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15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Xing, Baixi Song, Xinjie Chen, Qimeng Shi, Lei Pan, Yanhong Wang, Kaiqi Tang, Mengyue Industrial Design Institute Zhejiang University of Technology Hangzhou China School of Design and Architecture Zhejiang University of Technology Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798350326178

Users' viewing behavior could affect their perception and evaluation of design works. Taking into account users' visual attention as a subjective cognition cue, we used eye-tracking evidence to identify users' focus areas for further analysis. We conducted experiments to extract the image features of design images and the reviewers' eye-tracking data, aiming to predict the product design ranking in the competition through fusion data analysis. In particular, we collected 1,504 product design images from a design competition. Four deep convolutional neural networks were selected to explore the best aesthetics computation model. The experimental results show that using design images and eye-tracking data fusion can improve the model prediction performance. Finally, MobileNet-V3 achieves the highest classification accuracy of 74.75%. This suggests the proposed method can provide useful insights into personalized aesthetics evaluation and user-centered design perception. © 2023 IEEE.

关键词： Eye tracking

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Effects of Adopting Industry 4.0 on a Manufacturing Plant

Effects of Adopting Industry 4.0 on a Manufacturing Plant

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Pranav Waghanna Akash Reddy Sarthak Deshpande Siddhant Chavan Vishal R. Jaiswal Vishal Naranje BE IT Department of Pune Institute of Computer Technology Pune IT Department of Pune Institute of Computer Technology Pune School of Engineering Architecture and Interior Design Amity University Dubai

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

In the era of Industry 4.0, Smart Manufacturing is reshaping how things are made. At the heart of it is the Industrial Internet of Things (IIoT), where smart sensors and connections create a digital system for factories. This paper highlights how IIoT facilitates real-time data exchange, aiding quick decision-making and creating adaptable manufacturing setups. Cyber-Physical Systems (CPS) integration is also discussed, showing how the line between physical and digital elements in manufacturing is getting blurred. Big Data Analytics is another game-changer, supporting data-driven decisions and predictive maintenance. The paper also digs into Artificial Intelligence and Machine Learning, showing how they automate tasks and continuously learn, improving manufacturing processes. Real-world applications are examined, including Digital Twins, advanced robotics like collaborative robots (Cobots), 3D printing, and smart supply chain management. While Smart Manufacturing offers numerous benefits, it's not without challenges. The survey talks about security and privacy concerns, emphasizing the need for strong cybersecurity. Interoperability, ensuring different technologies work seamlessly together, and addressing the workforce skills gap are also explored. The paper ends with examples of successful implementations and looks at future trends, such as the impact of 5G, blockchain in supply chain management, human augmentation, and the push for sustainability in Smart Manufacturing. This survey is a practical guide for anyone interested in understanding the current state and where Smart Manufacturing is headed in the world of Industry 4.0.

关键词： Industries Surveys Technological innovation Supply chain management Market research Fourth Industrial Revolution Sustainable development

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Image super resolution using Enhanced Attention Block (EAB) for Grape Leaf Disease Detection 7

Image super resolution using Enhanced Attention Block (EAB) ...

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7th International Conference on computer Applications in Electrical Engineering-Recent Advances, CERA 2023

作者： Yeswanth, P.V. Thanya, Y. Deivalakshmi, S. Lokesh Kumar, K.M. George, Johaan National Institute of Technology Department of Electronics and Communication Engineering Tiruchirappalli India Amrita Vishwa Vidyapeetham Department of Computer and Communication Engineering Chennai India Indian Insitute of Technology Department of Ocean Engineering and Naval Architecture Kharagpur India

ISBN: (纸本)9798350305005

Accurate disease diagnosis in grape leaf analysis is heavily reliant on high-resolution (HR) images. To meet this need, we propose a model that incorporates the Enhanced Attention Block (EAB) to generate HR images for improved disease detection. The EAB employs advanced attention mechanisms and residual learning to effectively capture critical, spatial and channel-wise relationships. Proposed model is assessed using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) metrics, yielding PSNR scores of 31. 902dB, 32.785dB, and 34. 853dB, and SSIM score of 0.8709, 0.9058, and 97.46 for factors 2,4 and 6 respectively. This proposed approach holds great potential for widespread implementation in disease detection across various plant species, allowing for timely and accurate interventions for plant health management and increasing industry productivity. © 2023 IEEE.

