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Anthracnose Blight Phytophthora Cucumber leaf Prediction through Six Convolutional ADAM Optimized Deep Convolutional Neural network

Anthracnose Blight Phytophthora Cucumber leaf Prediction thr...

引用

Advance Computing and Innovative Technologies in Engineering (ICACITE), International conference on

作者： N Malarvizhi M. Shyamala Devi J. Aswini Manikanta Manikanta Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Computer Science & Engineering Saveetha Engineering College Chennai Tamilnadu India

Infections that harm the leaves of cucumber plants are referred as cucumber leaf diseases. These ailments can reduce cucumber yields significantly and have a detrimental effect on cucumber quality. Anthracnose, Blight and Phytophthora are a few typical cucumber leaf ailments. Rapid recognition and appropriate intervention of cucumber leaf disease detection are essential for effective control, but this can be challenging for farmers without access to special knowledge or equipment. This research proposes Six Convolutional ADAM DCNN to predict the cucumber leaf disease showing high efficiency. The proposed method utilizes the Cucumber leaf dataset from KAGGLE. The Cucumber leaf dataset having category of disease as Anthracnose, Blight and Phytophthora and Healthy with 4400 cucumber leaflets samples and applied for preprocessing. Single input, outcome, dense, and average pooling were used to create the six convolutional ADAM DCNN (6A-DCNN) that was suggested. Six convolutional layers and a maximum pooling layer associated with every convolution were used in the construction of the suggested 6A-DCNN model, which was then applied for optimization using ADAM optimizer. The cucumber leaf dataset was divided into 4000 training photos, 200 validation and testing images. For the purpose of evaluating performance indicators, the Cucumber leaf training dataset was applied to the proposed 6A-DCNN model as well as to the VGG, Resnet, MobileNetV2, DenseNet, Inception, Xception, and NASNet Large models. Using a Nvidia Geforce gtx V100 GPU server, 40 training epochs and a sample size of 96 were used while running Python. When compared to other CNN techniques, experimental findings reveal that the proposed 6A-DCNN exhibits the highest accuracy of 98.55%, precision of 98.22%, recall of 97.65%, and FScore of 97.67%.

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Synchrophasor Based Fault Location Identification in Radial Distribution networks

Synchrophasor Based Fault Location Identification in Radial ...

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International conference on Electrical Power and Energy Systems (ICEPES)

作者： Aneesh Rajeev D. R. Binu Ben Jose School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Deploying Phasor Measurement Units (PMUs) in power networks offers numerous benefits, such as optimizing power flow, enhancing system control, conducting contingency analysis, and detecting faults. However, installing PMUs at every bus in a power system is unnecessary and costly. Hence, the optimal positioning of PMUs is crucial, often achieved through the implementation of the binary based search algorithm. This algorithm has been evaluated on standard IEEE 7, 14 and 30 bus systems to identify the most effective PMU locations. Following the optimal PMU placement, the next crucial step involves fault location and diagnosis. This process aims to accurately pinpoint the location of a fault as soon as it occurs. The proposed fault location identification method consists of two simultaneous steps for determining the actual fault location. Firstly, candidate locations are identified by analyzing the data collected from one PMU at one location, iterating through each line section to pinpoint potential fault locations. Subsequently, a set of candidate locations is derived from this analysis. In the second step, by comparison of the voltage phasors the connection nodes a different set of potential location will be obtained. This comparison is based on synchrophasors measured from two terminals. By merging the two sets of candidate locations, the actual fault point can be accurately determined, ensuring efficient fault location and diagnosis within the power network.

关键词： Electric potential Power measurement Random access memory optimization methods Fault location Position measurement Phasor measurement units

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Fuzzy neural network simulation of vehicle yaw rate control based on PID 3

Fuzzy neural network simulation of vehicle yaw rate control ...

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3rd World conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)

作者： Zhang, Hongtu Xu, Donglai Li, Yansheng Hu, Xudong Beijing Univ Technol Dept Mat & Mfg Beijing Peoples R China

ISBN: (纸本)9781665441094

The core of driving force control strategy is the research core of electric vehicle driven by wheel motor. In order to give full play to the advantages of wheel motor drive system, a reasonable control strategy must be designed. In this paper, the stability of the vehicle is determined based on the sideslip angle of the center of mass of the vehicle. Through the speed and steering wheel angle, the actual yaw rate of the vehicle at a certain speed and the theoretical yaw rate at this speed are determined as the control objectives. The PID control is used to distribute the four-wheel brake pressure, generate additional yaw moment to adjust the vehicle attitude;and the traditional PID control is used to control the vehicle attitude. In contrast, fuzzy control using human expert control experience, for the control of nonlinear and complex objects shows the advantages of good robustness and high control performance. Therefore, on the basis of PID, through neural network learning, fuzzy neural network is used to optimize the output fuzzy rules and membership function to control the vehicle. The whole process is carried out with CarSim and Simulink Simulation is carried out to verify the control optimization process.

