This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrat...This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrating the CIPP model with AI technology,a novel intelligent educational evaluation system was designed.Through experimental validation and case studies,the system demonstrated significant effectiveness in improving teaching quality,facilitating personalized student development,and optimizing educational resource allocation.Additionally,the study predicts potential changes this system could bring to the education industry and proposes relevant policy recommendations.Although the current research has limitations,with technological advancements in the future,this system is expected to provide stronger support for innovations in engineering education models.展开更多
Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-ban...Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.展开更多
This study presents a teaching reform for the Object-oriented Software Construction(OOSC)course by integrating outcome-based education(OBE)and the BOPPPS(bridge-In,objectives,pre-assessment,participatory learning,post...This study presents a teaching reform for the Object-oriented Software Construction(OOSC)course by integrating outcome-based education(OBE)and the BOPPPS(bridge-In,objectives,pre-assessment,participatory learning,post-assessment,summary)instructional model.The reform addresses the gap between syntax-based programming instruction and the need for higher-level skills in abstraction,modularity,and software architecture.The course is anchored in a semester-long,project-based learning platform centered on a Java-based Aircraft Battle Game,progressing through six iterative experiments.Each experiment targets specific competencies within the structured BOPPPS teaching cycle and is aligned with specific OBE learning outcomes.A case study on the Factory Pattern illustrates how the BOPPPS model fosters conceptual understanding and practical application.Evaluation results from the 2023 and 2024 spring semesters show improved outcomes:Project completion rose from 87%to 95%,37%of students implemented innovative features,and average final grades increased by 7%.The results affirm that the OBE+BOPPPS integration strengthens engagement,deepens understanding,and equips students with real-world software development competencies.展开更多
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an...War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.展开更多
With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE ...With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.展开更多
Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelli...Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.展开更多
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i...Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.展开更多
In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signal...In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.展开更多
In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of sate...In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.展开更多
The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-...The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-seven landmarks from the second to fifth cervical vertebrae on the lat-eral cephalogram were identified.In this study,the system of CACVL was developed and used to iden-tify and calculate the landmarks by fast marching method and parabolic curve fitting.The accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodon-tic experts (OE) group and orthodontic novices (ON) group].The results showed that,as for the accu-racy,there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05),but there was significant difference between CACVL group and ON group,as well as OE group and ON group in both axes (P<0.05).As for the repeatability,CACVL group was more reli-able than OE group and ON group in both axes.It is concluded that CACVL has the same or higher ac-curacy,better repeatability and less workload than manual landmarking methods.It’s reliable for cervi-cal parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.展开更多
This paper optimizes the buried channel charge-coupled device(BCCD) structure fabricated by complementary metal oxide semiconductor(CMOS) technology. The optimized BCCD has advantages of low noise, high integration an...This paper optimizes the buried channel charge-coupled device(BCCD) structure fabricated by complementary metal oxide semiconductor(CMOS) technology. The optimized BCCD has advantages of low noise, high integration and high image quality. The charge transfer process shows that interface traps, weak fringing fields and potential well between adjacent gates all cause the decrease of charge transfer efficiency(CTE). CTE and well capacity are simulated with different operating voltages and gap sizes. CTE can achieve 99.999% and the well capacity reaches up to 25 000 electrons for the gap size of 130 nm and the maximum operating voltage of 3 V.展开更多
The chest radiograph has been one of the most frequently performed radiological investigation tools.In clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a serie...The chest radiograph has been one of the most frequently performed radiological investigation tools.In clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases(such as Atelectasis,Nodule,and Pneumonia,etc.).Importantly,it is of paramount importance for clinical screening,diagnosis,treatment planning,and efficacy evaluation.However,it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced radiologist.In recent years,many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and algorithms.How to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved.展开更多
