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A hybrid machine learning approach for enhanced anomaly detection in drinking water quality

International Journal of Environmental Studies 2024年第2期81卷 661-674页

作者： Kalaivanan, K. Vellingiri, J. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine learning has become important for anomaly detection in water quality prediction. Data anomalies are often caused by the difficulties of analysing large amounts of data, both technical and human, but approaches have been inadequate when water contamination increases. The paper reports an anomaly detection technique based on an autoencoder and an extreme learning machine (AEELM). This approach uses an autoencoder for feature selection and an ELM algorithm for classification purposes. It is estimated using the Cauvery River dataset. The autoencoder-based ELM techniques outperformed other models with 83% accuracy, 77% precision, 83% recall, and 83% F1-level characteristic performance indicators. These results demonstrate its great effectiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Water quality

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Sample-agnostic Adversarial Perturbation for Vision-Language Pre-training Models 24

Sample-agnostic Adversarial Perturbation for Vision-Language...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zheng, Haonan Jiang, Wen Deng, Xinyang Li, Wenrui School of Electronics and Information Northwest Polytechnical University Shaanxi Xi'an China Department of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798400706868

Recent studies on AI security have highlighted the vulnerability of Vision-Language Pre-training (VLP) models to subtle yet intentionally designed perturbations in images and texts. Investigating multimodal systems' robustness via adversarial attacks is crucial in this field. Most multimodal attacks are sample-specific, generating a unique perturbation for each sample to construct adversarial samples. To the best of our knowledge, it is the first work through multimodal decision boundaries to explore the creation of a universal, sample-agnostic perturbation that applies to any image. Initially, we explore strategies to move sample points beyond the decision boundaries of linear classifiers, refining the algorithm to ensure successful attacks under the top k accuracy metric. Based on this foundation, in visual-language tasks, we treat visual and textual modalities as reciprocal sample points and decision hyperplanes, guiding image embeddings to traverse text-constructed decision boundaries, and vice versa. This iterative process consistently refines a universal perturbation, ultimately identifying a singular direction within the input space which is exploitable to impair the retrieval performance of VLP models. The proposed algorithms support the creation of global perturbations or adversarial patches. Comprehensive experiments validate the effectiveness of our method, showcasing its data, task, and model transferability across various VLP models and datasets. Code: https://***/LibertazZ/MUAP © 2024 ACM.

关键词： Visual languages

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Federation Boosting Tree for Originator Rights Protection

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computers, Materials & Continua 2023年第2期74卷 4043-4058页

作者： Yinggang Sun Hongguo Zhang Chao Ma Hai Huang Dongyang Zhan Jiaxing Qu Harbin University of Science and Technology Harbin150040China School of Cyberspace Science Harbin Institute of TechnologyHarbin150001China The Ohio State University Columbus43202USA Heilongjiang Province Cyberspace Research Center Harbin150001China

The problem of data island hinders the application of big data in artificial intelligence model training,so researchers propose a federated learning *** enables model training without having to centralize all data in a central storage *** the current horizontal federated learning scheme,each participant gets the final jointly trained *** solution is proposed for scenarios where participants only provide training data in exchange for benefits,but do not care about the final jointly trained ***,this paper proposes a newboosted tree algorithm,calledRPBT(the originator Rights Protected federated Boosted Tree algorithm).Compared with the current horizontal federal learning algorithm,each participant will obtain the final jointly trained *** can guarantee that the local data of the participants will not be leaked,while the final jointly trained model cannot be *** is worth mentioning that,from the perspective of the participants,the scheme uses the batch idea to make the participants participate in the training in random ***,this scheme is more suitable for scenarios where a large number of participants are jointly ***,a small number of participants will not actually participate in the joint training ***,the proposed scheme is more *** analysis and experimental evaluations show that RPBT is secure,accurate and efficient.

关键词： Federated learning data privacy rights protection decision tree

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Boosting the direct conversion of NH4HCO3 electrolyte to syngas on Ag/Zn zeolitic imidazolate framework derived nitrogen-carbon skeleton

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Frontiers of Chemical science and Engineering 2023年第9期17卷 1196-1207页

作者： Huiyi Li Jianmin Gao Jingjing Shan Qian Du Yu Zhang Xin Guo Shaohua Wu Zhijiang Wang Institute of Combustion Engineering School of Energy Science and EngineeringHarbin Institute of TechnologyHarbin150001China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin150001China Harbin Bolier Co. Ltd.Harbin150040China Department of Environmental Engineering Shanxi UniversityTaiyuan030006China

