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An Optimized Approach to Hourly Temperature and Humidity Setpoint Generation for Reducing Tomato Disease and Saving Power Cost in Greenhouses

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SSRN

SSRN 2024年

作者： Sánchez-Molina, Jorge Antonio Wang, Hui Laktionov, Ivan Díaz, Francisco Rodríguez Li, Ming BIPP Institute of Plant Protection Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China Department of Computer Systems Software Dnipro University of Technology DniproUA49005 Ukraine Department of Informatics University of Almería ceiA3 CIESOL Almería04120 Spain Beijing Research Center for Information Technology in Agriculture National Engineering Research Centre for Information Technology in Agriculture National Engineering Laboratory for Agri-product Quality Traceability Key Laboratory of Agri-informatics Ministry of Agriculture Beijing100097 China

Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops. The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop. Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems. The results showed MOGA strategy performers good, the minimum power cost is only need 0.084€*day-1 in warm weather condition, as well as 3.74 €*day-1 in cold weather condition, the uninfected LAI (m2[Leaves](m-2[soils]*day-1)) is the range of [0.14 0.20]. The yearly power cost at least [308 1365]€.These are able to embed within a control scheme for achieving optimization purpose. The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth. © 2024, The Authors. All rights reserved.

关键词： Crops

An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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Procedia computer Science 2023年 220卷 470-477页

作者： Rima Kaafarani Leila Ismail Oussama Zahwe ICCS-Lab Computer Science Department American University of Culture and Education 1507 Beirut Lebanon Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

关键词： Blockchain Decision-Map Health Insurance Frauds Detection Hyperledger Fabric Neo Smart Contract Smart Healthcare

Smart room for patient monitoring based on IoT technologies 23

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Smart room for patient monitoring based on IoT technologies

Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference

作者： Nataly Zhukova Anna Motienko Dmitriy Levonevskiy Alexey Subbotin Pham Tuan Anh Laboratory of big data technologies in socio-cyberphysical systems St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Russia Faculty of Computer Technologies and Informatics St. Petersburg State Electrotechnical University Russia Faculty of Software Engineering and Computer Systems ITMO University Russia

ISBN: (纸本)9798400716225

This article presents a method for building a hospital room with extended intelligent features based on Internet of Things (IoT) technologies. We begin by reviewing existing vision-based health care systems and identifying important and useful functions that they should have. We then present a method for building a smart room that allows increase the level of the intelligence of the existing hospital rooms due to extensive use of the up-to-date machine learning methods. The proposed solution assumes using readily available hardware devices to reduce the cost of smart room building. Compared to commercially available assistive technologies, the proposed smart room turn out to have high level of intelligence and to be very cost effective. The system's non-intrusive nature makes it easy to use it both in hospitals and at home for patient care.

关键词： Internet of Things IoT automation in medicine intellectual room machine learning smart room smart ward

Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

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Robust Imagined Speech Production from Electrocorticography ...

International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

Unsupervised Recognition of Unknown Objects for Open-World Object Detection

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arXiv 2023年

作者： Fang, Ruohuan Pang, Guansong Zhou, Lei Bai, Xiao Zheng, Jin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University Singapore School of Computer Science and Artificial Intelligence Wuhan University of Technology China

Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models, such as ORE and OW-DETR, focus on pseudo-labeling regions with high objectness scores as unknowns, whose performance relies heavily on the supervision of known objects. While they can detect the unknowns that exhibit similar features to the known objects, they suffer from a severe label bias problem that they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this paper proposes a novel approach that learns an unsupervised discriminative model to recognize true unknown objects from raw pseudo labels generated by unsupervised region proposal methods. The resulting model can be further refined by a classification-free self-training method which iteratively extends pseudo unknown objects to the unlabeled regions. Experimental results show that our method 1) significantly outperforms the prior SOTA in detecting unknown objects while maintaining competitive performance of detecting known object classes on the MS COCO dataset, and 2) achieves better generalization ability on the LVIS and Objects365 datasets. Code is available at https://***/frh23333/mepu-owod. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