关键词： yaw rate vehicle control optimization vehicle stability CarSim joint Simulink simulation

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Reward Biased Maximum Likelihood Estimation for Reinforcement Learning 3

Reward Biased Maximum Likelihood Estimation for Reinforcemen...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Mete, Akshay Singh, Rahul Liu, Xi Kumar, P. R. Texas A&M Univ College Stn TX 77843 USA Indian Inst Sci Bangalore Karnataka India

The Reward-Biased Maximum Likelihood Estimate (RBMLE) for adaptive control of Markov chains was proposed in (Kumar and Becker, 1982) to overcome the central obstacle of what is variously called the fundamental "closed-identifiability problem" of adaptive control (Borkar and Varaiya, 1979), the "dual control problem" by Feldbaum (Feldbaum, 1960a,b), or, contemporaneously, the "exploration vs. exploitation problem". It exploited the key observation that since the maximum likelihood parameter estimator can asymptotically identify the closed-transition probabilities under a certainty equivalent approach (Borkar and Varaiya, 1979), the limiting parameter estimates must necessarily have an optimal reward that is less than the optimal reward attainable for the true but unknown system. Hence it proposed a counteracting reverse bias in favor of parameters with larger optimal rewards, providing a carefully structured solution to the fundamental problem alluded to above. It thereby proposed an optimistic approach of favoring parameters with larger optimal rewards, now known as "optimism in the face of uncertainty." The RBMLE approach has been proved to be long-term average reward optimal in a variety of contexts including controlled Markov chains, linear quadratic Gaussian (LQG) systems, some nonlinear systems, and diffusions. However, modern attention is focused on the much finer notion of "regret," or finite-time performance for all time, espoused by (Lai and Robbins, 1985). Recent analysis of RBMLE for multi-armed stochastic bandits (Liu et al., 2020) and linear contextual bandits (Hung et al., 2020) has shown that it not only has state-of-the-art regret, but it also exhibits empirical performance comparable to or better than the best current contenders, and leads to several new and strikingly simple index policies for these classical problems. Motivated by this, we examine the finite-time performance of RBMLE for reinforcement learning tasks that involve the general proble

关键词： Reinforcement Learning Markov Decision Process Adaptive control

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Accelerated Learning with Robustness to Adversarial Regressors 3

Accelerated Learning with Robustness to Adversarial Regresso...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Gaudio, Joseph E. Annaswamy, Anuradha M. Moreu, Jose M. Bolender, Michael A. Gibson, Travis E. MIT Cambridge MA 02139 USA US Air Force Res Lab Washington DC 20330 USA Brigham & Womens Hosp Boston MA 02115 USA Harvard Med Sch Boston MA 02115 USA

High order momentum-based parameter update algorithms have seen widespread applications in training machine learning models. Recently, connections with variational approaches have led to the derivation of new learning algorithms with accelerated learning guarantees. Such methods however, have only considered the case of static regressors. There is a significant need for parameter update algorithms which can be proven stable in the presence of adversarial time-varying regressors, as is commonplace in control theory. In this paper, we propose a new discrete time algorithm which 1) provides stability and asymptotic convergence guarantees in the presence of adversarial regressors by leveraging insights from adaptive control theory and 2) provides non-asymptotic accelerated learning guarantees leveraging insights from convex optimization. In particular, our algorithm reaches an epsilon sub-optimal point in at most (O) over tilde (1/root epsilon) iterations when regressors are constant - matching lower bounds due to Nesterov of Omega(1/root epsilon), up to a log(1/epsilon) factor and provides guaranteed bounds for stability when regressors are time-varying. We provide numerical experiments for a variant of Nesterov's provably hard convex optimization problem with time-varying regressors, as well as the problem of recovering an image with a time-varying blur and noise using streaming data.

关键词： control theory

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To Study and Analyze State-of-the-Art network optimization Techniques in an IoT Ecosystem

To Study and Analyze State-of-the-Art Network Optimization T...

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Advances in Computing, Communication control and networking (ICAC3N), International conference on

作者： Meenakshi Shruti Pal Munish Bhatia Lovely Professional University Phagwara Punjab India

ISBN: (纸本)9781665438124

With the technology's growth, the internet is being heavily used for exchanging data among various devices interconnected in billions. Huge data is transmitted over the network of the Internet of Things (IoT), which leads to various network problems such as routing, congestion, scalability, authentication, comprehensive security, and many more. Hence, it is necessary to provide the solution to these network problems. The optimization techniques play a vital role in the better performance of the network. This paper addresses, a detailed review, and analysis of network optimization in the IoT. This article explored several network optimization algorithms as well as network parameters in the IoT. Further, various challenges and issues about the optimization of networks in IoT have been described. At last, the paper is concluded with the state-of-the-art solution for network optimization in the IoT network.

