effectiveness of SAM-RL via extensive experiments. Supplemental materials and videos are available on our project webpage at https://sites.google.com/view/sam-rl.Comment: Submitted to IEEE International Conference on Robotics and Automation (ICRA) 202

Multi-Task Learning with Multi-Query Transformer for Dense Prediction

Previous multi-task dense prediction studies developed complex pipelines such as multi-modal distillations in multiple stages or searching for task relational contexts for each task. The core insight beyond these methods is to maximize the mutual effects between each task. Inspired by the recent query-based Transformers, we propose a simpler pipeline named Multi-Query Transformer (MQTransformer) that is equipped with multiple queries from different tasks to facilitate the reasoning among multiple tasks and simplify the cross task pipeline. Instead of modeling the dense per-pixel context among different tasks, we seek a task-specific proxy to perform cross-task reasoning via multiple queries where each query encodes the task-related context. The MQTransformer is composed of three key components: shared encoder, cross task attention and shared decoder. We first model each task with a task-relevant and scale-aware query, and then both the image feature output by the feature extractor and the task-relevant query feature are fed into the shared encoder, thus encoding the query feature from the image feature. Secondly, we design a cross task attention module to reason the dependencies among multiple tasks and feature scales from two perspectives including different tasks of the same scale and different scales of the same task. Then we use a shared decoder to gradually refine the image features with the reasoned query features from different tasks. Extensive experiment results on two dense prediction datasets (NYUD-v2 and PASCAL-Context) show that the proposed method is an effective approach and achieves the state-of-the-art result

Angle-Uniform Parallel Coordinates

We present angle-uniform parallel coordinates, a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot. Despite being a common method for visualizing multidimensional data, parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations. To address this issue, we introduce a transformation that bounds all points horizontally using an angle-uniform mapping and shrinks them vertically in a structure-preserving fashion; polygonal lines become smooth curves and a symmetric representation of data correlations is achieved. We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing. Our method enables accurate visual pattern interpretation of data correlations, and its data-independent nature makes it applicable to all multidimensional datasets. The usefulness of our method is demonstrated using examples of synthetic and real-world datasets.Comment: Computational Visual Media, 202

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously. The previous work solves this task by extending the existing panoptic segmentation method with an extra dense depth prediction and instance tracking head. However, the relationship between the depth and panoptic segmentation is not well explored -- simply combining existing methods leads to competition and needs carefully weight balancing. In this paper, we present PolyphonicFormer, a vision transformer to unify these sub-tasks under the DVPS task and lead to more robust results. Our principal insight is that the depth can be harmonized with the panoptic segmentation with our proposed new paradigm of predicting instance level depth maps with object queries. Then the relationship between the two tasks via query-based learning is explored. From the experiments, we demonstrate the benefits of our design from both depth estimation and panoptic segmentation aspects. Since each thing query also encodes the instance-wise information, it is natural to perform tracking directly with appearance learning. Our method achieves state-of-the-art results on two DVPS datasets (Semantic KITTI, Cityscapes), and ranks 1st on the ICCV-2021 BMTT Challenge video + depth track. Code is available at https://github.com/HarborYuan/PolyphonicFormer .Comment: Accepted by ECCV 202