150,155 results
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2. Socio‐technical issues in the platform‐mediated gig economy: A systematic literature review: An Annual Review of Information Science and Technology (ARIST) paper.
- Author
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Dedema, Meredith and Rosenbaum, Howard
- Subjects
INFORMATION science ,TECHNOLOGY ,CORPORATE culture ,ALGORITHMS ,ECONOMICS - Abstract
The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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3. 基于多目标优化的联邦学习进化.
- Author
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胡智勇, 于千城, 王之赐, and 张丽丝
- Subjects
FEDERATED learning ,ALGORITHMS ,PRIVACY - Abstract
Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
4. Superpolynomial Lower Bounds Against Low-Depth Algebraic Circuits.
- Author
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Limaye, Nutan, Srinivasan, Srikanth, and Tavenas, Sébastien
- Subjects
ALGEBRA ,POLYNOMIALS ,CIRCUIT complexity ,ALGORITHMS ,DIRECTED acyclic graphs ,LOGIC circuits - Abstract
An Algebraic Circuit for a multivariate polynomial P is a computational model for constructing the polynomial P using only additions and multiplications. It is a syntactic model of computation, as opposed to the Boolean Circuit model, and hence lower bounds for this model are widely expected to be easier to prove than lower bounds for Boolean circuits. Despite this, we do not have superpolynomial lower bounds against general algebraic circuits of depth 3 (except over constant-sized finite fields) and depth 4 (over any field other than F
2 ), while constant-depth Boolean circuit lower bounds have been known since the early 1980s. In this paper, we prove the first superpolynomial lower bounds against algebraic circuits of all constant depths over all fields of characteristic 0. We also observe that our super-polynomial lower bound for constant-depth circuits implies the first deterministic sub-exponential time algorithm for solving the Polynomial Identity Testing (PIT) problem for all small-depth circuits using the known connection between algebraic hardness and randomness. [ABSTRACT FROM AUTHOR]- Published
- 2024
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5. A review paper of optimal resource allocation algorithm in cloud environment.
- Author
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Patadiya, Namrata and Bhatt, Nirav
- Subjects
RESOURCE allocation ,LITERATURE reviews ,SERVICE level agreements ,ALGORITHMS ,ELECTRONIC data processing ,CLOUD computing - Abstract
Cloud computing has become a popular approach for processing data and running computationally expensive services on a pay-as-you-go basis. Due to the ever-increasing requirement for cloud-based apps, appropriately allocating resources according to user requests while meeting service-level agreements between customers and service providers has become increasingly complex. An efficient and versatile resource allocation method is required to properly deploy these assets and meet user needs. The technique of distributing resources has become more arduous as user demand has increased. One of the key areas of research experts is how to design optimal solutions for this approach. In this paper, a literature review on proposed dynamic resource allocation approaches is introduced. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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6. The Space Complexity of Consensus from Swap.
- Author
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Ovens, Sean
- Subjects
ALGORITHMS ,GENERALIZATION - Abstract
Nearly thirty years ago, it was shown that \(\Omega (\sqrt {n})\) read/write registers are needed to solve randomized wait-free consensus among n processes. This lower bound was improved to n registers in 2018, which exactly matches known algorithms. The \(\Omega (\sqrt {n})\) space complexity lower bound actually applies to a class of objects called historyless objects, which includes registers, test-and-set objects, and readable swap objects. However, every known n-process obstruction-free consensus algorithm from historyless objects uses Ω (n) objects. In this paper, we give the first Ω (n) space complexity lower bounds on consensus algorithms for two kinds of historyless objects. First, we show that any obstruction-free consensus algorithm from swap objects uses at least n-1 objects. More generally, we prove that any obstruction-free k-set agreement algorithm from swap objects uses at least \(\lceil \frac{n}{k}\rceil - 1\) objects. The k-set agreement problem is a generalization of consensus in which processes agree on no more than k different output values. This is the first non-constant lower bound on the space complexity of solving k-set agreement with swap objects when k > 1. We also present an obstruction-free k-set agreement algorithm from n-k swap objects, which exactly matches our lower bound when k=1. Second, we show that any obstruction-free binary consensus algorithm from readable swap objects with domain size b uses at least \(\frac{n-2}{3b+1}\) objects. When b is a constant, this asymptotically matches the best known obstruction-free consensus algorithms from readable swap objects with unbounded domains. Since any historyless object can be simulated by a readable swap object with the same domain, our results imply that any obstruction-free consensus algorithm from historyless objects with domain size b uses at least \(\frac{n-2}{3b+1}\) objects. For b = 2, we show a slightly better lower bound of n-2. There is an obstruction-free binary consensus algorithm using 2n-1 readable swap objects with domain size 2, asymptotically matching our lower bound. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. Digitalized Control Algorithm of Bridgeless Totem-Pole PFC with a Simple Control Structure Based on the Phase Angle.
- Author
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Lee, Gi-Young, Park, Hae-Chan, Ji, Min-Woo, and Kim, Rae-Young
- Subjects
ELECTRIC current rectifiers ,ELECTRONIC paper ,PHASE-locked loops ,ALGORITHMS ,ANGLES ,VOLTAGE - Abstract
Compared to the conventional boost power factor correction (PFC) converter, a totem-pole bridgeless PFC has high efficiency because it does not have an input diode rectifier stage, but a current spike may occur when the polarity of the grid voltage changes. This paper proposes a digital control algorithm for bridgeless totem-pole PFC with a simple control structure based on the phase angle of grid voltage. The proposed algorithm has a PI-based double-loop control structure and performs DC-link voltage and input inductor current control. Rectifying switches operate based on the proposed rectification algorithm using phase angle information calculated through a single-phase phase-locked loop (PLL) to prevent current spikes. The feed-forward duty ratio value is calculated according to the polarity of the grid voltage and added to the double-loop controller to perform appropriate power factor control. The performance and feasibility of the proposed control algorithm are verified through a 3 kW hardware prototype. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. Efficient and Effective Academic Expert Finding on Heterogeneous Graphs through (k, P)-Core based Embedding.
