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12th International Conference on Information Systems and Computing Technology

作者： Lu, Kailun Lu, Jiahao Shanghai Univ Elect Power Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350379860;9798350379877

With the globalization of integrated circuit design, the risks of intellectual property theft and hardware Trojan (HT) insertion have become increasingly prevalent. This paper addresses the challenges of sample imbalance and limitations of single detection models in HT detection. To tackle sample imbalance, Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) generates synthetic samples, which are further optimized by a genetic algorithm to improve classifier performance. Additionally, clustering algorithm is combined with supervised learning to enhance feature representation and improve detection accuracy. Light Gradient Boosting Machine (LightGBM) was chosen for its superior performance, achieving an average true positive rate (TPR) of 90.45%. Experimental results demonstrate that this method surpasses existing HT detection techniques.

关键词： Hardware trojan Synthetic samples Genetic algorithm clustering algorithm Supervised learning

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POSTERIOR BELIEF clustering algorithm FOR ENERGY-EFFICIENT TRACKING IN WIRELESS SENSOR NETWORKS

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INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 2014年第3期7卷 925-941页

作者： Wu, Bo Feng, Yanpeng Zheng, Hongyan Shenzhen Polytech Educ Technol & Informat Ctr Shenzhen 518055 Guangdong Peoples R China

In this paper, we propose a novel posterior belief clustering (PBC) algorithm to solve the tradeoff between target tracking performance and sensors energy consumption in wireless sensor networks. We model the target tracking under dynamic uncertain environment using partially observable Markov decision processes (POMDPs), and transform the optimization of the tradeoff between tracking performance and energy consumption into yielding the optimal value function of POMDPs. We analyze the error of a class of continuous posterior beliefs by Kullback-Leibler (KL) divergence, and cluster these posterior beliefs into one based on the error of KL divergence. So, we calculate the posterior reward value only once for each cluster to eliminate repeated computation. The numerical results show that the proposed algorithm has its effectiveness in optimizing the tradeoff between tracking performance and energy consumption.

关键词： Partially observable Markov decision processes wireless sensor networks target tracking energy consumption posterior belief clustering algorithm

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Multi-satellite flyby trajectory planning for uncertain target search

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ACTA ASTRONAUTICA 2025年 228卷 58-68页

作者： Wang, Ke-Mao Zhang, Jin Sun, Xue Gao, Lin-Tao Natl Univ Def Technol Coll Aerosp Sci & Technol Changsha 410073 Peoples R China Hunan Key Lab Intelligent Planning & Simulat Aeros Changsha 410073 Peoples R China Shanghai Inst Aerosp Syst Engn Shanghai 201109 Peoples R China

With the increase of space targets, it is significant to conduct rapid flyby monitoring of satellite clusters and identify Special Targets (STs), which have the distribution uncertainty among clusters. Moreover, there are observation uncertainties during the flybys of space targets. In order to complete the flyby and observation mission more efficiently, the trajectory design of Observation Satellites (OSs) is constructed as a Mixed-Integer Nonlinear Programming (MINLP) model. A two-stage optimization method is proposed to solve the MultiSatellite Flyby (MSF) planning problem. For the first stage, an improved K-means algorithm is used to determine the target assignment groups and the orbital planes of OSs. For the second stage, the mixed integer Differential Evolution (DE) algorithm is utilized to optimize the flyby sequences and maneuver impulses, and to determine the initial orbit parameters of OSs, thereby obtaining the optimal probability of successful search. The simulation results show that the proposed method is effective and can obtain the optimal MSF trajectory under the velocity increment and time constraints, and that the increase of allowed maximum velocity increment could expand the mission benefits. Furthermore, compared with non-revisitable mode which is commonly used in multi-satellite visit problems, the revisitable mode shows the considerable advantage.

关键词： Multi-satellite flyby Uncertain optimization Two-stage planning clustering algorithm Probability of success

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Research on clustering of non-uniformly distributed point clouds in road scenes 24

Research on clustering of non-uniformly distributed point cl...

