193 Result(s)
within
**bin Wu
Artificial Intelligence
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Article
An Improved Northern Goshawk Optimization Algorithm for Feature Selection
Feature Selection (FS) is an important data management technique that aims to minimize redundant information in a dataset. This work proposes DENGO, an improved version of the Northern Goshawk Optimization (NG...
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Article
Open AccessStructural entropy minimization combining graph representation for money laundering identification
Money laundering identification (MLI) is a challenging task for financial AI research and application due to its massive transaction volume, label sparseness, and label bias. Most of the existing MLI methods f...
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Article
Non-linear Feature Selection Based on Convolution Neural Networks with Sparse Regularization
The efficacy of feature selection methods in dimensionality reduction and enhancing the performance of learning algorithms has been well documented. Traditional feature selection algorithms often grapple with ...
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Article
Open AccessFast autonomous exploration with sparse topological graphs in large-scale environments
Exploring large-scale environments autonomously poses a significant challenge. As the size of environments increases, the computational cost becomes a hindrance to real-time operation. Additionally, while fron...
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Chapter and Conference Paper
Passenger’s Preference on Internal Interface Design in Driverless Buses: A Virtual Reality Experiment
Driverless buses are anticipated to enhance mobility services. Nevertheless, addressing passenger concerns about safety and information provision becomes crucial when there is no driver or onboard authority. C...
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Chapter and Conference Paper
Fast Autonomous Exploration with Sparse Topological Graphs in Large-Scale Environments
Exploring large-scale environments autonomously poses a significant challenge. As the size of environments increases, the computational cost becomes a hindrance to real-time operation. Additionally, while fron...
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Chapter and Conference Paper
ECNU-LLM@CHIP-PromptCBLUE: Prompt Optimization and In-Context Learning for Chinese Medical Tasks
Our team, ECNU-LLM, presents a method of in-context learning for enhancing the performance of large language models without fine-tuning in the 9th China Health Information Processing Conference (CHIP 2023) Ope...
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Chapter and Conference Paper
Enhancing Passenger Safety in an Autonomous Bus: A Multimodal Fall Detection Approach for Effective Remote Monitoring
With the rise of autonomous public transportation, passenger safety in autonomous buses is paramount. This paper introduces a novel Multimodal Long Short-Term Memory (LSTM) network-based fall detection system,...
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Article
A Novel Ensemble-Learning-Based Convolution Neural Network for Handling Imbalanced Data
Deep-learning-based fault diagnosis of wind turbine has played a significant role in advancing the renewable energy industry. However, the imbalanced data sampled by the supervisory control and data acquisitio...
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Article
HGTHP: a novel hyperbolic geometric transformer hawkes process for event prediction
Event sequences with spatiotemporal characteristics have been rapidly produced in various domains, such as earthquakes in seismology, electronic medical records in health care, and transactions in the financia...
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Article
AMMGAN: adaptive multi-scale modulation generative adversarial network for few-shot image generation
Deep learning-based methods have recently advanced image generation by exploiting the valuable information within immense training data, but they struggle to synthesize new images for rare categories that are ...
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Article
An autonomous navigation approach for unmanned vehicle in off-road environment with self-supervised traversal cost prediction
This paper presents a self-supervised learning-based terrain traversal cost prediction method that addresses different orientations and velocities to aid autonomous navigation in off-road environments. First, ...
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Article
Unsupervised image-to-image translation via long-short cycle-consistent adversarial networks
Cycle consistency conducts generative adversarial networks from aligned image pairs to unpaired training sets and can be applied to various image-to-image translations. However, the accumulation of errors that...
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Article
A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine
Harris hawks optimizer (HHO) is a relatively novel meta-heuristic approach that mimics the behavior of Harris hawk over the process of predating the rabbits. The simplicity and easy implementation of HHO have ...
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Article
An intelligent traceability method of water pollution based on dynamic multi-mode optimization
Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to ...
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Chapter and Conference Paper
Overview of the NLPCC 2023 Shared Task: Chinese Essay Discourse Coherence Evaluation
In this paper, we present an overview of the Chinese Essay Discourse Coherence Evaluation task in the NLPCC 2023 shared tasks. We give detailed descriptions of the task definition and the data for training as ...
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Chapter and Conference Paper
A Weighting Possibilistic Fuzzy C-Means Algorithm for Interval Granularity
Granular clustering is an emerging branch in the field of clustering. However, the existing granular clustering algorithms are still immature in terms of weight setting of granular data and noise resistance. I...
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Chapter and Conference Paper
The Relationship Between Older Drivers’ Cognitive Ability and Takeover Performance in Conditionally Automated Driving
In takeover process of conditionally automated driving, cognitive abilities, especially the executive function abilities, are found to play a significant role in driver’s performance. During the automated driv...
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Chapter and Conference Paper
CTC-Net: A Novel Coupled Feature-Enhanced Transformer and Inverted Convolution Network for Medical Image Segmentation
In recent years, the Vision Transformer has gradually replaced the CNN as the mainstream method in the field of medical image segmentation due to its powerful long-range dependencies modeling ability. However...
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Chapter and Conference Paper
Research on Day-Ahead Scheduling Strategy of the Power System Includes Wind Power Plants and Photovoltaic Power Stations Based on Big Data Clustering and Filling
Traditional power system scheduling optimization methods cannot fully deal with the massive data brought by the increase of new energy penetration. Aiming at the above problems, this paper proposes a day-ahead...
