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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Zhang, Xiaoyi Wu, Jun Xuan, Xuan Wang, Jun Zhao, Xuliang Yang, Jing State Grid Liaoning Electric Power Co. Ltd. Metering Center Marketing Service Center Shenyang City110000 China Wasion Group Co. Ltd. Smart Energy Efficiency Division Changsha City410000 China

ISBN: (纸本)9798331516550

Due to the complexity and diversity of virtual power plants, there are often outliers in the measurement data, which may have a negative impact on the state estimation, optimization scheduling, and decision support of the power system. Therefore, this paper proposes an anomaly detection method based on the isolation forest algorithm. Clean and normalize the measurement data of the virtual power plant. Introducing the isolation forest algorithm, combined with the characteristics and requirements of virtual power plant measurement data, the dataset is randomly divided to construct multiple isolation trees, and the degree of data anomaly is determined based on the path length from data points to the root node. The experimental results show that this method can accurately identify most outliers while maintaining good recognition ability for normal data. © 2024 IEEE.

关键词： Anomaly detection isolation forest algorithm Measurement data Power system Virtual power plant

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Automatic detection of crop lodging from multitemporal satellite data based on the isolation forest algorithm

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 215卷

作者： Guo, Rui Zhu, Xiufang Liu, Tingting Beijing Normal Univ Inst Remote Sensing Sci & Engn Fac Geog Sci Beijing 100875 Peoples R China Beijing Normal Univ State Key Lab Remote Sensing Sci Jointly Sponsored Beijing 100875 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100875 Peoples R China

Rapid identification of crop lodging has implications for optimizing disaster prevention and mitigation measures. This study proposes a sequentially integrated algorithm for crop lodging identification, which first automatically extracts lodged and non-lodged samples based on the isolation forest algorithm from the screened features dataset and then uses a supervised classifier for lodging identification. Taking the crop lodging disaster caused by the super typhoon "Maysak" in Bohetai Township as an example, we calculated 162 feature indices from Sentinel-1 and Sentinel-2 satellite data pre- and post-disaster and screened the optimal features. Then, the abnormal scores of the cultivated-land pixels were calculated using the isolation forest algorithm. After that, we calculated the coefficient of variation (CV) of the abnormal scores of each group, extracted the lodged and nonlodged samples based on the cumulative CV contribution rate, and input samples into three supervised classifiers (random forest, maximum likelihood, and support vector machine) to identify lodged crop pixels. The results showed that feature screening increased the identification accuracy of crop lodging by more than 10% and difference features of short-wave infrared (SWIR) and red edge bands contributed significantly to the identification. The accuracy of crop lodging automatic identification was verified using samples obtained from visual interpretation and digitization of UAV images as reference data and the overall accuracy reached 78%. The proposed method can simultaneously recognize lodging of different crop types without the need to select lodged samples and extract lodged ranges for each crop. With low data acquisition costs and high automation, the proposed method has promising applications in agricultural disaster emergency management and agricultural insurance loss assessment.

关键词： Crop lodging isolation forest algorithm Automatic identification Sentinel-1 Sentinel-2 Agricultural disaster

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Newest Collaborative and Hybrid Network Intrusion Detection Framework Based on Suricata and isolation forest algorithm 19

Newest Collaborative and Hybrid Network Intrusion Detection ...

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4th International Conference on Smart City Applications (SCA)

作者： Chiba, Zouhair Abghour, Noreddine Moussaid, Khalid El Omri, Amina Rida, Mohamed Hassan II Univ Casablanca Fac Sci LIMSAD Labs Casablanca 20100 Morocco

