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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Azgor, Sk Ruhul Rahman, Md. Saidur Graph Drawing and Information Visualization Laboratory Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9783031845420

The restricted input queue (rique) is a data structure which is recently introduced to study linear layout of graphs. The rique data structure is a special queue where insertions occur only at the head and removals occur at both the head and the tail. Considering a rique data structure the goal of a linear layout of a graph is to find a linear order of the vertices of the graph and a partition of its edges into pages such that the edges in each page follow the restriction of rique in the underlying order. The rique number of a graph is the smallest number of pages required for linear layout of the graph with rique data structure. A characterization of graphs for admitting a single page rique layout and some bounds on the rique number of complete and complete bipartite graphs are known. In this paper, we show that the rique number of series-parallel graphs as well as planar bipartite graphs is 2. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Directed graphs

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Theoretical and experimental research on static stiffness,performance,and lift-off characteristics of multi-layer gas foil thrust bearings

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Friction 2024年第11期12卷 2458-2479页

作者： Yang HU Pengjing DING Fangling WU Xiaojing WANG Bo LIANG Yonggang MENG School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing Engineering Institute of Aircraft SystemsNanjing 211106China State Key Laboratory of Tribology Tsinghua UniversityBeijing 100084China

In this study,a new comprehensive fully coupled elastic–hydrodynamic model is developed for a multi-layer gas foil thrust bearing(GFTB).The interaction effects among the top foil,back board,middle foil,and bottom foil,as well as the Coulomb friction effect,are *** stiffness and static characteristics obtained by the experimental and theoretical approaches are in good agreement,which verifies the accuracy of the *** contribution of each foil layer to the overall stiffness and the load-carrying mechanism are *** effects of the load,preload,and rotational speed on the static performance are investigated ***,start–stop tests are performed to achieve the lift-off speed,start-up torque,and shut-down torque under various operating conditions.

关键词： multi-layer gas foil thrust bearing(GFTB) load carrying mechanism static performance

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An Experimental Study on the Deviations in Performance of FNNS and CNNS in the Realm of Grayscale Adversarial Images

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Journal of engineering science and technology Review 2023年第3期16卷 66-73页

作者： Mathew, D. A. Steve Shree, N. Durga Chowdhary, Chiranji Lal Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore India Data Science and Applications Indian Institute of Technology Madras Chennai India Botho University Botswana

Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. It is observed that neural networks are prone to having lesser accuracy in the classification of images with noise perturbation. The following study resulted in inferences to establish that CNNs are extremely vulnerable at predicting noisy images while Feed-forward Neural Networks, FNNs are least affected due to noise perturbation, maintaining their accuracy almost undisturbed. FNNs showcase better classification accuracy when tested with noise-intensive, single-channelled images that are just sheer noise to human vision. The hand-written digit images from the MNIST dataset t are classified using the architectures of FNNs with 1 and 2 hidden layers and CNNs with 3, 4, 6, and 8 convolutions, which provide the stated experimental inferences. Deviations in the performances of these architectures analyzed systematically propose that FNNs stand out to show a classification accuracy of more than 85%, irrespective of the intensity of noise and CNNs witness a trend in the reduction in speed of classification accuracy against increasing noise intensities. Correlation analysis and mathematical modelling of the accuracy trends act as roadmaps to picture that the change in the speed of classification accuracy against increasing noise intensities for CNN with 8 convolutions is half of that of the rest of the CNNs. This experimental study is a step to quantify the performance of deep learning image classification models in the context of adversarial images. © 2023 School of science, IHU. All rights reserved.

关键词： Convolution

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Automated garden-insect recognition using improved lightweight convolution network

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information Processing in Agriculture 2023年第2期10卷 256-266页

作者： Zhankui Yang Xinting Yang Ming Li Wenyong Li College of Computer Science and Technology Beijing University of TechnologyBeijing 100124China National Engineering Research Center for Information Technology in Agriculture Beijing 100089China National Engineering Laboratory for Quality and Safety Traceability Technology and Application of Agricultural Products Beijing 100089China

Automated recognition of insect category,which currently is performed mainly by agriculture experts,is a challenging problem that has received increasing attention in recent *** goal of the present research is to develop an intelligent mobile-terminal recognition system based on deep neural networks to recognize garden insects in a device that can be conveniently deployed in mobile ***-of-the-art lightweight convolutional neural networks(such as SqueezeNet and ShuffleNet)have the same accuracy as classical convolutional neural networks such as AlexNet but fewer parameters,thereby not only requiring communication across servers during distributed training but also being more feasible to deploy on mobile terminals and other hardware with limited *** this research,we connect with the rich details of the low-level network features and the rich semantic information of the high-level network features to construct more rich semantic information feature maps which can effectively improve SqueezeNet model with a small computational *** addition,we developed an off-line insect recognition software that can be deployed on the mobile terminal to solve no network and the timedelay problems in the *** demonstrate that the proposed method is promising for recognition while remaining within a limited computational budget and delivers a much higher recognition accuracy of 91.64%with less training time relative to other classical convolutional neural *** have also verified the results that the improved SqueezeNet model has a 2.3%higher than of the original model in the open insect data IP102.

