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arXiv 2021年

作者： Jastrzebska, Agnieszka Hernández, Alejandro Morales Nápoles, Gonzalo Salgueiro, Yamisleydi Vanhoof, Koen Faculty of Mathematics and Information Science Warsaw University of Technology Poland Faculty of Business Economics Hasselt University Belgium Department of Cognitive Science & Artificial Intelligence Tilburg University Netherlands Department of Computer Sciences Faculty of Engineering Universidad de Talca Campus Curicó Chile

Time series processing is an essential aspect of wind turbine health monitoring. Despite the progress in this field, there is still room for new methods to improve modeling quality. In this paper, we propose two new approaches for the analysis of wind turbine health. Both approaches are based on abstract concepts, implemented using fuzzy sets, which summarize and aggregate the underlying raw data. By observing the change in concepts, we infer about the change in the turbine’s health. Analyzes are carried out separately for different external conditions (wind speed and temperature). We extract concepts that represent relative low, moderate, and high power production. The first method aims at evaluating the decrease or increase in relatively high and low power production. This task is performed using a regression-like model. The second method evaluates the overall drift of the extracted concepts. Large © 2021, CC BY-NC-ND.

关键词： Wind turbines

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Smart online charging algorithm for electric vehicles via customized actor-critic learning

arXiv

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arXiv 2021年

作者： Cao, Yongsheng Wang, Hao Li, Demin Zhang, Guanglin College of Information Science and Technology Donghua University Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai201620 China The Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia The Stanford Sustainable Systems Lab Stanford University StanfordCA94305 United States

With the advances in the Internet of Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This paper investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor-critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor-critic learning charging algorithm (CALC) by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs' expected cost by 24.03%, 21.49%, 13.80%, compared with the Eagerly Charging Algorithm, Online Charging Algorithm, RL-based Adaptive Energy Management Algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational efficiency

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Semantics-Guided Contrastive Network for Zero-Shot Object Detection

arXiv

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arXiv 2021年

作者： Yan, Caixia Chang, Xiaojun Luo, Minnan Liu, Huan Zhang, Xiaoqin Zheng, Qinghua The Ministry of Education Key Lab of Intelligent Networks and Network Security National Engineering Lab for Big Data Analytics School of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi710049 China The ReLER Lab AAAI Faculty of Engineering and Information Technology University of Technology Sydney Australia The College of Computer Science and Artificial Intelligence Wenzhou University China

Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision. Most existing approaches tackle the ZSD task with a strict mapping-transfer strategy that may lead to suboptimal ZSD results: 1) the learning process of these models neglects the available semantic information on unseen classes, which can easily bias towards the seen categories;2) the original visual feature space is not well-structured for the ZSD task due to the lack of discriminative information. To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection. Particularly, ContrastZSD incorporates two semantics-guided contrastive learning subnets that contrast between region-category and region-region pairs respectively. The pairwise contrastive tasks take advantage of supervision signals derived from both the ground truth label and class similarity information. By performing supervised contrastive learning over those explicit semantic supervision, the model can learn more knowledge about unseen categories to avoid the bias problem to seen concepts, while optimizing the visual data structure to be more discriminative for better visual-semantic alignment. Extensive experiments are conducted on two popular benchmarks for ZSD, i.e., PASCAL VOC and MS COCO. Results show that our method outperforms the previous state-of-the-art on both ZSD and generalized ZSD tasks. © 2021, CC BY.

关键词： Object detection

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OPR-Miner: Order-preserving rule mining for time series

arXiv

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arXiv 2022年

作者： Wu, Youxi Zhao, Xiaoqian Li, Yan Guo, Lei Zhu, Xingquan Fournier-Viger, Philippe Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Key Laboratory of Big Data Computing Tianjin300401 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China The Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Shenzhen University Shenzhen China Hefei University of Technology Hefei230009 China

Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by the order-preserving matching, the existing order-preserving pattern (OPP) mining algorithm employs order-preserving matching to calculate the support, which leads to low efficiency. To address this deficiency, this paper proposes an algorithm called efficient frequent OPP miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four parts: a pattern fusion strategy to generate candidate patterns, a matching process for the results of sub-patterns to calculate the support of super-patterns, a screening strategy to dynamically reduce the size of prefix and suffix arrays, and a pruning strategy to further dynamically prune candidate patterns. Moreover, this paper explores the order-preserving rule (OPR) mining and proposes an algorithm called OPR-Miner to discover strong rules from all frequent OPPs using EFO-Miner. Experimental results verify that OPR-Miner gives better performance than other competitive algorithms. More importantly, clustering and classification experiments further validate that OPR-Miner achieves good performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series

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Exploring an IOT-based smart charging algorithm for electric vehicles considering multiprocessing

Exploring an IOT-based smart charging algorithm for electric...

