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IoT-Enabled Smart Greenhouse for Robotic Enhancement of Tomato Production: Leveraging 5G and Edge Computing for Advanced Data-Driven Automation, Precision Irrigation, and Scalable Zoning Principles

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 88-116页

作者： Ariss, Anass Ennejjai, Imane Lamjid, Asmaa Mabrouki, Jamal Kharmoum, Nassim Ziti, Soumia Department of Computer Science Intelligent Processing Systems & Security Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Laboratory of Spectroscopy Molecular Modeling Materials Nanomaterial Water and Environment CERNE2D Faculty of Science Mohammed V University in Rabat Rabat Morocco

There are many difficulties in growing tomatoes, such as erratic weather patterns, variable yields, and short shelf lives. Tomatoes are a staple food that is grown all throughout the world, particularly in climate-friendly areas such as Morocco. These areas are not free from the difficulties that present major obstacles for farmers, though. Modern agricultural techniques are increasingly using cutting-edge technologies, such as Internet of Things (IoT)-enabled smart greenhouses, to address these problems. In order to improve tomato output, this study presents an enhanced smart greenhouse model that incorporates the zoning principle. The method provides optimal plant growth and resource efficiency by separating the greenhouse into various zones, each tailored for specific growth stages and environmental circumstances. Precision farming and sustainable practices are made possible by the suggested systems real-time monitoring and control, which integrates sensors, actuators, and data analytics. The automatic irrigation system of the intelligent greenhouse prototype is additionally set up to offer the best possible support for the growth of tomato plants. The functionality and efficacy of the prototype are investigated through extensive laboratory testing and experimentation, providing insight into its potential influence on agricultural practices and practical practicality. The greenhouse that has been created with the help of IoT capabilities and the zoning concept has proven to be an effective instrument in assisting farmers in their attempts to grow tomatoes in an efficient and sustainable manner. Our comprehensive testing yielded data that demonstrate the zoning principle-integrated IoT-enabled smart greenhouse greatly improves tomato quality and production. This system demonstrates the benefits and practical applicability of sophisticated data-driven automation in agriculture by providing a scalable and sustainable solution to the problems encountered by tomato pr

关键词： Fruits

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 313-328页

作者： Hanjiang Hu Hesheng Wang Zhe Liu Weidong Chen Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China IEEE Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Technology University of CambridgeCambridge CB30FDUnited Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous *** retrieval is an efficient and effective technique in image-based localization *** to the drastic variability of environmental conditions,e.g.,illumination changes,retrievalbased visual localization is severely affected and becomes a challenging *** this work,a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image ***,a novel gradientweighted similarity activation mapping loss(Grad-SAM)is incorporated for finer localization with high *** also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised *** final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM *** experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons *** strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons *** performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision,especially under challenging environments with illumination variance,vegetation,and night-time ***,real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.

关键词： Deep representation learning place recognition visual localization

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Retrospective of Kazakh-Russian Sign Language Corpus Formation 11

Retrospective of Kazakh-Russian Sign Language Corpus Formati...

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11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources, sign-lang@LREC-COLING 2024

作者： Imashev, Alfarabi Kydyrbekova, Aigerim Mukushev, Medet Sandygulova, Anara Islam, Shynggys Israilov, Khassan Makazhanov, Aibek Yessenbayev, Zhandos Department of Robotics Engineering Nazarbayev University Kazakhstan Computer Science Lab National Laboratory Astana Nazarbayev University Kazakhstan Public Association "Kazakh Deaf Society" Astana Branch Kazakhstan

