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Dual-modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM international conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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Development of Skin Cancer Detection Using Deep Spiking Neural Networks 10

Development of Skin Cancer Detection Using Deep Spiking Neur...

引用

10th IEEE international conference on Smart Instrumentation, measurement and Applications, ICSIMA 2024

作者： Habaebi, Mohamed Hadi Zulkarnain, Muhammad Amin Gunawan, Teddy Surya Fitriawan, Helmy Kartiwi, Mira Abd.rahman, Faridah International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia Universitas Lampung Department of Electrical Engineering Bandar Lampung35145 Indonesia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia

ISBN: (纸本)9798350365283

The limitations of conventional techniques necessitate the development of new diagnostic strategies for early detection of skin cancer, the most prevalent form of cancer. This investigation investigates the application of Spiking Neural Networks (SNNs) in creating a dependable and automated skin cancer detection system. The primary goal is to evaluate the feasibility and efficacy of SNNs for this purpose. The methodology entails using the SpikingJelly framework to create and assess an SNN-based model, specifically a modified Spiking VGG-11. Focusing on images classified as melanoma or non-melanoma, the investigation employs the ISIC 2019 dataset. The Spiking VGG-11 model, which features approximately 15 million parameters, achieved a test accuracy of 81.3%, specificity of 81.7%, and precision of 81.5%. This performance is noteworthy in light of its reduced complexity compared to models such as the Spiking VGG-13, which has approximately 134 million parameters. The model's high specificity is especially advantageous in clinical environments, as it minimizes false positives and unnecessary testing. These results emphasize the potential of SNNs to offer diagnostic tools that are both efficient and effective in the detection of skin cancer. These findings underscore the potential of SNNs to enhance the early detection and diagnosis of skin cancer, offering a promising alternative to conventional diagnostic methods. © 2024 IEEE.

关键词： Lung cancer

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modeling and performance analysis of an industrial transport platform manufacturing process 28

Modeling and performance analysis of an industrial transport...

引用

28th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Arzola, César Latorre, Daniel Sacramento, Germán Mahulea, Cristian Júlvez, Jorge University of Zaragoza Dept. of Computer Science and Systems Engineering Spain Alimak Group La Muela Spain Spain

ISBN: (纸本)9798350339918

This contribution focuses on the modeling and performance analysis of the manufacturing process for transport platforms at the Alimak Group facility in La Muela, Spain. The objective is to leverage formal tools to gain insights into this complex manufacturing process and explore potential applications such as control and optimization. This work presents a preliminary study, where a Generalized Stochastic Petri net model of the manufacturing process is proposed to be used for simulation, performance analysis, and optimization of the system. © 2023 IEEE.

关键词： Stochastic systems

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ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with Uncertainty Data 8

ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with ...

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8th IEEE international conference on Software Engineering and computer systems, ICSECS 2023

作者： Che Lah, Muhammad Shukri Arbaiy, Nureize Lin, Pei-Chun Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Malaysia Feng Chia University Department of Information Engineering and Computer Science Taiwan

ISBN: (纸本)9798350310931

Public health is seriously threatened by air pollution. systems for early warning are crucial for preventing its negative impacts on humans. But predicting air quality is difficult because it needs precise data from time series gathered at air monitoring sites. Due to things like measurement mistakes, this data may be ambiguous. Using fuzzy symmetry triangular fuzzy numbers, we suggest a strategy in this study for preparing data that contains uncertain information. Linear programming is utilized to obtain the midpoint value for defuzzification. The fuzzy pre-processed data is then used to build a predictive model using ARIMA. Our findings imply that the linear program can greatly lower prediction errors, resulting in more precise predictions. © 2023 IEEE.

关键词： Linear programming

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Axis-symmetric diesel spray flame model coupled with momentum flux distribution measurement and improved soot phi-T map 10

Axis-symmetric diesel spray flame model coupled with momentu...

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10th international conference on modeling and Diagnostics for Advanced Engine systems (COMODIA)

作者： Fuyuto, Takayuki Mandokoro, Yoshiyuki Tagita, Yuki Umehara, Tsutomu Toyota Cent R&D Labs Nagakute Aichi Japan DENSO CORP Kariya Aichi Japan Toyota Ind Corp Hekinan Aichi Japan Toyota Cent Res & Dev Labs Inc 41-1 Yokomichi Nagakute Aichi 4801192 Japan

