138 research outputs found
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery
This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery
This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers
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Text Segmentation Similarity Revisited: A Flexible Distance-based Approach for Multiple Boundary Types
Segmentation of texts into discourse and prosodic units is a ubiquitous problem in corpus linguistics and psycholinguistics, yet best practices for its evaluation – whether evaluating consistency between human segmenters or humanlikeness of machine segmenters – remain understudied. Building on segmentation edit distance (Fournier & Inkpen 2012, Fournier 2013), this paper introduces a new measure for evaluating similarity between two segmentations of the same text with multiple, mutually exclusive boundary types, accounting for varying identifiability and confusability between these types. We implement a dynamic programming algorithm for calculation specifically geared towards this type of segmentation problem, apply it to a case study of intonation unit segmentation measuring inter-annotator agreement, and make suggestions for interpreting results
Artificial intelligence in cancer target identification and drug discovery
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates
Sharpness-Aware Graph Collaborative Filtering
Graph Neural Networks (GNNs) have achieved impressive performance in
collaborative filtering. However, GNNs tend to yield inferior performance when
the distributions of training and test data are not aligned well. Also,
training GNNs requires optimizing non-convex neural networks with an abundance
of local and global minima, which may differ widely in their performance at
test time. Thus, it is essential to choose the minima carefully. Here we
propose an effective training schema, called {gSAM}, under the principle that
the \textit{flatter} minima has a better generalization ability than the
\textit{sharper} ones. To achieve this goal, gSAM regularizes the flatness of
the weight loss landscape by forming a bi-level optimization: the outer problem
conducts the standard model training while the inner problem helps the model
jump out of the sharp minima. Experimental results show the superiority of our
gSAM
A Conceptual Model to Optimize Operating Cost of Passenger Ships in Macau
To facilitate more convenient travel as the economy of Macau expands, the government of Macau has allowed shipping companies to add passenger ships and shipping lines. This paper demonstrates how shipping companies can reduce costs by optimizing passenger ships and crew size. It analyzes operating conditions for each shipping depot, including transit time, ships, and volume of passengers. A series of integer programming models is proposed. After a practical demonstration using Excel to solve the LP model, we show that the reduction in the number of passenger ships and crew size could reach 22.6% and 29.4%, respectively
Toward a Foundation Model for Time Series Data
A foundation model is a machine learning model trained on a large and diverse
set of data, typically using self-supervised learning-based pre-training
techniques, that can be adapted to various downstream tasks. However, current
research on time series pre-training has mostly focused on models pre-trained
solely on data from a single domain, resulting in a lack of knowledge about
other types of time series. However, current research on time series
pre-training has predominantly focused on models trained exclusively on data
from a single domain. As a result, these models possess domain-specific
knowledge that may not be easily transferable to time series from other
domains. In this paper, we aim to develop an effective time series foundation
model by leveraging unlabeled samples from multiple domains. To achieve this,
we repurposed the publicly available UCR Archive and evaluated four existing
self-supervised learning-based pre-training methods, along with a novel method,
on the datasets. We tested these methods using four popular neural network
architectures for time series to understand how the pre-training methods
interact with different network designs. Our experimental results show that
pre-training improves downstream classification tasks by enhancing the
convergence of the fine-tuning process. Furthermore, we found that the proposed
pre-training method, when combined with the Transformer model, outperforms the
alternatives
An Efficient Content-based Time Series Retrieval System
A Content-based Time Series Retrieval (CTSR) system is an information
retrieval system for users to interact with time series emerged from multiple
domains, such as finance, healthcare, and manufacturing. For example, users
seeking to learn more about the source of a time series can submit the time
series as a query to the CTSR system and retrieve a list of relevant time
series with associated metadata. By analyzing the retrieved metadata, users can
gather more information about the source of the time series. Because the CTSR
system is required to work with time series data from diverse domains, it needs
a high-capacity model to effectively measure the similarity between different
time series. On top of that, the model within the CTSR system has to compute
the similarity scores in an efficient manner as the users interact with the
system in real-time. In this paper, we propose an effective and efficient CTSR
model that outperforms alternative models, while still providing reasonable
inference runtimes. To demonstrate the capability of the proposed method in
solving business problems, we compare it against alternative models using our
in-house transaction data. Our findings reveal that the proposed model is the
most suitable solution compared to others for our transaction data problem
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