علوم رایانشی

علوم رایانشی

ارائه روشی برای خوشه‌بندی اشیاء دریایی با به‌کارگیری رویکرد تشخیص اجتماع در شبکه‌های اجتماعی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه اراک، اراک، ایران
2 دانشیار گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه اراک، اراک، ایران عضو پژوهشکده هوش مصنوعی، دانشگاه اراک، اراک، ایران
10.22034/csj.2025.222117
چکیده
شناسایی و گروه‌بندی وسایل دریایی در مناطق آبی، از اهمیت ویژه‌ای برخوردار است. اکثر تحقیقات در حوزه گروه‌‌بندی اشیاء دریایی، از رویکرد با ناظر استفاده می‌‌کنند و در نتیجه وابسته به فرایند آموزش بر روی مجموعه داده‌‌هایی می‌‌باشند که برچسب طبقه هر عضو، مشخص است. البته فراهم آوردن داده‌‌های برچسب‌‌دار، همیشه امکان‌‌پذیر نیست و در بسیاری از مواقع، مجموعه داده‌‌های واقعی، فاقد برچسب طبقه هستند. در این مقاله، یک روش جدید خوشه‌بندی اشیاء دریایی، با رویکرد بدون ناظر، ارائه شده است. روش پیشنهادی، شامل 4 مرحلة تولید ویژگی، ایجاد ماتریس‌‌های شباهت، ایجاد ماتریس شباهت کل و تشخیص اجتماع می‌باشد. عمل خوشه‌‌بندی، برخلاف عموم روش‌‌های مشابه، نه با در نظر گرفتن تنها یک خصوصیت از اعضای یک مجموعه داده، بلکه با در نظر گرفتن چندین خصوصیت و با به‌‌کارگیری چندین الگوریتم تولید ویژگی برای کشف خصوصیات مذکور، انجام شده است، تا قابلیت وفق‌‌پذیری بیشتری با کاربردهای مختلف و متفاوت داشته باشد. سپس، با توجه به پتانسیل تئوری گراف، عمل خوشه‌‌بندی اشیاء دریایی، با به‌‌کارگیری مباحث مرتبط با حوزه تشخیص اجتماع در شبکه‌‌های اجتماعی، که ناظر به عمل خوشه‌بندی بر روی گراف‌‌ها هستند، صورت پذیرفته است. در این تحقیق، عملکرد روش پیشنهادی، با انجام آزمایش بر روی دو مجموعه داده حاوی فایل‌‌های صوتی مربوط به اشیاء دریایی، بررسی شده است. به منظور ارزیابی عملکرد روش پیشنهادی، از معیار شاخص ژاکارد استفاده شده است. عملکرد روش پیشنهادی، با در نظر گرفتن سه مورد از بهترین الگوریتم‏های تشخیص اجتماع در مرحله چهارم آن، مقایسه شده است. نتایج نشان داده‏اند که روش پیشنهادی توانسته است در بهترین حالت، به مقدار شاخص ژاکارد 923/0 و 800/0، به ترتیب بر روی مجموعه داده‏های اول و دوم، دست پیدا کند. نتایج آزمایش مذکور، نشان از آن دارند که روش خوشه‌‌بندی ارائه شده، می‏تواند به نتایج مناسبی دست پیدا کند، و راه حل مناسبی برای خوشه‌بندی اشیاء دریایی ارائه دهد.
کلیدواژه‌ها
موضوعات

[1]   Gamage, C., Dinalankara, R., Samarabandu, J. & Subasinghe, A. 2023."A comprehensive survey on the applications of machine learning techniques on maritime surveillance to detect abnormal maritime vessel behaviors," WMU Journal of Maritime Affairs, vol. 22, no. 4, pp. 447-477.
[2]   Bishop, C. M. 2006. Pattern recognition and machine learning. springer.
[3]   Vanem E. & Brandsæter, A. 2021. "Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine," Journal of Marine Engineering & Technology, vol. 20, no. 4, pp. 217-234.
[4]   Guo, Z., Qiang, H., Xie, S. & Peng, X. 2024. "Unsupervised knowledge discovery framework: From AIS data processing to maritime traffic networks generating," Applied Ocean Research, vol. 146, p. 103924.
[5]   Liang, M., Weng, L., Gao, R., Li, Y. & Du, L. 2024. "Unsupervised maritime anomaly detection for intelligent situational awareness using AIS data," Knowledge-Based Systems, vol. 284, p. 111313.
[6]   Fortunato, S. 2010. "Community detection in graphs," Physics reports, vol. 486, no. 3-5, pp. 75-174.
[7]   Reihanian, A. 2014. "To Propose a New Framework for Topic-oriented Community Detection based on a Semantic Network (MSc Thesis in Persian)," Master of Science, Department of Computer and Information Technology, Mazandaran University of Science and Technology.