- Author
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YUXIANG WANG, JUN LIU, XIAOLIANG XU, XIANGYU KE, TIANXING WU, and XIAOXUAN GOU
- Subjects
COMMUNITIES ,SEMANTICS ,ALGORITHMS - Abstract
Expert finding is crucial for a wealth of applications in both academia and industry. Given a user query and trove of academic papers, expert finding aims at retrieving the most relevant experts for the query, from the academic papers. Existing studies focus on embedding-based solutions that consider academic papers’ textual semantic similarities to a query via document representation and extract the top-n experts from the most similar papers. Beyond implicit textual semantics, however, papers’ explicit relationships (e.g., co-authorship) in a heterogeneous graph (e.g., DBLP) are critical for expert finding, because they help improve the representation quality. Despite their importance, the explicit relationships of papers generally have been ignored in the literature. In this article, we study expert finding on heterogeneous graphs by considering both the explicit relationships and implicit textual semantics of papers in one model. Specifically, we define the cohesive (k, P)-core community of papers w.r.t. a meta-path P (i.e., relationship) and propose a (k, P)-core based document embedding model to enhance the representation quality. Based on this, we design a proximity graph-based index (PGIndex) of papers and present a threshold algorithm (TA)-based method to efficiently extract top-n experts from papers returned by PG-Index. We further optimize our approach in two ways: (1) we boost effectiveness by considering the (k, P)-core community of experts and the diversity of experts’ research interests, to achieve high-quality expert representation from paper representation; and (2) we streamline expert finding, going from “extract top-n experts from top-m (m > n) semantically similar papers” to “directly return top-n experts”. The process of returning a large number of top-m papers as intermediate data is avoided, thereby improving the efficiency. Extensive experiments using real-world datasets demonstrate our approach’s superiority. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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9. Performance Evaluation of the Extractive Methods in Automatic Text Summarization Using Medical Papers.
- Author
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Kus, Anil and Aci, Cigdem Inan
- Subjects
PERFORMANCE evaluation ,TEXT summarization ,MEDICAL sciences ,ALGORITHMS ,SEMANTICS - Abstract
Copyright of Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi is the property of Gazi Journal of Engineering Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
10. Special issue "Discrete optimization: Theory, algorithms and new applications".
- Author
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Werner, Frank
- Subjects
MATHEMATICAL optimization ,METAHEURISTIC algorithms ,ONLINE algorithms ,LINEAR matrix inequalities ,ALGORITHMS ,ROBUST stability analysis ,NONLINEAR integral equations - Abstract
This document is an editorial for a special issue of the journal AIMS Mathematics on the topic of discrete optimization. The issue includes 21 papers covering a range of subjects, including molecular trees, network systems, variational inequality problems, scheduling, image restoration, spectral clustering, integral equations, convex functions, graph products, optimization algorithms, air quality prediction, humanitarian planning, inertial methods, neural networks, transportation problems, emotion identification, fixed-point problems, structural engineering design, single machine scheduling, and ensemble learning. The papers present new theoretical results, algorithms, and applications in these areas. The guest editor expresses gratitude to the journal staff and reviewers and hopes that readers will find inspiration for their own research. [Extracted from the article]
- Published
- 2024
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11. Taming Algorithmic Priority Inversion in Mission-Critical Perception Pipelines.
- Author
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Liu, Shengzhong, Yao, Shuochao, Fu, Xinzhe, Tabish, Rohan, Yu, Simon, Bansal, Ayoosh, Yun, Heechul, Sha, Lui, and Abdelzaher, Tarek
- Subjects
ALGORITHMS ,SYSTEMS design ,CYBER physical systems ,COMPUTER scheduling ,ARTIFICIAL intelligence ,ARTIFICIAL neural networks ,FIRST in, first out (Queuing theory) - Abstract
The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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12. Committee-Based Blockchains as Games between Opportunistic Players and Adversaries.
- Author
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Amoussou-Guenou, Yackolley, Biais, Bruno, Potop-Butucaru, Maria, and Tucci-Piergiovanni, Sara
- Subjects
BLOCKCHAINS ,COMMITTEES ,GAMES ,COMPUTER network protocols ,ALGORITHMS - Abstract
We study consensus in a protocol capturing in a simplified manner the major features of the majority of Proof of Stake blockchains. A committee is formed; one member proposes a block; and the others can check its validity and vote for it. Blocks with a majority of votes are produced. When an invalid block is produced, the stakes of the members who voted for it are "slashed." Profit-maximizing members interact with adversaries seeking to disrupt consensus. When slashing is limited, free-riding and moral-hazard lead to invalid blocks in equilibrium. We propose a protocol modification producing only valid blocks in equilibrium. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
13. Autonomous localized path planning algorithm for UAVs based on TD3 strategy.
- Author
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Feiyu, Zhao, Dayan, Li, Zhengxu, Wang, Jianlin, Mao, and Niya, Wang
- Subjects
DRONE aircraft ,ALGORITHMS ,PROBLEM solving - Abstract
Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
14. Utilizing tables, figures, charts and graphs to enhance the readability of a research paper.
- Author
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Divecha C. A., Tullu M. S., and Karande S.
- Subjects
GRAPHIC arts ,READABILITY (Literary style) ,SERIAL publications ,RESEARCH methodology ,COPYRIGHT ,MEDICAL research ,ALGORITHMS - Abstract
The authors offer observation on utilizing tables, figures, charts and graphs to help understand the research presented in a simple manner but also engage and sustain the reader's interest. Topics discussed include benefits provided by the use of tables/figures/charts/graphs, general methodology of design and submission, and copyright issues of using material from government publications/public domain.
