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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Chaudhary, Jyoti Jain, Upma Sudarshan, N. Pai, Vasudeva Sutaria, Kamal Natrayan, L. Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysuru India Department of Information Science and Engineering NMAM Institute of Technology NITTE Deemed to be University Karkala India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Mechanical Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331504960

The expansion of the Internet of Things (IoT) is driven by the proliferation of interconnected wireless gadgets, made possible by ever-improving computer and Internet infrastructure. The IoT is an extensive system of interconnected sensors and gateways. In the course of their constant environmental interaction, IoT devices produce a wealth of data resources, including images, audio, video, digital signals, and more. Connectivity to the Internet allows all Internet of Things (IoT) equipment and apps to exchange data and resources more effectively. The proposed AI-Driven Digital Mirror Technology effectively enhances the security of IoT-enabled smart infrastructures by integrating real-time monitoring, anomaly detection, and adaptive response mechanisms. By employing advanced AI algorithms such as Graph Neural Networks (GNNs) and Reinforcement Learning (RL), the technology mirrors IoT environments digitally, enabling dynamic modeling and threat assessment. The system demonstrated a significant improvement in threat detection accuracy, achieving a precision rate of 95.6% and a recall rate of 93.2% during simulations. Additionally, the adaptive response mechanism reduced the average response time to security breaches by 40%, ensuring minimal disruption to infrastructure operations. Comparative analysis with traditional security solutions highlighted the system's superiority in mitigating complex, multi-layered attacks, and its scalability makes it suitable for various smart infrastructure applications, including smart cities, industrial automation, and intelligent transportation systems. The results underscore the potential of AI-Driven Digital Mirror Technology in securing IoT ecosystems against evolving cyber threats while maintaining operational efficiency and resilience. © 2024 IEEE.

关键词： Gateways (computer networks)

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EEG emotion recognition framework based on invariant wavelet scattering convolution network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2181-2199页

作者： Elrefaiy, Ahmed Tawfik, Nahed Zayed, Nourhan Elhenawy, Ibrahim Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt Mechanical Engineering Department The British University in Egypt Cairo Egypt

EEG signals for real-time emotion identification are crucial for affective computing and human-computer interaction. The current emotion recognition models, which rely on a small number of emotion classes and stimuli like music and images in controlled lab conditions, have poor ecological validity. Furthermore, identifying relevant EEG signal features is crucial for efficient emotion identification. According to the complexity, non-stationarity, and variation nature of EEG signals, which make it challenging to identify relevant features to categorize and identify emotions, a novel approach for feature extraction and classification concerning EEG signals is suggested based on invariant wavelet scattering transform (WST) and support vector machine algorithm (SVM). The WST is a new time-frequency domain equivalent to a deep convolutional network. It produces scattering feature matrix representations that are stable against time-warping deformations, noise-resistant, and time-shift invariant existing in EEG signals. So, small, difficult-to-measure variations in the amplitude and duration of EEG signals can be captured. As a result, it addresses the limitations of the previous feature extraction approaches, which are unstable and sensitive to time-shift variations. In this paper, the zero, first, and second order features from DEAP datasets are obtained by performing the WST with two deep layers. Then, the PCA method is used for dimensionality reduction. Finally, the extracted features are fed as inputs for different classifiers. In the classification step, the SVM classifier is utilized with different classification algorithms such as k-nearest neighbours (KNN), random forest (RF), and AdaBoost classifier. This research employs a principal component analysis (PCA) approach to reduce the high dimensionality of scattering characteristics and increase the computational efficiency of our classifiers. The proposed method is performed across four different emotional classific

关键词： Support vector machines

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Impact of Al2O3, CaO, and Fe2O3 additives on spirulina biodiesel blends in a comparative compression ignition engine study

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International Journal of Hydrogen Energy 2025年 139卷 1159-1167页

作者： Ahamed, T.S. Almoallim, Hesham S. Chinnathambi, Arunachalam Josephin JS, Femilda Varuvel, Edwin Geo Department of Mechanical Engineering Noorul Islam University Kanyakumari India Department of Oral and Maxillofacial Surgery College of Dentistry King Saud University PO Box-60169 Riyadh11545 Saudi Arabia Department of Botany and Microbiology College of Science King Saud University PO Box -2455 Riyadh11451 Saudi Arabia Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Mechanical Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Institute of Mechanical Science Vilnius Gediminas Technical University Plytinės Str. 25 Vilnius10105 Lithuania