ISBN: (纸本)9781450362894

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Indeed, intrusions or attacks can lead to irreparable damages, information leakage and significant financial losses. Hence, there is a great need for an effective Network Intrusion Detection System (NIDS). In the current study, we propose a hybrid NIDS to detect network attacks in the network environment by monitoring network traffic, thereby achieving a solid line of protection against inside and outside intruders and maintaining performance and service quality. In our NIDS framework, we use Suricata as a signature based detection to uncover known attacks, while for detecting network anomaly, we use isolation forest algorithm (IFA). By applying Suricata prior to the IFA classifier, IFA has to detect only unknown attacks. Therefore, detection time is reduced and computational power is saved. Suricata is an open source IDS, which has been advanced as a multi-threaded alternative to popular Snort IDS. The major benefits of a multi-threaded design is that it offers increased speed and efficiency in network traffic analysis and can also help divide up the IDS workload based on where the processing needs are. Consequently, Suricata shows an increase in accuracy and system performance over the de facto standard, single threaded NIDS Snort. While, IFA is one of the newest approaches to detect anomalies/outliers, which introduces the use of isolation as a more effective and efficient means to recognize anomalies than the popularly used basic distance and density measures. In fact, IFA uses no distance or density measures to identify outliers, this eliminates major computational cost o

关键词： Network intrusion detection system Signature detection Anomaly detection Hybrid IDS Suricata isolation forest algorithm

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Abnormal Electricity Detection of Users Based on Improved Canopy-Kmeans and isolation forest algorithms

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IEEE ACCESS 2024年 12卷 99110-99121页

作者： Wang, Jianyuan Li, Xiaoyao Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China

Aiming at the existing user abnormal electricity consumption detection methods that have the problem of difficult classification of user similar electricity consumption patterns, this paper proposes an unsupervised isolation forest abnormal electricity consumption detection model based on the Canopy-Kmeans algorithm with weighted density improvement. To start, we propose a composite parameter analysis method for user electricity consumption patterns, volatility, trends, and correlations using Irish smart meter data. This method involves joint data cleaning, interpolation, and feature construction. Additionally, principal component analysis is introduced to fuse features across layers and reduce dimensionality in user electricity consumption. Subsequently, we introduce the weighted density improvement Canopy-Kmeans clustering algorithm. This algorithm determines the K value and clustering centers using the maximum weight product method, based on definitions of sample density, average intra-class sample distance, and inter-class distance in the multilayer fusion feature data. Finally, we propose a fusion mechanism of weighted density improvement Canopy-Kmeans and isolation forest algorithms to jointly construct a model for detecting abnormal power usage based on multilayer fusion feature data analysis. The results demonstrate that multilayer fusion feature parameters vary in size and discretization among different user types, enabling classification of users with diverse electricity consumption patterns. Moreover, the anomaly detection model based on multilayer fusion feature data analysis improves accuracy rates, recall rates, and F1 scores compared to other algorithms.

关键词： Electricity Clustering algorithms Classification algorithms Anomaly detection forestry Prediction algorithms Feature extraction Detection algorithms Energy consumption Unsupervised learning Abnormal detection of electricity consumption by users Canopy-Kmeans algorithm isolation forest algorithm unsupervised learning weighted density

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A Novel Intelligent Learning Method for Identifying Gross Errors in Dam Deformation Monitoring Series

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WATER 2025年第2期17卷 148-148页

作者： Fang, Chunhui Wang, Xue Li, Jianchao Wu, Luobin Wang, Jiayi Gu, Hao Zhejiang Univ Water Resources & Elect Power Res Inst Water Sci Hangzhou 310018 Peoples R China Zhejiang Tongji Vocat Coll Sci & Technol Coll Water Conservancy Engn Hangzhou 311231 Peoples R China Water Conservancy Bur Agr & Rural Affairs Bur Shaoxing 312030 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

In view of the problem that traditional dam outlier identification methods mostly rely on single-monitoring-point models and do not fully consider the spatio-temporal correlation characteristics of deformation between monitoring points, which can easily lead to the misdiagnosis of outliers, this paper proposes a novel Ward-VMD-BiLSTM-Iforest method for identifying gross errors in dam deformation monitoring. By integrating spatio-temporal clustering, variational mode decomposition (VMD), and BiLSTM neural networks, the method effectively identifies outliers while avoiding the misclassification of data mutations caused by environmental changes. Compared to traditional models (GRU, LSTM, and BiLSTM), the HHO-BiLSTM model demonstrates superior performance, achieving an R2 of 0.97775 at TCN08, with a reduced MAE and better accuracy. In comparison with the Raida and Romanovsky criteria, the proposed method achieves 100% precision and 100% recall, significantly improving detection accuracy and reducing misjudgment. This method provides an effective and reliable solution for dam deformation outlier detection.

