arXiv subjects

Ran Wei

Publications and source records attributed to Ran Wei.

At least 55 records · Page 3Linked to original sources

arXiv2024

Cypher4BIM: Releasing the Power of Graph for Building Knowledge Discovery

Graph is considered a promising way for managing building information. A new graphic form of IFC (Industry Foundation Classes) data has just been developed, referred to as IFC-Graph. However, understanding of IFC-Graph is insufficient, especially for information query. This study aims to explore graphic building information query and develop a graph query language tailored for IFC-Graph. A series of tasks were carried out, including a) investigating the structure of IFC data and the main types of information in IFC, b) investigating the graph query language Cypher, and c) developing a set of tailored functional query patterns. The developed language is referred to as Cypher4BIM. Five IFC models were used for validation, and the result shows that Cypher4BIM can query individual instances and complex relations from IFC, such as spatial structure, space boundary, and space accessibility. This study contributes to applications that require effective building information query, such as digital twin.

cs.DB↗

arXiv2024

ACCESS: Assurance Case Centric Engineering of Safety-critical Systems

Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created documents, which are evaluated by system stakeholders through lengthy and complicated processes. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. This becomes increasingly important, as systems becomes more complex, it is a challenge to manage their development life-cycles, including coordination of development, verification and validation activities, and change impact analysis in inter-connected system assurance artifacts. Moreover, there is a need for assurance cases that support evolution during the operational life of the system, to enable continuous assurance in the face of an uncertain environment, as Robotics and Autonomous Systems (RAS) are adopted into society. In this paper, we contribute ACCESS - Assurance Case Centric Engineering of Safety-critical Systems, an engineering methodology, together with its tool support, for the development of safety critical systems around evolving model-based assurance cases. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts (e.g. system architectural models, system safety analysis, system behaviour models, etc.), and how formal methods can be integrated during the development process. We demonstrate how assurance cases can be automatically evaluated both at development and runtime. We apply our approach to a case study based on an Autonomous Underwater Vehicle (AUV).

cs.SE↗

arXiv2024

A Bayesian Approach to Robust Inverse Reinforcement Learning

We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL). The proposed framework differs from existing offline model-based IRL approaches by performing simultaneous estimation of the expert's reward function and subjective model of environment dynamics. We make use of a class of prior distributions which parameterizes how accurate the expert's model of the environment is to develop efficient algorithms to estimate the expert's reward and subjective dynamics in high-dimensional settings. Our analysis reveals a novel insight that the estimated policy exhibits robust performance when the expert is believed (a priori) to have a highly accurate model of the environment. We verify this observation in the MuJoCo environments and show that our algorithms outperform state-of-the-art offline IRL algorithms.

cs.LG↗

arXiv2024

A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning

Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment. While the capabilities of MBRL agents have significantly improved in recent years, how to best learn the model is still an unresolved question. The majority of MBRL algorithms aim at training the model to make accurate predictions about the environment and subsequently using the model to determine the most rewarding actions. However, recent research has shown that model predictive accuracy is often not correlated with action quality, tracing the root cause to the objective mismatch between accurate dynamics model learning and policy optimization of rewards. A number of interrelated solution categories to the objective mismatch problem have emerged as MBRL continues to mature as a research area. In this work, we provide an in-depth survey of these solution categories and propose a taxonomy to foster future research.

cs.LG↗

arXiv2024

Joint Planning of Charging Stations and Power Systems for Heavy-Duty Drayage Trucks

As global concerns about climate change intensify, the transition towards zero-emission freight is becoming increasingly vital. Drayage is an important segment of the freight system, typically involving the transport of goods from seaports or intermodal terminals to nearby warehouses. This sector significantly contributes to not only greenhouse gas emissions, but also pollution in densely populated areas. This study presents a holistic optimization model designed for an efficient transition to zero-emission drayage, offering cost-effective strategies for the coordinated investment planning for power systems, charging infrastructure, and electric drayage trucks. The model is validated in the Greater Los Angeles area, where regulatory goals are among the most ambitious. Furthermore, the model's design allows for easy adaptation to other regions. By focusing on drayage trucks, this study also paves the way for future research into other freight categories, establishing a foundation for a more extensive exploration in this field.

math.OC↗

arXiv2023

Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, a generalizable, interpretable, computational model of adaptive human driving behavior is still lacking. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

cs.RO↗

arXiv2023

Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations, which considers both local and global attentions. Specifically, considering local spatial coherence, local feature learning is performed through Relative Position Encoding and Attentive Feature Pooling. We incorporate the learned locality into the Transformer module. The local feature affects value component in Transformer to modulate the relationship between channels of each point, which can enhance self-attention mechanism with locality based channel interaction. We demonstrate its superiority experimentally on classification and segmentation tasks. The code is available at: https://github.com/jiamang/IBT