An axis-symmetrical diesel spray flame model coupled with momentum flux distribution measurement was developed as a tool for nozzle orifice development. Momentum flux distributions at several distances from the nozzle orifice exit are measured by a force sensor with a small-diameter aperture which traverses a cross-section. A theoretical momentum flux profile is determined by fitting with the measured momentum flux profile at the cross-section. The spray velocity profile and equivalence ratio profiles are quantified from the theoretical momentum flux profile. By interpolating the profiles of the measured cross-sections, the axis-symmetrical diesel spray flame model can calculate the equivalence ratio and other physical values as a function of the distance from the nozzle exit (x) and the radius from the spray axis (r). The equivalence ratio distribution in a cross-section involving the spray axis is converted into soot formation and oxidation distribution by coupling with the improved soot & phi;-T map. The conventional soot-NOx & phi;-T map did not show the borderline between the soot formation and oxidation. To quantify the residence time of the fuel element during soot formation, the narrow soot peninsula was expanded up to its maximum by replacing the soot yield with the soot particle diameter. Instead of contour lines for the of NO mole fraction, contour lines for the OH mole fraction were implemented to reveal the border between soot formation and soot oxidation on the & phi;-T map. The borderline appears clearly, consisting of a horizontal line in the lower-temperature region and a rising line in the higher-temperature region. The converted soot formation and oxidation regions in the axis-symmetric spray flame model exhibit a strong correlation with the measured soot distributions in a quasi-steady diesel spray flame. The shear-stress, which induces turbulence affecting soot formation/oxidation, is also quantified from the velocity distribution.

关键词： Diesel spray diesel spray flame model momentum flux soot shear stress

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Trend-aligned feature correlation: Holistic feature relevance metric on time series data 2

Trend-aligned feature correlation: Holistic feature relevanc...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： He, Runhai Song, Qingqing Zhou, Quanhua Zhang, Zhenxing Zhang, Shoujing Faculty of Applied Mathematics and Informatics Belarusian State University Minsk Belarus

ISBN: (纸本)9781510689053

Existing time series correlation research methods often rely too much on overall statistical characteristics and ignore the dynamic changes of data in the time dimension, especially in the identification of trend turning points and complex correlations. This study proposes an innovative trend-aligned feature correlation matching method (TAFC), which breaks through the limitations of traditional correlation analysis. TAFC adopts a sophisticated data preprocessing process and introduces two unique metrics: trend overlap index metric (Q) and numerical correlation metric (P). The P metric effectively captures the numerical correlation between data by combining the normalized difference measurement (NDM) and the Pearson correlation coefficient. In addition, TAFC also introduces a custom granular smoothing mechanism to balance trend feature preservation and noise suppression, aiming to comprehensively evaluate the multi-dimensional and multi-granular correlation of time series data. This study uses simulated scenario data based on chi-square distribution and multimodal distribution for experimental verification, and the verification range includes 36 sets of simulation data. The results show that this method can significantly distinguish data sets with different trend characteristics. The introduction of granularity has been verified to help make trend features more explicit. At the same time, when 5% random noise is introduced, the algorithm error is controlled within 5.60%-12.24%. This study provides a novel and accurate method for computer-aided time series data analysis, which has important application value. © 2025 SPIE.

关键词： Correlation coefficients computer simulations Analytical research Data modeling Time series analysis Data analysis Histograms Data processing Multifractal analysis Statistical analysis

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A Method of Hardware Implementation of Membrane Computing Architectures for Mobile Robot Control 16

A Method of Hardware Implementation of Membrane Computing Ar...

引用

16th international conference on Development and Application systems, DAS 2022

作者： Ababii, Victor Sudacevschi, Viorica Carbune, Viorel Munteanu, Silvia Alexei, Victoria Lasco, Victor Technical University of Moldova Computer Science and Systems Engineering Department Chisinau Moldova

ISBN: (纸本)9781665481625

This paper proposes a method of hardware implementation of the membrane computing architecture for the control of a mobile robot. The basic idea is to use in the development of control systems the models of functional description of living cells, which should ensure a process of design, modeling and implementation based on cognitive models. The implementation algorithm presents a sequence of operations involving: functional development and modeling of Computing Cells, development and modeling of the topology of the membrane computing system (P-System), their implementation in hardware description languages (AHDL), and configuration of the FPGA circuit for realization of the control system. For validation and analysis of the performance of the Computing Cells, Petri Net models are used, which ensure the identification of the concurrent processes while maintaining a maximum parallelism. Functional testing of membrane computing models was performed based on the Quartus II development environment, the AHDL hardware description language, and the Altera DE0 Board. © 2022 IEEE.

关键词： computer hardware description languages

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Person Re-Identification Based on Hybrid Dual-Branch ResNet-Transformer Network 9

Person Re-Identification Based on Hybrid Dual-Branch ResNet-...