关键词： dam deformation gross error spatio-temporal clustering deep learning monitoring model isolation forest algorithm

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Outlier Identification of Concrete Dam Displacement Monitoring Data Based on WAVLET-DBSCAN-IFRL

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WATER 2025年第5期17卷 716-716页

作者： Fang, Chunhui Wang, Xue Hu, Weixing He, Xiaojun Huang, Zihui Gu, Hao Zhejiang Univ Water Resources & Elect Power Res Inst Water Sci Hangzhou 310018 Peoples R China Zhejiang Tongji Vocat Coll Sci & Technol Coll Water Conservancy Engn Hangzhou 311231 Peoples R China Water Affairs Bur Wuyi Cty Jinhua 321200 Peoples R China Nat Resources & Planning Bur Wuyi Cty Jinhua 321200 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Monitoring data outliers comprises isolated mode outliers, cluster mode outliers, and normal points. To identify and distinguish the data hopping problems caused by outliers and environmental mutations in the displacement monitoring data of concrete AMS, this paper proposes a method based on wavelet transform, DBSCAN clustering algorithm combined with isolated forest and reinforcement learning algorithm to identify outliers in concrete dam monitoring data. In this paper, the trend line of measuring point data are extracted by the wavelet transform algorithm, and the residual data are obtained by subtracting it from the original process line. Subsequently, the DBSCAN clustering algorithm divides the residual data according to density. Therewith, the outlier scores of different data clusters are calculated, the iterative Q values are updated, and the threshold values are set. The data exceeding the threshold are finally marked as outliers. Finally, the water level and displacement data were compared by drawing the trend to ensure that the water level change did not cause the final identified concrete dam displacement data outliers. The results of the example analysis show that compared with the other two outlier detection methods ("Wavelet transform combined with DBSCAN clustering" or "W-D method", "Wavelet transform combined with isolated forest method" or "W-IF method"). The method has the lowest error rate and the highest precision rate, recall rate, and F1 score. The error rate, precision rate, recall rate, and F1 score were 0.0036, 0.870, 1.000, and 0.931, respectively. This method can effectively identify data jumps caused by an environmental mutation in deformation monitoring data, significantly improve the accuracy of outlier identification, reduce the misjudgement rate of outliers, and have the highest detection accuracy.

关键词： concrete dam displacement monitoring data outlier detection wavelet transform DBSCAN clustering algorithm isolation forest algorithm reinforcement learning

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Temperature Compensation of Laser Methane Sensor Based on a Large-Scale Dataset and the ISSA-BP Neural Network

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SENSORS 2024年第2期24卷 493页

作者： Yin, Songfeng Zou, Xiang Cheng, Yue Liu, Yunlong Anhui Jianzhu Univ Sch Elect & Informat Engn Hefei 230601 Peoples R China Tsinghua Univ Hefei Inst Publ Secur Hefei 230601 Peoples R China Hefei Tsingsensor Technol Co Ltd Hefei 230601 Peoples R China

We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a temperature compensation model based on the ISSA-BP neural network is proposed. On the data side, a large-scale dataset of 15,810 sets of laser methane sensors with different temperatures and concentrations was established, and an Improved isolation forest algorithm was used to clean the large-scale data and remove the outliers in the dataset. On the modeling framework, a temperature compensation model based on the ISSA-BP neural network is proposed. The quasi-reflective learning, chameleon swarm algorithm, Levy flight, and artificial rabbits optimization are utilized to improve the initialization of the sparrow population, explorer position, anti-predator position, and position of individual sparrows in each generation, respectively, to improve the global optimization seeking ability of the standard sparrow search algorithm. The ISSA-BP temperature compensation model far outperforms the four models, SVM, RF, BP, and PSO-BP, in model evaluation metrics such as MAE, MAPE, RMSE, and R-square for both the training and test sets. The results show that the algorithm in this paper can significantly improve the detection accuracy of the laser methane sensor under the wide temperature application environment.