cs.CV↗

arXiv2023

A Light-Weight Communication-Efficient Data Sharing Approach in 5G NR V2X

Timeliness of information is critical for Basic Safety Messages (BSMs) in Vehicle-to-Everything (V2X) communication to enable highly reliable autonomous driving. However, the current semi-persistent scheduling (SPS) algorithms in the 5th generation New Radio (5G NR) standard can still generate collisions probability, close to 20% with 100 vehicles per kilometer, such that they cannot meet this requirement for BSMs. This paper proposes a Ledger concept, which can communicate collision information to every vehicle in the network within one Resource Reservation Interval (RRI) through the broadcasting of Ledger information. The colliding vehicle is aware that it has collided with other vehicles and will reselect for the next SPS period. Besides that, other protocols follow the SPS. Although it sacrifices 14.29% of resources by including Ledger, it can eventually reduce the collision probability. In this paper, a Monte Carlo simulator is used to verify and analyze the performance of the Ledger system. Numerical results show that abide by the SPS protocol, the Ledger system can make the collision probability converge to zero after amount of RRIs.

eess.SY↗

arXiv2023

Dynamic Local Feature Aggregation for Learning on Point Clouds

Existing point cloud learning methods aggregate features from neighbouring points relying on constructing graph in the spatial domain, which results in feature update for each point based on spatially-fixed neighbours throughout layers. In this paper, we propose a dynamic feature aggregation (DFA) method that can transfer information by constructing local graphs in the feature domain without spatial constraints. By finding k-nearest neighbors in the feature domain, we perform relative position encoding and semantic feature encoding to explore latent position and feature similarity information, respectively, so that rich local features can be learned. At the same time, we also learn low-dimensional global features from the original point cloud for enhancing feature representation. Between DFA layers, we dynamically update the constructed local graph structure, so that we can learn richer information, which greatly improves adaptability and efficiency. We demonstrate the superiority of our method by conducting extensive experiments on point cloud classification and segmentation tasks. Implementation code is available: https://github.com/jiamang/DFA.

cs.CV↗

arXiv2022

One-dimensional polymers in random environments: stretching vs. folding

In this article we study a \emph{non-directed polymer model} on $\mathbb Z$, that is a one-dimensional simple random walk placed in a random environment. More precisely, the law of the random walk is modified by the exponential of the sum of "rewards" (or penalities) $βω_x -h$ sitting on the range of the random walk, where $(ω_x)_{x\in \mathbb Z}$ are i.i.d.\ random variables (the disorder), and where $β\geq 0$ (disorder strength) and $h\in \mathbb{R}$ (external field) are two parameters. When $β=0,h>0$, this corresponds to a random walk penalized by its range; when $β>0, h=0$, this corresponds to the "standard" polymer model in random environment, except that it is non-directed. In this work, we allow the parameters $β,h$ to vary according to the length of the random walk, and we study in detail the competition between the \emph{stretching effect} of the disorder, the \emph{folding effect} of the external field (if $h\ge 0$), and the \emph{entropy cost} of atypical trajectories. We prove a complete description of the (rich) phase diagram. For instance, in the case $β>0, h=0$ of the non-directed polymer, if $ω_x$ ha a finite second moment, we find a transversal fluctuation exponent $ξ=2/3$, and we identify the limiting distribution of the rescaled log-partition function.

math.PR↗

arXiv2022

Unusual electric polarization behavior in elemental quasi-2D allotropes of selenium

We investigate tunable electric polarization and electronic structure of quasi-two-dimensional (quasi-2D) allotropes of selenium, which are formed from their constituent one-dimensional (1D) structures through an inter-chain interaction facilitated by the multi-valence nature of Se. Our em ab initio calculations reveal that different quasi-2D Se allotropes display different types of electric polarization, including ferroelectric (FE) polarization normal to the chain direction in alpha and delta allotropes, non-collinear ferrielectric (FiE) polarization along the chain axis in tau-Se, and anti-ferroelectric (AFE) polarization in eta-Se. The magnitude and direction of the polarization can be changed by a previously unexplored rotation of the constituent chains. In that case, an in-plane polarization direction may change to out-of-plane in alpha-Se and delta-Se, flip its direction, and even disappear in tau-Se. Also, the band gap may be reduced and changed from indirect to direct by rotating the constituent chains about their axes in these quasi-2D Se allotropes.

cond-mat.mtrl-sci↗

arXiv2022

Non-directed polymers in heavy-tail random environment in dimension $d\geq 2$

In this article we study a \emph{non-directed} polymer model in dimension $d\ge 2$: we consider a simple symmetric random walk on $\mathbb{Z}^d$ which interacts with a random environment, represented by i.i.d. random variables $(ω_x)_{x\in \mathbb{Z}^d}$. The model consists in modifying the law of the random walk up to time (or length) $N$ by the exponential of $\sum_{x\in \mathcal{R}_N}β(ω_x-h)$ where $\mathcal{R}_N$ is the range of the walk, \textit{i.e.} the set of visited sites up to time $N$, and $β\geq 0,\, h\in \mathbb{R}$ are two parameters. We study the behavior of the model in a weak-coupling regime, that is taking $β:=β_N$ vanishing as the length $N$ goes to infinity, and in the case where the random variables $ω$ have a heavy tail with exponent $α\in (0,d)$. We are able to obtain precisely the behavior of polymer trajectories under all possible weak-coupling regimes $β_N = \hat βN^{-γ}$ with $γ\geq 0$: we find the correct transversal fluctuation exponent $ξ$ for the polymer (it depends on $α$ and $γ$) and we give the limiting distribution of the rescaled log-partition function. This extends existing works to the non-directed case and to higher dimensions.

