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检索条件"主题词=Detection Algorithms"

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Research on Improved FBD Harmonic detection Method Based on CNF-DSOGI

Research on Improved FBD Harmonic Detection Method Based on ...

引用

Power Systems and Electrical Technology (PSET), IEEE International Conference on

作者： Bo Jiang Ying Xue Cheng Pi Ziwei Zhou Information Engineering College East University of Heilongjiang Harbin China Intelligent Manufacturing Engineering College Harbin Huade University Harbin China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

In the traditional FBD algorithm based on traditional phase-locked loop (PLL), the detection accuracy and anti-interference performance are often limited when the grid voltage contains DC components and harmonic interference. In order to solve this problem, this paper proposes a phase-locked loop using a double second-order generalized integrator (DSOGI) of a complex notch filter (CNF) to improve the performance of the FBD algorithm. In this method, a second-order generalized integrator is added to the traditional phase-locked loop, and a complex notch filter is connected in series in the error processing link to effectively filter out the DC component and high-interference specific harmonics, obtain the grid synchronization information, and improve the ability to obtain the fundamental wave. The simulation results show that the proposed algorithm can quickly and effectively detect the harmonic current of the grid load under complex grid conditions, and significantly improve the accuracy of harmonic current detection.

关键词： Accuracy Simulation Voltage Harmonic analysis Steady-state Synchronization Phase locked loops detection algorithms Power harmonic filters Notch filters

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An improved YOLOv8-based safety helmet wearing detection algorithm

An improved YOLOv8-based safety helmet wearing detection alg...

引用

Computer Information Science and Application Technology (CISAT), International Conference on

作者： Shiyong Ning Xu Han Computer and Information Engineering Department Harbin University of Commerce Harbin China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Whether to wear a safety helmet is one of the key measures to ensure the safety of personnel in the construction process. This paper proposes a new detection algorithm for safety helmet wearing. Utilizing the YOLOv8n architecture, the algorithm incorporates a SENetV2-based channel-wise attention mechanism into the network’s backbone, thereby giving full consideration to global information, thereby allowing the network to allocate more attention to the safety helmet. To overcome the deficiencies of the initial backbone in combining features, we substitute the original network’s neck section with the CCFM module, thereby improving YOLOv8n’s capacity for feature integration following the extraction of features from the backbone network. A new detection head, DyHead3, is proposed to replace the detection head in the original network model. The empirical findings indicate that the algorithm enhances the mAP, P, and R metrics by 2.02%,3.41%,and 1.07%, respectively, when contrasted with the baseline YOLOv8n approach, respectively, which fully demonstrates the effectiveness and practicality of the improved algorithm proposed in this paper.

关键词： Measurement Information science Head Computational modeling Buildings Feature extraction Safety Neck Personnel detection algorithms

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A Forest Harmful Plant Goldenrod detection Method Based on Enhanced YOLOv5

A Forest Harmful Plant Goldenrod Detection Method Based on E...

引用

Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Hengyu Ma Bin Fang Yuting Zhang Tianzhu Qu Fuming Peng Xiaoyu Dai School of Automation Nanjing University of Science and Technology Nanjing China Automation Research Institute.NJCJ Ltd. Nanjing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The challenge of removing detrimental forest weeds like goldenrods remains unresolved, with weed detection playing a pivotal role in solving this issue. Factors such as irregular light distribution in wooded areas and the variety of weed characteristics frequently result in reduced precision during weed detection. To address these issues, this paper proposes a deep learning weed detection algorithm based on enhanced YOLOv5. Specifically, more accurate anchor boxes are obtained using the latest clustering algorithm K-means||, the location error of bounding boxes is reduced by improving the SIoU loss function, and the model's anti-interference capability is enhanced by incorporating the squeeze-and-excitation network (SENet) mechanism in the data preprocessing stage. Experimental results show that the improved algorithm outperforms the original YOLOv5 algorithm with an increase of 10.1%, 12.8%, and 11.2% in mean average precision (mAP), precision, and recall, respectively. Moreover, the utilization of the enhanced algorithm in weed detection within the project's demonstration forest yielded a detection accuracy of 99%, validating the effectiveness and superiority of the algorithm.

