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Smart Materials and Structures 2025年第6期34卷 065024-065024页

作者： Xiaoqiang Zhang Aodi Jin Shuangyou Li Xiaoqi Wang Xingyu Ma Gongyong Yang School of Mechanical Engineering Northeast Electric Power University Jilin 132012 People’s Republic of China School of Computer Science Northeast Electric Power University Jilin 132012 People’s Republic of China Engineering Training and Teaching Center Northeast Electric Power University Jilin 132012 People’s Republic of China

The promotion of smart agriculture is key to sustainable agriculture and reducing environmental impact. In order to achieve the development of self-powered sustainable smart agriculture, in this paper, a piezoelectric-electromagnetic hybrid energy harvester (PEHEH) is proposed, which based on the magnetic coupling. The PEHEH consists primarily of a magnetic turntable, coil, driving magnet, response magnet, and piezoelectric cantilever beam. By fully exploiting magnetic coupling, the PEHEH combines these two energy harvesters, ensuring efficient energy conversion. The non-contact magnetic coupling force excites the piezoelectric cantilever beam, facilitating efficient energy harvesting, enhancing conversion efficiency, and prolonging the operational lifespan. A series of experiments were conducted to investigate the impact of response magnet size and position on the output voltage of the PEHEH. The results show that the prototype, when assembled in accordance with the optimal structural parameters, is capable of providing a continuous and stable power supply for smart agricultural low-power sensors and small electronic devices. In particular, the PEHEH demonstrates high stability and reliability in soil temperature and humidity sensor monitoring, LED light compensation applications. It provides a new power supply solution for sustainable smart agriculture.

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Unsupervised embedding learning from uncertainty momentum modeling

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arXiv 2021年

作者： Zhou, Jiahuan Tang, Yansong Su, Bing Wu, Ying Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Engineering Science University of Oxford OX1 3PJ United Kingdom Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China

Existing popular unsupervised embedding learning methods focus on enhancing the instance-level local discrimination of the given unlabeled images by exploring various negative data. However, the existed sample outliers which exhibit large intra-class divergences or small inter-class variations severely limit their learning performance. We justify that the performance limitation is caused by the gradient vanishing on these sample outliers. Moreover, the shortage of positive data and disregard for global discrimination consideration also pose critical issues for unsupervised learning but are always ignored by existing methods. To handle these issues, we propose a novel solution to explicitly model and directly explore the uncertainty of the given unlabeled learning samples. Instead of learning a deterministic feature point for each sample in the embedding space, we propose to represent a sample by a stochastic Gaussian with the mean vector depicting its space localization and covariance vector representing the sample uncertainty. We leverage such uncertainty modeling as momentum to the learning which is helpful to tackle the outliers. Furthermore, abundant positive candidates can be readily drawn from the learned instance-specific distributions which are further adopted to mitigate the aforementioned issues. Thorough rationale analyses and extensive experiments are presented to verify our superiority. © 2021, CC BY.

关键词： Embeddings

Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency

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arXiv 2022年

作者： Li, Zihan Chen, Wentao Wei, Zhiqing Luo, Xingqi Su, Bing The School of Informatics Xiamen University Xiamen361005 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science & Technology Beijing Institute of Technology Beijing100081 China The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China

Supervised learning has been widely used for attack categorization, requiring high-quality data and labels. However, the data is often imbalanced and it is difficult to obtain sufficient annotations. Moreover, supervised models are subject to real-world deployment issues, such as defending against unseen artificial attacks. To tackle the challenges, we propose a semi-supervised fine-grained attack categorization framework consisting of an encoder and a two-branch structure and this framework can be generalized to different supervised models. The multilayer perceptron with residual connection is used as the encoder to extract features and reduce the complexity. The Recurrent Prototype Module (RPM) is proposed to train the encoder effectively in a semi-supervised manner. To alleviate the data imbalance problem, we introduce the Weight-Task Consistency (WTC) into the iterative process of RPM by assigning larger weights to classes with fewer samples in the loss function. In addition, to cope with new attacks in real-world deployment, we propose an Active Adaption Resampling (AAR) method, which can better discover the distribution of unseen sample data and adapt the parameters of encoder. Experimental results show that our model outperforms the state-of-the-art semi-supervised attack detection methods with a 3% improvement in classification accuracy and a 90% reduction in training time. Copyright © 2022, The Authors. All rights reserved.

关键词： Intrusion detection

Distinct factors predict use of active learning techniques by pre-tenure and tenured STEM faculty

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Journal of Geoscience Education 2021年第4期69卷 357-372页

作者： Indorf, Jane L. Benabentos, Rocio Daubenmire, Patrick Murasko, Donna Hazari, Zahra Potvin, Geoff Kramer, Laird Marsteller, Pat Thompson, Katerina V. Cassone, Vincent M. Stanford, Jennifer S. Department of Biology University of Miami Coral Gables FL United States STEM Transformation Institute Florida International University Miami FL United States Center for Science and Math Education and Department of Chemistry and Biochemistry Loyola University Chicago Chicago IL United States Department of Biology Drexel University Philadelphia PA United States Department of Teaching and Learning Florida International University Miami FL United States Department of Physics and Astronomy Florida International University Miami FL United States Department of Biology Emory University Atlanta GA United States College of Computer Mathematical and Natural Sciences University of Maryland College Park MD United States Department of Biology University of Kentucky Lexington KY United States Center for the Advancement of STEM Teaching and Learning Excellence (CASTLE) Drexel University Philadelphia PA United States