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4th International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2023)

作者： P. William K. S. Shashikala O. I. Khalaf I. H. Jebril B. Zaqaibeh S. Algburi Department of Information Technology Sanjivani College of Engineering Savitribai Phule Pune University Pune India Department of Artificial intelligence and Machine Learning New Horizon College of Engineering Bengaluru -560103 India Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Al-Zaytoonah University of Jordan Queen Alia Airport St. 594 11942 Amman Jordan Faculty of Science and Information Technology Jadara University Jordan Al-Kitab University College of Engineering Techniques kirkuk Iraq

ISBN: (数字)9781837240258

The transportation industry as a whole is now undergoing a transition towards more environmentally friendly business practices, and electric vehicles (EVs) are an essential component of this industry-wide shift. An intelligent charging algorithm that makes use of multiprocessing strategies and is based on the Internet of Things (IoT) is now the subject of study as a potential solution to the problems associated with inefficient and wasteful charging of electric vehicles (EVs). The development and evaluation of a smart charging algorithm for electric vehicles using the Internet of Things (IoT) is going to be the focus of this project. This integration makes it possible to create real-time communication between electric cars (EVs), charging stations, and the grid, which is necessary in order to offer charging that is intelligent and adaptable. The growth of the electric vehicle sector will only increase the need of coming up with innovative answers to problems. The purpose of this study is to evaluate the effectiveness of the algorithm and to determine the extent to which it has the potential to revolutionize the process of charging electric cars while also improving grid management, energy efficiency, and the prospects for a sustainable future.

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ADeepWeeD: An adaptive deep learning framework for weed species classification

Artificial Intelligence in Agriculture

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artificial intelligence in Agriculture 2025年第4期15卷 590-609页

作者： Md Geaur Rahman Md Anisur Rahman Mohammad Zavid Parvez Md Anwarul Kaium Patwary Tofael Ahamed David A. Fleming-Muñoz Saad Aloteibi Mohammad Ali Moni School of Computing Mathematics and Engineering Charles Sturt University Port Macquarie NSW 2444 Australia La Trobe Business School La Trobe University Melbourne VIC 3086 Australia School of Physics Maths and Computing The University of Western Australia Perth WA 6009 Australia Faculty of Life and Environmental Sciences University of Tsukuba Tsukuba Science City 305-8572 Ibaraki Japan La Trobe Institute for Sustainable Agriculture and Food La Trobe University Melbourne VIC 3086 Australia Computer Science Department Community College King Saud University Riyadh 11437 Saudi Arabia AI & Digital Health Technology Artificial Intelligence and Cyber Futures Institute Charles Sturt University Bathurst NSW 2795 Australia AI & Digital Health Technology Rural Health Research Institute Charles Sturt University Orange NSW 2800 Australia School of IT Washington University of Science and Technology VA USA

Efficient weed management in agricultural fields is essential for attaining optimal crop yields and safeguarding global food security. Every year, farmers worldwide invest significant time, capital, and resources to combat yield losses, approximately USD 75.6 billion, due to weed infestations. Deep Learning (DL) methodologies have been recently implemented to revolutionise agricultural practices, particularly in weed detection and classification. Existing DL-based weed classification techniques, including VGG16 and ResNet50, initially construct a model by implementing the algorithm on a training dataset comprising weed species, subsequently employing the model to identify weed species acquired during training. Given the dynamic nature of crop fields, we argue that existing methods may exhibit suboptimal performance due to two key issues: (i) the unavailability of all training weed species initially, as these species emerge over time, resulting in a progressively expanding training dataset, and (ii) the constrained memory and computational capacity of the system utilised for model development, which hinders the retention of all weed species that manifest over an extended duration. To address the issues, this paper introduces a novel DL-based framework called ADeepWeeD for weed classification that facilitates adaptive (i.e. incremental) learning so that it can handle new weed species by keeping track of historical information. ADeepWeeD is evaluated using two criteria, namely F 1 -Score and classification accuracy, by comparing its performances against four non-incremental and two incremental state-of-the-art methods on three publicly available large datasets. Our experimental results demonstrate that ADeepWeeD outperforms existing techniques used in this study. We believe that our developed model could be used to develop an automation system for weed identification. The code of the proposed method is available on GitHub: https://***/grahman20/ADeepWeed .