关键词： YOLO Training Deep learning Accuracy Data preprocessing Clustering algorithms Lighting Forestry Image preprocessing detection algorithms

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Large-Scale Graphs Community detection using Spark GraphFrames

Large-Scale Graphs Community Detection using Spark GraphFram...

引用

International Symposium on Parallel and Distributed Computing

作者： Elena-Simona Apostol Adrian-Cosmin Cojocaru Ciprian-Octavian Truică National University of Science and Technology Politehnica Bucharest 313 Independentei Bucharest Romania Academy of Romanian Scientists 3 Ilfov Bucharest Romania

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

With the emergence of social networks, online platforms dedicated to different use cases, and sensor networks, the emergence of large-scale graph community detection has become a steady field of research with real-world applications. Community detection algorithms have numerous practical applications, particularly due to their scalability with data size. Nonetheless, a notable drawback of community detection algorithms is their computational intensity [2], resulting in decreasing performance as data size increases. For this purpose, new frameworks that employ distributed systems such as Apache Hadoop and Apache Spark which can seamlessly handle large-scale graphs must be developed. In this paper, we propose a novel framework for community detection algorithms, i.e., K-Cliques, Louvain, and Fast Greedy, developed using Apache Spark GraphFrames. We test their performance and scalability on two real-world datasets. The experimental results prove the feasibility of developing graph mining algorithms using Apache Spark GraphFrames.

关键词： Distributed processing Social networking (online) Scalability Distributed databases Cluster computing Sparks detection algorithms

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Enhanced Student-graph Representation for At-risk Student detection

Enhanced Student-graph Representation for At-risk Student De...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Wei Qiu Andy W. H. Khong Fun Siong Lim Applications of Teaching & Learning Analytics for Students (ATLAS) Nanyang Technological University (NTU) Singapore School of Electrical and Electronic Engineering (EEE) Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Predicting examination grades is essential to facilitate early interventions and to enhance student retention rates in an academic institution. We propose a predictive model based solely on historical academic performance made available before the beginning of each semester. The proposed model employs singular value decomposition to distill the underlying student-course graph structure, resulting in a student representation vector that holistically captures a student’s academic ability across courses in relation to their cohort. This representation vector is then fused with the student’s historical academic records for grade prediction. Data for training the proposed prediction model was sourced from approximately five thousand Electrical and Electronic Engineering students across seventeen core courses, including Circuit Analysis and Analog Electronics taught in the sophomore year. Students identified as at risk of failing a course at the beginning of each semester may be offered targeted (academic) support such as peer tutoring programs.

关键词： Training Predictive models Vectors Data models Circuit analysis Engineering students detection algorithms

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A Particle Swarm Optimization Algorithm Based on Multi-Population Mutual Learning for SNP-SNP Interaction detection

A Particle Swarm Optimization Algorithm Based on Multi-Popul...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Linqian Zhao Yahan Li Junliang Shang Qianqian Ren Yuanyuan Zhang Jin-Xing Liu School of Computer Science Qufu Normal University Rizhao China School of Information and Control Engineering Qingdao University of Technology Qingdao China School of Health and Life Science University of Health and Rehabilitation Sciences Qingdao China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Single nucleotide polymorphism (SNPs) data have become abundant thanks to the quick advancement of high-throughput sequencing technology, which provides convenience for genome-wide association studies. Single SNPs have been proven to be the cause of some diseases, and the emergence of complex diseases is often thought to be the result of the interaction of multiple SNPs. However, the possible interaction of millions of SNPs imposes a heavy computational burden for uncovering complex disease mechanisms. The existing SNP-SNP interaction detection algorithms frequently have flaws including high computation complexity and poor optimization effectiveness. In this study, a particle swarm optimization algorithm based on multi-population mutual learning (PSOMPML) is proposed to detect SNP-SNP interactions. In this algorithm, the mutual learning strategy is introduced to deal with different particles in different sub-populations to facilitate knowledge exchange. In addition, the elite preservation mechanism is incorporated into PSOMPML, to better preserve the good SNPs in the elite particles. The promising region local search strategy searches the optimal solution along the target solution and its near space to increase the convergence speed of the proposed algorithm. Experiments on simulated data sets and real data also demonstrate the effectiveness of the proposed algorithm.