- Published
- 2023
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15. A fully-automated paper ECG digitisation algorithm using deep learning.
- Author
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Wu, Huiyi, Patel, Kiran Haresh Kumar, Li, Xinyang, Zhang, Bowen, Galazis, Christoforos, Bajaj, Nikesh, Sau, Arunashis, Shi, Xili, Sun, Lin, Tao, Yanda, Al-Qaysi, Harith, Tarusan, Lawrence, Yasmin, Najira, Grewal, Natasha, Kapoor, Gaurika, Waks, Jonathan W., Kramer, Daniel B., Peters, Nicholas S., and Ng, Fu Siong
- Subjects
DEEP learning ,ELECTROCARDIOGRAPHY ,ELECTRONIC paper ,ATRIAL fibrillation ,ALGORITHMS ,HEART failure ,HEART rate monitors - Abstract
There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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16. Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches--A Systematic Literature Review and Mapping Study.
- Author
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García-Peñalvo, Francisco José, Vázquez-Ingelmo, Andrea, and García-Holgado, Alicia
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ARTIFICIAL intelligence ,LITERATURE reviews ,SOFTWARE engineering ,ALGORITHMS ,HEURISTIC ,SOFTWARE engineers - Abstract
The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models' outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this specific expertise; non-AI-expert users could also benefit from applying these powerful algorithms to their domain problems, but they need basic guidelines to obtain the most out of AI models. The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features. The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering. As a result, 9 papers that tackle AI algorithm recommendation through tangible and traceable rules and heuristics were collected. The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
17. Research on fabric surface defect detection algorithm based on improved Yolo_v4.
- Author
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Li, Yuanyuan, Song, Liyuan, Cai, Yin, Fang, Zhijun, and Tang, Ming
- Subjects
SURFACE defects ,FEATURE extraction ,ALGORITHMS ,INDUSTRIAL sites ,TEXTILES ,PROBLEM solving - Abstract
In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
18. Visual recognition and location algorithm based on optimized YOLOv3 detector and RGB depth camera.
- Author
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He, Bin, Qian, Shusheng, and Niu, Yongchao
- Subjects
DETECTORS ,DIAMETER ,TOMATOES ,TRACKING algorithms ,CAMERAS ,ALGORITHMS - Abstract
Fruit recognition and location are the premises of robot automatic picking. YOLOv3 has been used to detect different fruits in complex environment. However, for the object with definite features, the complex network structure will increase the computing time and may cause overfitting. Therefore, this paper has carried out a lightweight design for the YOLOv3. This paper proposed an improved T-Net to detect tomato images. Firstly, the T-Net reduces the residual network layers. This paper changed the number of cycles in each group of the residual unit to 1, 2, 2, 1, and 1. Second, two feature layers with different scales are selected according to the features of tomatoes. Meanwhile, the convolutional layer at the neck has been reduced by two layers. Finally, the location and approximate diameter of the ripe tomato are obtained by combining the node information of the Intel D435i camera and T-Net in the Robot Operation System. T-Net obtains mean average precision (mAP) of 99.2%, F
1 -score of 98.9%, precision of 99.0%, and recall of 98.8% at a detection rate of 104.2 FPS. The proposed T-Net has outperformed the YOLOv3 with 0.4%, 0.1%, and 0.2% increase in precision, mAP, and F1 -score. The detection speed of T-Net is 1.8 times faster than YOLOv3. The mean errors of the center coordinates and diameter of the tomato are 8.5 mm and 2.5 mm, respectively. This model provides a method for efficient real-time detection and location of tomatoes. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
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19. Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements.
- Author
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Liang, Dong, Zhang, Zeyu, Zhang, Qiang, Wu, Erpeng, and Huang, Haibin
- Subjects
POINT cloud ,SYNCHRONIZATION ,CLOUD storage ,ALGORITHMS ,BRIDGES ,STRUCTURAL health monitoring - Abstract
Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
20. A lightweight license plate detection algorithm based on deep learning.
- Author
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Zhu, Shuo, Wang, Yu, and Wang, Zongyang
- Subjects
AUTOMOBILE license plates ,DEEP learning ,INTELLIGENT transportation systems ,TRAFFIC engineering ,ALGORITHMS ,COMPUTATIONAL complexity - Abstract
License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7.
- Author
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Yongliang Yang, Linghua Xu, Maolin Luo, Xiao Wang, and Min Cao
- Subjects
LABORATORY personnel ,COLLEGE environment ,ALGORITHMS ,PERSONNEL management ,FEATURE extraction - Abstract
Due to the complex environment of the university laboratory, personnel flow intensive, personnel irregular behavior is easy to cause security risks. Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed. Therefore, the current management of personnel behavior mainly relies on institutional constraints, education and training, on-site supervision, etc., which is time-consuming and ineffective. Given the above situation, this paperproposes animprovedYouOnlyLookOnce version7 (YOLOv7) to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy. First, to better capture the shape features of the target, deformable convolutional networks (DCN) is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed. Second, to enhance the extraction of important features and suppress useless features, this paper proposes a new convolutional block attention module_efficient channel attention (CBAM_E) for embedding the neck network to improve the model's ability to extract features from complex scenes. Finally, to reduce the influence of angle factor and bounding box regression accuracy, this paper proposes a new a-SCYLLA intersection over union (a-SIoU) instead of the complete intersection over union (CIoU), which improves the regression accuracy while increasing the convergence speed. Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes, with an increase of 2.92% in the precision rate, 4.14% in the recall rate, 0.0356 in the weighted harmonic mean, 3.60% in the mAP@0.5 value, and a reduction in the number of parameters and complexity. Compared with the mainstream algorithm, the improved algorithm has higher detection accuracy, faster convergence speed, and better actual recognition effect, indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning.