关键词： Diagnosis

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Evaluating Machine Learning Classifiers for Predicting Liver Disease Outcomes

Evaluating Machine Learning Classifiers for Predicting Liver...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Khaled Mohamad AlMustafa Juliano Katrib Department of Electrical and Computer Engineering College of Engineering and Architecture Gulf University for Science and Technology (GUST)

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The increasing prevalence of liver disease necessitates the development of accurate predictive models to aid in early diagnosis and treatment. This study investigates the effectiveness of several machine learning classifiers, Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), and Logistic Regression, in predicting liver disease outcomes based on clinical and biochemical features. Each model's performance was rigorously evaluated using multiple metrics, including F1-score, accuracy, root mean square error (RMSE), mean absolute error (MAE), precision, recall, and cross-validation ROC-AUC scores. The Random Forest Classifier emerged as the most effective model, achieving a perfect accuracy of 100% and an F1-score of 1.00, suggesting its robustness in handling complex, high-dimensional data. The Gradient Boosting Classifier also demonstrated commendable performance with an accuracy of 89% and an F1-score of 0.85, indicating its efficacy in capturing the underlying patterns of liver disease. Conversely, Logistic Regression and SVM exhibited lower performance metrics, pointing to potential limitations in their ability to model the intricacies of liver disease data effectively. The insights from this study not only enhance our understanding of liver disease prediction using machine learning but also emphasize the need for further research to validate these models in diverse clinical populations. This study contributes to the growing body of literature advocating for the integration of machine learning techniques in clinical decision-making processes, ultimately aiming to improve patient outcomes in liver disease management.

关键词： Support vector machines Measurement Logistic regression Accuracy Liver diseases Biological system modeling Predictive models Boosting Root mean square Random forests

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Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT-Based Self-Sustainable IoT Network

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4311-4327页

作者： Sadia, Haleema Kamal Hassan, Ahmad Haq Abbas, Ziaul Abbas, Ghulam Baker, Thar Saeed, Nasir United Arab Emirates University College of Engineering Department of Electrical and Communication Engineering Al Ain United Arab Emirates Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Topi23460 Pakistan Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Computer Science and Engineering Topi23460 Pakistan University of Brighton School of Architecture Technology and Engineering BrightonBN2 4GJ United Kingdom

Power splitting based simultaneous wireless information and power transfer (PS-SWIPT) appears to be a promising solution to support future self-sustainable Internet of Things (SS-IoT) networks. However, the performance of these networks is constrained by radio frequency signal strength and channel impairments. To overcome this challenge, intelligent reflecting surfaces (IRSs) have recently been introduced in IoT networks to improve network efficiency by controlling signal reflections. In this article, we propose a novel IRS-enabled phase cooperative framework that aims to maximize the energy efficiency (EE) of the PS-SWIPT based SS-IoT network. Our framework leverages the idea of phase cooperation between two distinct networks without requiring additional hardware resources. We employed transmit beamforming (BF) at access points (APs) and phase shift optimization at the IRS with effective phase cooperation between APs. The EE maximization problem turns out to be NP-hard, so first, an alternating optimization (AO) is solved for the user’s network using heuristic BF approaches and phase optimization via semidefinite relaxation (SDR) approaches. To combat the computational complexity of AO, we also propose a low-complexity alternative solution by exploiting heuristic BF schemes and an iterative algorithm, i.e., the element-wise block-coordinate descent method for phase shifts optimization. The proposed solutions’ analysis shows low computational complexity and fast convergence, achieving near-optimal EE performance for different network settings. We conducted extensive simulations to evaluate our proposed framework’s performance and unveiled significant EE performance achieved by the IoT network consistent with the numerical findings. This work demonstrates the potential of IRS phase cooperation for enhancing the EE of different networks without constraining hardware resources. © 2020 IEEE.

关键词： Energy transfer

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Research on the Design Method of Prefabricated Concrete Structure Based on BIM

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computer-Aided Design and Applications 2023年 20卷 148-164页

作者： Yang, Fan Hussain, Saddam Fadhel, Mustafa Abbas Ghafoor, Kayhan Zrar Gannan University of Science and Technology Jiangxi Ganzhou China Department of Civil and Architecture Engineering Kyushu Institute of Technology Fukuoka Kitakyushu8040015 Japan University of Al Muthanna Samawa66001 Iraq Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq

With the gradual development in industrialization, the advent of computer and information technology has evolved in the field of engineering and construction industry. BIM technology has become a valuable part of prefabricated concrete engineering with the advancement of modern technology based on 3D modeling simulation. In order to realize the industrialization and automation of prefabricated concrete structures, this paper analyzes the characteristics of fabricated concrete structure and BIM, combining the two techniques. This work applies BIM to fabricated concrete design and chooses finite element analysis and calculation software ETABS to analyze the structure model. The results show that the mass participation ratio of the model studied in this paper reaches more than 90%, the interlayer displacement angle is less than 1/550, and the modal and shell internal force analysis results meet the requirements. In this paper, modal analysis, shell internal force analysis under live load and structural dynamic analysis under response spectrum load conditions were carried out on the ETABS model converted from the Revit model and the model directly established by ETABS. The ETABS model converted from the Revit model by the data interface plug-in can be used for structural analysis. © 2023, CAD Solutions, LLC. All rights reserved.

