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2024 International Conference on Control, automation and Diagnosis, ICCAD 2024

作者： Knorr, Felix Pannosch, Jürgen Steindl, Gernot Tu Wien Institute of Computer Engineering Research Unit of Automation Systems Vienna Austria

ISBN: (纸本)9798350361025

This paper introduces an innovative model-driven approach for seamlessly integrating control logic into the building planning phase, ensuring consistency throughout deployment. Leveraging an open building information model (BIM), the approach enables model engineers to embed control logic directly into the BIM using an open language. The integrated model facilitates automatic extraction of control logic information, supporting efficient modifications. A proof-of-concept implementation using SIMULTAN, IEC 61131-3 Structured Text, and IDA ICE demonstrates successful deployment in a Heating, Ventilation, and Air Conditioning (HVAC) use case. © 2024 IEEE.

关键词： Air conditioning

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Skill-Based engineering for the Description and Mapping of Smart Grid Applications 29

Skill-Based Engineering for the Description and Mapping of S...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Knorr, Felix Frühwirth, Thomas Institute of Computer Engineering Research Unit of Automation Systems Tu Wien Vienna Austria

ISBN: (纸本)9798350361230

This paper introduces an innovative approach for the description and mapping of applications in the smart grid domain. By utilizing a skill-based engineering approach, not only flexibility and changeability will increase, but also the need for describing the same application multiple times for different grids decays. As a first use-case, Fault Location, Isolation, and Service Restauration (FLISR) as an example application and a real-world grid in Vietnam have been chosen. By showing how this novel approach could be applied to these two entities, its potential is demonstrated. The IEC 61850-based approach uses a two-step procedure for the mapping. After defining the application's requirements, a mapping algorithm can check if the application can be deployed to a specific grid. In the second step, the actual mapping is described to deploy the application to the devices in the field automatically. Having the requirement description, a recommender system can suggest possible adaptions to the grid in case the check fails. © 2024 IEEE.

关键词： Power grids

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Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5475-5488页

作者： Wang, Youze Hu, Wenbo Dong, Yinpeng Liu, Jing Zhang, Hanwang Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China Tsinghua University Beijing100084 China Chinese academy of science Institute of automation Beijing100190 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Large Language Models (LLMs) have evolved into Multimodal Large Language Models (MLLMs), significantly enhancing their capabilities by integrating visual information and other types, thus aligning more closely with the nature of human intelligence, which processes a variety of data forms beyond just text. Despite advancements, the undesirable generation of these models remains a critical concern, particularly due to vulnerabilities exposed by text-based jailbreak attacks, which have represented a significant threat by challenging existing safety protocols. Motivated by the unique security risks posed by the integration of new and old modalities for MLLMs, we propose a unified multimodal universal jailbreak attack framework that leverages iterative image-text interactions and transfer-based strategy to generate a universal adversarial suffix and image. Our work not only highlights the interaction of image-text modalities can be used as a critical vulnerability but also validates that multimodal universal jailbreak attacks can bring higher-quality undesirable generations across different MLLMs. We evaluate the undesirable context generation of MLLMs like LLaVA, Yi-VL, MiniGPT4, MiniGPT-v2, and InstructBLIP, and reveal significant multimodal safety alignment issues, highlighting the inadequacy of current safety mechanisms against sophisticated multimodal attacks. This study underscores the urgent need for robust safety measures in MLLMs, advocating for a comprehensive review and enhancement of security protocols to mitigate potential risks associated with multimodal capabilities. © 1991-2012 IEEE.

关键词： Human form models

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1360-1372页

作者： Wang, Shuyue Liu, Zhunga Zhang, Zuowei Bennamoun, Mohammed Northwestern Polytechnical University School of Automation Shaanxi Xi’an710072 China The University of Western Australia Department of Computer Science and Software Engineering PerthWA6009 Australia

Multimodal domain adaptation (MMDA) aims to transfer knowledge across different domains that contain multimodal data. Current methods typically assume that both the source and target domains have paired multimodal data with the same modalities, allowing for direct knowledge transfer between corresponding types of data. However, in certain applications, the source domain benefits from advanced sensors and equipment, capturing more modalities than those available in the target domain. As a result, the information from the source modalities may not strongly align with that of the target modalities. This weak correlation hinders the effective utilization of all source data for the target domain. To address this challenge, we propose a weakly correlated multimodal domain adaptation (WCMMDA) method for pattern classification. WCMMDA is designed to acquire the modality-independent and category-related knowledge from the source domain, enabling the full utilization of available source modalities for effective knowledge transfer. Specifically, modality-invariant features are first extracted from the multimodal data to bridge the heterogeneity gap within each domain. Subsequently, domain-invariant features are further learned from these modality-invariant features to align the feature distributions across the source and target domains. A source-specific classifier is employed here, which predicts pseudo-labels for the target data and enables the feature extractor to explore category-related information in source features. Finally, a target-specific classifier is trained using the pseudolabeled target data, where highly reliable pseudolabels are selected based on confidence to improve classification performance. Extensive experiments are performed on the real-world multimodal datasets to demonstrate the superiority of WCMMDA. © 2020 IEEE.

