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Disruptive Technologies (ICDT), International Conference on

作者： Ramakrishna M M Nageswara Rao Atyam Nikhil Chaurasia L Sahaya Senthamil Mohd Asif Shah V. L. Raja Dept of ECE/ICE Dr. M.G.R Educational and Research Institute Chennai Tamil Nadu India Electrical and Electronics Department CMR Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Sage University Bhopal Madhya Pradesh India Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India University School of Business Chandigarh University Mohali Punjab India School of Computing Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

There are many vehicle exchanges around the world are using the manual transmission and automatic transmission are the most popular. At this time, many popular manufacturers have started using the robot version in their new products. The gearbox of the robot is, in fact, mechanical; it has an additional automatic clutch and gear shift. Accordingly, the operation of the transmission is not completely dependent on the driver, as in other options, but on the electronic control unit. For the proper functioning of the transmission the driver needs to correctly transmit only the incoming information. In this paper an automation innovation was proposed to control the vehicle gear shifting functions by using the machine learning based on the robotic operation model. An automaton or robot, after some time, must first learn the device of a new invention. The automatic gearbox received a friction type clutch. It is a disk set or a built-in separate mechanism. The most reliable and durable design can be called the double clutch design.

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GCNs-Based Context-Aware Short Text Similarity Model

GCNs-Based Context-Aware Short Text Similarity Model

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International Conference on Pattern Recognition

作者： Xiaoqi Sun Shaochun Wu Yue Liu School of Computer Engineering and Science Shanghai University School of Computer Engineering and Science Shanghai University Shanghai Institute for Advanced Communication and Data Science Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9781728188089;9781728188096

Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which can not capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extends the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

关键词： Text mining Semantics Syntactics Natural language processing Pattern recognition Task analysis Context modeling

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Impact of Overcoming digitalization Implementation Barriers on Building Projects Development Success: A Hybrid Structural Equation Modeling and Deep Neural Network Approaches

SSRN

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SSRN 2023年

作者： Kineber, Ahmed Farouk Ali, Ali Hassan Elshaboury, Nehal Massoud, Mostafa Mo. Rady, Mohammed Al-Mhdawi, M.K.S. Rahimian, Farzad Pour Department of Civil Engineering College of Engineering in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Civil Engineering Canadian Higher Engineering Institute Canadian International College 6th October 12577 Egypt Department of Building and Real Estate The Hong Kong Polytechnic University Hong Kong Cairo Egypt Construction and Project Management Research Institute Housing and Building National Research Centre Giza12311 Egypt B 2401 Smart Village Giza12577 Egypt B 2401 Smart Village Giza12577 Egypt Trinity Dublin College Ireland School of Computing Engineering & Digital Technologies Teesside University Middlesbrough United Kingdom

digital methods in buildings have matured over the past two decades to become a standard practice informed by set principles. Implementing digitalization can aid in improving the primary goals in terms of quality, time, and cost. However, the building industry is slow to embrace technology due to the industry’s reliance on informal methods. Thus, this study aims to establish a framework for successfully incorporating digital technologies into building construction projects by analyzing the link between overcoming digitalization implementation barriers and overall project success (OPS).To leverage the power of data science, a questionnaire was disseminated to building industry experts to assess the significance of digitalization adoption barriers. Subsequently, these barriers were classified using data science techniques such as exploratory factor analysis (EFA). To further explore the relationship between these barriers and the OPS, advanced data science methods including deep artificial neural networks (ANN) and partial least squares-structural equation modeling (PLS-SEM) were utilized. The use of deep ANN and PLS-SEM allowed for a more comprehensive analysis of the correlations between digitalization implementation barriers and OPS. These data science techniques enhanced the accuracy of the results and facilitated more accurate forecasting, providing valuable insights into the impact of digitalization barriers on *** findings indicated a moderate correlation, with mitigating obstacles associated with digitalization execution accounting for 55.5% of the overall project performance. The results can assist decision-makers as they adopt digitalization to cut costs and increase productivity in the building construction sector. © 2023, The Authors. All rights reserved.

关键词： Least squares approximations

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High-Ni Low-Co High-Capacity Li-Rich Cathode Materials Via Adjusting Li2mno3 Content and Li/Ni Mixing Structures

SSRN

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SSRN 2023年

作者： Wu, Zhen Zou, Kunyang Dai, Xin Zhang, Yu-Han Zhang, Xudong Wang, Hao Hou, Zhufeng Ma, Lijing Liu, Yan Chen, Yuanzhen Guo, Shengwu Liu, Yongning State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong University Xi’an710049 China Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess Technology Chinese Academy of Sciences Qingdao266101 China Center for High Performance Computing Network Information Center Xi’an Jiaotong University Xi’an710049 China Hefei Advanced Computing Center Operation Management Corp Ltd Hefei230088 China State Key Laboratory of Structural Chemistry Fujian Institute of Research the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong University Xi’an710049 China

Li-rich layered oxides (LLOs) are considered as the promising cathode materials for next-generation high energy density lithium-ion batteries (LIBs). However, severe voltage fade and capacity decay hinder their commercial applications. Increasing the Ni content in LLOs effectively improves the stability and discharge voltage, but shows reduced capacities, typically © 2023, The Authors. All rights reserved.

