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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset

Performance Comparison of Lightweight CNN Models for Drone C...

International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Rifqi Nabila Zufar David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Center of Excellence in Intelligent Control Automation of Process Systems Chulalongkorn University254 Phayathai Road Pathumwan Bangkok Thailand

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work.

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Experimental Comparison of Three Moroccan Biomass Combustion 4

Experimental Comparison of Three Moroccan Biomass Combustion

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4th International Conference on Innovative Research in Applied Science, engineering and Technology, IRASET 2024

作者： El Idrissi, Ayoub Najah Benbrahim, Mohammed Abouallal, Hafida Rassai, Nadia National School Of Applied Science Ibn Tofail University Advanced Systems-Engineering Laboratory university campus Po Box 242 Kenitra Morocco Ibn Tofail University Laboratory Of Advanced Materiel Engineering Process Faculty Of Sciences Department Of Chemistry university campus Po Box 242 Kenitra Morocco

ISBN: (纸本)9798350309508

This project aims to valorize and maximize the use of Moroccan solid biomass for forest and agricultural waste. To this end, three samples of cork, olive cake, and Argan nut shell are collected from Moroccan cities, pretreated, and measured. The HHV, ultimate analysis, proximate analysis, and density are determined for each biomass in order to identify its physicochemical properties. The experimental results demonstrate that cork is the best biomass for combustion because it generates high temperatures and less pollutant emissions. © 2024 IEEE.

关键词： Biomass

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Improving Robot Safety and Efficiency: Integrating Reachability-Based Trajectory Design with Nonlinear Model Predictive control

SSRN

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SSRN 2024年

作者： Shafikhah, Mahmood Razminia, Abolhassan Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran Process Control Laboratory Faculty of Natural Sciences and Engineering Åbo Akademi University Turku Finland

In today's high-tech world, robots are increasingly used in industry and human assistance, raising safety concerns. This paper proposes a Reachability-based Trajectory Design (RTD) method to ensure safe robot motion. By computing the robot's Forward Reachable Set (FRS), a safe trajectory is chosen for the robot to follow. This approach enhances robot safety and reliability, making them more applicable across industries. Offline RTD and FRS calculations combined with Nonlinear Model Predictive control (NMPC) further improve navigation, motion planning, and control precision. This unique combination significantly improves robot operations, ensuring safety and efficiency. © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

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Temperature field simulation of polyolefin-absorber mixture by FDTD-FDM model during microwave heating

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Chinese Journal of Chemical engineering 2020年第11期28卷 2900-2917页

作者： Xiaodong Jing Hao Wen Zhihong Xu State Key Laboratory of Multiphase Complex Systems Beijing Engineering Research Centre of Process Pollution ControlInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

This work has performed a numerical simulation of the temperature field during microwave heating of polyolefin-absorber mixture by means of a combined electric and thermal model.A finite difference time domain was used to model the electric field distribution within the cavity,while the finite difference method was used to calculate the temperature field distribution in different *** study has focused only on the process from room temperature to 500 K for reducing heating time and energy *** temperature range is a process with high energy consumption,difficult to control and great influence on the follow-up *** dependence of dielectric properties and thermal properties of heated materials are fully considered and simulated through an iterative *** simulation results show that input power,the size and location of the heated materials,the position of the waveguide,and the kinds of microwave absorbers are important factors affecting the heating *** a result,the uniform temperature distribution(the temperature difference Tdb10 K)can be achieved by choosing the appropriate input power(500–2000 W),the appropriate proportion of microwave absorber(the volume ratio of Si C to HDPE is 30:70),and combining with the moving and rotating of the heated *** uniform temperature field obtained without mechanical stirring is very important for reducing energy consumption and subsequent reactions.

