Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods.

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Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling

Sensors ◽

10.3390/s21051646 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1646

Author(s):

Alireza Entezami ◽

Hassan Sarmadi ◽

Behshid Behkamal ◽

Stefano Mariani

Keyword(s):

Multidimensional Scaling ◽

Damage Detection ◽

Health Monitoring ◽

Large Scale ◽

Environmental Variability ◽

Data Driven ◽

High Dimensional ◽

Data Driven Approach ◽

Low Dimensional ◽

High Dimensional Datasets

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

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BALLU2: A Safe and Affordable Buoyancy Assisted Biped

Frontiers in Robotics and AI ◽

10.3389/frobt.2021.730323 ◽

2021 ◽

Vol 8 ◽

Author(s):

Hosik Chae ◽

Min Sung Ahn ◽

Donghun Noh ◽

Hyunwoo Nam ◽

Dennis Hong

Keyword(s):

Real World ◽

Data Driven ◽

Simulation Data ◽

Statistical Process ◽

Essential Information ◽

Full Disclosure ◽

Data Driven Approach ◽

Cartesian Coordinate ◽

Low Dimensional ◽

External Forces

This work presents the first full disclosure of BALLU, Buoyancy Assisted Lightweight Legged Unit, and describes the advantages and challenges of its concept, the hardware design of a new implementation (BALLU2), a motion analysis, and a data-driven walking controller. BALLU is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to the lightweight body, which solves many issues that hinder current robots from operating close to humans. The advantages gained also lead to the platform’s distinct difficulties caused by severe nonlinearities and external forces such as buoyancy and drag. The paper describes the nonconventional characteristics of BALLU as a legged robot and then gives an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process using Spearman Correlation Coefficient is proposed to form low-dimensional state vectors from the simulation data, and an artificial neural network-based controller is trained on the same data. The controller is tested both on simulation and on real-world hardware. Its performance is assessed by observing the robot’s limit cycles and trajectories in the Cartesian coordinate. The controller generates periodic walking sequences in simulation as well as on the real-world robot even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLU’s walking.

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A data-driven approach for a class of stochastic dynamic optimization problems

Computational Optimization and Applications ◽

10.1007/s10589-021-00320-4 ◽

2021 ◽

Author(s):

Thuener Silva ◽

Davi Valladão ◽

Tito Homem-de-Mello

Keyword(s):

Dynamic Optimization ◽

Optimization Problems ◽

Data Driven ◽

Stochastic Dynamic ◽

Dynamic Optimization Problems ◽

Stochastic Dynamic Optimization ◽

Data Driven Approach

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Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking

10.1101/2020.12.31.425008 ◽

2021 ◽

Author(s):

Hua Xie ◽

Roger E. Beaty ◽

Sahar Jahanikia ◽

Caleb Geniesse ◽

Neeraj S. Sonalkar ◽

...

Keyword(s):

Creative Thinking ◽

Dimensional Space ◽

Memory Task ◽

Process Models ◽

Dual Process ◽

Data Driven ◽

Fmri Session ◽

Cognitive Space ◽

Data Driven Approach ◽

Low Dimensional

AbstractDespite substantial progress in the quest of demystifying the brain basis of creativity, several questions remain open. One such issue concerns the relationship between two latent cognitive modes during creative thinking, i.e., deliberate goal-directed cognition and spontaneous thought generation. Although an interplay between deliberate and spontaneous thinking is often indirectly implicated in the creativity literature (e.g., dual-process models), a bottom-up data-driven validation of the cognitive processes associated with creative thinking is still lacking. Here, we attempted to capture the latent modes of creative thinking by utilizing a data-driven approach on a novel continuous multitask paradigm (CMP) that widely sampled a hypothetical two-dimensional cognitive plane of deliberate and spontaneous thinking in a single fMRI session. The CMP consisted of eight task blocks ranging from undirected mind wandering to goal-directed working memory task, while also including two of the widely used creativity tasks, i.e., alternate uses task (AUT) and remote association task (RAT). Using data-driven eigen-connectivity (EC) analysis on the multitask whole-brain functional connectivity (FC) patterns, we embedded the multitask FCs into a low-dimensional latent space. The first two latent components, as revealed by the EC analysis, broadly mapped onto the two cognitive modes of deliberate and spontaneous thinking, respectively. Further, in this low-dimensional space, both creativity tasks were located in the upper right corner of high deliberate and spontaneous thinking (creative cognitive space). Neuroanatomically, the creative cognitive space was represented by not only increased intra-network connectivity within executive control and default mode networks, but also by a higher inter-network coupling between the two. Further, individual differences reflected in the low-dimensional connectivity embeddings were related to differences in deliberate and spontaneous thinking abilities. Altogether, using a continuous multitask paradigm and data-driven approach, we provide direct empirical evidence for the contribution of both deliberate and spontaneous modes of cognition during creative thinking.

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