The current study investigates the impact of three different nano-additives such as Al2O3, CaO, and Fe2O3 on the engine performance and emissions of biodiesel made from Spirulina microalgae blends. The nano-additives were mixed at a concentration of 100 ppm with a blend of 20 % Spirulina biodiesel and 80 % pure diesel to create A20 (with Al2O3), C20 (with CaO), and F20 (with Fe2O3). The Fe2O3 blend (F20) showed the greatest improvement, with brake thermal efficiency 11.8 % higher than neat diesel at maximum engine speed. The CaO (C20) and Al2O3 (A20) blends saw efficiency gains of 3.1 % and 4.2 % respectively under the same conditions. Fuel consumption was reduced for all blends across the range of engine speeds tested. In terms of emissions, the nano-additive blends also outperformed neat diesel, with the Al2O3 blend demonstrating the greatest reductions in CO and HC. These improvements in performance and emissions were due to the high oxygen content in biodiesel blends and large surface area-to-volume ratio of the nanoparticles. © 2025 Hydrogen Energy Publications LLC

关键词： Microalgae

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Research on the Layout of Electromagnetic Radiation Measurement Points for Explosion of Energetic Materials 3

Research on the Layout of Electromagnetic Radiation Measurem...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Cui, Yuanbo Zhou, Hang School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

The adaptability of an electromagnetic radiation measurement system in the explosive field environment of energetic materials is studied, the installation method of the instrument is demonstrated, and the measurement point layout is studied. The effective measurement area of the explosion field is determined from the radiation source field area, the antenna measurement field area, the radius of the explosion fireball, the sensitivity of the data acquisition instrument, etc. The number of layouts at the same distance is determined and the dislocation arrangement is adopted to achieve omnidirectional coverage of the measurement range, finally a complete electromagnetic radiation measurement system has been established. © 2023 SPIE.

关键词： Data acquisition

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UNDERSTANDING RESILIENCE OF AGRICULTURAL SYSTEMS: A SYSTEMATIC LITERATURE REVIEW 24

UNDERSTANDING RESILIENCE OF AGRICULTURAL SYSTEMS: A SYSTEMAT...

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24th International Conference on engineering Design, ICED 2023

作者： Boahen, Samuel Oviroh, Peter Ozaveshe Austin-Breneman, Jesse Miyingo, Emmanuel W. Papalambros, Panos Y. Department of Mechanical Engineering Kwame Nkrumah University of Science and Technology Pmb Kumasi Ghana Department of Mechanical Engineering University of Michigan Ann Arbor United States Department of Mechanical Engineering Science University of Johannesburg South Africa Department of Electrical and Computer Engineering Makerere University Kampala Uganda

Resilience is a widely studied concept that is a key objective in the design and development of sustainable systems. This is especially true for the agricultural systems critical to food production, economic viability, and sustainability of our communities, as farmers seek to meet increasing demand in the face of shocks such as climate change and natural disasters. Although there is a rich body of work examining resilience, there is limited understanding of how the concept of resilience should be tailored for agricultural systems. This study seeks to address this gap by performing a systematic literature review of 50 papers selected from SCOPUS using the PRISMA protocol. A summary of research topics and characteristics by geographical region is presented. The paper also categorizes the types of shocks studied and the corresponding response methods. Results suggest that the focus of resilience research changes by region, which may indicate that design strategies and objectives should also differ by region. Furthermore, the work identifies a need for more simulation-based quantitative research into the impact of resilience. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Sustainable development

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Efficiency Evaluation of a Combining Compact 1D CNN and Signal Filtering Prototype Algorithm for Vibration Signal Classification 4

Efficiency Evaluation of a Combining Compact 1D CNN and Sign...

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4th International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Krinsukont, Thosapon Sirithanapipat, Taweedej National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Kasetsart University Department of Mechanical Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents an experiment and results of the modified CNN algorithm, it was developed by combining a compact 1D convolution neural network with a tuned signal filter (low-pass filter in this experiment). The algorithm was specifically designed for signal shape detection, prediction, and classification. An experiment aimed to evaluate the created algorithm's structure and signal filter to ensure its suitability for vibration signal data analysis. A testbed was built using an inverter motor equipped with various unbalanced conditions by increasing screws. Four-class unbalanced conditions were studied compared with the normal condition. Each conditions produced the distinct signal shape, with slight differences, making them suitable for evaluating the proposed algorithm's performance. The results demonstrate the satisfied performance of the algorithm, which effectively classifies vibration signals unbalance-3 (precision 54%), unbalance-4 (precision 77%) and a group of normal, unbalance-1, and unbalance-2. The group of normal, unbalance-1, and unbalance-2 can't be easily classified due to the tiny differences in the signal shapes, caused by small mass variations of the unbalanced screw unbalance-1, and unbalance-2. In conclusion, the algorithm proved satisfied effectiveness for its intended purpose of vibration signal classification. © 2025 IEEE.