关键词： Weed classification Adaptive learning Deep learning artificial intelligence Agricultural automation Precision agriculture

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An ample investigation on effective design technologies of autonomous vehicles

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AIP Conference Proceedings 2024年第1期3161卷

作者： C. Vijayakumaran K. Revathi R. Selvi L. C. Yong Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Tambaram Tamil Nadu India School of Engineering Asia Pacific University 57000 Kuala Lumpur Malaysia

Self-driving automobiles typically refer to fully automated cars or trucks that can be driven and controlled without any human involvement. While still in its infancy, autonomous driving technology is rapidly gaining popularity and has the probability to drastically alter the way people travel by addressing societal and economic issues. The proposed research demands the Internet of Things (IoT) to deploy precise wireless sensors that are important for perceiving the surroundings in order to build an appropriate design for driverless cars. Further, deep learning (DL) techniques are used to derive the autonomous and secure driving pattern. The main goal of the suggested design is to create a readily deployable autonomous module that records important environmental characteristics and is capable of enabling accurate driving patterns automatically at a reasonable price. The suggested kit can act as a ready-made item that must be installed at the user end when automation is required.

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artificial intelligence in gastric cancer:applications and challenges

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Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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A dynamic model using k-NN algorithm for predicting diabetes and breast cancer

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Computers in Biology and Medicine 2025年第Pt B期192卷 110276-110276页

作者： Al-Khamees, Hussein A.A. Sani, Nor Samsiah Gifal, Ahmed Sileh Liu, Luan Xiang Wei Esa, Mohd Isrul Computer Techniques Engineering Department College of Engineering and Technology Al-Mustaqbal University Babil51001 Iraq Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor43600 Malaysia Department of Information Technology College of Computer Science and Information Technology University of Kerbala Karbala56001 Iraq

Healthcare remains a critical focus due to its direct impact on human well-being. Diabetes, currently the fastest-growing chronic disease globally, poses severe health risks, including cardiovascular complications and kidney failure. Simultaneously, breast cancer has become the most prevalent cancer among women, particularly those in their 40s, surpassing other types. Early detection and diagnosis of these two diseases remain a substantial challenge, yet they are crucial for reducing mortality rates. Machine learning algorithms emerged as powerful tools in healthcare for disease classification and prediction, with the k-nearest neighbors (k-NN) being one of the most widely used supervised learning algorithm. Different traditional machine learning methods have been proposed, which are heavily specialized for specific datasets. More deeply, traditional k-NN relies on a static k-value, which may not provide optimal results across diverse datasets. This paper proposes a dynamic k-NN model that adjusts ‘k’ value based on local data characteristics, enhancing prediction accuracy. The proposed model is testing on two publicly available datasets;PIMA Diabetes and Breast Cancer Wisconsin (BCW) datasets. Our results are evaluated using different metrics that are;accuracy, precision, recall, F1_score, and execution time. The results of these metrics are as follows;(81.17%, 97.37%), (83.33% 100%), (54.55%, 86.05%), and (65.93%, 92.5%) for PIMA and BCW datasets respectively. These results demonstrate that the proposed model outperformed several state-of-the-art models. Thus, further highlighting its effectiveness and efficiency in medical data classification. © 2025

关键词： Diseases

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ReSSL: relational self-supervised learning with weak augmentation 21

ReSSL: relational self-supervised learning with weak augment...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Mingkai Zheng Shan You Fei Wang Chen Qian Changshui Zhang Xiaogang Wang Chang Xu School of Computer Science Faculty of Engineering The University of Sydney and SenseTime Research SenseTime Research and Department of Automation Tsinghua University Institute for Artificial Intelligence Tsinghua University (THUAI) Beijing National Research Center for Information Science and Technology (BNRist) University of Science and Technology of China SenseTime Research Department of Automation Tsinghua University Institute for Artificial Intelligence Tsinghua University (THUAI) Beijing National Research Center for Information Science and Technology (BNRist) SenseTime Research and The Chinese University of Hong Kong School of Computer Science Faculty of Engineering The University of Sydney

ISBN: (纸本)9781713845393

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance level information (i.e., the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduced a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as relation metric, which is thus utilized to match the feature embeddings of different augmentations. Moreover, to boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. Experimental results show that our proposed ReSSL significantly outperforms the previous state-of-the-art algorithms in terms of both performance and training efficiency.

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