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16th International Conference on Machine Learning and Computing (ICMLC)

作者： Li Lin Zhao Haili Hui Zhen Mi Rongxin Li Weikuang Chen Zixuan Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China Changan Commun Technol Co LTD Beijing Peoples R China

ISBN: (纸本)9798400709234

This paper presents a clustering algorithm for non-uniformly distributed point clouds in road scenes, which is used to alleviate the performance effect of classical Density-Based Spatial clustering of Applications with Noise (DBSCAN) algorithm in non-uniformly distributed scenes. Because of the limitations of the DBSCAN algorithm, it's difficult to show good results in the space where the parameters aren't convergent. So we proposes a solution, which calculates the node density, average density, density variation coefficient and other parameters of each point which is divided the space into several small spaces with uniform density. Through this method we can achieve better clustering effect in small spaces. Finally, we analyze the proposed solution through Python code on some KITTI data sets. The analysis results show that our proposed scheme can effectively improve the performance of classical DBSCAN algorithm in non-density uniform space.

关键词： DBSCAN clustering algorithm point cloud road scenes

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Multi-dimensional DBSCAN clustering Approach for Millimeter Wave Radar Point Cloud Data 16

Multi-dimensional DBSCAN Clustering Approach for Millimeter ...

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16th International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Li, Hanchen Fu, Chaowei Xi, Guangrong Wang, Qingmian Peng, Hongli Shanghai Jiao Tong Univ State Key Lab Radio Frequency Heterogeneous Integ Shanghai Peoples R China Shanghai Radio Equipment Res Inst Shanghai Peoples R China Shanghai Shentian Ind Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350389968

Addressing the poor resolution challenge of traditional Density-Based Spatial clustering of Applications with Noise (DBSCAN) algorithms in differentiating objects in close proximity, this paper introduces a multi-dimensional DBSCAN (MD-DBSCAN) clustering approach, considering the Doppler characteristics of millimeter wave (mmW) radar. Our approach is based on the idea that the whole clustering procedure is divided into two stages, i.e., velocity clustering and spatial one. Specifically, for each group, the clusters of varying radial velocities are firstly segmented, then followed by spatial dimension clustering. Experimental results demonstrate a 14.2% improvement in number of point clouds with this approach over traditional methods, underscoring its effectiveness in more accurately distinguishing closely situated objects in autonomous driving scenarios.

关键词： mmW Radar DBSCAN clustering algorithm

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Load Balanced clustering Based on Imperialist Competitive algorithm in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2020年第1期112卷 371-385页

作者： Dehestani, Fahimeh Jabraeil Jamali, Mohammad Ali Islamic Azad Univ Shabestar Branch Dept Comp Engn Shabestar Iran

Energy consumption is one of the most serious issues in designing Wireless Sensor Networks (WSNs) for maximizing its lifetime and stability. clustering is considered as one of the topology control methods for maintaining the stability of WSNs which can significantly reduce energy consumption in WSNs. However, using different methods for the selection of cluster head is an important challenge in this domain of research. Load balanced clustering is known as an NP-hard problem for a WSN along with unequal load for sensor nodes. The Imperialist Competitive algorithm (ICA) is regarded as an evolutionary method which can be used for finding a quick and efficient solution to such problems. In this paper, a clustering method with an evolutionary approach is introduced which investigates the issues of load balance and energy consumption of WSNs in the equal and unequal load modes so as to select optimal cluster heads. Simulation of the proposed method, carried out via NS2, indicated that it improves the criteria of energy consumption, the number of active sensor nodes and execution time.

关键词： Wireless sensor network clustering algorithm Imperialist competitive algorithm Load balancing

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Self-supervised Speaker Verification Employing Augmentation Mix and Self-augmented Training-Based clustering 25th

Self-supervised Speaker Verification Employing Augmentation ...

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25th International Conference on Speech and Computer (SPECOM)

作者： Fathan, Abderrahim Alam, Jahangir Comp Res Inst Montreal Montreal PQ H3N 1M3 Canada

ISBN: (纸本)9783031483110;9783031483127

Using clustering algorithms to optimize speaker embedding networks via pseudo-labels is a widely used practice to train self-supervised speaker verification systems. Although pseudo-label-based self-supervised training scheme showed outstanding performance, this latter depends on high-quality pseudo-labels, and recent studies have shown that label noise can remarkably impact downstream performance. In this paper, we propose a general-purpose clustering algorithm called CAMSAT that outperforms all other baselines used to cluster speaker embeddings. Moreover, using the generated pseudo-labels to train our speaker embedding systems allows us to further improve speaker verification performance. CAMSAT is based on two principles: (1) Augmentation Mix (AM) by mixing predictions of augmented samples to provide a complementary supervisory signal for clustering and enforce symmetry within augmentations and (2) Self-Augmented Training (SAT) to enforce representation invariance and maximize the information-theoretic dependency between samples and their predicted pseudo-labels. We provide a thorough comparative analysis of the performance of our clustering method compared to all baselines using a variety of clustering metrics and perform an ablation study to analyze the contribution of each component of our system.

