The proceedings contain 26 papers. The special focus in this conference is on Simulation of Adaptive Behavior. The topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Mode...
ISBN:
(纸本)9783031715327
The proceedings contain 26 papers. The special focus in this conference is on Simulation of Adaptive Behavior. The topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of Varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch Through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent Systems: A Computational modeling Approach;transient Milling Dynamics in Collective Motion with Visual Occlusions;extended Swarming with Embodied Neural Computation for Human control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg Vehicles;neural Chaotic Dynamics for Adaptive Exploration control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of Exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"Value" Emerges from Imperfect Memory;the Role of Theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy analysis in Education: A Multimodal Computational Model.
The proceedings contain 76 papers. The special focus in this conference is on Smart Electrical Grid and Renewable Energy. The topics include: Point Cloud Denoising Method Based on Improved PointCleanNet;dynamic Dual-S...
ISBN:
(纸本)9789819624553
The proceedings contain 76 papers. The special focus in this conference is on Smart Electrical Grid and Renewable Energy. The topics include: Point Cloud Denoising Method Based on Improved PointCleanNet;dynamic Dual-Strategy Update of Offline-to-Online Reinforcement Learning for Optimal Energy System Scheduling;knowledge Extraction Method of Electric Power Equipment Defects Based on Transfer Learning;a Secondary Bidding Strategy for the Charging Station Day-Ahead Market Based on the Evaluation of the Dispatchable Capacity of Electric Vehicles;primary Frequency Modulation control Strategy of Energy Storage System Based on State of Charge;building Integrated Energy System Expansion Planning Considering Reward-Punishment Ladder Carbon Mechanism and Load Uncertainty;Simulation analysis and Verification of Four-Air-Gap Current Transducer Based on TMR;optimal control of Large Number of Air Conditioners Driven by Dynamic Electric Carbon Emission Factor;application of a Bi-level Optimization Model for Energy Storage Capacity Allocation in Distribution Network with Renewable Energy Integration;power Load Disaggregation Method Based on Sparse Constraint;optimal Primary Frequency Support Demand Dispatch for Multiple Wind Turbines Considering Loss of Captured Wind Energy;promoting Eco-Friendly Power and Traffic Network Operations Using Machine Learning Techniques;Advanced-data Analytics for Household Power Consumption Forecasting Using CNN-LSTM Hybrid Network;research on the Interruption Performance of Environmentally Friendly 126 kV Circuit Breaker;research on Identification of Electricity Theft in Regional Power Grid Under New Energy Scenario;nonlinear modeling for One Pipe with Multiple Units Under Gird Connected of Pumped Storage Stations;a Methodology for the Evaluation of the Power Supply Capability of Subway Traction Power Supply System.
Business processes modeling typically faces challenges in the integrated representation of processes’ lifecycle, informational, and organizational models. Despite the plethora of processmodeling languages (workflow ...
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Big data is regarded as the secret to releasing the following massive surges of economic development. In light of a variety of new apps and platforms that are integrated into our everyday routines, such as smartphones...
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An essential task in supplying the world's food requirement is soil analysis. It is the backbone of agriculture, particularly in developing nations like India, so if data mining methods are used to the fields, spe...
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ISBN:
(纸本)9798331527495
An essential task in supplying the world's food requirement is soil analysis. It is the backbone of agriculture, particularly in developing nations like India, so if data mining methods are used to the fields, specifically to the soils, the pledge-making scenario may be altered, improving cultivations in the process. A significant portion of the decisions made about the majority of problems pertaining to the sector of agriculture include soil analysis. In addition to highlighting several data mining methods and the corresponding work done by various authors in the context of soil analysis, the primary emphasis of this study is on the function that data mining plays in soil analysis in the agricultural area. These data mining methods are quite modern in the soil analysis domain. The study's advancedness lies in its use of sophisticated data mining tools to better understand the characteristics of the soil, its nutrient content, and possible production results. Improving soil management techniques is the primary goal, since it has a direct impact on the agricultural system's sustainability and production. This research makes use of extensive data from soil samples collected from various agricultural locations. Consequently, a number of data mining methods, including cluster analysis, principal component analysis (PCA), and decision trees, will be used in the present study to discover patterns and relationships between crop performance and soil composition. Important soil characteristics, such as pH, the amount of organic matter, nitrogen, and potassium, all affect crop health and productivity. The objective of this study is to identify' soil clusters,' or groups of related prevalent conditions that either support or constrain agricultural output. Additionally, PCA gave me insight into the relevance of soil characteristics that have an impact on crop performance, making them very helpful for focused interventions aimed at improving the soil. It constructed the decision
Currently, most of the research on Internet financial models is based on traditional machine learning models, which often fail to adequately capture the complex features and potential non-linear interactions within fi...
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The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to prote...
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The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber and physical components, and a safety breach in the element is central to catastrophic consequences. Cyberattack recognition and mitigation techniques in CPSs include using numerous models like intrusion detection systems (IDSs), access control mechanisms, encryption, and firewalls. Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. Thus, the study proposes an automated Cyberattack Detection using Binary Metaheuristics with Deep Learning (ACAD-BMDL) method in a CPS environment. The ACAD-BMDL method mainly focuses on enhancing security in the CPS environment via the cyberattack detection process. The ACAD-BMDL method uses Z-score normalization to scale the input dataset. In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. Furthermore, the Archimedes Optimization Algorithm (AOA) model is employed to select the optimum hyperparameter for the EESNN model. The empirical analysis of the ACAD-BMDL technique is performed on a benchmark dataset. The experimental validation of the ACAD-BMDL technique portrayed a superior accuracy value of 99.12% and 99.36% under NSLKDD2015 and CICIDS2017 datasets in the CPS environment.
The field of laser-ion acceleration faces significant challenges in handling high-dimensional, computationally intensive problems, often constrained by budgets and available computational power. Reliably achieving hig...
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Identifying upstream processes responsible for wafer defects is challenging due to the combinatorial nature of process flows and the inherent variability in processing routes, which arises from factors such as rework ...
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ISBN:
(数字)9798331531850
ISBN:
(纸本)9798331531867
Identifying upstream processes responsible for wafer defects is challenging due to the combinatorial nature of process flows and the inherent variability in processing routes, which arises from factors such as rework operations and random process waiting times. This paper presents a novel framework for wafer defect root cause analysis, called Partial Trajectory Regression (PTR). The proposed framework is carefully designed to address the limitations of conventional vector-based regression models, particularly in handling variable-length processing routes that span a large number of heterogeneous physical processes. To compute the attribution score of each process given a detected high defect density on a specific wafer, we propose a new algorithm that compares two counterfactual outcomes derived from partial process trajectories. This is enabled by new representation learning methods, proc2vec and route2vec. We demonstrate the effectiveness of the proposed framework using real wafer history data from the NY CREATES fab in Albany.
Leprosy, characterized by dermatological manifestations and peripheral nervous system impairment due to Mycobacterium leprae infection in Schwann cells, poses persistent challenges in treatment efficacy. Stem cell the...
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