关键词： Microwave heating Temperature field Finite difference time domain Finite difference method

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Effect of Graphite Addition on Microstructure, Mechanical Properties and Thermal Properties of Injection Molded AZ91D Alloy+1

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Materials Transactions 2024年第4期65卷 374-380页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Course of Advanced Systems Engineering Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Corp. Suwa399-0211 Japan Department of Systems Engineering Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan

Magnesium chips were coated with a high concentration of graphite using a binder and were used as the raw material for injection molding. The microstructure of the magnesium injection-molded product with added graphite exhibited a dispersion of needle-like graphite particles. No significant voids were observed at the interfaces between the graphite and the matrix. The addition of more than 0.5 mass% graphite decreased the proof stress and tensile strength of the injection-molded products. The Young’s modulus of the graphite-added products tended to decrease with an increase in the graphite content, which is consistent with the lower limit of the rule of mixtures. The thermal conductivity of the 6.9 mass% graphite-added product increased compared with that of the AZ91D magnesium alloy and the coefficient of linear thermal expansion decreased. Both values are within a range that satisfied the rules of mixtures. © 2024 The Japan Institute of Metals and Materials.

关键词： Thermal expansion

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The need for neuromorphic computing in industrial robotics

The need for neuromorphic computing in industrial robotics

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Artificial Intelligence and Power engineering (AIPE), International Conference on

作者： Tapanta Bhanja Jonathan Nußbaum Khalil Abuibaid Tatjana Legler Achim Wagner Martin Ruskowski Department of Mechanical and Process Engineering Chair for Machine Tools and Control Systems University of Kaiserslautern-Landau Kaiserslautern Germany Innovative Factory Systems German Research Centre for Artificial Intelligence Kaiserslautern Germany

With Moore’s law coming to its limits, the rate of increase in compute power available for processing applications is similarly coming to a halt. This implies that the compute intensive tasks, such as robotics, artificial intelligence, and high-performance space computing need innovative ways to cater their ever-increasing compute needs. One innovative way to solve computational bottlenecks is to bring compute and memory together, as opposed to the Von Neumann computational model, with greater degree of parallelism in an event-based, asynchronous computation paradigm. Neuromorphic computing is one such paradigm that draws its inspiration from the brain. Energy and computational efficiency, asynchronous and event-based processing being its salient features, neuromorphic computing is an area worth exploring for compute intensive tasks. In this paper, the authors explore the possibilities and benefits of neuromorphic computing in robotics, and establish possible research directions that could benefit the robotics domain.

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Analysis of Free Fatty Acids (FFA) in Palm Oils Based on the Raman Spectra

Analysis of Free Fatty Acids (FFA) in Palm Oils Based on the...

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2023 IEEE International Conference on Automatic control and Intelligent systems, I2CACIS 2023

作者： Shaharuzzaman, Siti Nurshaheera Hashim, Fazida Hanim Sajab, Mohd Shaiful Huddin, Aqilah Baseri Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Selangor Bandar Baru Bangi Malaysia Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Chemical and Process Engineering Malaysia

ISBN: (纸本)9798350321302

Palm oil is a stable vegetable oil with various uses in both the food and non-food industries. The quality of edible palm oil is measured by its content of free fatty acids (FFA), which should be below 5% to be safely consumed and used, according to The Palm Oil Refiners Association of Malaysia (PORAM). Current titration methods require that the oil sample be dissolved in neutralized ethanol/diethyl, which is a long process that can damage the sample and cause delays and losses in the process. The focus of this study is to analyze the FFA in palm oils based on the Raman spectra of palm oil samples and oil palm fruitlets. Four samples of commercially available palm oils of different qualities, as well as 47 samples of oil palm fruitlets, were used in this study. The Raman spectra of all samples were collected via point scanning using a confocal microRaman imaging spectrometer with a 532 nm laser source. Manual spectrum pre-processing was performed with baseline correction and data filtering using Savitzky-Golay filters. Possible FFA peaks characteristics on the Raman spectrum were then identified through signal interpolation, signal deconvolution, and curve fitting. Statistical analysis was conducted on the extracted features to remove those with low statistical values, thus helping the machine learning algorithms to train the classifier models more efficiently. From this study, it was found that peaks at 1155 cm-1, 1525 cm-1, 1440 cm-1, and 1655 cm-1 from the Raman spectrum were highly correlated with the FFA levels of palm oils obtained through titration. The peak ratios of 1525 cm-1/1655 cm-1 and 1155 cm-1/1655cm-1 also showed a good and fast relationship for analyzing the FFA content of palm oil. © 2023 IEEE.