关键词： laser methane sensors temperature compensation isolation forest algorithm BP neural network sparrow search algorithm

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Situation Awareness Method for Electric Vehicles Charging Pile Clusters Based on Edge Computing 36

Situation Awareness Method for Electric Vehicles Charging Pi...

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36th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Zhi Liu, Hengyu Hu, Dawei Chen, Yanru Sun, Jiazheng State Grid Liaoning Elect Power Co Ltd Elect Power Res Inst Shenyang Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In response to challenges characterized by strong randomness, wide spatial distribution, and high power demand within short time frames by a large volume of Electric Vehicles (EVs), a situation awareness method for electric vehicle charging pile clusters is proposed based on edge computing. In this method, some factors such as EVs charging, EVs flow speed in road network are comprehensively considered by using edge computing, a physical framework is established for situation awareness electric vehicle charging pile clusters and some computational resources are reasonably allocated. In view of situation awareness for Electric Vehicles Charging Pile Clusters, an isolation forest(IF) algorithm is used to perceive of the operational status of electric vehicles and the Variational Mode Decomposition( VMD) algorithm is used to understand the operational characteristics of electric vehicles. On this basis, the power demand of EVs charging is forecasted by Using Multilayer Perceptron(MLP) neural network model and the Gated Recurrent Unit(GRU) algorithm. Finally, the case is studied and the proposed method is feasible.

关键词： electric vehicles situation awareness edge computing isolation forest algorithm power forecasted

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Km-IFC: A Framework for Road Course Estimation Using Radar Detections 10

Km-IFC: A Framework for Road Course Estimation Using Radar D...

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10th IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Aaptha, B., V Kadali, Phani Gupta, Saurabh Saju, Vinolia Continental Autonomous Mobil India Pvt Ltd Radar Percept Grp Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

In recent years, the increasing demand for technological advances has driven the usage of state-of-the-art technologies like machine learning in automated systems. In this study, a novel framework, Km-IFC, is presented that effectively estimates the course of the road using radar detections, and its results are discussed. It uses a combination of the K-means clustering algorithm for separation of data, the isolation forest algorithm for removal of outliers, and the least squares method to estimate the curve parameters of the roads' course. The estimated curve using radar data is compared with the vehicle-driven path (considered as ground truth). Km-IFC achieved favorable results in both optimal and non-optimal scenarios, with almost negligible differences between the estimated curve and ground truth. Consequently, the estimated curve had an average difference of 0.365 meters and an overall confidence coefficient of 0.88, inclusive of all scenarios. Furthermore, the study conducted takes in raw sensor detection data and estimates the roads' course without relying on any external sensor outputs, thus making it very computationally efficient.

关键词： K-means clustering algorithm isolation forest algorithm Curve fitting Radar detections Road trajectory

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Sea-Surface Floating Small Target Detection by Multifeature Detector Based on isolation forest

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 704-715页

作者： Xu, Shuwen Zhu, Jianan Jiang, Junzheng Shui, Penglang Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Mcmaster Univ Hamilton ON L8S 4L8 Canada Nanjing Res Inst Elect Technol Signal Proc Dept L8S4L8 Nanjing Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China

In this article, a multifeature detector based on isolation forest (iforest) algorithm is developed to detect floating small targets in sea clutter. The conventional multifeature detector can only process three features or less. The proposed detector aims to break the limitation of feature dimensions' number of the existed feature-based detectors and to improve the detection performance. It transforms the detection of floating small target into an anomaly detection problem in a high-dimensional feature space, breaking the limitation of the number of features. First, a modified isolation forest is constructed from multiple features extracted from sea clutter. Meanwhile, the relative Doppler coefficient of variation is proposed and added into the feature library. Then, taking the average path length as detection statistic, the detection threshold is obtained by Monte-Carlo technique at the given false alarm probability. Finally, the final decision is made by comparing the path length calculated from the cell under test of radar returns with the detection threshold. Detection performances are evaluated based on twenty measured IPIX radar datasets. The experiment results show that the multifeature detector based on isolation forest can obtain a significant performance improvement and has lower computation cost compared with the existed detectors.

关键词： Detectors Clutter Feature extraction Object detection Radar cross-sections forestry Feature detection floating small target detection isolation forest algorithm sea clutter

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