Hidden high-risky states identification from routine urban traffic

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arXiv 2024年

作者： Liu, Shiyan Bai, Mingyang Guo, Shengmin Gao, Jianxi Sun, Huijun Gao, Ziyou Li, Daqing School of Reliability and Systems Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States School of Systems Science Beijing Jiaotong University Beijing100044 China

One of the core risk management tasks is to identify hidden high-risky states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to high dimensionality and nonlinear interaction embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risky states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic, and construct system energy landscape. In this way, we can locate hidden high-risky states that have never been observed from real data. These states can serve as risk signals with high probability of entering hazardous minima in energy landscape, which lead to huge recovery cost. Our finding might provide insights for complex system risk management. Significance Statement Hidden high-risky states are lethal system states with catastrophic consequences for large-scale complex systems. Existing methods for identifying hidden high-risky states face challenges in routine urban traffic, where over 99% of possible system states are not yet visited in empirical data. Based on large-scale traffic-speed data, we build a least-structured network entropy model that captures intricate traffic interactions. This model can generate the entire state space and help the identification of hidden high-risky states that may lead to extreme events. Our results suggest a new way to provide reliable early warning signals for system breakdown based on critical risk scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

Scalable analysis of stop-and-go waves

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arXiv 2024年

作者： Ji, Junyi Gloudemans, Derek Wang, Yanbing Zachár, Gergely Barbour, William Sprinkle, Jonathan Piccoli, Benedetto Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States Department of Computer Science Vanderbilt University United States The Transportation and Power Systems Division Argonne National Laboratory United States School of Sustainable Engineering and the Built Environment Arizona State University United States Department of Mathematical Sciences Rutgers University Camden United States

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. In the past, datasets were of limited scale and could be easily analyzed by hand or with rudimentary methods to identify a very limited set of traffic waves present within the data. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in this data. This paper makes a first step towards addressing this shortcoming by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph-based representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. New insights revealed from this demonstration with emerging phenomena include: (a) we demonstrate that waves do generate, propagate, and dissipate at a scale (miles and hours) and ubiquity never observed before;(b) wave fronts and tails travels at a consistent speed for a critical speed between 10-20 mph, with propagation variation across lanes, where wave speed on the outer lane are less consistent compared to those on the inner lane;(c) wave fronts and tails propagate at different speeds;(d) wave boundaries capture rich and non-trivial wave topologies (with

关键词： Traffic congestion

Federated Graph Learning for Cross-Domain Recommendation

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arXiv 2024年

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. © 2024, CC BY.

关键词： Federated learning

Towards Adjoint Sensing and Acting Schemes and Interleaving Task Planning for Robust Robot Plan

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Towards Adjoint Sensing and Acting Schemes and Interleaving ...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Shuo Yang Xinjun Mao Shuo Wang Huaiyu Xiao Yuanzhou Xue Key Laboratory of Software Engineering for Complex Systems College of Computer National University of Defense Technology

Robots operating in open environments expect to have robust plans to achieve tasks successfully under environment uncertainties. However, both partial observability and dynamics of environment states have significantly decreased the robustness of task achievement, making robot task planning much more challenging. The partially observable states require the robot to obtain observations for optimally acting of the task goal. Also, state dynamics expects the robot to continuously observe surroundings for acting safely. Both challenges practically demand the purposeful and tight interactions between robot state-changing actuating actions and sensor-based observation actions. This paper proposes a novel model of Adjoint Sensing and Acting (ASA) that explicitly defines two parallel and sequential interaction schemes between actuating and observation actions, as well as an extended Behavior Tree for a concrete implementation of above schemes. We further propose an interleaving task planning approach for planning ASA-style plans, which integrates a deliberative POMDP planner for pursuing task goals, and a reactive Behavior Tree executive for fast responding to unexpected events. We experimentally demonstrate that ASA interaction schemes are practical and applicable to model and plan the open environment robot tasks. The plans from the interleaving task planning approach are both reactive in run-time response and efficient in task achievement.

关键词： Uncertainty Automation Conferences Robot sensing systems Robustness Planning Sensors