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Facile synthesis of W-Mo bimetallic oxides with high adsorption properties from secondary resources

Tungsten 2024年第4期6卷 806-820页

作者： Jia-Li Cheng Yang-Si Liu Xiao-Li Xi Zuo-Ren Nie Collaborative Innovation Center of Capital Resource-Recycling Material Technology College of Materials Science and EngineeringBeijing University of TechnologyBeijing100124China National Engineering Laboratory for Industrial Big-Data Application Technology Beijing University of TechnologyBeijing100124China Key Laboratory of Advanced Functional Materials Ministry of EducationBeijing University of TechnologyBeijing100124China

A simple solvothermal method was used to obtain W-Mo bimetallic oxides from W-Mo alloy scrap,and pure metal powders were also used as the raw materials to simulate *** products had a sea urchin-like structure with abundant oxygen vacancies and the products prepared at low temperatures forms a sosoloid resembling orthorhombic W_(0.4)Mo_(0.6)O_(3).The WMo bimetallic oxide prepared at the reaction temperature of 120℃exhibited excellent selective adsorption performance for methylene blue(MB),which the adsorption rate of MB reached 99%in 12 min and the adsorption rate reached 90%after6 adsorption *** the W-Mo molar ratio is 1:3,the maximum adsorption capacity of sample for MB can reach1148 mg·g^(-1).The adsorption process followed the Langmuir and pseudo-second-order models,which is surface-controlled monolayer *** experimental results show the feasibility of preparing W-Mo bimetal oxide products from pure materials and *** process is simple and effective,which offered a potential approach for secondary resource recycling and reusing.

关键词： W-Mo bimetallic oxide Oxygen vacancy Selective adsorption W-Mo alloy scrap Solvothermal

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A remote sensing work-flow recommendation algorithm based on logical structure

A remote sensing work-flow recommendation algorithm based on...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Wu, Yaohua Liu, Yang Lin, Yinghao Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9781510664616

With the development of remote sensing products towards the direction of civilianization and popularization, work-flow customization plays an important role in the production of remote sensing products. The traditional customized work-flow model has large time cost, complex operation and high requirements for professional knowledge. The work-flow recommendation system can improve the construction efficiency of remote sensing work-flow to some extent and assist users to design high-quality remote sensing work-flow models. However, most of the existing remote sensing work-flow modeling methods ignore the logical structure characteristics of the work-flow, leading to large errors in the calculation results of similarity. Difficult to make work-flow recommendations effectively. Therefore, this paper proposes a customized recommendation algorithm for remote sensing work-flow based on logical structure. By focusing on logical structure, the reliability of similarity calculation between work-flow is improved, so as to find similar work-flow to help users recommend the next modeling node. Firstly, the work-flow model needs to be preprocessed: the user converts the constructed work-flow model into a process structure tree by using Petri net workflow, and uses the path table generation algorithm based on logical structure to convert the model information into data information and store it in the database for subsequent data processing;Then the flow data in the flow tree set was converted into a path table according to certain rules, and then the longest common subsequence similarity of each data in the path table was calculated to obtain the similarity calculation results based on the logical structure characteristics, the most similar work-flow in the work-flow library is found and the recommendation is made for the user. The method proposed in this paper is evaluated experimentally on the real data set, in terms of recall, precision and F1-score, which shows that the method

关键词： Remote sensing

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

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China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary Research Peng Cheng LaboratoryShenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Data-driven Intelligent Information System Laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

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YOLOvis: a yolo style object detection network for drone vision 3

YOLOvis: a yolo style object detection network for drone vis...

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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Peng, Yanfei Li, Jincheng Guo, Xinyue School of Electronic and Information Engineering Liaoning University of Technology and Engineering Huludao125105 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China

ISBN: (纸本)9781510681880

In recent years, with the rapid development of drone photography technology, drones have emerged as a new method for photography and observation. We have applied object detection technology to drone photography and proposed a YOLO-style 2D object detection network called YOLOvis based on the YOLOv8 architecture. We introduced the PatchFPN structure to improve the network's robustness in detecting small objects and proposed the TranConcat structure to scale features for enhanced usability. YOLOvis utilizes the challenging VisDrone data set as the model's task data set. With a slight increase in computational cost, the model achieves an accuracy of mAP50 33.7%, which is a 3.7% improvement compared to YOLOv8 of the same size and a 17.0% improvement compared to YOLOv5 of the same size. Additionally, to better tailor the model to specific needs, we have proposed YOLOvis models of different sizes. The code and pretrained models are in https://***/BarryGUN/bgyolo_v8_***. © 2024 SPIE.