Despite decades of research indicating that active learning techniques (ALTs) are more effective than traditional lecture, ALTs are not widely implemented in STEM undergraduate classrooms. While ALT implementation is happening in a number of disciplines, including the geosciences, most STEM teaching remains lecture-based. Understanding the contexts that correlate with ALT adoption may allow expanded implementation. ALT use was documented among STEM pre-tenure and tenured faculty respondents across 66 research-intensive, U.S. universities, using the Change in Implementation of Pedagogical Practices survey. Personal, professional, and institutional/departmental factors were analyzed for association with ALT use. Most respondents reported using some ALTs in their teaching, with no significant distinctions between pre-tenure and tenured faculty. teaching was perceived to carry less weight in practice (e.g., in tenure and promotion decisions) than the theoretical value stated by institutions (e.g., in faculty assignments and hiring). ALT use among tenured faculty was predicted by number of recent publications, their knowledge of education initiatives, and receiving informal feedback on their teaching. ALT use among pre-tenure faculty was predicted by the number of STEM education talks given. Perception of obstacles was a negative predictor of ALT use among tenured faculty only, though both pre-tenure and tenured faculty identified barriers to ALT use. Considering these identified barriers and how to remove them could potentially increase ALT use. The findings that ALT use by tenured faculty was predicted by number of recent publications and that grant funding was neither a positive nor negative predictor of ALT use suggest that ALT use does not hinder faculty research productivity. © 2021 National Association of Geoscience Teachers.

关键词： Active learning communities of practice faculty change professional satisfaction STEM undergraduate

Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection

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arXiv 2023年

作者： Kovacs, Balint Netzer, Nils Baumgartner, Michael Eith, Carolin Bounias, Dimitrios Meinzer, Clara Jäger, Paul F. Zhang, Kevin S. Floca, Ralf Schrader, Adrian Isensee, Fabian Gnirs, Regula Görtz, Magdalena Schütz, Viktoria Stenzinger, Albrecht Hohenfellner, Markus Schlemmer, Heinz-Peter Wolf, Ivo Bonekamp, David Maier-Hein, Klaus H. Heidelberg Germany Division of Radiology DKFZ Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Helmholtz Imaging DKFZ Heidelberg Germany Interactive Machine Learning Group DKFZ Heidelberg Germany Department of Urology University of Heidelberg Medical Center Germany Junior Clinical Cooperation Unit’Multiparametric methods for early detection of prostate cancer’ DKFZ Heidelberg Germany Institute of Pathology University of Heidelberg Medical Center Germany Mannheim University of Applied Sciences Mannheim Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis systems still rely on simplistic spatial transformations to preserve the pathological label post transformation. However, such augmentations do not substantially increase the organ as well as tumor shape variability in the training set, limiting the model’s ability to generalize to unseen cases with more diverse localized soft-tissue deformations. We propose a new anatomy-informed transformation that leverages information from adjacent organs to simulate typical physiological deformations of the prostate and generates unique lesion shapes without altering their label. Due to its lightweight computational requirements, it can be easily integrated into common DA frameworks. We demonstrate the effectiveness of our augmentation on a dataset of 774 biopsy-confirmed examinations, by evaluating a state-of-the-art method for PCa detection with different augmentation settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

Bioelectrical brain activity can predict prosocial behavior

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arXiv 2021年

作者： Kunavin, Mikhail A. Kozitsina, Tatiana S. Myagkov, Mikhail G. Kozhevnikova, Irina S. Pankov, Mikhail N. Sokolova, Ludmila V. Federal University Arkhangelsk Russia Moscow Institute of Physics and Technology National Research University 9 Institutskiy per. Dolgoprudny Moscow Region 141701 Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow119333 Russia Laboratory of Experimental Methods in Cognitive and Social Sciences Tomsk State University Tomsk Russia Department of Political Science University of Oregon EugeneOR United States Federal University Arkhangelsk Russia

Generally, people behave in social dilemmas such as proself and prosocial. However, inside social groups, people have a tendency to choose prosocial alternatives due to in-group favoritism. The bioelectrical activity of the human brain shows the differences between proself and prosocial exist even out of a socialized group. Moreover, a group socialization strengthens these differences. We used EEG System, "Neuron-Spectrum-4/EPM" (16 channels), to track the brain bioelectrical activity during decision making in laboratory experiments with the Prisoner's dilemma game and the short-term socialization stage. We compared the spatial distribution of the spectral density during the different experimental parts. The noncooperative decision was characterized by the increased values of spectral the beta rhythm in the orbital regions of prefrontal cortex. The cooperative choice, on the contrary, was accompanied by the theta-rhythm activation in the central cortex regions in both hemispheres and the high-frequency alpha rhythm in the medial regions of the prefrontal cortex. People who increased the cooperation level after the socialization stage was initially different from the ones who decreased the cooperation in terms of the bioelectrical activity. Well-socialized participants differed by increased values of spectral density of theta-diapason and decreased values of spectral density of beta-diapason in the middle part of frontal lobe. People who decreased the cooperation level after the socialization stage was characterized by decreased values of spectral density of alpha rhythm in the middle and posterior convex regions of both hemispheres. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

An Efficient Task Allocation Scheme with Capability Diversity in Crowdsensing 12th

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An Efficient Task Allocation Scheme with Capability Diversit...

