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Smart Ontology-Based System for Recommending Practices in Melon Cultivation

ieee ACCESS 2024年 12卷 162204-162216页

作者： Umar, Ubaidillah Sardjono, Tri Arief Kusuma, Hendra Inst Teknol Sepuluh Nopember Fac Intelligent Elect & Informat Technol Dept Elect Engn Surabaya 60111 Indonesia Telkom Univ Fac Elect Engn Dept Comp Engn Surabaya 60231 Indonesia Inst Teknol Sepuluh Nopember Fac Intelligent Elect & Informat Technol Dept Biomed Engn Surabaya 60111 Indonesia

Smart agriculture systems are models that offer significant potential to improve farming practices by providing advanced tools for land analysis, plant monitoring, weed management, and produce estimation. Many small-scale farmers, particularly in major agricultural regions such as East Java, Indonesia, continue to rely on inefficient traditional methods. Therefore, this research aimed to introduce the Smart Agriculture Ontology System, an innovative framework that incorporates computer vision, deep learning, and semantic web technologies to manage agricultural knowledge effectively. The system integrated traditional observational data with sensor data into a unified knowledge graph, accessible via a query language designed for retrieving and manipulating data stored in the graph. In a case analysis at Puspalebo Orchard, Sidoarjo, East Java, this system provided real-time recommendations for seed selection, soil management, irrigation, pest control, and post-harvest handling. The results from this research showed that the system improved productivity and efficiency by delivering accurate, data-driven recommendations, making it a valuable tool for modern farming. Moreover, the methodology was designed to be generalizable and applicable to various agricultural contexts, allowing it to be a versatile method for different crops and farming conditions. The potential incorporation of external data sources, such as weather information, demonstrated the adaptability of the system for future agricultural management.

关键词： Ontologies Decision making Soft sensors Smart agriculture Deep learning Computer vision Farming Accuracy Soil Knowledge representation ontology deep learning computer vision melon cultivation semantic web recommendation system

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Augmented Mixed Vehicular Platoon control With Dense Communication Reinforcement learning for Traffic Oscillation Alleviation

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ieee INTERNET OF THINGS JOURNAL 2024年第22期11卷 35989-36001页

作者： Li, Meng Cao, Zehong Li, Zhibin Southeast Univ Sch Transportat Nanjing 210096 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Univ South Australia STEM Adelaide SA 5000 Australia

Traffic oscillations present significant challenges to road transportation systems, resulting in reduced fuel efficiency, heightened crash risks, and severe congestion. Recently emerging Augmented Intelligence of Things (AIoT) technology holds promise for enhancing traffic flow through vehicle-road cooperation. A representative application involves using deep reinforcement learning (DRL) techniques to control connected autonomous vehicle (CAV) platoons to alleviate traffic oscillations. However, uncertainties in human-driven vehicles (HDVs) driving behavior and the random distribution of CAVs make it challenging to achieve effective traffic oscillation alleviation in the Internet of Things environment. Existing DRL-based mixed vehicular platoon control strategies underutilize downstream traffic data, impairing CAVs' ability to predict and mitigate traffic oscillations, leading to inefficient speed adjustments and discomfort. This article proposes a dense communication cooperative RL policy for mixed vehicular platoons to address these challenges. It employs a parameter-sharing structure and a dense information flow topology, enabling CAVs to proactively respond to traffic oscillations while accommodating arbitrary vehicle distributions and communication failures. Experimental results demonstrate superior performance of the proposed strategy in driving efficiency, comfort, and safety, particularly in scenarios involving multivehicle cut-ins or cut-outs and communication failures.

关键词： Oscillators Training Fluctuations Safety Internet of Things Optimization Vehicle dynamics Augmented Intelligence of Things (AIoT) connected autonomous vehicle (CAV) mixed vehicular platoon reinforcement learning traffic oscillation

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Enforcing structure in data-driven reduced modeling through nested Operator Inference 63

Enforcing structure in data-driven reduced modeling through ...

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63rd ieee conference on Decision and control, CDC 2024

作者： Aretz, Nicole Willcox, Karen Oden Institute for Computational Engineering & Sciences University of Texas at Austin AustinTX United States

ISBN: (纸本)9798350316339

We introduce the data-driven nested Operator Inference method for learning projection-based reduced-order models (ROMs) from snapshot data of high-dimensional dynamical systems. These ROMs achieve significant computational speed-up by exploiting the intrinsic low-dimensionality of the full-order solution trajectory through projection onto a low-dimensional subspace. Our nested Operator Inference approach builds upon a nested structure of the projection-based reduced-order matrices and a hierarchy within the subspace's basis vectors to partition the Operator Inference learning problem into multiple regression problems defined on subspaces. Each regression problem is provably better conditioned than when all reduced-order operators are learned together, reducing the need for additional regularization. Since only O(1) unknowns are learned at a time, nested Operator Inference is particularly applicable to higher-order polynomial systems. We demonstrate our method for the shallow ice equations with eighth order polynomial operators. © 2024 ieee.