关键词： Labeled data

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Unpredictability of Digital Twin for Connected Vehicles

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China Communications 2023年第2期20卷 26-45页

作者： Xiaoxu Wang Zeyin Huang Songmiao Zheng Rong Yu Miao Pan School of Automation Guangdong University of TechnologyGuangzhou 510006China Electrical and Computer Engineering Department University of HoustonHoustonTX 77004USA

Digital twin is an essential enabling technology for 6G connected *** highfidelity mobility simulation,digital twin is expected to make accurate prediction about the vehicle trajectory,and then support intelligent applications such as safety monitoring and self-driving for connected ***,it is observed that even if a digital twin model is perfectly derived,it might still fail to predict the trajectory due to tiny measurement noise or delay in the initial vehicle *** paper aims at investigating the sources of unpredictability of digital *** the car-following behaviors in connected vehicles for case *** theoretical analysis and experimental results indicate that the predictability of digital twin naturally depends on its system *** a system enters a complex pattern,its longterm states are ***,our study discloses that the complexity is determined,on the one hand,by the intrinsic factors of the target physical system such as the driver’s response sensitivity and delay,and on the other hand,by the crucial parameters of the digital twin system such as the sampling interval and twining latency.

关键词： digital twin connected vehicles unpredictability complexity

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A method for recognizing facial expression intensity based on facial muscle variations

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Multimedia Tools and Applications 2024年第18期84卷 1-30页

作者： Zhang, Yukun Fei, Zixiang Li, Xia Zhou, Wenju Fei, Minrui The School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China The School of Computer Engineering and Science Shanghai University Shanghai200444 China The Shanghai Jiaotong University Shanghai200240 China

Expression intensity recognition is a research problem in the field of computer vision and pattern recognition, which can be understood as the salience, or intensity, of an expression. Existing techniques for expression intensity recognition find it difficult to capture the texture features of expressions, which results in low recognition accuracy. Therefore, this paper proposes a method for recognizing expression intensity based on facial muscle changes to address this issue. This method captures texture motion information of facial muscles using optical flow, determining the motion regions and static regions of facial texture based on optical flow magnitude. The movement areas of facial expressions are set as salient regions, and the intensity of emotions is classified based on the variation rate of muscles within these regions. The proposed algorithm exhibits good recognition accuracy on publicly available datasets such as CK +, MMI, and datasets collected from real scenarios, including those involving elderly individuals. The average recognition accuracy reaches 82.7%. The proposed algorithm demonstrates a strong ability to recognize the intensity of facial expressions. This study provides technical support for the development of a real-time mood monitoring system. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optical flows

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From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks

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IEEE Internet of Things Magazine 2024年第3期7卷 30-37页

作者： Wen, Jinbo Nie, Jiangtian Kang, Jiawen Niyato, Dusit Du, Hongyang Zhang, Yang Guizani, Mohsen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Computer Science and Engineering Nanyang Technological University China School of Automation Guangdong University of Technology China United Arab Emirates

Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent IoT applications, such as smart surveillance and voice assistants. In this article, we present the concept of GIoT and conduct an exploration of its potential prospects. Specifically, we first overview four GAI techniques and investigate promising GIoT applications. Then, we elaborate on the main challenges in enabling GIoT and propose a general GAI-based secure incentive mechanism framework to address them, in which we adopt Generative Diffusion Models (GDMs) for incentive mechanism designs and apply blockchain technologies for secure GIoT management. Moreover, we conduct a case study on modern Internet of Vehicle traffic monitoring, which utilizes GDMs to generate effective contracts for incentivizing users to contribute sensing data with high quality. Numerical results demonstrate the superiority of the proposed scheme. Finally, we suggest several open directions worth investigating for the future popularity of GIoT. © 2018 IEEE.

关键词： Internet of things

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Vehicle and pedestrian detection method based on improved YOLOv4-tiny

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Optoelectronics Letters 2023年第10期19卷 623-628页

作者： LI Jing XU Zhengjun XU Liang School of Computer Science and Engineering Tianjin University of TechnologyTianjin300384China School of Electrical Engineering and Automation Tianjin University of TechnologyTianjin300384China

Aiming at the problem of low detection accuracy of vehicle and pedestrian detection models,this paper proposes an improved you only look once v4(YOLOv4)-tiny vehicle and pedestrian target detection *** block attention module(CBAM)is introduced into cross stage partial Darknet-53(CSPDarknet53)-tiny module to enhance feature extraction *** addition,the cross stage partial dense block layer(CSP-DBL)module is used to replace the original simple convolutional module superposition,which compensates for the high-resolution characteristic information and further improves the detection accuracy of the ***,the test results on the BDD100K traffic dataset show that the mean average precision(mAP)value of the final network of the proposed method is 88.74%,and the detection speed reaches 63 frames per second(FPS),which improves the detection accuracy of the network and meets the real-time detection speed.

关键词： replace network method

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System for Centralized Collection and Storage of Data on Household Resource Consumption 24

System for Centralized Collection and Storage of Data on Hou...

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24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Kazakov, Lev Y. Rabinovich, Evgeniy V. Novosibirsk State Technical University Automation and Computer Engineering Novosibirsk Russia

ISBN: (纸本)9798350336870

In this paper, information can be found on how was designed, developed and implemented a system for the centralized collection and storage of data on the consumption of household resources, such as electricity, water and gas. Two independent services, Smappee and Glowmarkt, which produce sensors to collect data on household resource consumption, were successfully integrated into the developed system. The developed system will make it possible to control the consumption of energy resources in the household, as well as to forecast their consumption for several days in advance. The user will be given access to various graphs and charts that will clearly show his current power consumption compared to past time periods. The system also offers a solution to optimize the consumption of household resources. As a result of the work, a client-server application was developed and implemented in a working environment. Two additional applications were developed for data collection and also put into operation. At the moment, the system successfully operates in several cities in England. © 2023 IEEE.

关键词： Energy resources

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