关键词： Insect classification Mobile-terminal recognition SqueezeNet model Deep lightweight convolution network

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Hydrogel electrodes with conductive and substrate-adhesive layers for noninvasive long-term EEG acquisition

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Microsystems & Nanoengineering 2023年第3期9卷 357-370页

作者： Hailing Xue Dongyang Wang Mingyan Jin Hanbing Gao Xuhui Wang Long Xia Dong’ang Li Kai Sun Huanan Wang Xufeng Dong Chi Zhang Fengyu Cong Jiaqi Lin Key State Laboratory of Fine Chemicals School of BioengineeringDalian University of Technology116024 DalianChina School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of Technology116024 DalianChina Key Laboratory of Energy Materials and School of Materials Science and Engineering Dalian University of Technology116024 DalianChina

Noninvasive brain–computer interfaces (BCIs) show great potential in applications including sleep monitoring, fatigue alerts, neurofeedback training, etc. While noninvasive BCIs do not impose any procedural risk to users (as opposed to invasive BCIs), the acquisition of high-quality electroencephalograms (EEGs) in the long term has been challenging due to the limitations of current electrodes. Herein, we developed a semidry double-layer hydrogel electrode that not only records EEG signals at a resolution comparable to that of wet electrodes but is also able to withstand up to 12 h of continuous EEG acquisition. The electrode comprises dual hydrogel layers: a conductive layer that features high conductivity, low skin-contact impedance, and high robustness;and an adhesive layer that can bond to glass or plastic substrates to reduce motion artifacts in wearing conditions. Water retention in the hydrogel is stable, and the measured skin-contact impedance of the hydrogel electrode is comparable to that of wet electrodes (conductive paste) and drastically lower than that of dry electrodes (metal pin). Cytotoxicity and skin irritation tests show that the hydrogel electrode has excellent biocompatibility. Finally, the developed hydrogel electrode was evaluated in both N170 and P300 event-related potential (ERP) tests on human volunteers. The hydrogel electrode captured the expected ERP waveforms in both the N170 and P300 tests, showing similarities in the waveforms generated by wet electrodes. In contrast, dry electrodes fail to detect the triggered potential due to low signal quality. In addition, our hydrogel electrode can acquire EEG for up to 12 h and is ready for recycled use (7-day tests). Altogether, the results suggest that our semidry double-layer hydrogel electrodes are able to detect ERPs in the long term in an easy-to-use fashion, potentially opening up numerous applications in real-life scenarios for noninvasive BCI.

关键词： conductive electrode impedance

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Integrating Friction Noise for In Situ Monitoring of Polymer Wear Performance: A Machine Learning Approach in Tribology

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Journal of Tribology 2025年第6期147卷 061701页

作者： Chen, Shengshan Cheng, Ganlin Guo, Fei Jia, Xiaohong Wen, Xiaohao State Key Laboratory of Tribology in Advanced Equipment Tsinghua University Beijing100084 China Macau Institute of Systems Engineering Macau University of Science and Technology 999078 China

Friction and wear between mating surfaces significantly affect the efficiency and performance of mechanical systems. Traditional tribological research relies on post-observation methods, limiting the understanding of dynamic friction behavior. In contrast, in situ monitoring provides real-time insights into evolving friction dynamics. This study employs machine learning to monitor polymer wear performance through friction noise. The predictive accuracy of various machine learning methods, including Extremely Randomized Trees, Gradient-Boosting Decision Trees, AdaBoost, LightGBM, Deep Forest, and Deep Neural Networks, is compared for wear-type classification. Additionally, the LSBoost regression is selected as the optimal method for predicting polymer wear-rates across various temperatures. The results underscore the potential of using friction noise and machine learning for real-time wear monitoring, offering valuable insights for tribological system maintenance and failure prediction. © 2024 by ASME.

