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Chinese Journal of Chemical Engineering 2024年第4期68卷 216-223页

作者： Yuhang Wei Qingpeng Zhu Weiwei Xie Xinyue Wang Song Li Zhiming Chen School of Chemical and Environmental Engineering Anhui Laboratory of Functional Coordinated Complexes for Materials Chemistry and ApplicationAnhui Polytechnic UniversityWuhu 241000China School of Biology and Food Engineering Anhui Polytechnic UniversityWuhu 241000China

Efficient and convenient treatment of industrial dyeing wastewater is of great significance to guarantee human and animal *** work presented the enhanced catalytic activity at pH 3.0 of laccase immobilized on amino-functionalized ZnFe_(2)O_(4) nanoparticles(ZnFe_(2)O_(4)-laccase)and its application for the degradation of textile *** to the existence of a large number of oxygen vacancies on the surface of the ZnFe_(2)O_(4) nanoparticles,negative ions accumulated on the magnetic carriers,which resulted in a harsh optimal pH value of the ZnFe_(2)O_(4)-*** activity assays revealed that the ZnFe_(2)O_(4)-laccase possessed superior pH and thermal stabilities,excellent reusability,and noticeable organic solvent ***,the ZnFe_(2)O_(4) laccase presented efficient and sustainable degradation of high concentrations of textile *** initial decoloration efficiencies of malachite green(MG),brilliant green(BG),azophloxine,crystal violet(CV),reactive blue 19(RB19),and procion red MX-5B were approximately 99.1%,95.0%,93.3%,87.4%,86.1%,and 85.3%,*** 10 consecutive reuses,the degradation rates of the textile dyes still maintained about 98.2%,92.5%,83.2%,81.5%,79.8%and 65.9%,*** excellent dye degradation properties indicate that the ZnFe_(2)O_(4)-laccase has a technical application in high concentrations of dyestuff treatment.

关键词： ZnFe_(2)O_(4)-laccase Catalytic activity Stability and reusability Degradation of textile dye

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Asymmetric two-dimensional diffraction grating via a vortex field in an inverted-Ytype atomic system

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Communications in Theoretical Physics 2024年第5期76卷 77-85页

作者： Duo Zhang Ke Wang Kunpeng Zhao Mei Wang Zhaoyu Sun School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan 430023China

We propose a theoretical scheme to realize a two-dimensional(2D)diffraction grating in a four-level inverted-Y-type atomic system coupled by a standing-wave(SW)field and a Laguerre-Gaussian(LG)vortex *** to asymmetric spatial modulation of the LG vortex field,the incident probe field can be lopsidedly diffracted into four domains and an asymmetric 2D electromagnetically induced grating is *** adjusting the detunings of the probe field and the LG vortex field,the intensities of the LG vortex field and the coherent SW field,as well as the interaction length,the diffraction properties and efficiency,can be effectively *** addition,the effect of the azimuthal parameter on the Fraunhofer diffraction of the probe field is also *** asymmetric 2D diffraction grating scheme may provide a versatile platform for designing quantum devices that require asymmetric light transmission.

关键词： asymmetric diffraction grating standing-wave field Laguerre-Gaussian vortex field diffraction property

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Research on Modeling and Parameter Identification of Comprehensive Load Model of Distribution Network in Industrial Park

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Chinese Journal of Electronics 2024年第1期33卷 264-273页

作者： Tingling WANG Xiaohe YAN School of Electric Engineering North China University of Water Resources and Electric Power Wenzhou polytechnic

With the rapid development of industrial parks, its load model research has become a hot spot. In order to study the load of power system in industrial park, based on the characteristics of the industrial park load, a comprehensive load admittance static model with full voltage range adaptability is considered, and a comprehensive load model of distribution network of the industrial park is established. A complete parameter identification of the model is carried out through chaos particle swarm optimization. The simulation results show that the model can effectively describe the load characteristics of the distribution network in industrial parks.

关键词： Chaos Adaptation models Analytical models Parameter estimation Induction motors Simulation Distribution networks

Collaborative Charging Scheduling in Wireless Charging Sensor Networks

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Computers, Materials & Continua 2024年第4期79卷 1613-1630页

作者： Qiuyang Wang Zhen Xu Lei Yang School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430048China

Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides apromising solution to this problem, which is not easily affected by the external environment. In this paper, we studythe recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers(MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with theobjective of maximizing the number of surviving sensors, and further propose a collaborative charging schedulingalgorithm(CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary *** can adaptively collaboratively charge the sensors based on the energy limit ofMCs and the energy demandof sensors. Finally, we conducted comparative simulations. The simulation results show that the proposed algorithmcan effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertaintyof node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios.