关键词： Lithium compounds

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Testing whether a learning procedure is calibrated

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The Journal of Machine Learning research 2022年第1期23卷 9213-9248页

作者： Jon Cockayne Matthew M. Graham Chris J. Oates T. J. Sullivan Onur Teymur Mathematical Sciences University of Southampton Southampton UK Centre for Advanced Research Computing University College London London UK School of Mathematics Statistics & Physics Newcastle University Newcastle Upon Tyne UK Mathematics Institute and School of Engineering University of Warwick Coventry UK School of Mathematics Statistics & Actuarial Science University of Kent Cantebury UK

A learning procedure takes as input a dataset and performs inference for the parameters θ of a model that is assumed to have given rise to the dataset. Here we consider learning procedures whose output is a probability distribution, representing uncertainty about θ after seeing the dataset. Bayesian inference is a prime example of such a procedure, but one can also construct other learning procedures that return distributional output. This paper studies conditions for a learning procedure to be considered calibrated, in the sense that the true data-generating parameters are plausible as samples from its distributional output. A learning procedure whose inferences and predictions are systematically over- or under-confident will fail to be calibrated. On the other hand, a learning procedure that is calibrated need not be statistically efficient. A hypothesis-testing framework is developed in order to assess, using simulation, whether a learning procedure is calibrated. Several vignettes are presented to illustrate different aspects of the framework.

关键词： calibratedness credible sets uncertainty quantification

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Uncertain Location Transmitter and UAV-Aided Warden Based LEO Satellite Covert Communication Systems

arXiv

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arXiv 2025年

作者： Peng, Pei Chen, Xianfu Xu, Tianheng Wu, Celimuge Zou, Yulong Ni, Qiang Soljanin, Emina School of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China Shenzhen CyberAray Network Technology Company Ltd. Guangdong Shenzhen518042 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Meta-Networking Research Center The University of Electro-Communications Tokyo182-8585 Japan School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom Department of Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States

We propose a novel covert communication system in which a ground user, Alice, transmits unauthorized message fragments to Bob, a low-Earth orbit satellite (LEO), and an unmanned aerial vehicle (UAV) warden (Willie) attempts to detect these transmissions. The key contribution is modeling a scenario where Alice and Willie are unaware of each other’s exact locations and move randomly within a specific area. Alice utilizes environmental obstructions to avoid detection and only transmits when the satellite is directly overhead. LEO satellite technology allows users to avoid transmitting messages near a base station. We introduce two key performance metrics: catch probability (Willie detects and locates Alice during a message chunk transmission) and overall catch probability over multiple message chunks. We analyze how two parameters impact these metrics: 1) the size of the detection window and 2) the number of message chunks. The paper proposes two algorithms to optimize these parameters. The simulation results show that the algorithms effectively reduce the detection risks. This work advances the understanding of covert communication under mobility and uncertainty in satellite-aided systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

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Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior

arXiv

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arXiv 2022年

作者： Qin, Chen Wang, Shuo Chen, Chen Bai, Wenjia Rueckert, Daniel Institute for Digital Communications School of Engineering University of Edinburgh Edinburgh United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University China Biomedical Image Analysis Group Department of Computing Imperial College London London United Kingdom Department of Brain Sciences Imperial College London United Kingdom Institute for AI and Informatics Klinikum rechts der Isar Technical University of Munich Munich Germany

Myocardial motion and deformation are rich descriptors that characterize cardiac function. Image registration, as the most commonly used technique for myocardial motion tracking, is an ill-posed inverse problem which often requires prior assumptions on the solution space. In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model. Particularly, the proposed method leverages a variational autoencoder-based generative model to learn a manifold for biomechanically plausible deformations. The motion tracking then can be performed via traversing the learnt manifold to search for the optimal transformations while considering the sequence information. The proposed method is validated on three public cardiac cine MRI datasets with comprehensive evaluations. The results demonstrate that the proposed method can outperform other approaches, yielding higher motion tracking accuracy with reasonable volume preservation and better generalizability to varying data distributions. It also enables better estimates of myocardial strains, which indicates the potential of the method in characterizing spatiotemporal signatures for understanding cardiovascular diseases. © 2022, CC BY.

关键词： Image registration

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Study of multi-tariff influence on the distributed generation remuneration 16th

Study of multi-tariff influence on the distributed generatio...

