18 results on '"node detection"'
Search Results
2. Based on wavelet-Lipschitz function for node detection method on armor subsequent damage optimization.
- Author
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Chang, Hanjui, Sun, Yue, Lu, Shuzhou, and Zhang, Guangyi
- Subjects
- *
WAVELETS (Mathematics) , *INJECTION molding , *GENERATING functions , *PROBLEM solving , *MULTICASTING (Computer networks) - Abstract
In this paper, a node detection method is proposed to detect the damaged parts of armor automatically. The traditional detection of armor damage is judged by experience first, and then the parts are decomposed and confirmed, which makes the problem solving time long and efficiency low. In this paper, the in-mold electronic decoration technology is used on the surface of armor, and the node displacement changes after the surface film is damaged are used to achieve the purpose of damage detection. By using the wavelet analysis, the singularity of the wavelet function can be found and the damaged part can be determined. The Lipschitz index can be used to judge the singularity of the wavelet function to detect the local damage on the armor surface. Finally, the changes of Lipschitz index of the signal with different damage degrees on the helmet deck were simulated. In terms of influencing factors of optimization process, moldex3D was used to simulate and analyze the damaged parts of armor—different injection molding parameter schemes were set to optimize the armor film. In the first stage, the displacement change at the damaged location is detected by the probe node through simulation. In the second stage, the armor was simulated by setting appropriate process parameters such as melt temperature, filling time, filling pressure, and filling time. In the third stage, the wavelet analysis and a Lipschitz index were used to detect the location of damaged nodes. In the fourth stage, the change curve of wavelet analysis is verified by the analysis experiment. Through the experimental verification, it can be seen that the position displacement of the damaged armor changes and the singularity is generated on the wavelet function to achieve the purpose of damage detection. The influencing factors of the film injection molding are temperature and pressure. Through the simulation analysis, we optimize the injection molding parameters of the film, thus improving the damage resistance of the armor. Finally, the optimal parameters of vulnerable parts are obtained and the optimization scheme is determined. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Securing Network Resilience: Leveraging Node Centrality for Cyberattack Mitigation and Robustness Enhancement
- Author
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Hamouda, Essia, ElHafsi, Mohsen, and Son, Joon
- Published
- 2024
- Full Text
- View/download PDF
4. Crop Node Detection and Internode Length Estimation Using an Improved YOLOv5 Model.
- Author
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Hu, Jinnan, Li, Guo, Mo, Haolan, Lv, Yibo, Qian, Tingting, Chen, Ming, and Lu, Shenglian
- Subjects
CROPS ,FEATURE extraction ,EGGPLANT ,PIXELS ,PRECISION farming - Abstract
The extraction and analysis of plant phenotypic characteristics are critical issues for many precision agriculture applications. An improved YOLOv5 model was proposed in this study for accurate node detection and internode length estimation of crops by using an end-to-end approach. In this improved YOLOv5, a feature extraction module was added in front of each detection head, and the bounding box loss function used in the original network of YOLOv5 was replaced by the SIoU bounding box loss function. The results of the experiments on three different crops (chili, eggplant, and tomato) showed that the improved YOLOv5 reached 90.5% AP (average precision) and the average detection time was 0.019 s per image. The average error of the internode length estimation was 41.3 pixels, and the relative error was 7.36%. Compared with the original YOLOv5, the improved YOLOv5 had an average error reduction of 5.84 pixels and a relative error reduction of 1.61%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
5. The critical node detection problem in hypergraphs using weighted node degree centrality
- Author
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Tamás-Zsolt Képes
- Subjects
Node detection ,Hypergraphs ,Centrality measures ,Genetic algorithm ,Critical nodes ,Graph theory ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Network analysis is an indispensable part of today’s academic field. Among the different types of networks, the more complex hypergraphs can provide an excellent challenge and new angles for analysis. This study proposes a variant of the critical node detection problem for hypergraphs using weighted node degree centrality as a form of importance metric. An analysis is done on both generated synthetic networks and real-world derived data on the topic of United States House and Senate committees, using a newly designed algorithm. The numerical results show that the combination of the critical node detection on hypergraphs with the weighted node degree centrality provides promising results and the topic is worth exploring further.
