71 research outputs found
3D Path Planning and Obstacle Avoidance Algorithms for Obstacle-Overcoming Robots
This article introduces a multimodal motion planning (MMP) algorithm that
combines three-dimensional (3-D) path planning and a DWA obstacle avoidance
algorithm. The algorithms aim to plan the path and motion of
obstacle-overcoming robots in complex unstructured scenes. A novel A-star
algorithm is proposed to combine the characteristics of unstructured scenes and
a strategy to switch it into a greedy best-first strategy algorithm. Meanwhile,
the algorithm of path planning is integrated with the DWA algorithm so that the
robot can perform local dynamic obstacle avoidance during the movement along
the global planned path. Furthermore, when the proposed global path planning
algorithm combines with the local obstacle avoidance algorithm, the robot can
correct the path after obstacle avoidance and obstacle overcoming. The
simulation experiments in a factory with several complex environments verified
the feasibility and robustness of the algorithms. The algorithms can quickly
generate a reasonable 3-D path for obstacle-overcoming robots and perform
reliable local obstacle avoidance under the premise of considering the
characteristics of the scene and motion obstacles.Comment: 2nd IEEE International Conference on Electronic Communications,
Internet of Things and Big Data Conference 2022 (IEEE ICEIB 2022
Development of digital image correlation method for displacement and shape measurement
Master'sMASTER OF ENGINEERIN
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
Meta-learning for offline reinforcement learning (OMRL) is an understudied
problem with tremendous potential impact by enabling RL algorithms in many
real-world applications. A popular solution to the problem is to infer task
identity as augmented state using a context-based encoder, for which efficient
learning of robust task representations remains an open challenge. In this
work, we provably improve upon one of the SOTA OMRL algorithms, FOCAL, by
incorporating intra-task attention mechanism and inter-task contrastive
learning objectives, to robustify task representation learning against sparse
reward and distribution shift. Theoretical analysis and experiments are
presented to demonstrate the superior performance and robustness of our
end-to-end and model-free framework compared to prior algorithms across
multiple meta-RL benchmarks.Comment: 21 pages, 7 figure
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
Causal inference permits us to discover covert relationships of various
variables in time series. However, in most existing works, the variables
mentioned above are the dimensions. The causality between dimensions could be
cursory, which hinders the comprehension of the internal relationship and the
benefit of the causal graph to the neural networks (NNs). In this paper, we
find that causality exists not only outside but also inside the time series
because it reflects a succession of events in the real world. It inspires us to
seek the relationship between internal subsequences. However, the challenges
are the hardship of discovering causality from subsequences and utilizing the
causal natural structures to improve NNs. To address these challenges, we
propose a novel framework called Mining Causal Natural Structure (MCNS), which
is automatic and domain-agnostic and helps to find the causal natural
structures inside time series via the internal causality scheme. We evaluate
the MCNS framework and impregnation NN with MCNS on time series classification
tasks. Experimental results illustrate that our impregnation, by refining
attention, shape selection classification, and pruning datasets, drives NN,
even the data itself preferable accuracy and interpretability. Besides, MCNS
provides an in-depth, solid summary of the time series and datasets.Comment: 9 pages, 6 figure
Jointly Embedding Multiple Single-Cell Omics Measurements
Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of cells. Effectively, MMD-MA performs an in silico co-assay by embedding cells measured in different ways into a learned latent space. In the MMD-MA algorithm, single-cell data points from multiple domains are aligned by optimizing an objective function with three components: (1) a maximum mean discrepancy (MMD) term to encourage the differently measured points to have similar distributions in the latent space, (2) a distortion term to preserve the structure of the data between the input space and the latent space, and (3) a penalty term to avoid collapse to a trivial solution. Notably, MMD-MA does not require any correspondence information across data modalities, either between the cells or between the features. Furthermore, MMD-MA\u27s weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data. We demonstrate the utility of MMD-MA in simulation experiments and using a real data set involving single-cell gene expression and methylation data
Integrated Sensing and Communications: Recent Advances and Ten Open Challenges
It is anticipated that integrated sensing and communications (ISAC) would be
one of the key enablers of next-generation wireless networks (such as beyond 5G
(B5G) and 6G) for supporting a variety of emerging applications. In this paper,
we provide a comprehensive review of the recent advances in ISAC systems, with
a particular focus on their foundations, system design, networking aspects and
ISAC applications. Furthermore, we discuss the corresponding open questions of
the above that emerged in each issue. Hence, we commence with the information
theory of sensing and communications (SC), followed by the
information-theoretic limits of ISAC systems by shedding light on the
