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Observer-Based Control for a New Stochastic Maximum Power Point Tracking for Photovoltaic Systems With Networked Control System

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IEEE Transactions on Fuzzy Systems 2023年第6期31卷 1870-1884页

作者： Aslam, Muhammad Shamrooz Tiwari, Prayag Pandey, Hari Mohan Band, Shahab S. Guangxi University of Science and Technology School of Automation Liuzhou545006 China Halmstad University School of Information Technology Halmstad301 18 Sweden Bournemouth University Data Science and Artificial Intelligence Department of Information and Computing PooleBH12 United Kingdom National Yunlin University of Science and Technology Future Technology Research Center College of Future Douliou64002 Taiwan

This study discusses the new stochastic maximum power point tracking control approach toward the photovoltaic cells (PCs). A PC generator is isolated from the grid, resulting in a direct current microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed networked control systems beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi-Sugeno (T - S) fuzzy model is another option. The T - S fuzzy model is presented in a unified framework, for which 1) the fuzzy observer based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer-based controller can be created using the same premises being the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulation in the PC generator model for the real-time climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov-Krasovskii functional for combining with the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the tensor-product (T-P) transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators. © 1993-2012 IEEE.

关键词： Linear matrix inequalities

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Middle architecture criteria

Middle architecture criteria

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Beverley, John De Colle, Giacomo Jensen, Mark Benson, Carter Smith, Barry Department of Philosophy University at Buffalo NY United States National Center for Ontological Research NY United States Institute for Artificial Intelligence and Data Science University at Buffalo NY United States U.S. Customs and Border Protection United States

Mid-level ontologies are used to integrate data across disparate domains using vocabularies more specific than top-level ontologies and more general than domain-level ontologies. There are no clear, defensible criteria for determining whether a given ontology should count as mid-level, because we lack a rigorous characterization of what the middle level of generality is supposed to contain. Attempts to provide such a characterization have failed, we believe, because they have focused on the goal of specifying what is characteristic of those single ontologies that have been advanced as mid-level ontologies. Unfortunately, single ontologies of this sort are generally a mixture of top- and mid-level, and sometimes even of domain-level terms. To gain clarity, we aim to specify conditions for membership in what we call the middle architecture, which consists solely of mid-level ontologies. © 2024 Copyright for this paper by its authors.

关键词： Ontology

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Reflecting real-time monitoring of Agricultural equipment in Rythu Bharosa Kendra

Reflecting real-time monitoring of Agricultural equipment in...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： S. Vasavi Bhargav Mupparisetty Narasimha Reddy Vemula A. Nageswara Rao Department of Artificial Intelligence & Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Andhra Pradesh Space Application Center (APSAC) Vijayawada India

The Rythu Bharosa Kendra (RBK), an initiative by the Andhra Pradesh government, seeks to bolster farming communities by supplying quality seeds, fertilizers, and agricultural supplements while offering vital extension advisories at the village level. To modernize their operations, a proposed automated system will replace manual record-keeping by storing data directly into a database, facilitating seamless facility transactions, and generating comprehensive service reports. By outfitting RBK vehicles with GPS devices and integrating React technology for a user interface, administrators gain real-time insights into vehicle movements, optimize routes, and efficiently monitor the distribution of agricultural resources. This innovative approach not only streamlines RBK's processes but also ensures timely and effective support to farmers while minimizing paperwork and enhancing overall operational efficiency.

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Sememe knowledge computation:a review of recent advances in application and expansion of sememe knowledge bases

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Frontiers of Computer science 2021年第5期15卷 13-23页

作者： Fanchao QI Ruobing XIE Yuan ZANG Zhiyuan LIU Maosong SUN Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Institute for Artificial Intelligence Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Beijing 100084China Search Product Center WeChat Search Application DepartmentTencentBeijing 100080China Beijing Academy of Artificial Intelligence Beijing 100191China

A sememe is defined as the minimum semantic unit of languages in *** knowledge bases are built by manually annotating sememes for words and *** is the most well-known sememe knowledge *** has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using *** the era of deep learning,although data are thought to be of vital importance,there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system *** successful attempts have been made in the tasks including word representation learning,language modeling,semantic composition,*** addition,considering the high cost of manual annotation and update for sememe knowledge bases,some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge ***,some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new *** this paper,we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes.