- Published
- 2023
- Full Text
- View/download PDF
6. Cluster Performance by Dynamic Load and Resource-Aware Speculative Execution
- Author
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Mathew, Juby, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Suma, V., editor, Chen, Joy Iong-Zong, editor, Baig, Zubair, editor, and Wang, Haoxiang, editor
- Published
- 2021
- Full Text
- View/download PDF
7. Crop Node Detection and Internode Length Estimation Using an Improved YOLOv5 Model
- Author
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Jinnan Hu, Guo Li, Haolan Mo, Yibo Lv, Tingting Qian, Ming Chen, and Shenglian Lu
- Subjects
plant phenotyping ,node detection ,internode length ,YOLOv5 ,Agriculture (General) ,S1-972 - Abstract
The extraction and analysis of plant phenotypic characteristics are critical issues for many precision agriculture applications. An improved YOLOv5 model was proposed in this study for accurate node detection and internode length estimation of crops by using an end-to-end approach. In this improved YOLOv5, a feature extraction module was added in front of each detection head, and the bounding box loss function used in the original network of YOLOv5 was replaced by the SIoU bounding box loss function. The results of the experiments on three different crops (chili, eggplant, and tomato) showed that the improved YOLOv5 reached 90.5% AP (average precision) and the average detection time was 0.019 s per image. The average error of the internode length estimation was 41.3 pixels, and the relative error was 7.36%. Compared with the original YOLOv5, the improved YOLOv5 had an average error reduction of 5.84 pixels and a relative error reduction of 1.61%.
- Published
- 2023
- Full Text
- View/download PDF
8. 基于固定邻域规模的动态网络影响力最大化探测算法.
- Author
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赵永伟 and 班志杰
- Subjects
- *
ALGORITHMS , *SOCIAL networks - Abstract
Previous research on influence maximization are mostly based on static graph optimization, but in reality, the net- work data volume increase rapidly with time,so the system cannot obtain the connection between nodes in the whole network in real time. Based on the traditional MaxG detection model, this paper proposed the RAS-MaxG detection algorithm, which combined the regular area scale and node neighborhood level to calculate the node influence. Finally, the experimental results on real datasets show that the proposed detection algorithm has better performance in the final influence coverage. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
9. RFDCAR: Robust failure node detection and dynamic congestion aware routing with network coding technique for wireless sensor network.
- Author
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Gobinath, T. and Tamilarasi, A.
- Subjects
LINEAR network coding ,WIRELESS sensor networks ,WIRELESS sensor nodes ,ALGORITHMS ,ROUTING algorithms ,ENERGY consumption ,MATHEMATICAL optimization - Abstract
Wireless sensor networks is an attractive concept that is being implemented in all fields of work with diverse applications. Hence it is not a surprise that there are several wireless routing algorithms available and they mainly focus on reducing the consumption of energy in WSN, the direction of the fact that a sensor node point work on batteries. But algorithms do not study on these energy deficient nodes and their collision effects. There are various reasons for node failure that can fall under mechanical or electrical problems, battery depletion, environmental degradation or hostile tampering. But the most common failure of nodes occur due to limited energy availability. Failure caused due to a group of nodes can minimize the network paths. These activities can lead to failures in the subset of acting nodes resulting in a disconnected or no path situation from the network. This algorithm introduces the multipath node disjoint routing by combining local and global procedures for adaptive route. The capable nodes in the network are located using Lyapunoy optimization technique through network coding technique that enhances the operation and lifetime of the entire network. Through weight of the packet and along with the packet receiving ratio the algorithm separates the packets and route them to a different path to the base station thereby improving delivery and optimizing time and energy. Simulations are conducted in a NS3 environment and proved that this algorithm is efficient in performance than the existing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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10. Dynamic performance of an attacker replacement algorithm for wireless sensor networks
- Author
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Hemalatha, P. and Vijithaananthi, J.
- Published
- 2017
- Full Text
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11. M optimal routes hops strategy: detecting sinkhole attacks in wireless sensor networks.
