175 research outputs found
Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning
The two fields of urban planning and artificial intelligence (AI) arose and
developed separately. However, there is now cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In the
present paper, we introduce the importance of urban planning from the
sustainability, living, economic, disaster, and environmental perspectives. We
review the fundamental concepts of urban planning and relate these concepts to
crucial open problems of machine learning, including adversarial learning,
generative neural networks, deep encoder-decoder networks, conversational AI,
and geospatial and temporal machine learning, thereby assaying how AI can
contribute to modern urban planning. Thus, a central problem is automated
land-use configuration, which is formulated as the generation of land uses and
building configuration for a target area from surrounding geospatial, human
mobility, social media, environment, and economic activities. Finally, we
delineate some implications of AI for urban planning and propose key research
areas at the intersection of both topics.Comment: TSAS Submissio
Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks
Modeling and forecasting forward citations to a patent is a central task for
the discovery of emerging technologies and for measuring the pulse of inventive
progress. Conventional methods for forecasting these forward citations cast the
problem as analysis of temporal point processes which rely on the conditional
intensity of previously received citations. Recent approaches model the
conditional intensity as a chain of recurrent neural networks to capture memory
dependency in hopes of reducing the restrictions of the parametric form of the
intensity function. For the problem of patent citations, we observe that
forecasting a patent's chain of citations benefits from not only the patent's
history itself but also from the historical citations of assignees and
inventors associated with that patent. In this paper, we propose a
sequence-to-sequence model which employs an attention-of-attention mechanism to
capture the dependencies of these multiple time sequences. Furthermore, the
proposed model is able to forecast both the timestamp and the category of a
patent's next citation. Extensive experiments on a large patent citation
dataset collected from USPTO demonstrate that the proposed model outperforms
state-of-the-art models at forward citation forecasting
A Method for Optimising the Weight and Response of Brush-Type Wound- Field Direct Current Motors
Abstract Weight optimised direct current (DC) motors have been used lately as actuators for micro-robotics and biomedical equipment. The evolution of these motors originated from the need for lightweight compact motors with good response in the industry. Many techniques have been used in developing permanent magnet (PM) and wound-field (WF) DC motors, such as numerical analysis, selecting adequate magnetic material, finite element analysis and optimisation models. Yet, published articles on optimising the weight and response of WF DC motors reveal the development of WF weight optimised motors with problems for example non-proportional geometry, saturated armature teeth, weak output torque and high operating specific electric loading. To overcome these problems, this paper presents a method that separately optimises wound-field DC motors operating with closed-loop proportional, integral and derivative (PID) controllers. A 900-watts DC motor and its PID controllers are optimised as an example for illustrating the proposed method
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
For both investors and policymakers, forecasting the stock market is
essential as it serves as an indicator of economic well-being. To this end, we
harness the power of social media data, a rich source of public sentiment, to
enhance the accuracy of stock market predictions. Diverging from conventional
methods, we pioneer an approach that integrates sentiment analysis,
macroeconomic indicators, search engine data, and historical prices within a
multi-attention deep learning model, masterfully decoding the complex patterns
inherent in the data. We showcase the state-of-the-art performance of our
proposed model using a dataset, specifically curated by us, for predicting
stock market movements and volatility
Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
Predicting stock market is vital for investors and policymakers, acting as a
barometer of the economic health. We leverage social media data, a potent
source of public sentiment, in tandem with macroeconomic indicators as
government-compiled statistics, to refine stock market predictions. However,
prior research using tweet data for stock market prediction faces three
challenges. First, the quality of tweets varies widely. While many are filled
with noise and irrelevant details, only a few genuinely mirror the actual
market scenario. Second, solely focusing on the historical data of a particular
stock without considering its sector can lead to oversight. Stocks within the
same industry often exhibit correlated price behaviors. Lastly, simply
forecasting the direction of price movement without assessing its magnitude is
of limited value, as the extent of the rise or fall truly determines
profitability. In this paper, diverging from the conventional methods, we
pioneer an ECON. The framework has following advantages: First, ECON has an
adept tweets filter that efficiently extracts and decodes the vast array of
tweet data. Second, ECON discerns multi-level relationships among stocks,
sectors, and macroeconomic factors through a self-aware mechanism in semantic
space. Third, ECON offers enhanced accuracy in predicting substantial stock
price fluctuations by capitalizing on stock price movement. We showcase the
state-of-the-art performance of our proposed model using a dataset,
specifically curated by us, for predicting stock market movements and
volatility
High Frequency, High Accuracy Pointing onboard Nanosats using Neuromorphic Event Sensing and Piezoelectric Actuation
As satellites become smaller, the ability to maintain stable pointing
decreases as external forces acting on the satellite come into play. At the
same time, reaction wheels used in the attitude determination and control
system (ADCS) introduce high frequency jitter which can disrupt pointing
stability. For space domain awareness (SDA) tasks that track objects tens of
thousands of kilometres away, the pointing accuracy offered by current
nanosats, typically in the range of 10 to 100 arcseconds, is not sufficient. In
this work, we develop a novel payload that utilises a neuromorphic event sensor
(for high frequency and highly accurate relative attitude estimation) paired in
a closed loop with a piezoelectric stage (for active attitude corrections) to
provide highly stable sensor-specific pointing. Event sensors are especially
suited for space applications due to their desirable characteristics of low
power consumption, asynchronous operation, and high dynamic range. We use the
event sensor to first estimate a reference background star field from which
instantaneous relative attitude is estimated at high frequency. The
piezoelectric stage works in a closed control loop with the event sensor to
perform attitude corrections based on the discrepancy between the current and
desired attitude. Results in a controlled setting show that we can achieve a
pointing accuracy in the range of 1-5 arcseconds using our novel payload at an
operating frequency of up to 50Hz using a prototype built from
commercial-off-the-shelf components. Further details can be found at
https://ylatif.github.io/ultrafinestabilisatio
Carbon electrode sensitivity enhancement for lead detection using polypyrrole, ionic liquid, and nafion composite
This paper concerns enhancing a lead detection sensor using a combination of polypyrrole (PPy), Nafion (N), and ionic liquid (IL) with thick-film or screen-printing technology on sensitive material-based carbon electrodes. Electrode characterization using a scanning electron microscope (SEM) was conducted to see the morphology of sensitive materials, showing that the spherical particles were distributed evenly on the electrode surface. Analysis using energy dispersive spectroscopy (EDS) shows that the element's atomic composition is 84.92 %, 8.81 %, 6.26 %, and 0.01 % for carbon, nitrogen, oxygen, and bismuth, respectively. Potentiostat measurement with the ambient temperature of 25 °C on a standard lead solution with concentration ranging from 0.05 to 0.5 mg/l yields an average output voltage ranging from 2.16 to 2.27 V. It can be concluded that the sensor is able to detect lead with a sensitivity of 0.21 V in each addition of solution concentration (mg/l) and give an 84 % concentration contribution to the voltage
- …