Deep Bayesian Active Learning Using In-Memory Computing Hardware

Chemistry, Nanoscience, Engineering, Electrical Engineering, Information Science, Computer Science, Semiconductors and Information Devices, Artificial Intelligence,
in-memory computing   deep Bayesian learning   uncertainty capture   robot skill learning   efficient learning  

With the rapid development of artificial intelligence (AI) technologies, deep learning has made significant progress in complex tasks. However, the success of deep learning largely relies on massive amounts of labeled data, and the data labeling process is not only time-consuming and labor-intensive but also requires specialized domain knowledge, m...

Migrant Resettlement by Evolutionary Multiobjective Optimization

Mathematics, Engineering, Information Science, Computer Science, Artificial Intelligence,
migrant resettlement   multiobjective optimization   evolutionary algorithms   greedy algorithm   submodular optimization   humanitarianism   employment rate  

A Research Report on a New Framework for Solving Migrant Resettlement Using Multiobjective Evolutionary Optimization Against the backdrop of accelerated globalization and evolving socio-economic conditions, migration has become an undeniable global trend. From the perspective of humanitarian relief or the sustainable development of a globalized eco...

Reinforcement Learned Multiagent Cooperative Navigation in Hybrid Environment with Relational Graph Learning

Engineering, Mechanical Engineering, Electrical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
multiagent system   reinforcement learning   relational graph learning   hybrid environment   cooperative navigation   decentralized execution   collision avoidance  

Multi-agent Cooperative Navigation in Hybrid Environments: A New Reinforcement Learning Approach Based on Relational Graph Learning Mobile robotics is witnessing a surge in applications, fueled by advancements in artificial intelligence, with navigation capabilities being one of the core focus areas of research. Traditional navigation methods often...

Adaptive Composite Fixed-Time RL-Optimized Control for Nonlinear Systems and Its Application to Intelligent Ship Autopilot

Engineering, Mechanical Engineering, Ocean Engineering, Transportation Engineering, Electrical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
adaptive control   fixed-time control   reinforcement learning   nonlinear systems   neural networks   intelligent ships   optimization methods  

Nonlinear Fixed-Time Reinforcement Learning Optimized Control for Intelligent Ship Autopilots In recent years, intelligent autopilot technology has gradually become a research hotspot in the field of automation control. For complex nonlinear systems, the design of optimized control strategies, especially the achievement of system stability and perf...

Policy Consensus-Based Distributed Deterministic Multi-Agent Reinforcement Learning

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence,
policy consensus   distributed algorithms   multi-agent reinforcement learning   deterministic policy gradient   directed graphs   continuous control   deep reinforcement learning  

Policy Consensus-Based Distributed Deterministic Multi-Agent Reinforcement Learning Research Report Reinforcement Learning (RL) has made significant breakthroughs in recent years in various fields such as robotics, smart grids, and autonomous driving. However, in real-world scenarios, multi-agent collaboration problems, also known as Multi-Agent Re...

An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach

Engineering, Energy Engineering, Information Science, Electronic Information, Artificial Intelligence,
electricity price forecasting   explainable artificial intelligence   SHAP method   error compensation   neural networks   electricity markets   price volatility  

Improved Electricity Price Forecasting Model Based on SHAP and Its Explainability Analysis Background and Research Motivation Electricity price forecasting (EPF) models have become a hot research topic in recent years, particularly due to the financial impact of market volatility on stakeholders. Especially in European energy markets, recent years ...

Multiobjective Dynamic Flexible Job Shop Scheduling with Biased Objectives via Multitask Genetic Programming

Engineering, Mechanical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
dynamic scheduling   flexible job shop   genetic programming   multitask learning   multiobjective optimization   scheduling heuristics   algorithm efficiency  

Breakthrough Research in Multiobjective Dynamic Flexible Job Shop Scheduling: An Innovative Approach to Optimize Biased Objectives via Multitask Learning in Genetic Programming Background Introduction Dynamic Flexible Job Shop Scheduling (DFJSS) is an essential combinatorial optimization problem with extensive real-world applications in areas such ...

NPE-DRL: Enhancing Perception-Constrained Obstacle Avoidance with Nonexpert Policy-Guided Reinforcement Learning

Engineering, Aerospace Engineering, Information Science, Computer Science, Artificial Intelligence,
UAVs   Obstacle avoidance   Deep reinforcement learning   Nonexpert policy   Perception-constrained   Sample efficiency   Monocular vision  

Research on Improving UAV Obstacle Avoidance in Vision-Constrained Environments Based on Nonexpert Policy Reinforcement Learning In recent years, unmanned aerial vehicles (UAVs) have gained widespread application in civilian fields such as package delivery, risk assessment, and emergency rescue, owing to their superior maneuverability and versatili...

Efficient CORDIC-based Activation Function Implementations for RNN Acceleration on FPGAs

Engineering, Electrical Engineering, Information Science, Electronic Information, Computer Science, Artificial Intelligence,
CORDIC   RNN   activation function   FPGA   hardware acceleration   natural language processing   edge devices  

Efficient Implementation of RNN Activation Functions: Breakthroughs in CORDIC Algorithms and FPGA Hardware Acceleration Background and Research Significance In recent years, with the rapid advancement of deep learning technologies, Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have demonstrated powerful capa...

A Monolithic 3D IGZO-RRAM-SRAM-Integrated Architecture for Robust and Efficient Compute-in-Memory

Engineering, Electrical Engineering, Information Science, Computer Science, Semiconductors and Information Devices, Artificial Intelligence,
compute-in-memory   monolithic 3D   RRAM   IGZO   3D stacking   neural networks   system optimization  

Monolithic 3D IGZO-RRAM-SRAM Compute-in-Memory Architecture: A Breakthrough in Improving Neural Network Computation Efficiency Background and Research Motivation As neural networks (NNs) continue to find applications in artificial intelligence, traditional computing architectures struggle to meet their needs for energy efficiency, speed, and densit...