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16th International Conference on Distributed computing and Artificial Intelligence, DCAI 2019

作者： Silva, Cátia Faria, Pedro Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP - Polytechnic Institute of Porto Rua DR. Antonio Bernardino de Almeida Porto431 4200-072 Portugal

ISBN: (纸本)9783030239459

The energy market, with the introduction of the smart grids concept, opens the door to small distributed energy resources. However, these resources introduce an added level of difficulty to market management, requiring an entity to aggregate and manage them optimally. This paper proposes an approach that integrates these small resources. The methodology is composed of optimal scheduling, aggregation and remuneration based on aggregation. The method chosen for aggregation is k-means. In relation to previous works, the innovation goes through the multi-period and the comparison that this can have in the formation of groups. Thus, three scenarios were created: Whole Week, Work Days and Weekend. Profiles were added for 548 units of DG. The justification for the formation of groups will be a fairer remuneration and according to the contribution of each resource to the management of the network. © 2020, Springer Nature Switzerland AG.

关键词： Distributed power generation

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SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems

arXiv

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arXiv 2024年

作者： Dave, Kshitij Innan, Nouhaila Behera, Bikash K. Mumtaz, Zahid Al-Kuwari, Saif Farouk, Ahmed Institute of Advanced Research Gandhinagar India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M’sick Hassan II University of Casablanca Morocco NYUAD Research Institute Abu Dhabi United Arab Emirates Pvt. Ltd. WB Mohanpur741246 India London School of Economics and Political Science London United Kingdom Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Department of Computer Science Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt

Sentiment analysis is an essential component of natural language processing, used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. researchers have developed various classical machine learning and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in sentiment analysis. However, these approaches often fail to determine the optimal number of clusters, interpret results accurately, handle noise or outliers efficiently, and scale effectively to high-dimensional data. Additionally, they are frequently insensitive to input variations. In this paper, we propose a novel hybrid approach for sentiment analysis called the Quantum Fuzzy Neural Network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical sentiment analysis algorithms. In this study, we test the proposed approach on two Twitter datasets: the Coronavirus Tweets Dataset (CVTD) and the General Sentimental Tweets Dataset (GSTD), and compare it with classical and hybrid algorithms. The results demonstrate that QFNN outperforms all classical, quantum, and hybrid algorithms, achieving 100% and 90% accuracy in the case of CVTD and GSTD, respectively. Furthermore, QFNN demonstrates its robustness against six different noise models, providing the potential to tackle the computational complexity associated with sentiment analysis on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyses different forms of textual data, thereby enhancing sentiment classification and insights associated with sentiment analysis. © 2024, CC BY.

关键词： Tweets

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Nanoscopic Insights into Fouling and Cleaning of Membranes Using Ptychographic X-Ray Nanotomography

SSRN

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SSRN 2024年

作者： Rudolph-Schöpping, Gregor Larsson, Emanuel Guizar-Sicairos, Manuel Nilsson Pingel, Torben Holler, Mirko Hall, Stephen A. Lipnizki, Frank Division of Chemical Engineering Lund University Lund22100 Sweden LINXS - Institute of Advanced Neutron and X-ray Science Lund Sweden Division of Solid Mechanics Department of Construction Sciences Lund University Lund Sweden LUNARC Centre for Scientific and Technical Computing Lund University Lund Sweden MAX IV Laboratory Lund University Lund Sweden RISE Research Institutes of Sweden Bioeconomy and Health - Agriculture and Food Gothenburg Sweden Paul Scherrer Institute Villigen Switzerland Lausanne Switzerland

Membrane processes are key in a sustainable economy, but membrane fouling is hampering theirfurther distribution. In this study ptychographic X-ray nanotomography (PXCT) was used to investigatethe nanoscale structural changes resulting from membrane fouling and cleaning in andon commercial microfiltration and ultrafiltration membranes. Using a complex feed solution forfouling and a commercial cleaning agent, PXCT achieved unprecedented 26 nm resolution withhigh contrast, allowing a groundbreaking exploration of the membrane morphology and foulinglayers in radiation resistant samples. Visual differences between pristine, fouled, and cleanedmembranes were found, with quantifiable variations for both, porosity and tortuosity. It was possibleto identify the position and character of the fouling in and on the membranes. Membranecleaning effectively restored the performance but altered the cut-off and porosity of the *** experimental achievements, this study establishes a benchmark for membranecharacterisation, providing novel insights into fouling and cleaning processes and making thisseparation process more sustainable. The application of PXCT for the characterisation of polymericmembranes represents a significant step forward that will contribute to a comprehensiveunderstanding of membrane processes and holds a potential for the advancement of the field ofmembranes science in general. © 2024, The Authors. All rights reserved.

关键词： Ultrafiltration

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