关键词： Speaker verification Speaker embeddings clustering algorithm Pseudo-labels

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A machine learning method for the recognition of ship behavior using AIS data

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OCEAN ENGINEERING 2025年 315卷

作者： Ma, Quandang Lian, Sunrong Zhang, Dingze Lang, Xiao Rong, Hao Mao, Wengang Zhang, Mingyang Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Sch Nav Wuhan Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China Chalmers Univ Technol Dept Mech & Maritime Sci Gothenburg Sweden Univ Tecn Lisboa Inst Super Tecn Ctr Marine Technol & Ocean Engn CENTEC Ave Rovisco Pais Lisbon Portugal Aalto Univ Sch Engn Dept Mech Engn Espoo Finland

The efficiency of maritime traffic management and the safety of ship navigation have become top priorities. This study introduces a ship behavior recognition method that utilizes the Extreme Gradient Boosting (XGBoost) classification model, in conjunction with the Sparrow Search algorithm (SSA), to enhance proactive maritime traffic management. The method leverages Automatic Identification System (AIS) data as its primary source and involves several critical steps. Initially, the AIS data is preprocessed, and ship behaviors are encoded. Subsequently, the encoded behaviors are clustered using spectral clustering to create a labeled dataset. Then, this dataset is employed to train and validate the SSA-XGBoost classification algorithm for identifying ship behaviors. Finally, an example analysis is performed in the Yangtze River. The results indicate that the proposed method can accurately and swiftly identify ship behaviors, achieving an accuracy of 97.28%, precision of 96.97%, recall of 97.43%, and an F1 score of 97.19%, surpassing the performance of the existing algorithms. The findings have the potential to aid maritime supervision authorities in promptly assessing ship navigation statuses and provide a valuable reference for developing ship scheduling decisions.

关键词： Maritime traffic safety Ship behavior recognition AIS data processing clustering algorithm Machine learning

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An inversion-based clustering approach for complex clusters

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BMC RESEARCH NOTES 2024年第1期17卷 1页

作者： Jozan, Mohammad Mahdi Barati Lotfata, Aynaz Hamilton, Howard J. Tabesh, Hamed Mashhad Univ Med Sci Fac Med Dept Med Informat Mashhad Iran Univ Calif Davis Sch Vet Med Dept Pathol Microbiol & Immunol Davis CA 95616 USA Univ Regina Dept Comp Sci Regina SK Canada

Background The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various *** In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand *** The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various *** The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic mana

关键词： clustering algorithm Inversion-based similarity measure Overlapping clusters Adjusted Rand index

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A hybrid framework for regional climate seasonality study and trend analysis

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ENVIRONMENTAL MODELLING & SOFTWARE 2025年 188卷

作者： Suleman, Masooma Khaiter, Peter A. York Univ Sch Informat Technol 4700 Keele St Toronto ON M3J1P3 Canada

One of the profound effects produced by climate change is shifting the seasons in terms of both duration and start/end dates. It is important for sustainable management to detect and predict any such seasonal changes as they may trigger earlier-than-usual timing of plant phenology, animal migration, and other ecological, environmental, economic, and social implications. In this study, we are using meteorological data recorded in four cities across Southern Ontario, Canada over the past 70 years (1953-2022) to explore regional relationship between climate variables and seasonal shifts. Applying a combination of statistical and machine learning (ML) algorithms, a novel hybrid framework is suggested for detecting, quantifying, and visualizing seasonal clusters and trends. A comparative analysis of different ML clustering algorithms to identify variations in seasonality timing and to establish phenological seasons is conducted. The resultant seasonal clusters are then used to detect shifts in seasonality dynamics and trends in climate parameters.

关键词： Climate change Seasonality shift Machine learning clustering algorithm Trend analysis

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