- Author
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Rong, Zhang
- Subjects
DEEP learning ,TABLE tennis ,CONVOLUTIONAL neural networks ,TENNIS tournaments ,ATHLETE training ,ALGORITHMS - Abstract
This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
23. Secure Messaging using Blockchain Technology.
- Author
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D., Ruby and Magadum, Hrishikesh
- Subjects
BLOCKCHAINS ,DATA encryption ,COMPUTER hacking ,ALGORITHMS ,EMAIL security - Abstract
All messaging platforms rely on a centralized server which has the vulnerability of getting hacked by outside agencies and posing risk to private data. Authentication between users is an important asset in electronic messaging today, which is one of the most widely used network applications. By replacing the need for reliable mediators, blockchain technology overcomes these threats and allows for a reduction in power-intensive operations. In this work, we propose a blockchain-based solution for secure messaging. Blockchain-based communication can increase communication effectiveness and security, and this paper proposes the creation of a blockchainbased messaging model that will improve the performance and security of blockchain-recorded data, with a smart contract to verify ownership. The system is still fully distributed and allows users to exchange messages securely. This paper has tried to implement a blockchain with a few users and implemented secure end-to-end encryption using the SHA-256 algorithm and multiple crypto techniques so that messages cannot be vulnerable. [ABSTRACT FROM AUTHOR]
- Published
- 2024
24. A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model.
- Author
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Yaoyao Du and Xiangkui Jiang
- Subjects
OBJECT recognition (Computer vision) ,ALGORITHMS ,EDGE computing ,FEATURE extraction ,COMPUTATIONAL complexity ,TRACKING algorithms - Abstract
To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the detection capability for small targets. Finally, this paper applies L1 regularization to train the improved network, followed by pruning and fine-tuning operations to remove redundant channels, reducing computational and parameter complexity and enhancing the detection efficiency of the network. Training is conducted on the VisDrone2019-DET dataset. The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%, respectively. The average detection accuracy improves by 5.15%, and the detection speed reaches 47 images per second, satisfying real-time requirements. Compared with existing approaches, including YOLOv5m and classical vehicle detection algorithms, our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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25. Remote Sensing Image Retrieval Algorithm for Dense Data.
- Author
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Li, Xin, Liu, Shibin, and Liu, Wei
- Subjects
IMAGE retrieval ,GREEDY algorithms ,INFORMATION retrieval ,ALGORITHMS ,DATA quality - Abstract
With the rapid development of remote sensing technology, remote sensing products have found increasingly widespread applications across various fields. Nevertheless, as the volume of remote sensing image data continues to grow, traditional data retrieval techniques have encountered several challenges such as substantial query results, data overlap, and variations in data quality. Users need to manually browse and filter a large number of remote sensing datasets, which is a cumbersome and inefficient process. In order to cope with these problems of traditional remote sensing image retrieval methods, this paper proposes a remote sensing image retrieval algorithm for dense data (DD-RSIRA). The algorithm establishes evaluation metrics based on factors like imaging time, cloud coverage, and image coverage. The algorithm utilizes the global grids to create an ensemble coverage relation between images and grids. A locally optimal initial solution is obtained by a greedy algorithm, and then a local search is performed to search for the optimal solution by combining the strategies of weighted gain-loss scheme and novel mechanism. Ultimately, it achieves an optimal coverage of remote sensing images within the region of interest. In this paper, it is shown that the method obtains a smaller number of datasets with lower redundancy and higher data utilization and ensures the data quality to a certain extent in order to accurately meet the requirements of the regional coverage of remote sensing images. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
26. CyMac: Diving Deep into the Application of Machine Learning Algorithms in Cyber Security.
- Author
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Das, Bishwajit, Yadav, Nikita, Chauhan, Deepa, and Gupta, Sanju
- Subjects
INTERNET security ,ALGORITHMS ,MACHINE learning ,PHISHING prevention ,JURISDICTION - Abstract
Machine learning has emerged as a climatic technology in contemporary and prospective cyber threat intel systems, with numerous jurisdictions seamlessly integrating it into their operations. However, the current state of machine learning in cyber defence is still in its early stages, foreshadowing a noticeable unexplored research territory and practical implementation. This paper marks the initial endeavour to offer a comprehensive understanding of machine learning within the entire spectrum of cybersecurity jurisdictions, catering to potential end users with enthusiasm in this field of study. This paper aims to serve as a source of inspiration for significant advancements in ML within the cyber defence zone, laying the groundwork for the broader adoption of ML mitigations to safeguard present and heuristic systems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
27. Hybrid System for Image Restoration.
- Author
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KASIM, Ghada Mohammad Tahir, ALKATTAN, Zahraa Mazin, and MOHAMMED, Nadia Maan
- Subjects
IMAGE reconstruction ,IMAGE processing ,ALGORITHMS ,COMPUTER passwords ,COMPUTER security - Abstract
The image processing field is considered one of the highly sensitive fields for accuracy due to the quality of the processing in view of the visual view of the user and due to the development in modern means of communication and the use of these means in the transfer of images and the impact of these means on several factors, including external, including those related to the quality of the source signal and the impact of the transmitted images by these conditions, digital correction processes have emerged to reach a high quality of the received image. Most of the studies and research on digital image correction have focused on the quality and time required for correction processes, and some have focused on using traditional optimization algorithms to obtain acceptable visual quality, while others have focused on shortening time regardless of quality, and due to the fact that all studies and research that have been viewed were focused on the use of speculative methods and hybrid algorithms to address distortion in images, as all weaknesses were related to time, quality and calculations because the size of the image data is large Very. The research aims to study digital images and then process images, optimization methods, genetic algorithms and accomplish an algorithm with high features. In this paper, the simple genetic algorithm is used in the process of correcting images of the type (.JPG), as this method is characterized by the fact that it includes many of the advantages of the previous methods in addition to additional features that provided quality, accuracy and shortening time in calculations. The paper has been completed in five phases: The first stage: Providing external protection for the system by entering the password. Second Stage: Creating the system's database. Third stage: Create (code book) in a new style based on the size of the file used. Fourth stage: Building the genetic algorithm for correction Fifth stage: Using a mathematical model to add distortion to a clear image, correct it and compare the results. [ABSTRACT FROM AUTHOR]
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- 2024
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28. Infrared image enhancement algorithm based on detail enhancement guided image filtering.