关键词： Sequential analysis Genomics Search problems Particle swarm optimization Bioinformatics detection algorithms Computational complexity Optimization Diseases Convergence

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Demo - Unmasking the Unseen: CryptojackingTrap, the Most Resilient Evasion-Proof Cryptojacking Malware detection Algorithm

Demo - Unmasking the Unseen: CryptojackingTrap, the Most Res...

引用

IEEE International Workshop on Information Forensics and Security (WIFS)

作者： Atefeh Zareh Chahoki Hamid Reza Shahriari Marco Roveri Inf. Eng. and Computer Science Dept. University of Trento Trento Italy Computer Engineering Dept. Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350364422

ISBN: (纸本)9798350364439

This demonstration presents CryptojackingTrap, an advanced cryptojacking malware detection solution that stands out among similar works due to its superior evasion resilience. The demonstration covers the architecture, design, and implementation of CryptojackingTrap, highlighting its effectiveness in detecting cryptojacking attempts, even in scenarios where attackers employ evasion techniques to reduce the hash rate by up to tenfold. The evaluation includes rigorous experimental testing against both miner and non-miner executables. This demonstration provides cybersecurity specialists with a deep understanding of the algorithm, showcasing its robustness and potential for extension, and offering insights into benchmarking detection solutions using publicly available datasets and opensource codebases.

关键词： Instruments Forensics Conferences Benchmark testing Malware Robustness detection algorithms Computer security Resilience

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Label Propagation Algorithm Based on 2-Hop Neighbor and Weighted Path for Community detection

Label Propagation Algorithm Based on 2-Hop Neighbor and Weig...

引用

Data Science and Information Technology (DSIT), International Conference on

作者： Boya Feng Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Community detection is a research focus in the field of complex networks and has wide applications in various domains. However, traditional label propagation algorithms and modularity maximization algorithms often fail to achieve optimal results in many community detection tasks. By observing the characteristics of real-world social networks, this paper proposes a label propagation algorithm based on 2-hop neighbor and weighted path (LPANW) for community detection. The proposed algorithm is compared with three other algorithms on both synthetic networks and real-world datasets, demonstrating better NMI index and robustness.

关键词： Social networking (online) Heuristic algorithms Complex networks Data science Robustness Iterative algorithms Indexes Time complexity Information technology detection algorithms

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A Review of Lane Line detection Research

A Review of Lane Line Detection Research

引用

International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Hao Wang Yuyang Gu Shanghai Institute of Technology SIT Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Lane line detection is an important technology in the field of intelligent driving system, which has a significant impact on the development of intelligent driving system. This paper reviews the research progress of lane line detection algorithms, describes the process of traditional lane line detection algorithms and deep learning based lane line detection algorithms respectively, and elaborates the representative implementation methods. At the same time, this paper also describes in detail some public datasets commonly used for lane line detection tasks. Finally, some existing problems of lane line detection are summarized, and the development direction of lane line detection research is prospected.

关键词： Deep learning Reviews Training data Focusing detection algorithms Informatics Meteorology

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Fire detection algorithm with multi-scale feature focus and diffusion

Fire detection algorithm with multi-scale feature focus and ...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Qi Xiangming Zhang Yiran School of Software Engineering Liaoning Technical University Huludao China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

Flame and smoke detection in fire detection systems can suffer from low accuracy and high false detection rates due to background and environmental factors. To address this, a multi-scale feature focusing and diffusion multilayer fusion fire detection algorithm is proposed, using YOLOv8s as the baseline model. This approach introduces large separable convolutional kernel attention at the end of the backbone network to expand the receptive field, enhance the understanding of spatial feature relationships, and improve feature extraction capabilities. The neck network is enhanced by refining the PANet structure, adding a feature focusing module for multi-scale feature fusion, and incorporating a feature diffusion mechanism to boost the model's utilization of global context information and multi-layer feature integration. The detection head is upgraded with a dynamic detection head featuring unified attention, which consolidates three types of attention to improve detection accuracy. After algorithm optimization and testing, the improved model shows a 1.4 percentage point increase in mAP@0.5, a 2.6 percentage point increase in mAP@0.5:0.95, and a detection speed of 84 fps, outperforming other mainstream object detection algorithms.

关键词： Accuracy Scientific computing Heuristic algorithms Refining Fires Focusing Feature extraction detection algorithms Optimization Testing

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