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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

Computer systems Science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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Computer systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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Chinese Control Conference (CCC)

作者： De-Xiu Wang Qing-Hao Meng Rui-Xue Han Hong-Yue Li Hui-Rang Hou Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Navigation Instruments Research Institute Tianjin China

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations.

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Producing Lunar Steel and Oxygen using Molten Lunar Regolith Electrolysis

Producing Lunar Steel and Oxygen using Molten Lunar Regolith...

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Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2023

作者： Patel, Palak B. Soto, Jose Adams, Zachary K. Koller, Scarlett Latyshev, Kir Boothe, Evan Cooper, Amber Fontanez, Jonatan Jalil, Laman Ma, Beverly Meza, Cesar Misquitta, Kristoff Na, Hyein Nguyen, Tom Pendley, Hezekiah Sangabattula, Lokesh Usey, Helena Zehner, Alice Allanore, Antoine Culpepper, Martin De Weck, Olivier Lordos, George Hoffman, Jeffrey Bergman, Dean Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology Sloan School of Management CambridgeMA02139 United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Material Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Honeybee Robotics Exploration Systems AltadenaCA91001 United States

ISBN: (纸本)9781624107054

With the current Artemis missions planned, by 2028, NASA and its international and commercial partners may be establishing a permanent presence at the lunar South pole. The Artemis Base Camp will be the locus of international and public-private collaboration under the Artemis Accords, helping to bootstrap a lunar economy. As the lunar economy grows, traffic of people and goods will grow with it. Thus, we envision that NASA and its international and commercial partners will have increasing long-term needs for producing metal from in situ resources such as lunar regolith. These metals can be used to produce many end products on the moon including large pressure vessels, both for spacious habitats and industrial-scale storage and operations. In this paper, as a part of the NASA 2023 BIG Idea Challenge ‘Lunar Forge: Producing Metal Products on the Moon’, we design and manufacture a molten regolith electrolysis (MRE) reactor capable of producing various alloys of steel and other metal products from lunar regolith, with oxygen as a useful byproduct. We have built ARTEMIS Steelworks (Advancing Reactor Technologies for Electrolytic Manufacturing of In situ Steel), which consists of an electrolytic reactor, supporting the addition of alloying elements to produce molten steel from lunar regolith simulant. To prepare for a lunar technology demonstration, a preliminary test of the reaction on Earth was performed in a vacuum, a sonicator was tested to dislodge oxygen bubbles from the anode to increase electrode life, and an automated iron and slag collector was made to reduce the need for astronaut operation. The project’s technical objectives are to demonstrate steel-making capability while also quantifying energy efficiency, steel quality, and the expected useful life of the electrodes and vessel under different configurations relative to the state of the art. The comprehensive testing plan presented here includes functional subsystem and integrated testing in ambient conditio

关键词： Electrolysis

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QuerySOD: A Small Object Detection Algorithm Based on Sparse Convolutional Network and Query Mechanism

QuerySOD: A Small Object Detection Algorithm Based on Sparse...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Junnian Li Jie Niu MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects’ information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on ‘Jinghai’ unmanned survey vehicles and receive excellent SOD performance from this real-world application.

关键词： Surveys Accuracy Object detection Seaports Lakes Feature extraction Prediction algorithms Convolutional neural networks Security Noise measurement

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Uapgo: Uncertainty-Aware Pose Graph Optimization for Lidar-Based Slam systems

SSRN

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SSRN 2024年

作者： Zhi, Tian Xu, Jie Ma, Yongxin Yu, Wenlu Liu, Shenglan Yuan, Shenghai College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China School of Mechanical Engineering Shandong University Jinan250061 China

Addressing uncertainty errors, although crucial, may sometimes be deprioritized in the optimization process ofLiDAR-based SLAM systems due to the inherent complexity of the task. The practice of applying uniform weights, which simplifies the uncertainty management, when incorporating factors can inadvertently degrade the pose graph optimization, particularly within complex environments. In response to this, we propose anuncertainty-aware pose graph optimization technique, along with an innovative keyframe selection strategy. We develop a model for estimating the covariance of relative pose transformations, utilizing frontend odometry output and the Huber robust kernel function. This model assigns weights based on the uncertainty error of pose transformations. Upon detection of a loop closure, the system optimizes keyframe poses, effectively reducing cumulative error. Our keyframe selection strategy takes into account the maximum eigenvalue of the translational component of pose covariance as an uncertainty measure. If this value surpasses a certain threshold, the pose is selected as a keyframe, thereby enhancing data precision and computational efficiency, and subsequently improving the SLAM system’s ability to handle uncertainty. Ablation studies validate our approach, demonstrating its effectiveness in enhancing performance, especially in complex environments and conditions of data degradation. Wealso provide the associated open-source code for further research. © 2024, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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A Context-Enhanced Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping

A Context-Enhanced Full-Resolution Floor Plan Segmentation N...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Yiyang Sun Zhe Ma MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Topological semantic maps provide a practical solution to enhance indoor navigation for the Partially Sighted or Visually Impaired (PSVI). Segmenting indoor floor plans and extracting boundaries are key to constructing these maps. The existing methods exhibit low accuracy in segmentation. To achieve desired high segmentation accuracy, we introduce a Context-Enhanced Full-Resolution Network (CEFRN) for floor plan segmentation. It is designed to harness the shallow detailed features and inter-category contextual dependencies inherent in floor plans. CEFRN integrates modified residual blocks to capture the low-stage full-resolution features while maintaining its compactness. A position attention module is employed to refine the deep-stage contextual information. We also propose a two-dimensional deep supervision method to merge features from both stages, which significantly boosts the feature representation ability of CEFRN. Finally, a practical topological semantic mapping method for PSVI indoor navigation is introduced. Experimental results demonstrate that CEFRN’s segmentation accuracy well exceeds the state-of-the-art methods’. It can be used to well support accurate topological semantic mapping.

关键词： Accuracy Hospitals Indoor navigation Semantics Feature extraction Mobile robots Floors Intelligent robots Autonomous vehicles Drones

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New Type of Insoles Based on Air Pressure Arrays for Fall Detection in the Elderly

New Type of Insoles Based on Air Pressure Arrays for Fall De...

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International Conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2024

作者： Xu, Yiwei Lu, Junjie Wan, Xiao Chang, Tao Lei, Qian Huang, Zixin School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9789819633166

For traditional fall detection insoles, the number of sensors is large, the cost is high, the sensor must correspond to the detection site one by one, and the resolution is low when the distance is far from the geometric center of the sensor, and the high sensitivity cannot be maintained in a large range. The rigid conduction of the force formed by the sensor and the soles of the elderly leads to poor comfort and low trust of the machine. A new soft insole for fall detection, based on an air pressure array, was designed and produced. Insole uses BMP280 pressure sensor and a 3D printed mold for silicone multi-layer injection molding. With four built-in airbags in one foot, tactile pressure can be converted to air pressure. STM32 microprocessor is used to construct the pressure information acquisition and processing system. Finally, Kalman filter algorithm is used to process the collected information. The experiment shows that the pressure detection system based on pressure change has high sensitivity and small repeatability error, and can effectively detect the pressure change and distribution of the sole. And reduce the noise interference, effectively improve the comfort and improve the precision and stability of recognition and acquisition equipment at the same time reduce the cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pressure sensors

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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