This paper analyzes the requirement of professional computer talents in Chinese universities,and introduces the practice in innovative educational methods taken by Shandong University in an open and research environme...This paper analyzes the requirement of professional computer talents in Chinese universities,and introduces the practice in innovative educational methods taken by Shandong University in an open and research environment.In order to improve educational quality,we have carried out a serial of reforms,including "Four Experiences" aiming at diversifying study environments and fostering their adaptability and extending their vision.Students are encouraged to join "Research Assistant" program and participate in scientific projects to improve their ability in research and innovation.They also conduct "Engineering Practice" to learn latest modeling and programming skills.Compound talents characterized of solid foundation,high quality and strong practical ability are shaped through these initiatives.展开更多
In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to in...In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecastin...Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.展开更多
Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a c...Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).展开更多
According to the building requirements of “China’s double first class” discipline, traditional computer organization and architecture (COA) course has new challenges including various course expectations and the li...According to the building requirements of “China’s double first class” discipline, traditional computer organization and architecture (COA) course has new challenges including various course expectations and the limited teaching hours. Considering the hierarchy feature of COA, a bottom-up teaching mode is adopted in teaching reformation to meet the challenges. In this paper, details about COA teaching reforms were shown from aspects of teaching contents, teaching methods, handson assignments, and examination methods. These reform experience will benefit teachers who embark on courses related to computer hardware.展开更多
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
Rice crops are frequently threatened by pests such as rice planthoppers(Nilaparvata lugens,Sogatella furcifera,and Laodelphax striatellus)and leafhoppers(Cicadellidae),which cause significant yield losses.Accurate ide...Rice crops are frequently threatened by pests such as rice planthoppers(Nilaparvata lugens,Sogatella furcifera,and Laodelphax striatellus)and leafhoppers(Cicadellidae),which cause significant yield losses.Accurate identification of both pest developmental stages and their natural predators is crucial for effective pest control and maintaining ecological balance.However,conventional field surveys are often subjective,inefficient,and lack traceability.To overcome these limitations,this study proposed RiceInsectID,a two-stage cascaded detection method designed to identify and count tiny rice pests and their natural predators from white flat plate images captured by head-worn AR glasses.The method recognizes 25 insect classes,including 17 instars of rice planthoppers,2 instars of leafhoppers,4 spider species(Araneae),as well as Miridae and rove beetles(Staphylinidae Latreille).At the first coarse-grained detection stage,16 visually similar classes are consolidated into 6 broader categories and detected using an enhanced YOLOv6 model.To improve small object detection and address class imbalance,the fullregion overlapping sliding slices and target pasting(FOSTP)algorithm was applied,increasing the mean average precision at a 50%IoU threshold(mAP50)by 35.46%over the baseline YOLOv6.Feature extraction and fusion were further improved by incorporating an efficient channel attention path aggregation feature pyramid network(ECA-PAFPN)and adaptive structure feature fusion(ASFF)modules,while the balanced classification mosaic(BCM)enhanced detection of minority classes.With test-time augmentation(TTA),mAP50 improved by an additional 2.06%,reaching 84.71%.At the second fine-grained classification stage,each of the six broad classes from the first stage is further classified using individual ResNet50 models.Online data augmentation and transfer learning were employed to significantly enhance generalization.Compared with the baseline YOLOv6,the two-stage cascaded method improved recall by 4.06%,precision by 3.79%,and the F1-score by 3.92%.Overall,RiceInsectID achieved 82.85%recall,80.62%precision,and an F1-score of 81.72%,demonstrating an efficient and practical solution for monitoring tiny rice pests and their natural predators in paddy fields.This study provides valuable insights for ecosystem monitoring and supporting sustainable pest management in rice agriculture.展开更多
基金Liaoning Provincial Social Science Planning Fund“Research on the Educational Intelligent Evaluation System Based on the CIPP Model and Artificial Intelligence under the Background of New Engineering”(L22BTJ005)。
文摘This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrating the CIPP model with AI technology,a novel intelligent educational evaluation system was designed.Through experimental validation and case studies,the system demonstrated significant effectiveness in improving teaching quality,facilitating personalized student development,and optimizing educational resource allocation.Additionally,the study predicts potential changes this system could bring to the education industry and proposes relevant policy recommendations.Although the current research has limitations,with technological advancements in the future,this system is expected to provide stronger support for innovations in engineering education models.
基金supported by the‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang(Grant No.2023C02018)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002)+2 种基金National Natural Science Foundation of China(Grant No.42371385)Funds of the Natural Science Foundation of Hangzhou(Grant No.2024SZRYBD010001)Nanxun Scholars Program of ZJWEU(Grant No.RC2022010755).
文摘Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.