[8]   Reihanian, A. 2018. "Design and Implementation of a New Framework for Community Detection in Social Networks with Node Attributes using Metaheuristic Algorithms (PhD Thesis in Persian)," Doctor of Philosophy, Department of Computer Engineering, University of Tabriz.
[9]   Jamali, S. M. 2007. "Mining Persian Weblogs' Social Network (MSc Thesis in Persian)," Master of Science, Department of Computer Engineering, Sharif University of Technology.
[10] Webb, A. R. 2003. Statistical pattern recognition. John Wiley & Sons.
[11] Leskovec, J., Lang, K. J. & Mahoney, M. 2010. "Empirical comparison of algorithms for network community detection," in Proceedings of the 19th international conference on World wide web, ACM, pp. 631-640.
[12] Easley D. & Kleinberg, J. 2010. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.
[13] Newman, M. E. & Girvan, M. 2004. "Finding and evaluating community structure in networks," Physical review E, vol. 69, no. 2, p. 026113.
[14] Newman, M. E. 2004. "Analysis of weighted networks," Physical Review E, vol. 70, no. 5, p. 056131.
[15] Liu, K., Yi, S., Wang, G. & Liu, F. 2017."Passive target classification based on mode energy difference characteristic of the wavenumber spectrum," in 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC),: IEEE, pp. 1-4.
[16] Aslam M. A. et al., 2024. "Underwater sound classification using learning based methods: A review," Expert Systems with Applications, vol. 255, p. 124498.
[17] Jiang, J., Wu, Z., Lu, J., Huang, M. & Xiao, Z. 2021. "Interpretable features for underwater acoustic target recognition," Measurement, vol. 173, p. 108586.
[18] Zelada Leon, A., Huvenne, V. A., Benoist, N. M., Ferguson, M., Bett, B. J. & Wynn, R. B. 2020. "Assessing the repeatability of automated seafloor classification algorithms, with application in marine protected area monitoring," Remote Sensing, vol. 12, no. 10, p. 1572.
[19] Harakawa, R., Ogawa, T., Haseyama, M. & Akamatsu, T. 2018. "Automatic detection of fish sounds based on multi-stage classification including logistic regression via adaptive feature weighting," The Journal of the Acoustical Society of America, vol. 144, no. 5, pp. 2709-2718.
[20] Gomez, B. & Kadri, U. 2021. "Earthquake source characterization by machine learning algorithms applied to acoustic signals," Scientific Reports, vol. 11, no. 1, p. 23062.
[21] Feng, S., Ma, S., Zhu, X. & Yan, M. 2024. "Artificial Intelligence-Based Underwater Acoustic Target Recognition: A Survey," Remote Sensing, vol. 16, no. 17, p. 3333.
[22] Li, H., Cheng, Y., Dai, W. & Li, Z. 2014. "A method based on wavelet packets-fractal and SVM for underwater acoustic signals recognition," in 2014 12th International Conference on Signal Processing (ICSP): IEEE, pp. 2169-2173.
[23] De Moura N. N. & De Seixas, J. M. 2015. "Novelty detection in passive sonar systems using support vector machines," in 2015 Latin America Congress on Computational Intelligence (LA-CCI), IEEE, pp. 1-6.
[24] Yao Q. et al., 2023. "Recognition method for underwater imitation whistle communication signals by slope distribution," Applied Acoustics, vol. 211, p. 109531.
[25] Sherin, B. & Supriya, M. 2015. "Selection and parameter optimization of SVM kernel function for underwater target classification," in 2015 IEEE Underwater Technology (UT), IEEE, pp. 1-5.
[26] Wang, B., Wu, C., Zhu, Y., Zhang, M., Li, H. & Zhang, W. 2021. "Ship Radiated Noise Recognition Technology Based on ML‐DS Decision Fusion," Computational Intelligence and Neuroscience, vol. 2021, no. 1, p. 8901565.
[27] Liu, F., Li, G. & Yang, H. 2024. "Application of multi-algorithm mixed feature extraction model in underwater acoustic signal," Ocean Engineering, vol. 296, p. 116959.
[28] Li, Y.-X., Jiao, S.-B., Geng, B., Zhang, Q. & Zhang, Y.-M. 2022. "A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise," Defence Technology, vol. 18, no. 2, pp. 183-193.
[29] Jin, S.-Y., Su, Y., Guo, C.-J., Fan, Y.-X. & Tao, Z.-Y. 2023. "Offshore ship recognition based on center frequency projection of improved EMD and KNN algorithm," Mechanical Systems and Signal Processing, vol. 189, p. 110076.