关键词： Polynomials

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Stochastic data-driven Predictive control: Regularization, Estimation, and Constraint Tightening 20

Stochastic Data-Driven Predictive Control: Regularization, E...

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20th IFAC Symposium on System Identification (SYSID)

作者： Yin, Mingzhou Iannelli, Andrea Smith, Roy S. Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany

data-driven predictive control methods based on the Willems' fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control with nonparametric data-driven predictors instead of model-based predictors. This study addresses three problems of applying such algorithms under unbounded stochastic uncertainties: 1) tuning-free regularizer design, 2) initial condition estimation, and 3) reliable constraint satisfaction, by using stochastic prediction error quantification. The regularizer is designed by leveraging the expected output cost. An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction. A novel constraint-tightening method, using second-order cone constraints, is presented to ensure high-probability chance constraint satisfaction. Numerical results demonstrate that the proposed methods lead to satisfactory control performance in terms of both control cost and constraint satisfaction, with significantly improved initial condition estimation. Copyright (c) 2024 The Authors.

关键词： data-driven control model predictive control estimation and filtering stochastic optimal control stochastic system identification

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Hierarchical Multiclass Continual learning for Network Intrusion Detection 10

Hierarchical Multiclass Continual Learning for Network Intru...

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10th International conference on Network Softwarization (NetSoft)

作者： Talpini, Jacopo Sartori, Fabio Savi, Marco Univ Milano Bicocca Dept Informat Syst & Commun DISCo Milan Italy

ISBN: (纸本)9798350369588;9798350369595

The evolution of Internet and its related communication technologies have consistently increased the risk of cyberattacks. In this context, a crucial role is played by Intrusion Detection systems (IDSs), which are security devices designed to identify and mitigate attacks to modern networks. In the last decade, data-driven approaches based on Machine learning (ML) have gained more and more popularity for executing the classification tasks required by signature-based IDSs. However, typical ML models adopted for this purpose are trained in static settings while new attacks - and variants of known attacks - dynamically emerge over time. As a consequence, there is the need of keeping the IDS capability constantly updated, which poses peculiar challenges especially in resourced-constrained scenarios. To this end, we propose a novel hierarchical model based on a binary classification of benign and malicious traffic performed by a Bayesian Neural Network that is trained continuously and efficiently by exploiting Continual learning. A generative multiclass classifier is then adopted to incrementally classify new kinds of attacks with respect to the malicious traffic. We prove the effectiveness of our approach showing that it removes the need of storing network traffic data samples related to historical data, representative of all the kinds of attacks, while ensuring good detection capabilities.

关键词： Hierarchical systems

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Novel Adaptive Multiscale Vertical Federated KELM Algorithm for Cross-unit Fault Diagnosis in Interconnected Industrial System 14

Novel Adaptive Multiscale Vertical Federated KELM Algorithm ...

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14th Asian control conference (ASCC)

作者： Tong, Ruining Xu, Zhiyu Zhang, Xin Wei, Ruihua Fu, Shengcheng Tan, Yaxin Tongji Univ Coll Elect & Informat Engn Sino Gennan Res Ctr Intelligent Sci & Technol Shanghai Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9798331540845;9789887581598

A key challenge that needs to be addressed in data-driven fault diagnosis technology based on artificial intelligence algorithms is how to effectively perform cross-unit fault diagnosis in interconnected industrial system while preserving the data privacy of each subunit. Fortunately, it is found from our research that the kernel extreme learning machine (KELM) algorithm with the vertical federated (VF) learning mechanism shows great effect for solving this challenge. However, it assigns single and equal contribution scale to the multiple feature variables of each subunit participated in VF learning, which leads its fault diagnosis rate (FDR) is still insufficient. To address this shortcoming, a novel adaptive multiscale vertical federated kernel extreme learning machine (AMVF-KELM) algorithm is proposed in this paper. For AMVF-KELM, the multiple feature variables of each subunit were assigned different contribution scales according to Laplacian Score, that is, multiscale. In addition, the adaptive adjustment factor is designed in AMVF-KELM for the multiscale, which enables the algorithm to adapt more flexibly to the complexity of industrial process data, that is, adaptive. The effectiveness and superiority of our method is validated on the benchmark Tennessee-Eastman industrial system datasets. The results demonstrated that our method can achieve better FDR while preserving the data privacy of each subunit effectively.

关键词： data privacypreserving vertical federated learning kernel extreme learning machine Laplacian score cross-unit fault diagnosis

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Resilience of data-driven Cyberattack Detection systems in Smart Power Grids 32

Resilience of Data-Driven Cyberattack Detection Systems in S...