关键词： natural language process semantics knowledge base sememe HowNet

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A Multi-Objective Evolutionary Algorithm Based on Indicator and Performing Local Searches 4

A Multi-Objective Evolutionary Algorithm Based on Indicator ...

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4th International Conference on Information science and Education, ICISE-IE 2023

作者： Chen, Hanning Zeng, Xiangrui Jia, Kun Hu, Yabao Wang, Guopeng School of Computer Science and Technology Tiangong University Tianjin China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China School of Artificial Intelligence Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd Beijing China College of Artificial Intelligence University of Science and Technology Tianjin China The Open University of China China

ISBN: (纸本)9798350394610

The indicator-based multi-objective optimization evolutionary algorithm has gained significant attention for its strong performance across various optimization problems. We draw on the advantages and try to make improvements in the environmental selection process based on different selection criteria. From this, we developed a distinctive multi-objective evolutionary algorithm that based on indicator and performing local searches, namely INLS. The INLS algorithm includes a novel selection mechanism that does not rely on weight vectors, which employs a strategy choosing solutions with relatively poor convergence but good diversity to perturb the decision variables of the selected solutions, aiming to explore and discover solutions that demonstrate enhanced convergence and diversity characteristics. The experimental results obtained from conducting tests on the ZDT and DTLZ test suites provide clear evidence of the remarkable performance exhibited by the proposed INLS in comparison to various other well-known MOEAs. These results serve to underscore the competitiveness of the proposed method. © 2023 IEEE.

关键词： Evolutionary algorithms

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Synthesis of electric vehicle charging data: A real-world data-driven approach

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Communications in Transportation research 2024年第1期4卷 163-177页

作者： Zhi Li Zilin Bian Zhibin Chen Kaan Ozbay Minghui Zhong Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU ShanghaiShanghai200126China Shanghai Key Laboratory of Urban Design and Urban Science NYU ShanghaiShanghai200126China Department of Civil and Urban Engineering New York UniversityNew York11201USA C2SMARTER Center(A Tier 1 USDOT UTC) Department of Civil and Urban EngineeringNew York UniversityNew York11201USA Shanghai Electric Vehicle Public Data Collecting Monitoringand Research CenterShanghai201805China

Nowadays,electric vehicles(EVs)are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging *** analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes,particularly in the deployment of charging infrastructure and the formulation of EV-focused ***,the challenges of collecting these data are significant,primarily due to privacy concerns and the high costs associated with data *** response,this study introduces an innovative methodology for generating large-scale and diverse EV charging data,mirroring real-world patterns for cost-efficient and privacy-compliant ***,this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs(BEVs)in Shanghai over a *** illustrate that the proposed model can effectively capture the underlying distribution of the original charging data,enabling the generation of synthetic samples that closely resemble real-world charging *** approach is readily employed for data imputation and augmentation,and it can also help simulate future charging distributions by conditional generation based on anticipated development premises.

关键词： Electric vehicles Charging data data augmentation data generation Gibbs sampling Conditional density network

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Causal Discovery via Conditional Independence Testing with Proxy Variables 41

Causal Discovery via Conditional Independence Testing with P...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Mingzhou Sun, Xinwei Qiao, Yu Wang, Yizhou School of Computer Science Peking University China Peking University China Sch. of Data Science Fudan University China Dep. of Automation Shanghai Jiao Tong University China Inst. for Artificial Intelligence Peking University China Nat'l Eng. Research Center of Visual Technology Peking University China Nat'l Key Lab of General Artificial Intelligence Peking University China

Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing commonly employed in constraint-based causal discovery for identifying causal relations. To address this issue, existing methods introduced proxy variables to adjust for the bias caused by unobserveness. However, these methods were either limited to categorical variables or relied on strong parametric assumptions for identification. In this paper, we propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables, without any parametric constraint. Our procedure is based on discretization, which under completeness conditions, is able to asymptotically establish a linear equation whose coefficient vector is identifiable under the causal null hypothesis. Based on this, we introduce our test statistic and demonstrate its asymptotic level and power. We validate the effectiveness of our procedure using both synthetic and real-world data. Code is publicly available at https://***/lmz123321/proxy_causal_discovery. Copyright 2024 by the author(s)

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A novel feature alignment-based cell nucleus identification scheme for medical decision-making systems