关键词： Finite element method

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EVOAL: A Domain-Specific Language-Based Approach to Optimisation

EVOAL: A Domain-Specific Language-Based Approach to Optimisa...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Berger, Bernhard J. Plump, Christina Drechlser, Rolf Institute of Embedded Systems Hamburg University of Technology Hamburg Germany Dfki GmbH-Cyber-Physical Systems Bremen28359 Germany University Bremen - Computer Architecture Bremen28359 Germany

ISBN: (纸本)9798350314588

Adapting optimisation algorithms, such as evolutionary algorithms, to a problem is a necessity. The required collection and exchange of domain information is an important but tedious task in real-world projects involving several experts from different areas of expertise (e.g. the domain and the optimisation area). This paper presents a structured approach that allows the experts to systematically provide their knowledge using domain-specific languages. The presented approach defines a different language for the involved experts that enables them to add their knowledge and use the information provided by other experts. The languages are extensible, allowing the addition of new optimisation aspects without changing the actual language. These languages are the front-end to a versatile open-source optimisation tool, that we built, enabling the actual execution of the optimisation. It additionally provides features for surrogate models as well as data generation and different benchmarks for evaluation. Conducting a user study, we show that the language is suitable to express the domain knowledge and domain experts can use the language to describe their domain knowledge after a short introduction. This way, the approach reduces the effort for domain experts in providing their information. As a side effect, the complete configuration of the optimisation execution through these languages allows an easy and reliable reproduction. © 2023 IEEE.

关键词： Genetic algorithms

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NEURAL PROGRAM SYNTHESIS WITH QUERY 10

NEURAL PROGRAM SYNTHESIS WITH QUERY

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10th International Conference on Learning Representations, ICLR 2022

作者： Huang, Di Zhang, Rui Hu, Xing Zhang, Xishan Jin, Pengwei Li, Nan Du, Zidong Guo, Qi Chen, Yunji SKL of Computer Architecture Institute of Computing Technology CAS China University of Chinese Academy of Sciences China University of Science and Technology of China China Cambricon Technologies

Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output examples is unrealistic because it requires the users to have the ability to describe the underlying program with a few input-output examples under the training distribution. In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space. The quality of the query depends on the amount of the mutual information between the query and the corresponding program, which can guide the optimization of the query framework. To estimate the mutual information more accurately, we introduce the functional space (F-space) which models the relevance between the input-output examples and the programs in a differentiable way. We evaluate the effectiveness and generalization of the proposed query-based framework on the Karel task and the list processing task. Experimental results show that the query-based framework can generate informative input-output examples which achieve and even outperform well-designed input-output examples. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

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Haze Risk Assessment and Trend Evolution in Fenwei Plain Based on PCA-XGBoost

Haze Risk Assessment and Trend Evolution in Fenwei Plain Bas...

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2022 International Conference on Environmental Science and Green Energy, ICESGE 2022

作者： Dai, Hongbin Huang, Guangqiu Zeng, Huibin Yu, Rongchuan Wang, Jingjing School of Management Xi'an University of Architecture and Technology Xi'an China School of Mathematics and Computer Science Guangxi Science & Technology Normal University Laibin546199 China

ISBN: (纸本)9781665453165

According to the regional disaster system theory, haze risk assessment indicators for the Fenwei Plain were chosen to effectively study the urban haze hazards in terms of the sensitivity of the disaster-causing environment, the hazard of haze components, and the vulnerability of disaster-bearing bodies. The risk level of haze hazards in Fenwei Plain cities was calculated using Principal Component Analysis (PCA). A quantitative assessment of the haze risk in the Fenwei Plain urban agglomeration was carried out using the extreme gradient boosting method (XGBoost) to investigate the spatial and temporal distribution patterns of PM2.5 and its trends. The results show that Xi'an exhibited the highest haze risk index and the PCA-XGBoost model performed best in the three evaluation indicators. The main factors affecting haze are the economy and population density. © 2022 IEEE.

关键词： Principal component analysis

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The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection

The Seeker's Dilemma: Realistic Formulation and Benchmarking...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Sarihi, Amin Patooghy, Ahmad Badawy, Abdel-Hameed A. Jamieson, Peter New Mexico State University Klipsch School of Electrical and Computer Engineering Las CrucesNM United States MIPS Architecture and Performance AustinTX United States North Carolina A and T State University Department of Computer Systems Technology GreensboroNC United States Miami University Department of Electrical and Computer Engineering OxfordOH United States

ISBN: (纸本)9798350367942

This work focuses on advancing the security field in the hardware design space by formally defining the problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker's Dilemma"(an extension of Hide&Seek on a graph), where a detecting agent is unaware of whether HTs infect circuits or not. Using this problem formulation, we create a benchmark that consists of a mixture of HT-free and HT-infected restructured circuits while preserving their original functionalities. The restructured circuits are randomly infected by HTs, causing a situation where the defender is uncertain if a circuit is infected. Our innovative dataset will help the community better judge the detection quality of different methods by comparing their success rates in circuit classification. We use our benchmark to evaluate three state-of-the-art HT detection tools to show baseline results for this approach. We use Principal Component Analysis to assess the strength of our benchmark, where we observe that some restructured HT-infected circuits are mapped closely to HT-free circuits, leading to significant label misclassification by detectors. © 2024 IEEE.

关键词： Hardware security

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