基金supported in part by the Guangdong Province Education Science Planning Project(Higher Education Project,Project No.2024GXJK410)Shenzhen Education Science“14th Five-Year Plan”2023 Annual Project on Artificial Intelligence Special Project under Grant No.rgzn23001,the Guangdong Province Higher Education Research and Reform Project under Grant No.YueJiaoGaoHan(2024)No.9+1 种基金the Guangdong Province General Colleges and Universities Innovation Team Project under No.2022KCXTD038the Guangdong Provincial Hardware and System Teaching&Research Office Quality Engineering Project under No.HITSZERP22002.
文摘This study presents a teaching reform for the Object-oriented Software Construction(OOSC)course by integrating outcome-based education(OBE)and the BOPPPS(bridge-In,objectives,pre-assessment,participatory learning,post-assessment,summary)instructional model.The reform addresses the gap between syntax-based programming instruction and the need for higher-level skills in abstraction,modularity,and software architecture.The course is anchored in a semester-long,project-based learning platform centered on a Java-based Aircraft Battle Game,progressing through six iterative experiments.Each experiment targets specific competencies within the structured BOPPPS teaching cycle and is aligned with specific OBE learning outcomes.A case study on the Factory Pattern illustrates how the BOPPPS model fosters conceptual understanding and practical application.Evaluation results from the 2023 and 2024 spring semesters show improved outcomes:Project completion rose from 87%to 95%,37%of students implemented innovative features,and average final grades increased by 7%.The results affirm that the OBE+BOPPPS integration strengthens engagement,deepens understanding,and equips students with real-world software development competencies.
文摘War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.
基金supported by the Shanghai Municipal Education Research Project“Exploring the Practical Application of Generative Artificial Intelligence in Cultivating Innovative Thinking and Capabilities of Interdisciplinary Application Technology Talents‘Practice Path’”(C2025299)the university-level postgraduate course project“Software Process Management”(PX-2025251502)of Shanghai Sanda Universitythe key course project at the university level of Shanghai Sanda University,“Introduction to Software Engineering”(PX-5241216).
文摘With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.
基金support from the Scientific Funding for the Center of National Railway Intelligent Transportation System Engineering and Technology,China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ354)。
文摘Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.
基金supported by the Meteorological Joint Funds of the National Natural Science Foundation of China(Grant No.U2142211)the National Natural Science Foundation of China(Grant Nos.42075141,42341202 and 62088101)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.
基金supported by National Natural Science Foundation of China (62171390)Central Universities of Southwest Minzu University (ZYN2022032,2023NYXXS034)the State Scholarship Fund of the China Scholarship Council (NO.202008510081)。
文摘In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.
基金supported in part by the National Natural Science Foundation of China under Grant No.U2268204,62172061 and 61871422National Key R&D Program of China under Grant No.2020YFB1711800 and 2020YFB1707900+2 种基金the Science and Technology Project of Sichuan Province under Grant No.2023ZHCG0014,2023ZHCG0011,2022YFG0155,2022YFG0157,2021GFW019,2021YFG0152,2021YFG0025,2020YFG0322Central Universities of Southwest Minzu University under Grant No.ZYN2022032,2023NYXXS034the State Scholarship Fund of the China Scholarship Council under Grant No.202008510081。
文摘In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.
基金supported by grants from National Natural Sciences Foundation of China (No. 30801314)China Hubei Provincial Science and Technology Department (No.2008CBD088)
文摘The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-seven landmarks from the second to fifth cervical vertebrae on the lat-eral cephalogram were identified.In this study,the system of CACVL was developed and used to iden-tify and calculate the landmarks by fast marching method and parabolic curve fitting.The accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodon-tic experts (OE) group and orthodontic novices (ON) group].The results showed that,as for the accu-racy,there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05),but there was significant difference between CACVL group and ON group,as well as OE group and ON group in both axes (P<0.05).As for the repeatability,CACVL group was more reli-able than OE group and ON group in both axes.It is concluded that CACVL has the same or higher ac-curacy,better repeatability and less workload than manual landmarking methods.It’s reliable for cervi-cal parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.
基金supported by the National Natural Science Foundation of China(Nos.61306070,61404090 and 61674115)
文摘This paper optimizes the buried channel charge-coupled device(BCCD) structure fabricated by complementary metal oxide semiconductor(CMOS) technology. The optimized BCCD has advantages of low noise, high integration and high image quality. The charge transfer process shows that interface traps, weak fringing fields and potential well between adjacent gates all cause the decrease of charge transfer efficiency(CTE). CTE and well capacity are simulated with different operating voltages and gap sizes. CTE can achieve 99.999% and the well capacity reaches up to 25 000 electrons for the gap size of 130 nm and the maximum operating voltage of 3 V.