[30] Quraishi, S. J., Singh, M., Prasad, S. K., Arora, K., Pathak, S. & Singh, A. 2023. "A Machine Learning Approach to Rock and Mine Classification in Sonar Systems Using Logistic Regression," in 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), IEEE, pp. 462-468.
[31] Mousavipour, F. & Mosavi, M. R. 2023. "Sonar data classification using neural network trained by hybrid dragonfly and chimp optimization algorithms," Wireless Personal Communications, vol. 129, no. 1, pp. 191-208.
[32] Mahale, V. P., Chanda, K., Chakraborty, B., Salkar, T. & Sreekanth, G. 2023. "Biodiversity assessment using passive acoustic recordings from off-reef location—Unsupervised learning to classify fish vocalization," The Journal of the Acoustical Society of America, vol. 153, no. 3, pp. 1534-1553.
[33] Sun, Y., Yuan, P. & Li, G. 2020. "Research on Classification and Recognition of Underwater Targets Based on Spark’s Decision Tree Technology," in 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), IEEE, pp. 1-5.
[34] Wang, P. & Peng, Y. 2020. "Research on feature extraction and recognition method of underwater acoustic target based on deep convolutional network," in 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), IEEE, pp. 863-868.
[35] Doan, V.-S., Huynh-The, T. & Kim, D.-S. 2020."Underwater acoustic target classification based on dense convolutional neural network," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5.
[36] Qi, P., Yin, G. & Zhang, L. 2024. "Underwater acoustic target recognition using RCRNN and wavelet-auditory feature," Multimedia Tools and Applications, vol. 83, no. 16, pp. 47295-47317.
[37] Yao, Q., Wang, Y. & Yang, Y. 2023. "Underwater acoustic target recognition based on data augmentation and residual CNN," Electronics, vol. 12, no. 5, p. 1206.
[38] Yang, S., Xue, L., Hong, X. & Zeng, X. 2023. "A lightweight network model based on an attention mechanism for ship-radiated noise classification," Journal of Marine Science and Engineering, vol. 11, no. 2, p. 432.
[39] Cai, W., Zhu, J., Zhang, M. & Yang, Y. 2022. "A parallel classification model for marine mammal sounds based on multi-dimensional feature extraction and data augmentation," Sensors, vol. 22, no. 19, p. 7443.
[40] Tian, G., Haiyang, Y., Haiyan, W., Fan, W. & Xiao, C. 2023. "CA_MobileNetV2 for Underwater Acoustic Target Recognition," in 2023 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), IEEE, pp. 1-5.
[41] Jiang, Z., Zhao, C. & Wang, H. 2022. "Classification of underwater target based on S-ResNet and modified DCGAN models," Sensors, vol. 22, no. 6, p. 2293.
[42] Yan, C. et al., 2023. "Underwater target recognition using a lightweight asymmetric convolutional neural network," in Proceedings of the 17th International Conference on Underwater Networks & Systems, pp. 1-8.
[43] Jin, A. & Zeng, X. 2023. "A novel deep learning method for underwater target recognition based on res-dense convolutional neural network with attention mechanism," Journal of Marine Science and Engineering, vol. 11, no. 1, p. 69.
[44] Wang, B., Zhang, W., Zhu, Y., Wu, C. & Zhang, S. 2023. "An underwater acoustic target recognition method based on AMNet," IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5.
[45] Chen, L., Liu, F., Li, D., Shen, T. & Zhao, D. 2022. "Underwater acoustic target classification with joint learning framework and data augmentation," in 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), IEEE, pp. 23-28.
[46] Ren, J., Xie, Y., Zhang, X. & Xu, J. 2022. "UALF: A learnable front-end for intelligent underwater acoustic classification system," Ocean Engineering, vol. 264, p. 112394.
[47] Tian, S.-Z., Chen, D.-B., Fu, Y. & Zhou, J.-L. 2023. "Joint learning model for underwater acoustic target recognition," Knowledge-Based Systems, vol. 260, p. 110119.
[48] Alouani, Z., Hmamouche, Y., El Khamlichi, B. & Seghrouchni, A. E. F. 2022."A spatio-temporal deep learning approach for underwater acoustic signals classification," in 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp. 1-7.
[49] Yang, H., Huang, Y. & Liu, Y. 2022. "Spatial Attention Deep Convolution Neural Network for Call Recognition of Marine Mammal," in International Conference on Autonomous Unmanned Systems, Springer, pp. 2725-2733.
[50] Feng, S. & Zhu, X. 2022. "A transformer-based deep learning network for underwater acoustic target recognition," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5.
[51] Gong, Y., Chung, Y.-A. & Glass, J. 2021. "Ast: Audio spectrogram transformer," arXiv preprint arXiv:2104.01778.
[52] Li, P., Wu, J., Wang, Y., Lan, Q. & Xiao, W. 2022. "STM: Spectrogram transformer model for underwater acoustic target recognition," Journal of Marine Science and Engineering, vol. 10, no. 10, p. 1428.