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9th IEEE international conference on Computational Intelligence and Applications, ICCIA 2024

作者： Duan, Wenjian Wang, Zhaohui Faculty of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350352214

Person re-identification is an important branch in the realm of computer vision, which aims to match the images of the same individual captured by non-overlapping cameras based on their characteristics. It has broad applications in security surveillance, intelligent transportation systems, and urban planning. Recently, EfficientFormerV2 has achieved a breakthrough due to the utilization of self-attention and the interconnections of global characteristic. Compared with traditional convolutional neural networks, EfficientFormerV2 excels in extracting global abstract features with its global receptive fields and the modeling ability of long-range dependency. Conversely, convolutional neural networks are more adept at extracting local detailed features. To combine the strengths of both, this paper proposes a hybrid Dual-Branch ResNet-Transformer network to capture both global and local features. It comprises a dual backbone, including EfficientFormerV2 and ResNet-50, a global average pooling layer, and a feature fusion module. Specifically, the global branch-based on EfficientFormerV2 backbone-is utilized to extract the global abstract semantic features from images. The local branch-based on the ResNet-50 backbone-is employed to capture local detailed features. Experimental results on the Market1501 and DukeMTMC-reID datasets validate the effectiveness of the proposed model. © 2024 IEEE.

关键词： Intelligent systems

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Fuzzy Logic-Based Fall Detection System for Elderly Using a Single Inertial measurement Unit

Fuzzy Logic-Based Fall Detection System for Elderly Using a ...

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2024 international conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Syamlan, Muhammad Ariq Noor, Raihan Aria Muhamad Alif Syihabudin Fawwaz, S. Syamlan, Muhammad Adib Arifin, Achmad Pramudijanto, Josaphat Arrofiqi, Fauzan Institut Teknologi Sepuluh Nopember Biomedical Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350368802

Accidental Falls are common occurrences among elderly people that can lead to serious injuries. Wearable fall detection systems are widely used to help detect and monitor the elderly during their Activity of Daily Living (ADLs). This paper presents a fall detection system using fuzzy logic algorithm based on a wearable inertial measurement unit (IMU) sensor placed on the user's waist. The main system is constructed using master-slave architecture. ESP-NOW protocol is implemented for the master-slave wireless data transmission. We calculate the magnitude of total acceleration based on IMU's three axes, which are used to trigger the fuzzy logic algorithm. The fuzzy logic is used to detect falls and determine fall position based on the pitch and roll angular position of the waist. Experiment is conducted on two subjects, which we get a total accuracy of 94.2%, precision of 97.14%, and recall of 97.13%. Based on our analysis, the error occurred because the user found it hard to demonstrate a natural fall position even when using protective gear and a mattress. Still, this device is able to achieve high accuracy with a low computational load, so the algorithm can be implemented on a low-cost chip or MCU. © 2024 IEEE.

关键词： Wearable sensors

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Innovating Trajectory Similarity measurement with Distributional Techniques for Anomaly Detection Applications 2

Innovating Trajectory Similarity Measurement with Distributi...

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2nd IEEE international conference on Data Science and Network Security, ICDSNS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi, R. India Srv Media India Sri Balaji University Pune India Parul Institute of Technology Parul University Department of Computer Science & Engineering Engineering and Technology India Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350373110

In the field of geospatial data analysis, accurately measuring trajectory similarity is crucial for effective anomaly detection, impacting areas from urban planning to security surveillance. Traditional methods often inadequately capture the intricate distributional characteristics of trajectory data. This study introduces a novel approach that leverages distributional statistics and machine learning algorithms to refine trajectory similarity measurement. By considering the holistic properties of trajectories, including their spatial distribution and temporal dynamics, our method offers a nuanced quantification of similarity. Implementing the Distributional Trajectory Similarity Algorithm (DTSA) has led to a notable enhancement in anomaly detection within geospatial datasets. Experimental validation using real-world datasets shows that our approach outperforms traditional methods, achieving substantial improvements in accuracy (94.7%), precision (92.4%), recall (93.6%), and F1 score (93.0%). This research not only overcomes the limitations of conventional metrics but also significantly advances the capability of anomaly detection systems. The DTSA provides a robust framework that could redefine anomaly detection practices, with potential applicability in various domains beyond geospatial analysis. This study represents a significant step forward, merging the theoretical depth of distributional statistics with practical algorithmic application, and sets a foundation for future advancements by integrating emerging technologies. © 2024 IEEE.

关键词： Contrastive Learning

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