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32nd European Signal Processing conference (EUSIPCO)

作者： Takiddin, Abdulrahman Atat, Rachad Mbayed, Hayat Ismail, Muhammad Serpedin, Erchin Florida State Univ Dept Elect & Comp Engn Tallahassee FL 32306 USA Texas A&M Univ Qatar Elect & Comp Engn Program Doha Qatar Florida State Univ Ctr Adv Power Syst Tallahassee FL USA Tennessee Technol Univ Dept Comp Sci Cookeville TN USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX USA

ISBN: (纸本)9789464593617;9798331519773

data communications within the smart power grid components are susceptible to cyberattacks due to the interconnected nature of the grid and reliance on communication networks. Such cyberattacks can exploit the integrity of the exchanged data and result in operational instability. Existing data-driven cyberattack detection systems (CDSs) are proposed in the literature but their effectiveness is only verified against one type of cyberattacks. In reality, a smart grid system could encounter more than one attack type at once. Thus, in this paper, we investigate the resilience of state-of-the-art data-driven CDSs against replay false data injection, adversarial evasion, and adversarial data poisoning attacks on a realistic ieee 118-bus system model. It turns out that a convolutional recurrent graph autoencoder-based CDS offers an attack detection rate of 96 - 97.5%, which outperforms other machine learning and deep learning-based data-driven CDSs by 16 - 54% since it captures the recurrent and spatial aspects of the data without being trained on attack data.

关键词： Adversarial attacks cyberattack detection false data injection graph neural networks smart grid resilience

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Linearised data-driven LSTM-Based control of Multi-Input HVAC systems 6

Linearised Data-Driven LSTM-Based Control of Multi-Input HVA...

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6th Annual learning for Dynamics and control conference

作者： Hinderyckx, Andreas Guillaume, Florence Katholieke Univ Leuven Brussels Belgium Sirris EluciDATA Lab Brussels Belgium

The pursuit of sustainability has paved the way for building management systems (BMSs) that can steer buildings in an energy-efficient way. In this article, a deep learning approach is proposed to control multi-input heating, ventilation, and air conditioning (HVAC) systems in order to minimise both thermal discomfort and operational cost. More particularly, a long short-term memory (LSTM)-based encoder-decoder process model, trained on historical weather data and control sequences generated while the building was steered by a modern rule-based controller (RBC), is fed into an optimisation problem, to which a change of variables is applied to efficiently model the effect of interdependent control inputs. Both the nonlinear LSTM process model and the cost function of the optimisation problem are linearised to formulate the control problem as a mixed integer linear programming (MILP) problem, which ensures that the controller can operate in near realtime and with limited computational power. Moreover, to avoid resorting to model extrapolation and to improve the model's predictive performance, the set of allowed control signal values is restricted using a percentile-based approach. In addition to the purely data-driven controller (DDC), a hybrid controller is designed to leverage the strengths of the RBC and the DDC. The performance of both controllers is benchmarked against the RBC's performance using the BOPTEST simulation environment under various experiment settings, highlighting how the hyperparameters affect the controller's performance. Compared to the RBC, we show that the proposed controllers realise substantial improvements in terms of both thermal comfort and operational cost while controlling a single zone or two zones simultaneously.

关键词： HVAC control Encoder-Decoder LSTM Model PNMPC Interdependent Inputs

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Pilot-Free Semantic Communication systems for Frequency-Selective Fading Channels

Pilot-Free Semantic Communication Systems for Frequency-Sele...

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ieee International conference on Communications (ieee ICC)

作者： Cao, Zijian Zhang, Hua Liang, Le Wang, Haotian Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

ISBN: (纸本)9798350333077

With the paradigm shift from traditional communication systems to machine learning-driven communication systems, deep learning-based semantic communication is considered as a potential approach to improve transmission efficiency. However, the time-varying and multipath effects of wireless channels significantly influence the performance of existing semantic communication systems. The conventional solutions to overcome channel impairments, i.e., pilot-driven channel estimation and channel equalization approaches, will complicate the system and introduce extra overhead. Therefore, this paper proposes a deep learning-driven pilot-free semantic communication framework for frequency-selective fading channels. The convolutional neural network-based channel feature extraction and data recovery modules are introduced in the receiver to implicitly extract channel information directly from the received signal and recover the semantic information. The simulation demonstrates that the proposed network can be applied to various frequency-selective fading channels and outperforms existing schemes in terms of computational complexity and semantic transmission accuracy.

关键词： deep learning pilot-free semantic communication end-to-end communication convolutional neural network

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Numerical evidence for sample efficiency of model-based over model-free reinforcement learning control of partial differential equations

Numerical evidence for sample efficiency of model-based over...

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European control conference (ECC)

作者： Werner, Stefan Peitz, Sebastian SICK Sensor Intelligence Waldkirch Germany Paderborn Univ Dept Comp Sci Paderborn Germany

ISBN: (纸本)9798331540920;9783907144107

The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional Long-Short Term Memory network with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstrate our findings.

关键词： reinforcement learning surrogate modeling partial differential equations feedback control

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