407 research outputs found
Otkrivanje pogreške u analognim sklopovima analizom relativne amplitude i faze
A new method for detection of parametric faults occurring in analog circuits based on relative amplitude and relative phase analysis of the Circuit Under Test (CUT) is proposed. The relative amplitude is the common power change of the signals and the relative phase presents the relative phase offset of the signals. In the proposed method, the value of each component of the CUT is varied within its tolerance limit using Monte Carlo simulation. The upper and lower bounds of relative amplitude and phase of the CUT sampling series are obtained. While testing, the relative amplitude and phase value of the analog circuit are obtained. If any one of the relative amplitude and phase values exceed the bounds then the CUT is declared faulty. The effectiveness of the proposed method is validated through HSpice/MATLAB simulations of two benchmark circuits and the practical circuit test of Tow-Thomas circuit.U ovome članku predložena je nova metoda otkrivanja parametarskih pogrešaka u analognim sklopovima temeljena na analizi relativne amplitude i faze promatranog sklopa (eng. Circuit Under Test, CUT). Relativna amplituda predstavlja zajedničku promjenu snage signala, dok relativna faza predstavlja pomak u fazi među signalima. U predloženoj metodi, koristeći Monte Carlo simulacije, vrijednost svake komponente CUT-a mijenja se unutar svojih granica tolerancije. Na taj način dobivaju se gornja i donja granica relativne amplitude i faze CUT uzoraka, dok se sama relativna amplituda i faza dobivaju tijekom testiranja. U slučaju da ijedan od tih dvaju faktora prelazi granicu, CUT se proglašava neispravnim. Učinkovitost predložene metode ispitana je pomoću HSpice/MATLAB simulacija nad dva referentna sklopa te na Tow-Thomas sklopu
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
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
The distribution shift in Time Series Forecasting (TSF), indicating series
distribution changes over time, largely hinders the performance of TSF models.
Existing works towards distribution shift in time series are mostly limited in
the quantification of distribution and, more importantly, overlook the
potential shift between lookback and horizon windows. To address above
challenges, we systematically summarize the distribution shift in TSF into two
categories. Regarding lookback windows as input-space and horizon windows as
output-space, there exist (i) intra-space shift, that the distribution within
the input-space keeps shifted over time, and (ii) inter-space shift, that the
distribution is shifted between input-space and output-space. Then we
introduce, Dish-TS, a general neural paradigm for alleviating distribution
shift in TSF. Specifically, for better distribution estimation, we propose the
coefficient net (CONET), which can be any neural architectures, to map input
sequences into learnable distribution coefficients. To relieve intra-space and
inter-space shift, we organize Dish-TS as a Dual-CONET framework to separately
learn the distribution of input- and output-space, which naturally captures the
distribution difference of two spaces. In addition, we introduce a more
effective training strategy for intractable CONET learning. Finally, we conduct
extensive experiments on several datasets coupled with different
state-of-the-art forecasting models. Experimental results show Dish-TS
consistently boosts them with a more than 20% average improvement. Code is
available.Comment: Accepted by AAAI 202
Reinforced Imitative Graph Learning for Mobile User Profiling
Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user’s profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach
An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model
High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these modelsâ?? capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective
In this paper, we study the problem of mobile user profiling, which is a
critical component for quantifying users' characteristics in the human mobility
modeling pipeline. Human mobility is a sequential decision-making process
dependent on the users' dynamic interests. With accurate user profiles, the
predictive model can perfectly reproduce users' mobility trajectories. In the
reverse direction, once the predictive model can imitate users' mobility
patterns, the learned user profiles are also optimal. Such intuition motivates
us to propose an imitation-based mobile user profiling framework by exploiting
reinforcement learning, in which the agent is trained to precisely imitate
users' mobility patterns for optimal user profiles. Specifically, the proposed
framework includes two modules: (1) representation module, which produces state
combining user profiles and spatio-temporal context in real-time; (2) imitation
module, where Deep Q-network (DQN) imitates the user behavior (action) based on
the state that is produced by the representation module. However, there are two
challenges in running the framework effectively. First, epsilon-greedy strategy
in DQN makes use of the exploration-exploitation trade-off by randomly pick
actions with the epsilon probability. Such randomness feeds back to the
representation module, causing the learned user profiles unstable. To solve the
problem, we propose an adversarial training strategy to guarantee the
robustness of the representation module. Second, the representation module
updates users' profiles in an incremental manner, requiring integrating the
temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM),
we introduce a gated mechanism to incorporate new and old user characteristics
into the user profile.Comment: AAAI 202
Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
Feature transformation aims to generate new pattern-discriminative feature
space from original features to improve downstream machine learning (ML) task
performances. However, the discrete search space for the optimal feature
explosively grows on the basis of combinations of features and operations from
low-order forms to high-order forms. Existing methods, such as exhaustive
search, expansion reduction, evolutionary algorithms, reinforcement learning,
and iterative greedy, suffer from large search space. Overly emphasizing
efficiency in algorithm design usually sacrifices stability or robustness. To
fundamentally fill this gap, we reformulate discrete feature transformation as
a continuous space optimization task and develop an
embedding-optimization-reconstruction framework. This framework includes four
steps: 1) reinforcement-enhanced data preparation, aiming to prepare
high-quality transformation-accuracy training data; 2) feature transformation
operation sequence embedding, intending to encapsulate the knowledge of
prepared training data within a continuous space; 3) gradient-steered optimal
embedding search, dedicating to uncover potentially superior embeddings within
the learned space; 4) transformation operation sequence reconstruction,
striving to reproduce the feature transformation solution to pinpoint the
optimal feature space.Comment: Accepted by NeurIPS 202
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
Urban traffic speed prediction aims to estimate the future traffic speed for
improving the urban transportation services. Enormous efforts have been made on
exploiting spatial correlations and temporal dependencies of traffic speed
evolving patterns by leveraging explicit spatial relations (geographical
proximity) through pre-defined geographical structures ({\it e.g.}, region
grids or road networks). While achieving promising results, current traffic
speed prediction methods still suffer from ignoring implicit spatial
correlations (interactions), which cannot be captured by grid/graph
convolutions. To tackle the challenge, we propose a generic model for enabling
the current traffic speed prediction methods to preserve implicit spatial
correlations. Specifically, we first develop a Dual-Transformer architecture,
including a Spatial Transformer and a Temporal Transformer. The Spatial
Transformer automatically learns the implicit spatial correlations across the
road segments beyond the boundary of geographical structures, while the
Temporal Transformer aims to capture the dynamic changing patterns of the
implicit spatial correlations. Then, to further integrate both explicit and
implicit spatial correlations, we propose a distillation-style learning
framework, in which the existing traffic speed prediction methods are
considered as the teacher model, and the proposed Dual-Transformer
architectures are considered as the student model. The extensive experiments
over three real-world datasets indicate significant improvements of our
proposed framework over the existing methods
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