- Author
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Zhang, Zhaohui, Liu, Sanyang, Bai, Yiguang, and Zheng, Yalin
- Subjects
- *
WIRELESS sensor networks , *SINKHOLES , *HOPS , *DYNAMIC programming , *DATA packeting - Abstract
Sinkhole malicious nodes attacks are quite hard to handle in wireless sensor networks, due to the reason that the results of the attacks may cut off the communications by spreading false hops, absorbing data packets. Those terrible attacks are formed from the unique destroying method, which will preferentially destroy the nodes with higher communication function and shorter distance from the Sink node (Base station). In this paper, we propose a secure and energy-efficient detection scheme which can detect the malicious nodes more precisely than the traditional methods. In particularly, a new measure method is introduced in this paper, which is the frequency of each node by establishing M routes with optimal hops from per node to the Sink node. Using the proposed measure and dynamic programming, the malicious nodes can easily tell apart from the network according to the various satisfaction levels and hop difference. Compared with the traditional algorithms, simulation results show that our scheme can significantly promote the detection rate and the false positive rate. The detection rate increased about by 6–30%, the false positive rate decreased about by 5–25%. We also obtain a high energy-saving results in wireless sensor networks. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
12. RETRACTED ARTICLE: RFDCAR: Robust failure node detection and dynamic congestion aware routing with network coding technique for wireless sensor network
- Author
-
Gobinath, T. and Tamilarasi, A.
- Published
- 2020
- Full Text
- View/download PDF
13. The critical node detection problem in hypergraphs using weighted node degree centrality.
- Author
-
Képes TZ
- Abstract
Network analysis is an indispensable part of today's academic field. Among the different types of networks, the more complex hypergraphs can provide an excellent challenge and new angles for analysis. This study proposes a variant of the critical node detection problem for hypergraphs using weighted node degree centrality as a form of importance metric. An analysis is done on both generated synthetic networks and real-world derived data on the topic of United States House and Senate committees, using a newly designed algorithm. The numerical results show that the combination of the critical node detection on hypergraphs with the weighted node degree centrality provides promising results and the topic is worth exploring further., Competing Interests: The authors declare there are no competing interests., (©2023 Képes.)
- Published
- 2023
- Full Text
- View/download PDF
14. Reconstruction of Curve Networks from Unorganized Spatial Points
- Author
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Jianjun Zhang, Shuangbu Wang, Yu Xia, and Lihua You
- Subjects
Reverse engineering ,General Computer Science ,Iterative method ,business.industry ,Computer science ,Curve network ,curve extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,curve network ,QA75.5-76.95 ,computer.software_genre ,Automation ,Theoretical Computer Science ,Euclidean distance ,Computer graphics ,Set (abstract data type) ,node detection ,curve reconstruction ,node detectio ,Electronic computers. Computer science ,Principal component analysis ,business ,computer ,Algorithm ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
Curve network reconstruction from a set of unorganized points is an important problem in reverse engineering and computer graphics. In this paper, we propose\ud an automatic method to extract curve segments and reconstruct curve networks from\ud unorganized spatial points. Our proposed method divides reconstruction of curve networks into two steps: 1) detecting nodes of curve segments and 2) reconstructing curve\ud segments. For detection of nodes of curve segments, we present a principal component\ud analysis-based algorithm to obtain candidate nodes from unorganized spatial points\ud and a Euclidean distance-based iterative algorithm to remove peripheral nodes and\ud find the actual nodes. For reconstruction of curve segments, we propose an extraction\ud algorithm to obtain the points on each of curve segments. We present quite a number of examples which use our proposed method to reconstruct curve networks from\ud unorganized spatial points. The results demonstrate the effectiveness of our proposed\ud method and its advantages of good automation and high reconstruction efficiency.
- Published
- 2020
15. Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning.