关键词： Curve fitting

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Experimental and Numerical Evaluation of Models for Metallic Honeycomb Sandwich Structures

Experimental and Numerical Evaluation of Models for Metallic...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Ali Mohammed Hussien Sewara Mohsin Mahealdeen Shekhan Asmaa Mehasn Abreesm Petroleum & Gas refinery Engineering Department College of Petroleum process Engineering Tikrit University Iraq Technical Institute of Kirkuk Northern Technical University Iraq Petroleum systems control Engineering Department College of Petroleum Process es Engineering Tikrit University Iraq

Sandwich constructions with metallic honeycomb cores are used in numerous mechanical applications, especially in aerospace, where high explicit modulus or solidity to weight ratios are needed. Square honeycomb sandwich panels are easier to make than hexagonal ones. There is also evidence that square honeycomb sandwich panels have a greater structural integrity-to-weight ratio. Scholarly research on square honeycomb sandwich boards has focused on predicting the structure's response to shear, twisting, in-plane and out- of-plane strain, and dynamic shots. This study compares experimental models of square metallic honeycomb collapse under a three-point twist. Disillusioned curiosity can be shown through face-clasping, face-yielding, center- clasping, and center-yielding. Scientists compare their hypotheses to traditional views that suggest a clash between the minute and fact sheets, the shear and the centre. These tools measure face sheet shear stress and rotating minute centre pressure. The section that deviates from the central region becomes more important as it thickens. Limited component research evaluates explanatory models. The major goals of post-processing limited component models are to understand neighbourhood yielding and locking inside the structure and to examine how adjacent disappointment affects honeycomb power migration. Parametric analysis using multiple honeycomb structures was used to test disappointment ideas. We aim to create separate explanatory frameworks that can anticipate discontentment across stacking levels, geometries, and relative densities. Diagnostic models are combined with key improvement ideas to help architects choose materials and geometry under implementation constraints.

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Optical fiber-based surface plasmon resonance sensor for simultaneous measurement of relative humidity and temperature

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Applied Optics 2025年第9期64卷 2261-2268页

作者： Zhang, Qi Chen, Xiaopeng Li, Xinghui Wang, Huifang Zhou, Xue Yan, Xin Cheng, Tonglei State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Foshan Graduate School of Innovation Northeastern University Guangdong Foshan528311 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

In this work, an optical fiber sensor based on surface plasmon resonance (SPR) is proposed for simultaneous measurement of relative humidity (RH) and temperature, which is constructed by connecting two segments of thin-core fibers (TCFs) using a multimode fiber (MMF). The gold film was coated on TCFs to excite the SPR effect. One was further coated with carboxymethyl cellulose (CMC) to sense RH (Channel I), and the other was coated with gold nanoparticles (AuNPs) and polydimethylsiloxane (PDMS) to sense temperature (Channel II). The detection was performed via the resonance wavelength shift induced by the refractive index (RI) change of CMC and PDMS in relation to RH variation and temperature variation, respectively. The incorporation of gold nanoparticles enhances the sensitivity of the SPR sensor and shifts the resonance wavelengths, allowing for more outstanding demarcation of the SPR dip between the two portions inside the equivalent spectrum. Experimental results show that the sensor has an RH sensitivity of −1.053 nm/%RH and a temperature sensitivity of −2.306 nm/◦C. Moreover, the cross-sensitivity issue present in RH and temperature measurements was resolved by introducing a dual-wavelength matrix method. The proposed sensor offers high sensitivity, a simple structure, and ease of manufacturing, making it broadly applicable in fields such as food safety, biotechnology, and manufacturing. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Gold nanoparticles

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