[53] Liu et al., Z. 2021. "Swin transformer: Hierarchical vision transformer using shifted windows," in Proceedings of the IEEE/CVF international conference on computer vision, pp. 10012-10022.
[54] Wu, F., Yao, H. & Wang, H. 2024. "Recognizing the State of Motion by Ship-Radiated Noise Using Time-Frequency Swin-Transformer," IEEE Journal of Oceanic Engineering.
[55] Yao, H., Gao, T., Wang, Y., Wang, H. & Chen, X. 2024. "Mobile_ViT: Underwater Acoustic Target Recognition Method Based on Local–Global Feature Fusion," Journal of Marine Science and Engineering, vol. 12, no. 4, p. 589.
[56] Xue, L., Zeng, X. & Jin, A. 2022. "A novel deep-learning method with channel attention mechanism for underwater target recognition," Sensors, vol. 22, no. 15, p. 5492.
[57] Honghui, Y., Junhao, L. & Meiping, S. 2022. "Underwater acoustic target multi-attribute correlation perception method based on dep learning," Applied Acoustics, vol. 190, p. 108644.
[58] Shen, S., Yang, H., Li, J., Xu, G. & Sheng, M. 2018. "Auditory inspired convolutional neural networks for ship type classification with raw hydrophone data," Entropy, vol. 20, no. 12, p. 990.
[59] Yang, Y. 2017. "A signal theoretic approach for envelope analysis of real-valued signals," IEEE Access, vol. 5, pp. 5623-5630.
[60] Bradbury, J. 2000. "Linear predictive coding," Mc G. Hill.
[61] Logan, B. 2000. "Mel frequency cepstral coefficients for music modeling," in Ismir, vol. 270, pp. 1-11.
[62] Ahmed, N., Natarajan, T. & Rao, K. R. 1974. "Discrete cosine transform," IEEE transactions on Computers, vol. 100, no. 1, pp. 90-93.
[63] Abdi, H. & Williams, L. J. 2010. "Principal component analysis," Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433-459.
[64] Thomson, D. J. 1982. "Spectrum estimation and harmonic analysis," Proceedings of the IEEE, vol. 70, no. 9, pp. 1055-1096.
[65] Alexander, W. & Williams, C. M. 2016. Digital Signal Processing: Principles, Algorithms and System Design. Academic Press.
[66] Bogert, B. P., Healy, M. J. R. & Tukey, J. W. 1963."The quefrency alanysis of time series for echoes; Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking," Time series analysis, pp. 209-243.
[67] Oppenheim, A. V. & Schafer, R. W. 2004. "From frequency to quefrency: A history of the cepstrum," IEEE signal processing Magazine, vol. 21, no. 5, pp. 95-106.
[68] Bishop, M. P., Young, B. W., Huo, D. & Chi, Z. 2020. "Spatial Analysis and Modeling in Geomorphology,".
[69] Esfetanaj, N. N. & Nojavan, S. 2018. "The use of hybrid neural networks, wavelet transform and heuristic algorithm of WIPSO in smart grids to improve short- term prediction of load, solar power, and wind energy," in Operation of Distributed Energy Resources in Smart Distribution Networks: Elsevier, pp. 75-100.
[70] Xiao, Z. 2012. "Compositing, smoothing, and gap- filling techniques," Advanced remote sensing terrestrial information extraction and applications, pp. 75-90.
[71] "What is a Notch Filter? - everything RF." (accessed.
[72] "2-D correlation coefficient - MATLAB corr2 - MathWorks." (accessed.
[73] Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. 2008. "Fast unfolding of communities in large networks," Journal of Statistical Mechanics: Theory and Experiment, vol. 2008, no. 10, p. P10008.
[74] Lancichinetti, A., Fortunato, S. & Kertész, J. 2009. "Detecting the overlapping and hierarchical community structure in complex networks," New Journal of Physics, vol. 11, no. 3, p. 033015.
[75] Hespanha, J. P. 2004."An efficient matlab algorithm for graph partitioning," Santa Barbara, CA, USA: University of California.
[76] Le Martelot, E. & Hankin, C. 2013. "Fast multi-scale detection of relevant communities in large-scale networks," The Computer Journal, vol. 56, no. 9, pp. 1136-1150.
[77] Simon, H. A. 1962. "The Architecture of Complexity," Proceedings of the American Philosophical Society, vol. 106, no. 6, pp. 467-482.
[78] Riehl, J. R. & Hespanha, J. P. 2007. "Cooperative graph search using fractal decomposition," in 2007 American Control Conference, IEEE, pp. 2557-2562.
[79] "Community Detection Toolbox - File Exchange - MATLAB Central." (accessed).