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Tan, Ailing, Liao, Hongping, Zhang, Bozhi, Gao, Meijing, Li, Shiyu, Bai, Yang, and Liu, Zehao
- Subjects
IMAGE intensifiers ,INFRARED imaging ,COST functions ,ENTROPY (Information theory) ,ALGORITHMS ,ENTROPY ,SIGNAL-to-noise ratio ,QUANTUM noise ,QUANTUM entropy - Abstract
Because of the unique imaging mechanism of infrared (IR) sensors, IR images commonly suffer from blurred edge details, low contrast, and poor signal-to-noise ratio. A new method is proposed in this paper to enhance IR image details so that the enhanced images can effectively inhibit image noise and improve image contrast while enhancing image details. First, for the traditional guided image filter (GIF) applied to IR image enhancement is prone to halo artifacts, this paper proposes a detail enhancement guided filter (DGIF). It mainly adds the constructed edge perception and detail regulation factors to the cost function of the GIF. Then, according to the visual characteristics of human eyes, this paper applies the detail regulation factor to the detail layer enhancement, which solves the problem of amplifying image noise using fixed gain coefficient enhancement. Finally, the enhanced detail layer is directly fused with the base layer so that the enhanced image has rich detail information. We first compare the DGIF with four guided image filters and then compare the algorithm of this paper with three traditional IR image enhancement algorithms and two IR image enhancement algorithms based on the GIF on 20 IR images. The experimental results show that the DGIF has better edge-preserving and smoothing characteristics than the four guided image filters. The mean values of quantitative evaluation of information entropy, average gradient, edge intensity, figure definition, and root-mean-square contrast of the enhanced images, respectively, achieved about 0.23%, 3.4%, 4.3%, 2.1%, and 0.17% improvement over the optimal parameter. It shows that the algorithm in this paper can effectively suppress the image noise in the detail layer while enhancing the detail information, improving the image contrast, and having a better visual effect. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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29. A taxonomy of load balancing algorithms and approaches in fog computing: a survey.
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Ebneyousef, Sepideh and Shirmarz, Alireza
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ALGORITHMS ,COMPUTER systems ,QUALITY of service ,CLOUD computing ,INTERNET of things ,TAXONOMY ,LOAD balancing (Computer networks) - Abstract
These days, cloud computing usage has been increasing with the rapid growth of Internet coverage all over the world to serve as a pay-per-use model using shared computing resources. Internet of Things (IoT) is a growing technology which is used in different applications and it needs cloud computing however the distance between cloud computing resources and the end system in IoT can cause a delay which is intolerable for delay-sensitive applications. Fog computing is a computing resource between cloud computing and end system to reduce the delay for the delay-sensitive applications in IoT. Therefore, load balancing functionality is a significant role to provide the required quality of service (QoS), quality of experience (QoE), and performance. Load balancing can be done based on response time, throughput, energy consumption, and utilization metrics. In this paper, the papers published in Elsevier, ACM, IEEE, Springer and Wiley between 2018 and 2022 have been examined to extract the load-balancing algorithms, system architecture, tools and applications, advantages and disadvantages. This review is useful for those working on load-balancing performance improvement. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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30. Quantum-Proof Secrets.
- Author
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HOUSTON-EDWARDS, KELSEY
- Subjects
QUANTUM computers ,CRYPTOGRAPHY ,QUANTUM cryptography ,COMPUTER systems ,ALGORITHMS - Abstract
This article discusses the urgent need to develop post-quantum cryptography in order to protect data from being compromised by future quantum computers. Public-key cryptography, which is currently used to secure information, would become ineffective if a quantum computer were able to break it. The National Institute of Standards and Technology (NIST) has launched a contest to find alternative cryptographic algorithms that are resistant to quantum attacks, and 26 algorithms have been selected for further testing. Lattice-based cryptography has emerged as a promising approach, but NIST is exploring other options to avoid relying solely on one type of algorithm. The transition to post-quantum cryptography will require time and upgrades to computer systems and protocols. [Extracted from the article]
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- 2024
31. A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field.
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Alohali, Yousef A., Fayed, Mahmoud S., Mesallam, Tamer, Abdelsamad, Yassin, Almuhawas, Fida, and Hagr, Abdulrahman
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DECISION trees ,SERIAL publications ,NATURAL language processing ,BIBLIOMETRICS ,MACHINE learning ,REGRESSION analysis ,RANDOM forest algorithms ,CITATION analysis ,DESCRIPTIVE statistics ,PREDICTION models ,ARTIFICIAL neural networks ,MEDICAL research ,MEDICAL specialties & specialists ,ALGORITHMS - Abstract
One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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32. Cost Optimal Production-Scheduling Model Based on VNS-NSGA-II Hybrid Algorithm—Study on Tissue Paper Mill.
- Author
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Zhang, Huanhuan, Li, Jigeng, Hong, Mengna, Man, Yi, and He, Zhenglei
- Subjects
PAPER mills ,FLOW shop scheduling ,PRODUCTION scheduling ,INDUSTRIAL costs ,ALGORITHMS - Abstract
With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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33. Tools and algorithms for the construction and analysis of systems: a special issue on tool papers for TACAS 2021.
- Author
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Jensen, Peter Gjøl and Neele, Thomas
- Subjects
ALGORITHMS ,SOFTWARE verification ,INTEGRATED circuit verification ,SYSTEMS software ,CONFERENCES & conventions - Abstract
This special issue contains six revised and extended versions of tool papers that appeared in the proceedings of TACAS 2021, the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. The issue is dedicated to the realization of algorithms in tools and the studies of the application of these tools for analysing hard- and software systems. [ABSTRACT FROM AUTHOR]
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- 2023
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34. Automated analysis of pen-on-paper spirals for tremor detection, quantification, and differentiation.