ISBN: (纸本)9782493814302

Sign language (SL) is a mode of communication that, in most cases, relies on visual perception exclusively and uses the visual-gestural modality. The advent of machine learning techniques has expanded the range of potential applications, not only in industry but also in addressing societal needs. Previous research has already demonstrated encouraging outcomes in developing sign language recognition systems that are both quite accurate and resilient. Nevertheless, the effectiveness and use of algorithms are impacted not only by their accessibility but also, at times to a greater extent, by the presence of substantial quantities of pertinent data. At the start of the local sign language corpus collection in 2015, there was a notable deficit of local Kazakh-Russian Sign Language data available for computer vision and machine-learning tasks. There were already corpora of another lexically close language, Russian Sign Language, but they were aimed at and tailored for lingustic research. We initiated the procedure by collecting data appropriate for machine-learning purposes. The subsets have been incorporated into the principal corpus and will be subject to future enhancements and refinements. This paper provides an overview of the collected components of the Kazakh-Russian Sign Language Corpus and the resulting outcomes derived from them. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

关键词： Machine learning

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Inverse Kinematics of Robotic Manipulators Using a New Learning-by-Example Method

Inverse Kinematics of Robotic Manipulators Using a New Learn...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Jacket Demby’s Ramy Farag Guilherme N. DeSouza Department of Electrical Engineering and Computer Science (EECS) Vision-Guided and Intelligent Robotics (ViGIR) Laboratory University of Missouri-Columbia Columbia Missouri

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Inverse Kinematics (IK) is one of the most fundamental challenges in robotics. It refers to the process of determining the joint configurations required to achieve the desired position and orientation (pose) of a robot end-effector. Although numerous Data-Driven (DD) IK solvers have demonstrated encouraging results, they have not achieved the same accuracy when compared to other IK methods for complex robot configurations (e.g., numerical methods for higher Degrees of Freedom (DoF)). In this work, we propose a new Learning-by-Example method, and show that such a scheme considerably improves the IK learning results when compared to other DD learners. In our approach, the network input incorporates an example of joint-pose pair along with the query pose to predict the desired robot joint configuration. We show that the example joint-pose pair does not need to be too close to the query – i.e. example and query can be as far as 20 degrees apart in the joint configuration space. Furthermore, we investigate the utilization of residual and dense skip connections in Multilayer Perceptron for DDIK solvers and employ the resulting networks for two redundant robotic manipulators: a 7-DoF-7R commensurate robot and a 7DoF-2RP4R incommensurate robot. Our experimental results show that the resulting DDIK solver can reliably predict IK solutions with accuracy better than 1mm in position and 1deg in orientation.

关键词： Hands Accuracy Kinematics Network architecture Multilayer perceptrons Numerical models Reliability Collision avoidance Robots intelligent robots

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Interaction model estimation-based robotic force-position coordinated optimization for rigid-soft heterogeneous contact tasks

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Biomimetic Intelligence & robotics 2025年第1期5卷 77-86页

作者： Haochen Zheng Xueqian Zhai Hongmin Wu Jia Pan Zhihao Xu Xuefeng Zhou Institute of Intelligent Manufacturing Guangdong Academy of SciencesGuangdong Key Laboratory of Modern Control TechnologyGuangzhou 510070China School of Rail Transportation Wuyi UniversityJiangmen 529020China Department of Computer Science The University of Hong KongHong Kong 999077China

Inspired by Model Predictive Interaction Control(MPIC),this paper proposes differential models for estimating contact geometric parameters and normal-friction forces and formulates an optimal control problem with multiple constraints to allow robots to perform rigid-soft heterogeneous contact *** the MPIC,robot dynamics are linearized,and Extended Kalman Filters are used for the online estimation of geometry-aware ***,a geometry-aware Hertz contact model is introduced for the online estimation of contact *** then implement the force-position coordinate optimization by incorporating the contact parameters and interaction force constraints into a gradient-based optimization *** validations were designed for two contact modes:“single-point contact”and“continuous contact”,involving materials with four different Young’s moduli and tested in human arm“relaxation-contraction”*** indicate that our framework ensures consistent geometry-aware parameter estimation and maintains reliable force interaction to guarantee *** method reduces the maximum impact force by 50%and decreases the average force error by 42%.The proposed framework has potential applications in medical and industrial tasks involving the manipulation of rigid,soft,and deformable objects.

关键词： Heterogeneous contact Interaction model estimation Coordination optimization Model Predictive Control

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 IEEE.