关键词： Wireless rechargeable sensor network mobile charger collaborative charging adaptive charging

A Study on Enhancing Chip Detection Efficiency Using the Lightweight Van-YOLOv8 Network

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Computers, Materials & Continua 2024年第4期79卷 531-547页

作者： Meng Huang Honglei Wei Xianyi Zhai School of Mechanical Engineering and Automation Dalian Polytechnic UniversityDalian116034China

In pursuit of cost-effective manufacturing,enterprises are increasingly adopting the practice of utilizing recycled semiconductor *** ensure consistent chip orientation during packaging,a circular marker on the front side is employed for pin alignment following successful functional ***,recycled chips often exhibit substantial surface wear,and the identification of the relatively small marker proves ***,the complexity of generic target detection algorithms hampers seamless *** these issues,this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips,termed ***,to alleviate the influence of diminutive,low-resolution markings on the precision of deep learning models,we utilize an upscaling approach for enhanced *** technique relies on the Super-Resolution Generative Adversarial Network with Extended Training(SRGANext)network,facilitating the reconstruction of high-fidelity images that align with input ***,we replace the original YOLOv8smodel’s backbone feature extraction network with the lightweight VanillaNetwork(VanillaNet),simplifying the branch structure to reduce network ***,a Hybrid Attention Mechanism(HAM)is implemented to capture essential details from input images,improving feature representation while concurrently expediting model inference *** results demonstrate that the Van-YOLOv8 network outperforms the original YOLOv8s on a recycled chip dataset in various ***,it demonstrates superiority in parameter count,computational intricacy,precision in identifying targets,and speed when compared to certain prevalent algorithms in the current *** proposed approach proves promising for real-time detection of recycled chips in practical factory settings.

关键词： Lightweight neural networks attention mechanisms image super-resolution enhancement feature extraction small object detection

An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7

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Computers, Materials & Continua 2024年第2期78卷 2829-2845页

作者： Liqiu Ren Zhanying Li Xueyu He Lingyan Kong Yinghao Zhang College of Information Science and Engineering Dalian Polytechnic UniversityDalian116034China

For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor ***,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection *** improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone *** Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference *** Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the *** experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the *** feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.

关键词： Deep learning underwater object detection improved YOLOv7 attention mechanism

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2421-2444页

作者： Fady Samann Shavan Askar Department of Energy Engineering Technical College of EngineeringDuhok Polytechnic UniversityDuhok42001Iraq Department of Information System Engineering Technical College of EngineeringErbil Polytechnic UniversityErbil44001Iraq

Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative ***,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering *** paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering *** traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time *** Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the ***++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in *** Scott’s formula estimation of the K number only matched the baseline when the road layout was ***,the clustering algorithm required one iteration on average to converge when used with LET.

关键词： Clustering vehicular network Scott’s formula FastPAM

DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet

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Chinese Journal of Mechanical Engineering 2024年第1期37卷 130-145页

作者： Yufeng Du Rongyun Zhang Peicheng Shi Linfeng Zhao Bin Zhang Yaming Liu School of Mechanical Engineering Anhui Polytechnic UniversityWuhu 241000China Automotive New Technology Anhui Engineering and Technology Research Center Anhui Polytechnic UniversityWuhu 241000China School of Automotive and Transportation Engineering Hefei University of TechnologyHefei 230009China

The advancement of autonomous driving heavily relies on the ability to accurate lane lines *** deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the ***,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane *** precision of lane line detection is limited by the unpredictable quantity and diversity of lane *** address the aforementioned challenges,we propose a novel deep learning approach for lane line *** method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane *** 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 *** programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://***/Duane 711/Lane-line-detec tion-ST-LaneNet.

关键词： Autonomous driving Lane line detection Deep learning Swin transformer

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Significant annealing-induced hardening effect in nanolaminate d-nanotwinne d(CrCoNi)_(97.4)Al_(0.8)Ti_(1.8)me dium-entropy alloy by severe cold rolling

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Journal of Materials Science & Technology 2024年第3期170卷 156-166页

作者： Shuqing Yuan Hui Fu Lei Qian Chi Fai Cheung Xu-Sheng Yang State Key Laboratory of Ultra-precision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Hong Kong polytechnic university Shenzhen Research Institute Shenzhen 518060China

Due to the easy coarsening caused by poor thermal stability,the verified annealing-induced hardening in nanograined metals can only maintain at a relatively low-temperature *** this study,a nanolam-inated(CrCoNi)_(97.4)Al_(0.8)Ti_(1.8)medium-entropy alloy with an average lamellae thickness of∼20 nm embedded by thinner nanotwins was fabricated by severe cold rolling to achieve superior thermal *** with the conventional nanotwinned CrCoNi with nanotwins inside ultra-fined grains,the hier-archical nanolaminated-nanotwinned(CrCoNi)_(97.4)Al_(0.8)Ti_(1.8) exhibits a significant annealing-induced hard-ening effect,i.e.,hardness increasing from∼250 HV in the original specimen to∼500 HV in the cold-rolled status and finally∼630 HV after annealing at 600℃for 1 *** microstructure characterizations reveal that the reduced dislocation density and formation of L1_(2)ordered domain are mainly responsible for such hardening effect,which is facilitated by the effectively suppressed coarsening with annealing temperature,i.e.,slow detwinning process and well-retained low-angle nanolamellar *** coarsening mechanisms from the cold-rolled nanolamellae to the fully recrystallized micro-equiaxed structures under the annealing temperatures ranging from 400 to 800℃ were also elucidated by atomic observations.

关键词： Annealing-induced hardening Thermal stability Nanolaminated-nanotwinned structure CrCoNi Detwinning Grain boundary migration