364 research outputs found
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
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