关键词： Technological innovation Scalability Ecosystems Authentication Routing Real-time systems Internet of Things

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optimization and control of Wireless Rechargeable Sensor network Based on Intelligent Algorithm 3

Optimization and Control of Wireless Rechargeable Sensor Net...

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3rd World conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)

作者： Liu, Jinfu Feng, Fan Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781665441094

Aiming to optimize the charging scheme and deployment cost of wireless rechargeable sensor network when the node coordinates are given. We first establish a TSP charging route model of a single mobile charger, use an improved ant colony algorithm to deal with the problem of the nearest charging route, and then establish an aperiodic greedy charging model based on the imbalance of sensor energy consumption of the shortest charging path obtained. Two reasonable charging strategies and a variable step size adaptive algorithm are used to search for the minimum battery capacity required for the sensor to work normally, so as to optimize the entire wireless rechargeable sensor network. The simulation using the data provided by MMCT indicates that the model and algorithm are able to commendably control the normal operation of the sensor network and reduce the operating cost of the entire network.

关键词： wireless rechargeable sensor network charging strategies simulation improved ant colony algorithm

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Near-Optimal Data Source Selection for Bayesian Learning 3

Near-Optimal Data Source Selection for Bayesian Learning

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Ye, Lintao Mitra, Aritra Sundaram, Shreyas Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

We study a fundamental problem in Bayesian learning, where the goal is to select a set of data sources with minimum cost while achieving a certain learning performance based on the data streams provided by the selected data sources. First, we show that the data source selection problem for Bayesian learning is NP-hard. We then show that the data source selection problem can be transformed into an instance of the submodular set covering problem studied in the literature, and provide a standard greedy algorithm to solve the data source selection problem with provable performance guarantees. Next, we propose a fast greedy algorithm that improves the running times of the standard greedy algorithm, while achieving performance guarantees that are comparable to those of the standard greedy algorithm. Finally, we validate the results using numerical examples, and show that the greedy algorithms work well in practice.

关键词： Bayesian Learning Combinatorial optimization Approximation Algorithms Greedy Algorithms

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Learning How to Solve "Bubble Ball" 3

Learning How to Solve "Bubble Ball"

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Lee, Hotae Bujarbaruah, Monimoy Borrelli, Francesco 2169 Etcheverry Hall Berkeley CA 94720 USA

"Bubble Ball" is a game built on a 2D physics engine, where a finite set of objects can modify the motion of a bubble-like ball. The objective is to choose the set and the initial configuration of the objects, in order to get the ball to reach a target flag. The presence of obstacles, friction, contact forces and combinatorial object choices make the game hard to solve. In this paper, we propose a hierarchical predictive framework which solves Bubble Ball. Geometric, kinematic and dynamic models are used at different levels of the hierarchy. At each level of the game, data collected during failed iterations are used to update models at all hierarchical level and converge to a feasible solution to the game. The proposed approach successfully solves a large set of Bubble Ball levels within reasonable number of trials (GitHub, Videos). This proposed framework can also be used to solve other physics-based games, especially with limited training data from human demonstrations.

关键词： MPC Physics-Based Games Trajectory optimization

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Robust Reinforcement Learning: A Constrained Game-theoretic Approach 3

Robust Reinforcement Learning: A Constrained Game-theoretic ...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Yu, Jing Gehring, Clement Schafer, Florian Anandkumar, Animashree CALTECH Elect Engn & Comp Sci Pasadena CA 91125 USA CALTECH Comp & Math Sci Pasadena CA 91125 USA

Reinforcement learning (RL) methods provide state-of-art performance in complex control tasks. However, it has been widely recognized that RL methods often fail to generalize due to unaccounted uncertainties. In this work, we propose a game theoretic framework for robust reinforcement learning that comprises many previous works as special cases. We formulate robust RL as a constrained minimax game between the RL agent and an environmental agent which represents uncertainties such as model parameter variations and adversarial disturbances. To solve the competitive optimization problems arising in our framework, we propose to use competitive mirror descent (CMD). This method accounts for the interactive nature of the game at each iteration while using Bregman divergences to adapt to the global structure of the constraint set. leveraging Lagrangian duality, we demonstrate an RRL policy gradient algorithm based on CMD. We empirically show that our algorithm is stable for large step sizes, resulting in faster convergence on constrained linear quadratic games.

关键词： robust reinforcement learning zero-sum game adversarial training competitive optimization policy gradient

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