文摘The chest radiograph has been one of the most frequently performed radiological investigation tools.In clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases(such as Atelectasis,Nodule,and Pneumonia,etc.).Importantly,it is of paramount importance for clinical screening,diagnosis,treatment planning,and efficacy evaluation.However,it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced radiologist.In recent years,many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and algorithms.How to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved.
文摘This paper analyzes the requirement of professional computer talents in Chinese universities,and introduces the practice in innovative educational methods taken by Shandong University in an open and research environment.In order to improve educational quality,we have carried out a serial of reforms,including "Four Experiences" aiming at diversifying study environments and fostering their adaptability and extending their vision.Students are encouraged to join "Research Assistant" program and participate in scientific projects to improve their ability in research and innovation.They also conduct "Engineering Practice" to learn latest modeling and programming skills.Compound talents characterized of solid foundation,high quality and strong practical ability are shaped through these initiatives.
文摘In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金supported by the Collaborative Innovation Center Project of Guangdong Academy of Agricultural Sciences,China(XTXM202202).
文摘Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.
基金funded by the National Natural Science Foundation of China(Grant No.6240072655)the Hubei Provincial Key Research and Development Program(Grant No.2023BCB151)+1 种基金the Wuhan Natural Science Foundation Exploration Program(Chenguang Program,Grant No.2024040801020202)the Natural Science Foundation of Hubei Province of China(Grant No.2025AFB148).
文摘Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).
文摘According to the building requirements of “China’s double first class” discipline, traditional computer organization and architecture (COA) course has new challenges including various course expectations and the limited teaching hours. Considering the hierarchy feature of COA, a bottom-up teaching mode is adopted in teaching reformation to meet the challenges. In this paper, details about COA teaching reforms were shown from aspects of teaching contents, teaching methods, handson assignments, and examination methods. These reform experience will benefit teachers who embark on courses related to computer hardware.
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
基金supported by the National Key Research Program of China during the 14th Five-Year Plan Period(Grant No.2021YFD1401100)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN24C140007)the‘San Nong Jiu Fang’Sciences and Technologies Cooperation Project of Zhejiang Province,China(Grant No.2024SNJF010)。
文摘Rice crops are frequently threatened by pests such as rice planthoppers(Nilaparvata lugens,Sogatella furcifera,and Laodelphax striatellus)and leafhoppers(Cicadellidae),which cause significant yield losses.Accurate identification of both pest developmental stages and their natural predators is crucial for effective pest control and maintaining ecological balance.However,conventional field surveys are often subjective,inefficient,and lack traceability.To overcome these limitations,this study proposed RiceInsectID,a two-stage cascaded detection method designed to identify and count tiny rice pests and their natural predators from white flat plate images captured by head-worn AR glasses.The method recognizes 25 insect classes,including 17 instars of rice planthoppers,2 instars of leafhoppers,4 spider species(Araneae),as well as Miridae and rove beetles(Staphylinidae Latreille).At the first coarse-grained detection stage,16 visually similar classes are consolidated into 6 broader categories and detected using an enhanced YOLOv6 model.To improve small object detection and address class imbalance,the fullregion overlapping sliding slices and target pasting(FOSTP)algorithm was applied,increasing the mean average precision at a 50%IoU threshold(mAP50)by 35.46%over the baseline YOLOv6.Feature extraction and fusion were further improved by incorporating an efficient channel attention path aggregation feature pyramid network(ECA-PAFPN)and adaptive structure feature fusion(ASFF)modules,while the balanced classification mosaic(BCM)enhanced detection of minority classes.With test-time augmentation(TTA),mAP50 improved by an additional 2.06%,reaching 84.71%.At the second fine-grained classification stage,each of the six broad classes from the first stage is further classified using individual ResNet50 models.Online data augmentation and transfer learning were employed to significantly enhance generalization.Compared with the baseline YOLOv6,the two-stage cascaded method improved recall by 4.06%,precision by 3.79%,and the F1-score by 3.92%.Overall,RiceInsectID achieved 82.85%recall,80.62%precision,and an F1-score of 81.72%,demonstrating an efficient and practical solution for monitoring tiny rice pests and their natural predators in paddy fields.This study provides valuable insights for ecosystem monitoring and supporting sustainable pest management in rice agriculture.