fundamental performance metrics. Next, we discuss their clock synchronization
and phase offset problems, the associated Pareto-optimal signaling strategies,
as well as the associated super-resolution ISAC system design. Moreover, we
envision that ISAC ushers in a paradigm shift for the future cellular networks
relying on network sensing, transforming the classic cellular architecture,
cross-layer resource management methods, and transmission protocols. In ISAC
applications, we further highlight the security and privacy issues of wireless
sensing. Finally, we close by studying the recent advances in a representative
ISAC use case, namely the multi-object multi-task (MOMT) recognition problem
using wireless signals.Comment: 26 pages, 22 figures, resubmitted to IEEE Journal. Appreciation for
the outstanding contributions of coauthors in the paper
Multi-scenario pear tree inflorescence detection based on improved YOLOv7 object detection algorithm
Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature maps. Additionally, the SPPCSPC module is optimized to preserve target area features as much as possible under different receptive fields, and the Soft-NMS algorithm is employed to reduce the likelihood of missing detections in overlapping regions. The model is trained on a diverse dataset collected from real-world field settings. Upon validation, the improved YOLOv7-E object detection algorithm achieves an average precision and recall of 91.4% and 89.8%, respectively, in inflorescence detection under various time periods, distances, and weather conditions. The detection time for a single image is 80.9 ms, and the model size is 37.6 Mb. In comparison to the original YOLOv7 algorithm, it boasts a 4.9% increase in detection accuracy and a 5.3% improvement in recall rate, with a mere 1.8% increase in model parameters. The YOLOv7-E object detection algorithm presented in this study enables precise inflorescence detection and localization across an entire tree at varying distances, offering robust technical support for differentiated and precise blossom thinning operations by thinning machinery in the future
Attribute-Based Equality Test over Encrypted Data without Random Oracles
© 2013 IEEE. Sensitive data would be encrypted before uploading to the cloud due to the privacy issue. However, how to compare the encrypted data efficiently becomes a problem. Public Key Encryption with Equality Test (PKEET) provides an efficient way to check whether two ciphertexts (of possibly different users) contain the same message without decryption. As an enhanced variant, Attribute-based Encryption with Equality Test (ABEET) provides a flexible mechanism of authorization on the equality test. Most of the existing ABEET schemes are only proved to be secure in the random oracle model. Their security, however, would not be guaranteed if random oracles are replaced with real-life hash functions. In this work, we propose a construction of CP-ABEET scheme and prove its security based on some reasonable assumptions in the standard model. We then show how to modify the scheme to outsource complex computations in decryption and equality test to a third-party server in order to support thin clients
Public Key Authenticated Encryption with Designated Equality Test and its Applications in Diagnostic Related Groups
Due to the massive growth of data and security concerns, data of patients would be encrypted and outsourced to the cloud server for feature matching in various medical scenarios, such as personal health record systems, actuarial judgements and diagnostic related groups. Public key encryption with equality test (PKEET) is a useful utility for encrypted feature matching. Authorized tester could perform data matching on encrypted data without decrypting. Unfortunately, due to the limited terminology in medicine, people within institutions may illegally use data, trying to obtain information through traversal methods. In this paper we propose a new PKEET notion, called public-key authenticated encryption with designated equality test (PKAE-DET), which could resist this kind of attacks launched by an inside adversary, known as offline message recovery attacks (OMRA). We propose a concrete construction of PKAE-DET, which only requires one single server to perform the feature matching job securely, and does not require any group mechanism. We prove its security based on some simple mathematical assumptions. Experimental results show that our scheme has efficiency comparable with those PKEET schemes which do not resist OMRA attacks or require group mechanism. We further show how our scheme could be effectively used in diagnostic related groups in medicine, demonstrating its practicabilit
The multifaceted influence of multidisciplinary background on placement and academic progression of faculty
Abstract This study delves into the implications of faculty’s multidisciplinary educational backgrounds on their academic placement and upward mobility, and underscores the moderating effects of gender and academic inbreeding. Grounded in the theories of knowledge recombination and limited attention, the study finds that having a multidisciplinary background tends to challenge favorable academic placements and upward mobility. However, it also shows that male faculty and those who have graduated from the same institution where they work (academic inbreeding) are better at overcoming these challenges. Additionally, elite universities seem to have a higher regard for multidisciplinary backgrounds. This study provides insights for individuals navigating academic careers and offers valuable information for university leaders and policymakers
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