An overview of data-driven battery health estimation technology for battery management system

The automated extraction of maritime routes that accurately resembles the real traffic of vessels is crucial for intelligent traffic management systems to understand vessel behaviour in sea areas, identify events, and support decision-making. Available solutions for maritime traffic route extraction utilize traditional clustering algorithms, which have high computational costs. Data reduction methods are proposed for use with these clustering methods to improve clustering performance, which involves a loss in movement pattern quality. Such solutions often result in low quality representations of traffic routes, which poorly estimate sailing distances for long journeys and time of arrivals and poorly identify non-conformities. In this paper, we propose a spatial clustering method (SPTCLUST-II) to extract spatial representations of sailing routes from historical Automatic Identification System (AIS) data. Our method can cluster huge volumes of trajectory data in a minimal amount of time without using any of the traditional clustering algorithms and with no reduction or modification of the spatio-temporal predicates of the original trajectories. A real-world AIS dataset captured in the area of the Gulf of Mexico is used for the evaluation of the proposed method. The results demonstrate that the proposed method extracts tankers maritime traffic routes with an accuracy of 97% and a f1-measure of 98.5% and cargoes maritime traffic routes with an accuracy of 98.1% and a f1-measure of 99%. This method can be utilized by surveillance authorities for stable and sustainable vessel traffic management.

Spatial Clustering Method of Historical AIS Data for Maritime Traffic Routes Extraction

The automated extraction of maritime routes that accurately resembles the real traffic of vessels is crucial for intelligent traffic management systems to understand vessel behaviour in sea areas, identify events, and support decision-making. Available solutions for maritime traffic route extraction utilize traditional clustering algorithms, which have high computational costs. Data reduction methods are proposed for use with these clustering methods to improve clustering performance, which involves a loss in movement pattern quality. Such solutions often result in low quality representations of traffic routes, which poorly estimate sailing distances for long journeys and time of arrivals and poorly identify non-conformities. In this paper, we propose a spatial clustering method (SPTCLUST-II) to extract spatial representations of sailing routes from historical Automatic Identification System (AIS) data. Our method can cluster huge volumes of trajectory data in a minimal amount of time without using any of the traditional clustering algorithms and with no reduction or modification of the spatio-temporal predicates of the original trajectories. A real-world AIS dataset captured in the area of the Gulf of Mexico is used for the evaluation of the proposed method. The results demonstrate that the proposed method extracts tankers maritime traffic routes with an accuracy of 97% and a f1-measure of 98.5% and cargoes maritime traffic routes with an accuracy of 98.1% and a f1-measure of 99%. This method can be utilized by surveillance authorities for stable and sustainable vessel traffic management. 

Detection of the vehicle license plate using a kernel density with default search radius algorithm filter

In the realm of contemporary medical research, the extraction of pivotal attributes associated with Parkinson’s disease (PD) from intricate Magnetic Resonance Imaging (MRI) data characterized by multifaceted dimensions and non-linearity stands as a formidable challenge in the pursuit of realizing automated diagnostic support. To address this issue, an approach is introduced, denoted as the Deep Frequency-Channel Attention Factorization (Deep FCAF), designed to address the complexities inherent to the neurodegenerative ailment of Parkinson’s. The Deep FCAF amalgamates three key elements: (1) the inherent capacity for non-linear processing exhibited by neural networks, (2) the inherent capability of the attention mechanism to capture global interdependencies, and (3) the underpinning principles of tensor decomposition within the order of multi-dimensional data structures. The experiments conducted on the identification of PD underscore the efficacy of Deep FCAF in assimilating structural and non-linear factor matrices, consequently leading to an improved classification performance. Notably, the elucidation of functional connectivity based on the acquired factor matrices align with previous scholarly investigations. The mathematical formulations, derived from the MRI data, facilitate the measurement of the dynamic mechanism inherent in the MRI data. 

Unsupervised deep frequency-channel attention factorization to non-linear feature extraction: A case study of identification and functional connectivity interpretation of Parkinson’s disease

A Non-negative Latent-factorization-of-tensors model relying on a <underline>N</underline>onnegative and <underline>M</underline>ultiplicative <underline>U</underline>pdate on <underline>I</underline>ncomplete <underline>T</underline>ensors (NMU-IT) algorithm facilitates efficient representation learning to a <underline>D</underline>ynamic <underline>W</underline>eighted <underline>D</underline>irected <underline>N</underline>etwork (DWDN). However, a NMU-IT algorithm leads to slow model convergence and inefficient selection of hyper-parameters. Aiming to address these challenging issues, this work proposes a Momentum-incorporated Biased Non-negative and Adaptive Latent-factorization-of-tensors (MNL) model. It adopts two-fold ideas: 1) incorporating a generalized momentum method into the NMU-IT algorithm to enable fast model convergence; 2) facilitating hyper-parameter slef-adaptation via Particle Swarm Optimization. Empirical studies on four real DWDNs indicate that the proposed MNL is superior to state-of-the-art models in performing efficient representation learning to a DWDN, which is definitely supported by its high computational efficiency and prediction accuracy for missing links of a DWDN. Moreover, its hyper-parameter-free training enables its high practicability in real scenes.

MNL: A Highly-Efficient Model for Large-scale Dynamic Weighted Directed Network Representation

At present, the correct recognition rate of face recognition algorithm is limited under unconstrained conditions. To solve this problem, a face recognition algorithm based on deep learning under unconstrained conditions is proposed in this paper. The algorithm takes LBP texture feature as the input data of deep network, and trains the network layer by layer greedily to obtain optimized parameters of network, and then uses the trained network to predict the test samples. Experimental results on the face database LFW show that the proposed algorithm has higher correct recognition rate than some traditional algorithms under unconstrained conditions. In order to further verify its effectiveness and universality, this algorithm was also tested in YALE and YALE-B, and achieved a high correct recognition rate as well, which indicated that the deep learning method using LBP texture feature as input data is effective and robust to face recognition.

Study on face recognition under unconstrained conditions based on LBP and deep learning

A vehicle recognition algorithm based on fusion feature and improved binary normalized gradient feature

In recent years, generative adversarial networks (GAN) have become a research hotspot in the field of deep learning. Researchers apply them to the field of anomaly detection and are committed to effectively and accurately identifying abnormal images in practical applications. In anomaly detection, traditional supervised learning algorithms have limitations in training with a large number of known labeled samples. Therefore, the anomaly detection model of unsupervised learning GAN is the research object for discussion and research. Firstly, the basic principles of GAN are introduced. Secondly, several typical GAN-based anomaly detection models are sorted out in detail. Then by comparing the similarities and differences of each derivative model, discuss and summarize their respective advantages, limitations and application scenarios. Finally, the problems and challenges faced by GAN in anomaly detection are discussed, and future research directions are prospected.

Analysis and Research of Generative Adversarial Network in Anomaly Detection

Research on bilateral filtering algorithm based on trigonometric function

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chun He

Author Tools

Chat about Author