154 research outputs found
A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China
This paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high.Computational intelligence; artificial neural networks; fuzzy optimization; early warning system; economic crises
Environmental regulation and sectoral disparity in labor demand: evidence from a quasi-natural experiment in China
The trade-off between environmental regulation and job creation
has been a dilemma for policymakers in the past decades.
Exploiting the enterprise-level sample data, we conduct a difference-
in-difference-in-differences (DDD) specification to estimate the
overall effect of China’s Two Control Zones (TCZ) policy on labor
demand, measured as the number of enterprise-level employees.
We find that the industrial enterprises in TCZ cities, where the TCZ
policy has been implemented after 1998, employed fewer workers
in more polluting industries. Furthermore, these employment
effects are very heterogeneous among different enterprise ownerships
and control zones. The TCZ policy significantly decreased the
labor demand in private enterprises or those located in the Acid
Rain Control Zones (ARCZ) but had little impact on their stateowned
and foreign-invested counterparts or those located in the
Sulfur Dioxide Pollution Control Zones (SPCZ)
A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China
This paper combines artificial neural networks (ANN), fuzzy
optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high
Enhanced Management of Personal Astronomical Data with FITSManager
Although the roles of data centers and computing centers are becoming more
and more important, and on-line research is becoming the mainstream for
astronomy, individual research based on locally hosted data is still very
common. With the increase of personal storage capacity, it is easy to find
hundreds to thousands of FITS files in the personal computer of an
astrophysicist. Because Flexible Image Transport System (FITS) is a
professional data format initiated by astronomers and used mainly in the small
community, data management toolkits for FITS files are very few. Astronomers
need a powerful tool to help them manage their local astronomical data.
Although Virtual Observatory (VO) is a network oriented astronomical research
environment, its applications and related technologies provide useful solutions
to enhance the management and utilization of astronomical data hosted in an
astronomer's personal computer. FITSManager is such a tool to provide
astronomers an efficient management and utilization of their local data,
bringing VO to astronomers in a seamless and transparent way. FITSManager
provides fruitful functions for FITS file management, like thumbnail, preview,
type dependent icons, header keyword indexing and search, collaborated working
with other tools and online services, and so on. The development of the
FITSManager is an effort to fill the gap between management and analysis of
astronomical data.Comment: 12 pages, 9 figures, Accepted for publication in New Astronom
Image-based Visual Servo Control for Aerial Manipulation Using a Fully-Actuated UAV
Using Unmanned Aerial Vehicles (UAVs) to perform high-altitude manipulation
tasks beyond just passive visual application can reduce the time, cost, and
risk of human workers. Prior research on aerial manipulation has relied on
either ground truth state estimate or GPS/total station with some Simultaneous
Localization and Mapping (SLAM) algorithms, which may not be practical for many
applications close to infrastructure with degraded GPS signal or featureless
environments. Visual servo can avoid the need to estimate robot pose. Existing
works on visual servo for aerial manipulation either address solely
end-effector position control or rely on precise velocity measurement and
pre-defined visual visual marker with known pattern. Furthermore, most of
previous work used under-actuated UAVs, resulting in complicated mechanical and
hence control design for the end-effector. This paper develops an image-based
visual servo control strategy for bridge maintenance using a fully-actuated
UAV. The main components are (1) a visual line detection and tracking system,
(2) a hybrid impedance force and motion control system. Our approach does not
rely on either robot pose/velocity estimation from an external localization
system or pre-defined visual markers. The complexity of the mechanical system
and controller architecture is also minimized due to the fully-actuated nature.
Experiments show that the system can effectively execute motion tracking and
force holding using only the visual guidance for the bridge painting. To the
best of our knowledge, this is one of the first studies on aerial manipulation
using visual servo that is capable of achieving both motion and force control
without the need of external pose/velocity information or pre-defined visual
guidance.Comment: Accepted by 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
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