关键词： Testbeds

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Feasibility-Guaranteed Safety-Critical Control with Applications to Heterogeneous Platoons 63

Feasibility-Guaranteed Safety-Critical Control with Applicat...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Shuo Xiao, Wei Belta, Calin Boston University Department of Mechanical Engineering BrooklineMA02215 United States Massachusetts Institute of Technology Computer Science and Artificial Intelligence Lab CambridgeMA United States University of Maryland Department of Electrical and Computer Engineering The Department of Computer Science College ParkMD United States

ISBN: (纸本)9798350316339

This paper studies safety and feasibility guarantees for systems with tight control bounds. It has been shown that stabilizing an affine control system while optimizing a quadratic cost and satisfying state and control constraints can be mapped to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBF) and Control Lyapunov Functions (CLF). One of the main challenges in this method is that the QP could easily become infeasible under safety constraints of high relative degree, especially under tight control bounds. Recent work focused on deriving sufficient conditions for guaranteeing feasibility. The existing results are case-dependent. In this paper, we consider the general case. We define a feasibility constraint and propose a new type of CBF to enforce it. Our method guarantees the feasibility of the above mentioned QPs, while satisfying safety requirements. We demonstrate the proposed method on an Adaptive Cruise Control (ACC) problem for a heterogeneous platoon with tight control bounds, and compare our method to existing CBFCLF approaches. The results show that our proposed approach can generate gradually transitioned control (without abrupt changes) with guaranteed feasibility and safety. © 2024 IEEE.

关键词： Adaptive cruise control

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Data-Driven Shielding of Online Reinforcement Learning: A Stormwater Pond Case Study 11th

Data-Driven Shielding of Online Reinforcement Learning: A ...

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11th IFIP WG 2.2 International Conference on Fundamentals of Software engineering, FSEN 2025

作者： Kim, Esther Hahyeon Goorden, Martijn Angelo Larsen, Kim Guldstrand Nielsen, Thomas Dyhre Department of Computer Science Aalborg University Aalborg East9220 Denmark Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5612 AZ Netherlands

ISBN: (纸本)9783031870538

To synthesize a safe and optimal controller for switched hybrid systems, one can first synthesize a shield that ensures safety, and then apply reinforcement learning within the constraints of the shield to obtain the desired controller. However, developing such a shield for switched hybrid systems typically requires a full model of the environment, which is not always available. Instead, historical data of the environment might be available. In this paper, we introduce a method for the construction of safety shields based on different scenarios captured in historical data. We show how individual shields for different scenarios can be combined to obtain a single shield that is provably safe within the bounds of the observed scenarios. We demonstrate the method using an industrial case study of a stormwater detention pond, which includes ten years of historical data of different rain events/scenarios. Our experimental results show that the shielded optimal controller ensures safety across all individual historical rain scenarios compared to the unshielded optimal controller. Additionally, we empirically show that the shield may also generalize for scenarios not covered by the historical data. © IFIP International Federation for Information Processing 2025.

关键词： Storm sewers

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An Overview of Methodologies and Prototypes involved in Speech Recognition Systems

An Overview of Methodologies and Prototypes involved in Spee...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Ojha, Ananta Pal, Ritu Batra, Raman Department of Computer Science and Information Technology Bangalore India Tula's Institute Department of Computer Science and Engineering Dehradun India Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350344387

The discipline of voice recognition is characterised by its multidisciplinary nature, incorporating the domains of linguistics, electrical engineering, and computer science. Its primary focus lies in the analysis and interpretation of spoken language. At present, automated voice recognition systems are employed across several fields such as commerce, government, and education, to effectively process mathematical statements. The present study is centred on the identification of impediments in speech recognition systems for mathematical phrases. This article offers a comprehensive examination of the current status of voice recognition techniques, encompassing a broad scope as well as focusing on the specialised domain of mathematical expressions. In this study, we examine many components including feature extraction approaches, classifiers, datasets, recording tools, and performance assessments. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Classification Feature extraction Pattern Recognition Speech Recognition

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Non-intrusive soil carbon content quantification methods using machine learning algorithms:A comparison of microwave and millimeter wave radar sensors

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Journal of Automation and Intelligence 2023年第3期2卷 152-166页

作者： Di An YangQuan Chen Electrical Engineering&Computer Science University of CaliforniaMerced95343CAUSA Department of Mechanical Engineering University of CaliforniaMerced95343CAUSA

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for *** biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water ***,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and *** address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine *** methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or *** paper provides details on the materials,methods,machine learning workflow,and results of our *** significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content *** validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the *** results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.

关键词： Soil carbon content sensing Carbon sequestration Microwave radar Millimeter wave radar Proximal sensing Machine learning

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