12th China Wireless Sensor Network Conference, CWSN 2018

作者： Li, Lixia Zhang, Lichen Wang, Xiaoming Yu, Sui Wang, Ana Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China

ISBN: (纸本)9789811368332

Crowdsensing offers an effective data collection platform where data requesters can create tasks dynamically and workers are assigned to tasks. Task assignment is a vital part in crowdsensing. Most existing researches consider single capability and basic cost of workers, while ignoring the diverse capabilities and both the basic and additional cost of performing a task. In this paper, we introduce the capability diversity of tasks and workers’ additional cost of workers and formulate the task assignment as a one-to-many matching problem, in which multiple workers with different capabilities are assigned to execute one task, and a task can be successfully completed only if all the required capabilities are fully covered by the capabilities of its assigned workers within its limited budget. Based on relationship between capability and profit, we propose three heuristic algorithms that try to increase the total profits of assigned workers within budget constraint. Through extensive simulations, we show that the proposed algorithms greatly improve the total profits and the coverage ratio of task accomplishment. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

High-Quality Learning Resource Dissemination Based on Opportunistic Networks in Campus Collaborative Learning Context 13th

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High-Quality Learning Resource Dissemination Based on Opport...

13th China Conference on Wireless Sensor Networks, CWSN 2019

作者： Li, Peng Liu, Hong Guo, Longjiang Zhang, Lichen Wang, Xiaoming Wu, Xiaojun Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China

ISBN: (纸本)9789811517846

In the campus scenario, a basic community of collaborative teams is formed among the nodes participating in the collaborative learning interaction in the mobile opportunistic network. Due to the existing research does not consider the weak connection, node influence and the contact characteristics between nodes. In this paper, a routing method using a collaborative group as a communication unit is proposed. The route mainly counts the contact characteristics among the groups according to the characteristics of the node movement and predicts the subsequent contact situation. Combined with the weak connection relationship and the node’s influence, the optimal node to be transmitted is selected. It has been experimentally verified that the routing method can greatly improve the speed of message dissemination and avoid unnecessary message redundancy and waste of contact opportunities. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Collaborative learning Community influence Opportunistic networks Weak connection

Personalized Recommendation Based on Tag Semantics in the Heterogeneous Information Network 13th

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Personalized Recommendation Based on Tag Semantics in the He...

13th China Conference on Wireless Sensor Networks, CWSN 2019

作者： Yan, Bin Zhang, Lichen Guo, Longjiang Ren, Meirei Wang, Ana Key Laboratory of Modern Teaching Technology Ministry of Education Xian710062 China Engineering Laboratory of Teaching Information Technonlogy of Shaanxi Province Xian710119 China School of Computer Science Shaanxi Normal University Xian710119 China

ISBN: (纸本)9789811517846

Heterogeneous information network (HIN) is widely used in recommendation system because of its superiority in complex information modeling. However, the existing HIN-based methods ignore two issues. First, low-quality information may cause users to be dissatisfied with the recommendation results. Secondly, HIN-based recommendations are difficult to predict the user’s attitude toward the item. Therefore, this paper proposes two improvement strategies: (1) propose a semantic information filtering strategy to filter low-quality information and improve recommendation efficiency;(2) integrate tag information into HIN-based recommendation system to achieve personalization recommend. This paper verifies the validity of the proposed model on two real data sets. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Semantics

Multiple Riemann wave solutions of the general form of quasilinear hyperbolic systems

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arXiv 2022年

作者： Grundland, A.M. de Lucas, Javier Centre de Recherches Mathématiques Université de Montréal Succ. Centre-Ville CP 6128 MontréalQCH3C 3J7 Canada Department of Mathematics and Computer Science Université du Québec CP 500 Trois-RivièresQCG9A 5H7 Canada Department of Mathematical Methods in Physics University of Warsaw ul. Pasteura 5 Warszawa02-093 Poland

The objective of this paper is to construct geometrically Riemann k-wave solutions of the general form of first-order quasilinear hyperbolic systems of partial differential equations. To this end, we adapt and combine elements of two approaches to the construction of Riemann k-waves, namely the symmetry reduction method and the generalized method of characteristics. We formulate a geometrical setting for the general form of the k-wave problem and discuss in detail the conditions for the existence of k-wave solutions. An auxiliary result concerning the Frobenius theorem is established. We use it to obtain formulae describing the k-wave solutions in closed form. Our theoretical considerations are illustrated by examples of hydrodynamic type systems including the Brownian motion *** Codes 35Q53 (primary), 35A30, 35Q58, 53A05 (secondary) © 2022, CC BY.

关键词： Brownian movement