math.PR↗

arXiv2022

Performance Analysis of Semi-Persistent Scheduling Throughput in 5G NR-V2X: A MAC Perspective

The packet throughput in 5th Generation (5G) New Radio (NR) Vehicle-to-Everything (V2X) is highly dependent on the Medium Access Control (MAC) based scheduling algorithm with no base station participation. In particular, the Semi-Persistent Scheduling (SPS) algorithm has been standardized by the 3rd Generation Partnership Project (3GPP) for V2X resource scheduling in the out-of-coverage scenario. This paper analyzes the NR-V2X SPS throughput from the MAC perspective, where the packet reception ratio (PRR) and half-duplex (HD) effect dominate. We first investigate the average throughput in the fully connected vehicular network, in which all the vehicles share the same throughput. Subsequently, the average throughput as a function of distance in the partially connected vehicular network is analyzed. The Monte Carlo simulation results show that increasing the resource keeping probability can improve the average throughput. Meanwhile, in the partially connected network, the lower resource keeping probability is prone to obtaining the higher throughput gain by increasing the number of subchannels.

eess.SY↗

arXiv2022

Ferroelectricity induced by the absorption of water molecules on double helix SnIP

We study the ferroelectricity in a one-dimensional system composed of a double helix SnIP with absorbing water molecules. Our ab initio calculations reveal two factors that are critical to the electrical polarization. The first one is the orientation of polarized water molecules staying in the R2 region of SnIP. The second one is the displacement of I atom which roots from subtle interaction with absorbed water molecules. A reasonable scenario of polarization flipping is proposed in this study. In the scenario, the water molecule is rolling-up with keeping the magnitude of its electrical dipole and changing its direction, meanwhile, the displacement of I atoms is also reversed. Highly tunable polarization can be achieved by applying strain, with 26.5% of polarization enhancement by applying tensile strain, with only 4% degradation is observed with 4% compressive strain. Finally, the direct band gap is also found to be correlated with strain.

cond-mat.mtrl-sci↗

arXiv2022

Weak coupling limits for directed polymers in tube environments

In this paper, we study a model of directed polymers in random environment, where the environment is restricted to a time-space tube whose spatial width grows polynomially with time. It can be viewed as an interpolation between the disordered pinning model and the classic directed polymer model. We prove weak coupling limits for the directed polymer partition functions in such tube environments in all dimensions, as the inverse temperature vanishes at a suitable rate. As the tube width varies, transitions between regimes of disorder irrelevance, marginal relevance and disorder relevance are observed.

math.PR↗

arXiv2022

Dilated convolutional neural network-based deep reference picture generation for video compression

Motion estimation and motion compensation are indispensable parts of inter prediction in video coding. Since the motion vector of objects is mostly in fractional pixel units, original reference pictures may not accurately provide a suitable reference for motion compensation. In this paper, we propose a deep reference picture generator which can create a picture that is more relevant to the current encoding frame, thereby further reducing temporal redundancy and improving video compression efficiency. Inspired by the recent progress of Convolutional Neural Network(CNN), this paper proposes to use a dilated CNN to build the generator. Moreover, we insert the generated deep picture into Versatile Video Coding(VVC) as a reference picture and perform a comprehensive set of experiments to evaluate the effectiveness of our network on the latest VVC Test Model VTM. The experimental results demonstrate that our proposed method achieves on average 9.7% bit saving compared with VVC under low-delay P configuration.

eess.IV↗

arXiv2021

Underwater Image Restoration via Contrastive Learning and a Real-world Dataset

Underwater image restoration is of significant importance in unveiling the underwater world. Numerous techniques and algorithms have been developed in the past decades. However, due to fundamental difficulties associated with imaging/sensing, lighting, and refractive geometric distortions, in capturing clear underwater images, no comprehensive evaluations have been conducted of underwater image restoration. To address this gap, we have constructed a large-scale real underwater image dataset, dubbed `HICRD' (Heron Island Coral Reef Dataset), for the purpose of benchmarking existing methods and supporting the development of new deep-learning based methods. We employ accurate water parameter (diffuse attenuation coefficient) in generating reference images. There are 2000 reference restored images and 6003 original underwater images in the unpaired training set. Further, we present a novel method for underwater image restoration based on unsupervised image-to-image translation framework. Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method. Our code and dataset are publicly available at GitHub.

eess.IV↗

arXiv2021

Single Underwater Image Restoration by Contrastive Learning

Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world. This paper elaborates on a novel method that achieves state-of-the-art results for underwater image restoration based on the unsupervised image-to-image translation framework. We design our method by leveraging from contrastive learning and generative adversarial networks to maximize mutual information between raw and restored images. Additionally, we release a large-scale real underwater image dataset to support both paired and unpaired training modules. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method.

cs.CV↗