- Author
-
Kyosuke Yamamoto, Wei Guo, and Seishi Ninomiya
- Abstract
Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this study, we develop a method for internode length estimation using image processing technology. The proposed method consists of three steps: node detection, node order estimation, and internode length estimation. This method has two main advantages: (i) as it uses machine learning approaches for node detection, it does not require adjustment of threshold values even though seedlings are imaged under varying timings and lighting conditions with complex backgrounds; and (ii) as it uses affinity propagation for node order estimation, it can be applied to seedlings with different numbers of nodes without prior provision of the node number as a parameter. Our node detection results show that the proposed method can detect 72% of the 358 nodes in time-series imaging of three seedlings (recall = 0.72, precision = 0.78). In particular, the application of a general object recognition approach, Bag of Visual Words (BoVWs), enabled the elimination of many false positives on leaves occurring in the image segmentation based on pixel color, significantly improving the precision. The internode length estimation results had a relative error of below 15.4%. These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
16. Imposter Detection Based On Location In Vehicular Ad-Hoc Network
- Author
-
Sanjoy Das, Akash Arya, and Rishi Pal Singh
- Subjects
node detection ,Authentication ,detection ,imposter node ,IDBL protocol - Abstract
Vehicular Ad hoc Network is basically the solution of several problems associated while vehicles are plying on the road. In this paper, we have focused on the detection of imposter node while it has stolen the ID's of the authenticated vehicle in the network. The purpose is to harm the network through imposter messages. Here, we have proposed a protocol namely Imposter Detection based on Location (IDBL), which will store the location coordinate of the each vehicle as the key of the authenticity of the message so that imposter node can be detected. The imposter nodes send messages from a stolen ID and show that it is from an authentic node ID. So, to detect this anomaly, the first location is checked and observed different from original vehicle location. This node is known as imposter node. We have implemented the algorithm through JAVA and tested various types of node distribution and observed the detection probability of imposter node., {"references":["S. Sesay, Z Yang and Jianhua He, \"A survey on Mobile Ad Hoc Network\", Information Technology Journal 3 (2), pp. 168-175, 2004.","Moustafa, H., Zhang, Y.: Vehicular networks: Techniques, Standards, and Applications. CRC Press, (2009).","Yaseer Toor et al., \"Vehicle Ad Hoc Networks: Applications and Related Technical issues\", IEEE Communications surveys & Tutorials, vol 10, No 3, pp. 74-88, 3rd quarter 2008.","Y.- C. Hu and K. Laberteaux, \"Strong Security on a Budget,\" Wksp. Embedded Security for Cars, Nov. 2006;http://www.crhc.uiuc.edu/~yihchun/","D. Sutariya, \"Data Dissemination Techniques in Vehicular Ad Hoc Network,\" International Journal of Computer Applications, Volume 8– No.10, PP.35-39, October 2010.","V. H. La and A. Cavalli, \"Security Attacks and Solutions in Vehicular Ad Hoc Networks: A Survey,\" International Journal on Ad Hoc Networking Systems (IJANS) Vol. 4, No. 2, April 2014.","A. Pathre, C. Agrawal, and A. Jain, \"Identification of Malicious Vehicle in Vanet Environment from Ddos Attack,\" J. Glob. Res. Comput. Sci., vol. 4, no. 6, pp. 1– 5, 2013.","U. Khan, S. Agrawal, and S. Silakari, \"Detection of Malicious Nodes (DMN) in Vehicular Ad-Hoc Networks,\" Procedia Comput. Sci, ICICT, Elsevier. vol. 46, 2014, pp. 965–972, 2015","J. Newsome, E. Shi, D. Song and A. Perrig. The Sybil Attack in Sensor Networks: Analysis and Defenses. In Proc. of the Third International Symposium on Information Processing in Sensor Networks (IPSN 2004).\n[10]\tHussain R, Son J, Oh H. Anti Sybil: Standing against Sybil attacks in privacy preserved VANETs. In: International Conference on Connected Vehicles and Expo, IEEE; 2012. p. 108-113.\n[11]\tC. Selva Lakshmi et al \"Secured Multi Message authentication protocol for Vehicular Communication,\" International Journal of Advanced Research in computer and communication Engineering. vol-2, Issue 12, December 2013\n[12]\tC. Zhang et al., \"An efficient message authentication scheme for vehicular communication,\" IEEE Trans. Vehicular Technology, vol. 57, no. 6, pp-3357-3368, Nov. 2008.\n[13]\tDilendrashukla, Akash Vaibhav, Sanjoy das, Prashant Johri, \"Security and attack analysis in VANET- A survey,\" to be published in the preceding of IEEE International conference on computing communication and automation (ICCCA 2016). 29-30 April 2016.\n[14]\tR. K. Schmidt et al., \"Exploration of Adaptive Beaconing for Efficient Intervehicle Safety Communication,\" In IEEE Network, vol. 24, Issues.1, pp. 14-19, Feb. 2010.\n[15]\tChen, Y., Jian, W.,& Jiang, W.(2009) \"An improved AOMDV routing protocol for V2V communication,\" In IEEE intelligent vehicles symposium (IV\"09, June 2009, pp. 1115-1120), 2009\n[16]\tTong Zhou et al., \"P2DAP – Sybil Attacks Detection in Vehicular Ad Hoc Networks,\" IEEE Journal on selected areas in communications, Vol. 29, Issues. 