- Author
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Rajan, Roopa, Anandapadmanabhan, Reghu, Nageswaran, Sharmila, Radhakrishnan, Vineeth, Saini, Arti, Krishnan, Syam, Gupta, Anu, Vishnu, Venugopalan Y., Pandit, Awadh K., Singh, Rajesh Kumar, Radhakrishnan, Divya M, Singh, Mamta Bhushan, Bhatia, Rohit, Srivastava, Achal, Kishore, Asha, and Padma Srivastava, M. V.
- Subjects
STATISTICS ,RESEARCH ,CONFIDENCE intervals ,ANALYSIS of variance ,TASK performance ,HANDWRITING ,ACCELEROMETERS ,DYSTONIA ,MOVEMENT disorders ,TREMOR ,DRAWING ,DESCRIPTIVE statistics ,PARKINSON'S disease ,SENSITIVITY & specificity (Statistics) ,DATA analysis ,RECEIVER operating characteristic curves ,DATA analysis software ,ALGORITHMS - Abstract
OBJECTIVE: To develop an automated algorithm to detect, quantify, and differentiate between tremor using pen-on-paper spirals. METHODS: Patients with essential tremor (n = 25), dystonic tremor (n = 25), Parkinson’s disease (n = 25), and healthy volunteers (HV, n = 25) drew free-hand spirals. The algorithm derived the mean deviation (MD) and tremor variability from scanned images. MD and tremor variability were compared with 1) the Bain and Findley scale, 2) the Fahn–Tolosa–Marin tremor rating scale (FTM–TRS), and 3) the peak power and total power of the accelerometer spectra. Inter and intra loop widths were computed to differentiate between the tremor. RESULTS: MD was higher in the tremor group (48.9±26.3) than in HV (26.4±5.3; p < 0.001). The cut-off value of 30.3 had 80.9% sensitivity and 76.0% specificity for the detection of the tremor [area under the curve: 0.83; 95% confidence index (CI): 0.75, 0.91, p < 0.001]. MD correlated with the Bain and Findley ratings (rho = 0.491, p = 0 < 0.001), FTM–TRS part B (rho = 0.260, p = 0.032) and accelerometric measures of postural tremor (total power, rho = 0.366, p < 0.001; peak power, rho = 0.402, p < 0.001). Minimum Detectable Change was 19.9%. Inter loop width distinguished Parkinson’s disease spirals from dystonic tremor (p < 0.001, 95% CI: 54.6, 211.1), essential tremor (p = 0.003, 95% CI: 28.5, 184.9), or HV (p = 0.036, 95% CI: -160.4, -3.9). CONCLUSION: The automated analysis of pen-on-paper spirals generated robust variables to quantify the tremor and putative variables to distinguish them from each other. SIGNIFICANCE: This technique maybe useful for epidemiological surveys and follow-up studies on tremor. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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35. 基于子空间多尺度特征融合的试卷语义分割.
- Author
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夏源祥, 刘 渝, 楚程钱, 万永菁, and 蒋翠玲
- Subjects
PYRAMIDS ,ALGORITHMS ,HANDWRITING ,CLASSIFICATION ,SUBSPACES (Mathematics) - Abstract
Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
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36. SOFTWARE DEFECT PREDICTION APPROACHES REVISITED.
- Author
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Shebl, Khaled S., Afify, Yasmine M., and Badr, Nagwa
- Subjects
SEMANTICS ,DATABASES ,ALGORITHMS ,COMPUTER software testing ,MACHINE learning - Abstract
A crucial field in software development and testing is Software Defect Prediction (SDP) because the quality, dependability, efficiency, and cost of the software are all improved by forecasting software defects at an earlier stage. Many existing models predict defects to facilitate software testing process for testers. A comprehensive review of these models from different perspectives is crucial to help new researchers enter this field and learn about its latest developments. Algorithms, method types, datasets, and tools were the only perspectives discussed in the current literature. A comprehensive study that takes into account a wide spectrum of viewpoints hasn't yet been published. Examining the development and advancement of SDP-related studies is the goal of this literature review. It provides a comprehensive and updated state-of-the-art that satisfies all stated criteria. Out of 591 papers retrieved from 6 reputable databases, 73 papers were eligible for analysis. This review addresses relevant research questions regarding techniques & method types, data details, tools, code syntax, semantics, structural and domain information. Motivation to conduct this comprehensive review is to equip the readers with the necessary information and keep them informed about the software defect prediction domain. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
37. Revisit the scheduling problem with assignable or generalized due dates to minimize total weighted late work.
- Author
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Chen, Rubing, Gao, Yuan, Geng, Zhichao, and Yuan, Jinjiang
- Abstract
We revisit the single-machine scheduling for minimising the total weighted late work with assignable due dates (ADD-scheduling) and generalised due dates (GDD-scheduling). In particular, we consider the following three problems: (i) the GDD-scheduling problem for minimising the total weighted late work, (ii) the ADD-scheduling problem for minimising the total weighted late work, and (iii) the ADD-scheduling problem for minimising the total late work. In the literature, the above three problems are proved to be NP-hard, but their exact complexity (unary NP-hardness or pseudo-polynomial-time solvability) are unknown. In this paper, we address these open problems by showing that the first two problems are unary NP-hard and the third problem admits pseudo-polynomial-time algorithms. For the third problem, we also present a 2-approximation solution and a fully polynomial-time approximation scheme. Computational experiments show that our algorithms and solutions are efficient. When the jobs have identical processing times, we further present more efficient polynomial-time algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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38. The Markovian Multiagent Monte-Carlo method as a differential evolution approach to the SCF problem for restricted and unrestricted Hartree–Fock and Kohn-Sham-DFT.