关键词： Undirected graphs

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Comprehensive Learning Strategy Enhanced Chaotic Whale Optimization for High-dimensional Feature Selection

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Journal of Bionic Engineering 2023年第6期20卷 2973-3007页

作者： Hanjie Ma Lei Xiao Zhongyi Hu Ali Asghar Heidari Myriam Hadjouni Hela Elmannai Huiling Chen Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China Department of Computer Sciences College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityP.O.Box 8442811671 RiyadhSaudi Arabia Department of Information Technology College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityP.O.Box 8442811671 RiyadhSaudi Arabia

Feature selection(FS)is an adequate data pre-processing method that reduces the dimensionality of datasets and is used in bioinformatics,finance,and *** FS approaches,however,frequently struggle to identify the most important characteristics when dealing with high-dimensional *** alleviate the imbalance of explore search ability and exploit search ability of the Whale Optimization Algorithm(WOA),we propose an enhanced WOA,namely SCLWOA,that incorporates sine chaos and comprehensive learning(CL)*** them,the CL mechanism contributes to improving the ability to *** the same time,the sine chaos is used to enhance the exploitation capacity and help the optimizer to gain a better initial *** hybrid performance of SCLWOA was evaluated comprehensively on IEEE CEC2017 test functions,including its qualitative analysis and comparisons with other *** results demonstrate that SCLWOA is superior to other algorithms in accuracy and converges faster than ***,the variant of Binary SCLWOA(BSCLWOA)and other binary optimizers obtained by the mapping function was evaluated on 12 UCI data ***,BSCLWOA has proven very competitive in classification precision and feature reduction.

关键词： Feature selection Whale Optimization Algorithm Binary optimizer Global optimization

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Serialization-Oriented Data Layout for Distributed and Real-Time Agent-Based Simulation 28

Serialization-Oriented Data Layout for Distributed and Real-...

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28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Meng, Zhuoxiao Gao, Mingyue Bortoli, Stefano Sommer, Christoph Knoll, Alois Technical University of Munich Department of Informatics Germany Huawei Munich Research Center Intelligent Cloud Technologies Laboratory Germany TU Dresden Faculty of Computer Science Germany

ISBN: (纸本)9798331527211

Data transfer efficiency is a frequent bottleneck of distributed (co-)simulations and X-in-the-loop systems. One of the key reasons, particularly in Agent-Based Simulation (ABS), is related to the low serialization performance caused by non-optimal data layout in memory. To address these challenges, this paper explores the potentials of a Serialization-oriented Data Layout (SoDaLa) approach for ABS building on Data Oriented Design (DOD) principles. In this work we also introduce ABS_M, a model-based abstraction of memory access in ABS. Using this model, we evaluate the impact of SoDaLa. This is done also to promote the adoption of SoDaLa and to ease the assessment of data layout strategies prior to full implementation in an existing codebases. The results indicate that our proposed approach enhances serialization efficiency and highlights the trade-offs between serialization efficiency and simulation performance across different model specifics and hardware conditions. © 2024 IEEE.

关键词： Digital elevation model

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Challenges and future directions of secure federated learning:a survey

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Frontiers of computer science 2022年第5期16卷 173-180页

作者： Kaiyue ZHANG Xuan SONG Chenhan ZHANG Shui YU Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Faculty of Engineering and Information Technology University of Technology SydneySydney 2007Australia SUSTech-UTokyo Joint Research Center on Super Smart City Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Federated learning came into being with the increasing concern of privacy security,as people’s sensitive information is being exposed under the era of big *** is an algorithm that does not collect users’raw data,but aggregates model parameters from each client and therefore protects user’s ***,due to the inherent distributed nature of federated learning,it is more vulnerable under attacks since users may upload malicious data to break down the federated learning *** addition,some recent studies have shown that attackers can recover information merely from ***,there is still lots of room to improve the current federated learning *** this survey,we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated *** open issues and existing solutions in federated learning are *** also point out the future research directions of federated learning.

关键词： federated learning privacy protection security

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