3, pp. 582-594, March 2011.\n[17]\tManik Lal Das et al., \"A novel remote user authentication scheme using bilinear pairings,\" In the proceeding of Elsevier computer and society, Vol. 25, Issues.3, pp. 184-189, 2006\n[18]\tAjay Rawati, Santosh Sharma and Rama Susil, \"VANET: Security Attacks On Its Possible Solutions,\" Journal of Information and Operations Management, Vol. 3, Issue.1, pp.301-304, 2012\n[19]\tTyagi, Pand Dembla, D., \"Investigating the security threats in Vehicular ad hoc Networks (VANETs): Towards security engineering for safer on-road transportation,\" International Conference on Advances in Computing, Communications and Informatics, pp. 2084 – 2090, Sept. 2014.\n[20]\tAkash Vaibhav, Dilendra Shukla, Sanjoy Das, Subrata Sahana, Prashant Johri, \"Security Challenges, Authentication, Application and Trust Models for Vehicular Ad Hoc Network- A Survey\", International Journal of Wireless and Microwave Technologies (IJWMT), Vol.7, No.3, pp.36-48, 2017.DOI: 10.5815/ijwmt.2017.03.04"]}
- Published
- 2018
- Full Text
- View/download PDF
17. Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning
- Author
-
Seishi Ninomiya, Kyosuke Yamamoto, and Wei Guo
- Subjects
0106 biological sciences ,Time Factors ,Image processing ,node detection ,internode length estimation ,image analysis ,machine learning ,BoVWs ,affinity propagation ,Machine learning ,computer.software_genre ,lcsh:Chemical technology ,01 natural sciences ,Biochemistry ,Article ,Analytical Chemistry ,Solanum lycopersicum ,Approximation error ,False positive paradox ,Image Processing, Computer-Assisted ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,Mathematics ,Plant Stems ,business.industry ,Cognitive neuroscience of visual object recognition ,04 agricultural and veterinary sciences ,Image segmentation ,Atomic and Molecular Physics, and Optics ,Plant Leaves ,Bag-of-words model in computer vision ,Seedlings ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Affinity propagation ,Node (circuits) ,Artificial intelligence ,business ,computer ,Cotyledon ,010606 plant biology & botany - Abstract
Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this study, we develop a method for internode length estimation using image processing technology. The proposed method consists of three steps: node detection, node order estimation, and internode length estimation. This method has two main advantages: (i) as it uses machine learning approaches for node detection, it does not require adjustment of threshold values even though seedlings are imaged under varying timings and lighting conditions with complex backgrounds; and (ii) as it uses affinity propagation for node order estimation, it can be applied to seedlings with different numbers of nodes without prior provision of the node number as a parameter. Our node detection results show that the proposed method can detect 72% of the 358 nodes in time-series imaging of three seedlings (recall = 0.72, precision = 0.78). In particular, the application of a general object recognition approach, Bag of Visual Words (BoVWs), enabled the elimination of many false positives on leaves occurring in the image segmentation based on pixel color, significantly improving the precision. The internode length estimation results had a relative error of below 15.4%. These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately.
- Published
- 2016
18. Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning.
- Author
-
Yamamoto K, Guo W, and Ninomiya S
- Subjects
- Cotyledon anatomy & histology, Plant Leaves anatomy & histology, Time Factors, Image Processing, Computer-Assisted, Solanum lycopersicum anatomy & histology, Machine Learning, Plant Stems anatomy & histology, Seedlings anatomy & histology
- Abstract
Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this study, we develop a method for internode length estimation using image processing technology. The proposed method consists of three steps: node detection, node order estimation, and internode length estimation. This method has two main advantages: (i) as it uses machine learning approaches for node detection, it does not require adjustment of threshold values even though seedlings are imaged under varying timings and lighting conditions with complex backgrounds; and (ii) as it uses affinity propagation for node order estimation, it can be applied to seedlings with different numbers of nodes without prior provision of the node number as a parameter. Our node detection results show that the proposed method can detect 72% of the 358 nodes in time-series imaging of three seedlings (recall = 0.72, precision = 0.78). In particular, the application of a general object recognition approach, Bag of Visual Words (BoVWs), enabled the elimination of many false positives on leaves occurring in the image segmentation based on pixel color, significantly improving the precision. The internode length estimation results had a relative error of below 15.4%. These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately.
- Published
- 2016
- Full Text
- View/download PDF
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