- Author
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Dittmer, Linus Bjarne and Dreuw, Andreas
- Subjects
ALGORITHMS ,DIFFERENTIAL evolution - Abstract
In this paper we present the Markovian Multiagent Monte-Carlo Second Order Self-Consistent Field Algorithm (M3-SOSCF). This algorithm provides a highly reliable methodology for converging SCF calculations in single-reference methods using a modified differential evolution approach. Additionally, M3 is embarrassingly parallel and modular in regards to Newton–Raphson subroutines. We show that M3 is able to surpass contemporary SOSCFs in reliability, which is illustrated by a benchmark employing poor initial guesses and a second benchmark with SCF calculations which face difficulties using standard SCF algorithms. Furthermore, we analyse inherent properties of M3 and show that in addition to its robustness and efficiency, it is more user-friendly than current SOSCFs. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
39. Research on WSN reliable ranging and positioning algorithm for forest environment.
- Author
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Wu, Peng, Yu, Le, Yi, Xiaomei, Xu, Liang, Liu, LiJuan, Yi, YuTong, Jiang, Tengteng, and Tao, Chunling
- Subjects
WIRELESS sensor networks ,ALGORITHMS - Abstract
Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Weather Radar High-Resolution Spectral Moment Estimation Using Bidirectional Extreme Learning Machine.
- Author
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Zhongyuan Wang, Ling Qiao, Yu Jiang, Mingwei Shen, and Guodong Han
- Subjects
MACHINE learning ,POWER spectra ,RADAR meteorology ,PROBLEM solving ,ALGORITHMS - Abstract
Since the performance of the spectral moment estimation algorithm commonly used in engineering degrades under the conditions of low SNR, this paper introduces the Extreme Learning Machine (ELM) to the spectral moment estimation of weather signals based on the correlation of the signals of adjacent range cells. To solve the problem that the hidden layer nodes of ELM algorithm are difficult to be determined, the Bidirectional Extreme Learning Machine (B-ELM) algorithm is applied to achieve the high resolution of spectral moments. Firstly, to improve the SNR of the training samples, time-domain pulse signals are converted into weather power spectrum by Welch method. Then, the parameters of the B-ELM hidden layer nodes are directly calculated by backpropagation of network residuals. The model parameters are optimized according to the least-squares solution, where the optimal number of hidden layer nodes is determined adaptively. Finally, the optimized B-ELM model is employed for the spectral moment estimation of weather signals. The algorithm is validated to be fast and accurate for spectral moment estimation using the measured IDRA weather radar data and is easy to implement in engineering. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. A Dynamic Task Scheduling Algorithm for Airborne Device Clouds.
- Author
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Deng, Bao and Zhai, Zhengjun
- Subjects
ALGORITHMS ,GENETIC algorithms ,SCHEDULING ,GENETIC models ,DATA transmission systems ,CLOUD computing ,WIRELESS Internet - Abstract
The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction.
- Author
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Hou, Xinyu, Chen, Peng, and Gu, Haishuo
- Subjects
IMAGE segmentation ,DEEP learning ,COMPUTER vision ,ALGORITHMS - Abstract
Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce the parameters and computations of the model while ensuring segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on this basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; and the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8.
- Author
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Wang, Haoyu, Yang, Haitao, Chen, Hang, Wang, Jinyu, Zhou, Xixuan, and Xu, Yifan
- Subjects
REMOTE sensing ,ALGORITHMS ,REMOTE-sensing images - Abstract
Aiming at the characteristics of remote sensing images such as a complex background, a large number of small targets, and various target scales, this paper presents a remote sensing image target detection algorithm based on improved YOLOv8. First, in order to extract more information about small targets in images, we add an extra detection layer for small targets in the backbone network; second, we propose a C2f-E structure based on the Efficient Multi-Scale Attention Module (EMA) to enhance the network's ability to detect targets of different sizes; and lastly, Wise-IoU is used to replace the CIoU loss function in the original algorithm to improve the robustness of the model. Using our improved algorithm for the detection of multiple target categories in the DOTAv1.0 dataset, the mAP@0.5 value is 82.7%, which is 1.3% higher than that of the original YOLOv8 algorithm. It is proven that the algorithm proposed in this paper can effectively improve target detection accuracy in remote sensing images. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception.
- Author
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Li, Pei, Wei, Wangjuan, Pan, Xiaoying, Wang, Hao, and Mu, Yuanzhen
- Subjects
ALGORITHMS ,IMAGE intensifiers ,IMAGE processing ,PEDESTRIANS ,HUMAN fingerprints - Abstract
Existing algorithms for enhancing low-light images predominantly focus on the low-light region, which leads to over-enhancement of the glare region, and the high complexity of the algorithm makes it difficult to apply it to embedded devices. In this paper, a GS-AGC algorithm based on regional luminance perception is proposed. The indirect perception of the human eye's luminance vision was taken into account. All similar luminance pixels that satisfied the luminance region were extracted, and adaptive adjustment processing was performed for the different luminance regions of low-light images. The proposed method was evaluated experimentally on real images, and objective evidence was provided to show that its processing effect surpasses that of other comparable methods. Furthermore, the potential practical value of GS-AGC was highlighted through its effective application in road pedestrian detection and face detection. The algorithm in this paper not only effectively suppressed glare but also achieved the effect of overall image quality enhancement. It can be easily combined with the embedded hardware FPGA for acceleration to improve real-time image processing. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. A Convex Combination–Variable-Step-Size Least Mean p -Norm Algorithm.
- Author
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Zhu, Boyu, Wang, Biao, Cai, Banggui, and Zhu, Yunan
- Subjects
CHANNEL estimation ,ALGORITHMS ,GAUSSIAN function ,PROBLEM solving ,ADAPTIVE filters - Abstract
Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Adaptive Super-Twisting Algorithm Sliding Mode Observer.