A novel feature alignment-based cell nucleus identification ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gou, Fangfang Dai, Tingyi Wu, Jia Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Hunan University of Medicine General Hospital Huaihua418000 China Monash University Research Center for Artificial Intelligence MelbourneVIC 3800 Australia

ISBN: (纸本)9798350386226

AI medical imaging diagnosis enables physicians to rapidly acquire and analyze image information using deep learning techniques. Consequently, it holds a significant position in medical decision-making assistance. Specifically, the complexity and large scale of digital pathology images necessitate accurate recognition of cell arrangement, size, and shape, which aids physicians in identifying cancerous regions. However, existing medical image segmentation techniques often fail to deliver sufficiently detailed results in unknown target domains, resulting in the blurring of cell nucleus boundaries. In order to introduce more display-like information to solve the problem of obscure cell nucleus boundaries, this study proposes an accurate recognition method (UNW) for cell nucleus recognition in medical decision-making systems. It solves the problem of the distribution difference between the source domain and the unknown target domain by inserting feature center alignment (FCA) and feature distribution alignment (FDA) modules into the encoder and decoder. UNW performs instance normalization (IN) and instance whitening (IW) on the feature images to better identify the feature detail information, which results in more distinct boundaries between the cell nucleus and other tissues. Experimental results on various backbone networks show that our method can obtain more accurate decision results. Among them, the IOU reaches 0.704, which outperforms existing models, while the FCA and FDA maintain the segmentation ability of the model in the face of test data that is inconsistent with the distribution of the training data. © 2024 IEEE.

关键词： Image segmentation

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AI-Informed Development for a Lactate Measurement Tool 32

AI-Informed Development for a Lactate Measurement Tool

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32nd Irish Conference on artificial intelligence and Cognitive science, AICS 2024

作者： Kiely, Cian Rossberg, Nicola Krishnamoorthy, Shree Visentin, Andrea School of Computer Science & IT University College Cork Ireland SFI Center for Research Training in Artificial Intelligence University College Cork Ireland Biophotonics@Tyndall IPIC Tyndall National Institute Ireland SFI Insight Centre for Data Analytics University College Cork Ireland

Lactate has been identified as a key biomarker, with spikes co-occurring with high-risk medical conditions including sepsis and hypoxia. Despite its high medical value, current methods of Lactate measurement require repeated blood sampling from the patient, which is both costly and invasive, and consequently tends to be limited to intensive care units. Spectroscopy, a non-invasive light-based system, presents a cost-effective alternative to these traditional methods, which permits continuous measurement and improved patient monitoring. Through the use of machine learning, spectroscopic measurements can be used to estimate blood Lactate levels in an accessible and low-cost manner. In this study, machine learning models were trained on Near-infrared (NIR) spectroscopy data, to identify the best set-up for high-precision estimation of Lactate levels. The results of the analysis are used to determine the best path length for spectroscopic measurements. Feature selection is implemented to establish the most important wavelengths for prediction and inform on the most relevant spectral regions for the given task. Explainability is implemented to analyse feature contributions and allow inference of potentially interfering components that should be considered for further testing. The results showed that by using a random forest, R2 values of 0.9986 can be achieved. Feature selection increased predictive performance considerably with R2 values as high as 0.9996 and the implementation of explainability allowed the identification of important wavelength ranges. © 2024 Copyright for this paper by its authors.

关键词： Near infrared spectroscopy

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Capabilities: An Ontology

Capabilities: An Ontology

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Beverley, John Limbaugh, David Merrell, Eric Koch, Peter M. Smith, Barry National Center for Ontological Research BuffaloNY United States University at Buffalo Department of Philosophy BuffaloNY United States Institute for Artificial Intelligence and Data Science University at Buffalo NY United States Villanova University Department of Philosophy PA United States

In our daily lives, as in science and in all other domains, we encounter huge numbers of dispositions (tendencies, potentials, powers) which are realized in processes such as sneezing, sweating, shedding, melting. Among this plethora of what we can think of as ‘mere dispositions’ is a subset of dispositions in whose realizations we have an interest – a car responding well when driven on ice, a rabbit’s lungs responding well when it is chased by a wolf, and so on. We call the latter ‘capabilities’ and we attempt to provide an ontological account of what capabilities are that is of sufficient generality to serve a variety of purposes, for example by providing a useful extension to ontology-based research in areas where capabilities data are being collected in siloed fashion. © 2024 Copyright for this paper by its authors.

关键词： Ontology

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