关键词： Tribology

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Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

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Computers, Materials & Continua 2024年第8期80卷 2815-2837页

作者： Mengmeng Zhao Haipeng Peng Lixiang Li Yeqing Ren Information Security Center State Key Laboratory ofNetworking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China National Engineering Laboratory for Disaster Backup and Recovery Beijing University of Posts and TelecommunicationsBeijing100876China Department of Information Science and Engineering Zaozhuang UniversityZaozhuang277160China

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working *** the systems are attacked,timely identification of outliers in time series is critical to ensure *** many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these *** to the superior capability of Transformer in learning time series *** paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved ***,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are ***,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each *** scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each *** interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory *** introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph *** on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

关键词： Multivariate time series anomaly detection spatial-temporal network Transformer

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Towards more reliable evaluation in pedestrian detection by rethinking “ignore regions”

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Li, Gang Li, Xiang Zhang, Shanshan Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China VCIP Nankai University Tianjing China

It remains a challenging task to detect pedestrians in crowds and it needs more efforts to understand why the detectors fail. When we perform an error analysis based on the traditional evaluation strategy, we find that it produces many misleading false positives, which in fact cover occluded pedestrians. The reason for this is that we usually have two kinds of annotations in the dataset: regular pedestrians (detection targets) labeled by full-body boxes and ignored pedestrians (NOT detection targets) labeled by visible boxes. Ignored pedestrians are labeled as an additional category termed the “ignore region”. Nevertheless, our detectors always predict a full-body box for each pedestrian. This gap results in the following case: when a detector successfully predicts a full-body box for those ignored pedestrians, a false positive is triggered due to the low overlap between the predicted full-body box and the labeled visible box for the ignored pedestrian. This becomes even more harmful as the detector improves and becomes more capable of locating occluded pedestrians. To alleviate this issue, we devise a new pedestrian detection pipeline, which considers the additional visible box at both the detection and evaluation stages. During detection, we predict an extra visible box apart from the full-body box for every instance;during evaluation, we employ visible boxes instead of full-body boxes to match the “ignore region”. We apply the new pipeline to dozens of detection methods and validate the effectiveness of our pipeline in reducing the over-reporting of false positives and providing more reliable evaluation results. © The Author(s) 2024.

关键词： Evaluation metric Object detection Occlusion handling Pedestrian detection

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Human activities rather than climate dominated the grassland net primary production in the three basins of the Hexi Corridor,North-Western China

River

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River 2023年第2期2卷 210-221页

作者： Yutao Wang Tianbao Dou Research Center Gansu Institute of Science and Technology InformationGansu Provincial Key Laboratory of Science and Technology EvaluationLanzhouChina Key Laboratory of Western China's Environmental Systems College of Earth and Environmental ScienceLanzhou UniversityLanzhouChina

Actual net primary production(ANPP)is affected by both natural conditions and human activities in many areas,while the contributions of these factors to NPP remain unknown because it is difficult to isolate their *** knowledge gap hinders our implementation of environmental protection and *** this study,we selected three basins of the Hexi Corridor,North-western China,as the study *** on the monthly growth of grassland and field observations,we identified the undisturbed grids and then calculated the potential net primary productivity(PNPP)of these *** the Carnegie-Ames-Stanford-Approach(CASA)model,we calculated the ANPP in these *** influence of human activities on NPP(HNPP)was then calculated as the difference between ANPP and *** results showed that from 2001 to 2017,the mean grassland HNPP was−236.25 gC m^(−2) a^(−1).The area with a relative contribution index(RCI)of human activity greater than 0.5 accounted for more than 50%of the total,indicating that human activities were a dominant factor in the NPP *** the implementation of ecological governance projects in the Hexi Corridor in 2006,HNPP exhibited a negative weakening rate of 2.79 gC m^(−2) a^(−1)(p<0.05),and the RCI decreased from 0.708 at the beginning of the study period to 0.522 at the end(p<0.01),indicating that reducing human interference with nature was an effective measure for ecological *** on these findings,we concluded that the changes in NPP in the three basins of the Hexi Corridor was dominated by human *** results suggest that future studies pertaining to the response of NPP to climate change should consider the impact of human activities in semi-arid regions.

关键词： coincidence ratio Hexi corridor human influence net primary productivity potential NPP

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Integrated Clustering and Routing Design and Triangle Path Optimization for UAV-Assisted Wireless Sensor Networks

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China Communications 2024年第4期21卷 178-192页

作者： Shao Liwei Qian Liping Wu Mengru Wu Yuan College of Information Engineering Zhejiang University of TechnologyHangzhou 310014China State Key Laboratory of Internet of Things for Smart City University of MacaoMacaoChina Department of Computer and Information Science University of MacaoMacaoChina

With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower ***,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery *** this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this ***,we use UAVs to assist the WSN in collecting *** the current UAV-assisted WSN,the clustering and routing schemes are determined ***,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and *** efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing *** the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing ***,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution *** point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor *** avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are *** emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of *** with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.

关键词： Monte-Las search strategy triangle path optimization unmanned aerial vehicles wireless sensor networks

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