关键词： Drones

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DCEL:classifier fusion model for Android malware detection

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Journal of Systems engineering and Electronics 2024年第1期35卷 163-177页

作者： XU Xiaolong JIANG Shuai ZHAO Jinbo WANG Xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computing and Engineering University of West LondonLondon W55RFUK

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android ***,the current methods of Android malware detection need a lot of time in the feature engineering ***,these models have the defects of low detection rate,high complexity,and poor practicability,*** analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other *** classify the software’s threat levels based on the correlation of ***,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware ***,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray ***,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single *** based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

关键词： Android malware detection deep learning ensemble learning model fusion

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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

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IEEE Transactions on knowledge and data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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Lightweight Dual Grouped Large-Kernel Convolutions for Salient Object Detection Network 31st

Lightweight Dual Grouped Large-Kernel Convolutions for Sali...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Liu, Jiajie Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Beijing China Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620609

Most existing Salient Object Detection (SOD) methods focus on achieving better performance, often resulting in models with a large number of parameters. However, there is limited research on lightweight models in this field. To address this gap, our goal is to maintain performance while reducing the number of network parameters. Thanks to the development of large-kernel convolutions in recent years, we have improved the U2Net as the base network by adding our lightweight dual large-kernel fusion module. Our module better utilizes the depth information of U2Net and, due to the large receptive field of large-kernel convolutions, better captures the relationships between image elements. This allows our network to remain lightweight while maintaining performance. We designed a large-kernel (DLK) fusion module and a lightweight dual grouped large-kernel Unet network (DGLKUNET). Our network uses SRUS (an improved Residual U-blocks module, RUS) to construct DGLKUNET that predicts image contours and labels. Compared to the base RUS network (U2Net), our network reduces the number of parameters by up to 70% and the computational cost by 40% while maintaining performance. Evaluation results on five datasets demonstrate the performance of our lightweight network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object recognition

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data-Driven Dynamic Optimization of Electronic Fences in Free-Float Bike Sharing Systems 24

Data-Driven Dynamic Optimization of Electronic Fences in Fre...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wei, Zhonghua Ma, Houqiang Li, Jiale Li, Zongze Li, Yunxuan Beijing Key Laboratory of Traffic Engineering Beijing Univ. of Technology Beijing China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong Univ. Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

As an emerging technical means of free-float bike sharing parking management, electronic fencing solves the problem of chaotic parking. Existing research mostly conducts static optimal site selection for electronic fences, without considering the spatial and temporal heterogeneity of free-float bike sharing, which to some extent causes the problem of resource utilization mismatch. This paper proposes a multi-scale siting method based on free-float bike sharing big data to select the site of free-float bike sharing electronic fence in a macro and micro superposition way;starting from users and enterprises, establishing a multi-objective hybrid planning model considering the characteristics of free-float bike sharing, and dynamically optimizing the free-float bike sharing electronic fence to determine the open location and capacity of the electronic fence in different time periods. An example analysis of the free-float bike sharing data set in the local area of Shenzhen is selected to verify the effectiveness of the method. © ASCE.

关键词： Parking

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A DL Intrusion Detection Method for Low-Rate Denial of Service Attacks

A DL Intrusion Detection Method for Low-Rate Denial of Servi...

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2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Wu, Yuhong Lv, Yunshan Hu, Xiangdong School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Public Big Data Security Technology Chongqing Chongqing China School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665457033

Denial of service (DoS) attacks is among the most harmful network threats. Low-rate DoS (LDoS) attacks feature good concealment and are likely to evade detection by conventional signal analysis-based methods. A LDoS attack detection method that combines the convolutional neural network and the gated recurrent unit is proposed in the present work. To evaluate the proposed method, we collect real user traffic from a university's website, label the traffic with LDoS attacks as normal traffic. In order to get the real attack traffic, this paper has collected some new LDoS attack programs, such as slowloris, slowhttptest, pwnlloris, torhammer and httplog, which can be obtained from public channels, and constructed a LAN environment that conformed to the working scenario of the program. By running the attack program, the attack traffic could be obtained. In a large sample scenario, the average detection rate of our proposed method can reach 98.68%. This article focuses on the research: the average detection rate of the detection method proposed in this article in the case that the security agency can only intercept a small number of attack samples. That is, in the small sample scenario, the detection of the collected traffic shows that the average detection rate of the proposed detection method can reach 89.54% in a small sample scenario. Its effective detection rate is obviously superior to the existing similar detection methods. © 2023 IEEE.

关键词： Denial-of-service attack

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