- Author
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Zou, Xinhong, Ding, Hongchang, and Li, Jinhong
- Subjects
PERMANENT magnet motors ,SENSORLESS control systems ,PHASE-locked loops ,ALGORITHMS - Abstract
A sensorless control method of permanent magnet synchronous motor (PMSM) based on adaptive super-twisting algorithm sliding mode observer (STASMO) is proposed in this paper. The traditional sliding mode observer (SMO) algorithm has the problem of inherent high-frequency chattering due to the use of switching function. The phase delay will be caused by using low-pass filter to deal with the problem of high-frequency buffeting. In this paper, the chattering of the system is effectively suppressed by combining the super-twisting algorithm and the SMO algorithm. The chattering suppression ability of the algorithm is further improved by adding an adaptive coefficient associated with speed in front of the higher-order integral term of the observer. At the same time, the speed and rotor position information are extracted by normalized phase-locked loop (PLL), which avoids the use of low-pass filter and phase compensation module. In order to overcome the influence of the change of motor parameters on the control system, an online estimation method of motor parameters is proposed. The values of stator resistance and stator inductance are estimated online in real time, and the estimated values are fed back to the SMO, which improves the system performance and estimation accuracy. Through simulations and experiments, it is proved that the proposed algorithm can effectively suppress high-frequency chattering, effectively improve the estimation performance of PMSM sensorless control system, and obtain more accurate speed and rotor position information. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. Numeric Validation of the Inversion Model of Electrical Resistivity Imaging Method using the Levenberg-Marquardt Algorithm.
- Author
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Tuan Anh Nguyen
- Subjects
STRUCTURAL health monitoring ,MODEL validation ,ALGORITHMS ,ELECTRICAL resistivity ,COMPUTER simulation ,MAGNETOTELLURICS - Abstract
This paper introduces a new application of the Electrical Resistivity Imaging (ERI) method within the realm of structural assessment, deviating from its conventional use in geology. The study presents an innovative inversion model that incorporates the Levenberg-Marquardt algorithm, representing a notable leap in seamlessly integrating ERI into structural analysis. Rigorous validation of the inversion methodology is conducted through extensive benchmarking against simulated reference data, focusing on 1D and 2D resistivity distributions within timber specimens. By utilizing known resistivity fields, the paper quantitatively validates the accuracy of reconstructed models obtained through numerical simulations. Notably, both longitudinal and transverse surveys exhibit exceptional outcomes, showcasing a high correlation with the actual resistivity profiles, achieved within a concise 10-13 iterations. This meticulous validation process conclusively underscores the effectiveness and precision of the proposed inversion approach. Beyond its scientific contribution, this research expands the conventional boundaries of ERI application and establishes it as an invaluable tool for structural monitoring. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm.
- Author
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Lieping Zhang, Xiaoxu Shi, Liu Tang, Yilin Wang, Jiansheng Peng, and Jianchu Zou
- Subjects
MOBILE robots ,BIAS correction (Topology) ,ALGORITHMS ,REFERENCE values ,STATISTICAL sampling ,PROBLEM solving - Abstract
A Rapid-exploration Random Tree (RRT) autonomous detection algorithm based on the multi-guide-node deflection strategy andKarto Simultaneous Localization andMapping (SLAM) algorithmwas proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot. Firstly, an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward, which introduces the reference value of guide nodes' deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability. After that, a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm. The algorithm simulation platform based on the Gazebo platform was built. The simulation results show that compared with the traditional RRT algorithm, the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection, plan the length of detection trajectory under the condition of high average detection coverage, and complete the task of autonomous detection mapping more efficiently. Finally, with the help of the ROS-based mobile robot experimental platform, the performance of the proposed algorithm was verified in the real environment of different obstacles. The experimental results show that in the actual environment of simple and complex obstacles, the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection, length of detection trajectory, and average coverage, thus improving the efficiency and accuracy of autonomous detection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. IIR Shelving Filter, Support Vector Machine and k-Nearest Neighbors Algorithm Application for Voltage Transients and Short-Duration RMS Variations Analysis.
- Author
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Liubčuk, Vladislav, Kairaitis, Gediminas, Radziukynas, Virginijus, and Naujokaitis, Darius
- Subjects
SUPPORT vector machines ,K-nearest neighbor classification ,LITERATURE reviews ,VOLTAGE ,ALGORITHMS - Abstract
This paper focuses on both voltage transients and short-duration RMS variations, and presents a unique and heterogeneous approach to their assessment by applying AI tools. The database consists of both real (obtained from Lithuanian PQ monitoring campaigns) and synthetic data (obtained from the simulation and literature review). Firstly, this paper investigates the fundamental grid component and its harmonics filtering with an IIR shelving filter. Secondly, in a key part, both SVM and KNN are used to classify PQ events by their primary cause in the voltage–duration plane as well as by the type of short circuit in the three-dimensional voltage space. Thirdly, since it seemed to be difficult to interpret the results in the three-dimensional space, the new method, based on Clarke transformation, is developed to convert it to two-dimensional space. The method shows an outstanding performance by avoiding the loss of important information. In addition, a geometric analysis of the fault voltage in both two-dimensional and three-dimensional spaces revealed certain geometric patterns that are undoubtedly important for PQ classification. Finally, based on the results of a PQ monitoring campaign in the Lithuanian distribution grid, this paper presents a unique discussion regarding PQ assessment gaps that need to be solved in anticipation of a great leap forward and refers them to PQ legislation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading.
- Author
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Wu, Nian, Hu, Wenshan, Liu, Guo-Ping, and Lei, Zhongcheng
- Subjects
LOADING & unloading ,TRUCKS ,ALGORITHMS ,WEATHER ,TRUCK loading & unloading ,MATHEMATICAL models ,SHIPPING containers - Abstract
Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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