The electrochemical reduction of NH4HCO3 to syngas can bypass the high energy consumption of high-purity CO_(2)release and compression after the ammonia-based CO_(2)capture *** technology has broad prospects in industrial applications and carbon neutrality.A zeolitic imidazolate framework-8 precursor was introduced with different Ag contents via colloid chemical *** material was carbonized at 1000℃to obtain AgZn zeolitic imidazolate framework derived nitrogen carbon catalysts,which were used for the first time for boosting the direct conversion of NH4HCO3 electrolyte to *** AgZn zeolitic imidazolate framework derived nitrogen carbon catalyst with a Ag/Zn ratio of 0.5:1 achieved the highest CO Faradaic efficiency of 52.0%with a current density of 1.15 mA·cm^(−2)at−0.5 V,a H2/CO ratio of 1-2(−0.5 to−0.7 V),and a stable catalytic activity of more than 6 *** activity is comparable to that of the CO_(2)-saturated NH4HCO3 *** highly discrete Ag-N_(x)and Zn-N_(x)nodes may have combined catalytic effects in the catalysts synthesized by appropriate Ag doping and sufficient *** nodes could increase active sites of catalysts,which is conducive to the transport and adsorption of reactant CO_(2)and the stability of*COOH intermediate,thus can improve the selectivity and catalytic activity of CO.

关键词： Ag catalyst zeolitic imidazolate framework CO_(2)electroreduction ammonium bicarbonate electrolyte syngas

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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A Diffusion Model for Traffic Data Imputation

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 606-617页

作者： Bo Lu Qinghai Miao Yahui Liu Tariku Sinshaw Tamir Hongxia Zhao Xiqiao Zhang Yisheng Lv Fei-Yue Wang the School of Artificial Intelligence University of Chinese Academy of Sciences Baidu Inc. IEEE the State Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Meituan Guangdong University of Technology the Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling *** now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multiscale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing *** experimental results demonstrate that the model improves imputation accuracy and generalization capability.

关键词： Data imputation diffusion model implicit feature time series traffic data

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Research on facial expression recognition based on multimodal data fusion and neural network

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 47-55页

作者： Han, Yi Wang, Xubin Lu, Zhengyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Anyang Institute of Technology Department of Computer Science and Information Engineering Henan Province Anyang China School of Computer Science and Information Engineering Shanghai Institute of Technology FengXian District Shanghai China

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data fusion

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Traffic Conflict Prediction for Basic Freeway Segments Considering Different Indicators 24

Traffic Conflict Prediction for Basic Freeway Segments Consi...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Zheng, Lai Jiao, Hansheng Wei, Wei School of Transportation Science and Engineering Harbin Institute of Technology–Harbin Harbin China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Freeways are facilities where it’s likely for severe traffic accidents to happen and thus have great safety concerns. This study predicts the safety of basic freeway segments using traffic conflict techniques. From the highD data set, two different conflict indicators, time to collision (TTC) and post encroachment time (PET), were extracted to measure the traffic conflicts, considering different thresholds. Road- and vehicle-factors which could influence the occurrence of traffic conflicts were considered independent variables. Both Poisson distribution models and Negative Binomial models were developed, and their performances were evaluated according to the goodness of fit and prediction accuracy. The result shows that the Negative Binomial distribution models are better because the data is overdispersed. The prediction accuracy of the models using PET is generally higher than that of the models using TTC. Variables such as lane-changing frequency and standard deviation of space-mean-speed have significant impact on the traffic conflicts. © ASCE.

关键词： Poisson distribution

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Radiation effects of 50-MeV protons on PNP bipolar junction transistors

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Chinese Physics B 2022年第2期31卷 655-659页

作者： Yuan-Ting Huang Xiu-Hai Cui Jian-Qun Yang Tao Ying Xue-Qiang Yu Lei Dong Wei-Qi Li Xing-Ji Li School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China

The effects of radiation on 3 CG110 PNP bipolar junction transistors(BJTs)are characterized using 50-Me V protons,40-Me V Si ions,and 1-Me V *** this paper,electrical characteristics and deep level transient spectroscopy(DLTS)are utilized to analyze radiation defects induced by ionization and displacement *** experimental results show a degradation of the current gain and an increase in the types of radiation defect with increasing fluences of 50-Me V ***,by comparing the types of damage caused by different radiation sources,the characteristics of the radiation defects induced by irradiation show that 50-Me V proton irradiation can produce both ionization and displacement defects in the 3 CG110 PNP BJTs,in contrast to 40-Me V Si ions,which mainly generate displacement defects,and 1-Me V electrons,which mainly produce ionization *** work provides direct evidence of a synergistic effect between the ionization and displacement defects caused in PNP BJTs by 50-Me V protons.

关键词： bipolar junction transistors electrical properties radiation defects synergistic effect

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