Abstract: Remote sensing images are usually characterized by complex backgrounds, scale and orientation variations, and large intraclass variance. General semantic segmentation methods usually fail to ...

Abstract: Modeling and solving the flexible job shop scheduling problem (FJSP) is critical for modern manufacturing. However, existing works primarily focus on the time-related makespan target, often ...

Abstract: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex ...

Abstract: Due to recent advancements in deep learning, techniques for urban structure extraction and semantic segmentation of multimodal remote sensing images have significant improvements. However, ...

Abstract: Integrated sensing and massive multiple-input-multiple-output (MIMO) communication (mMIMO-ISAC) at terahertz (THz) bands can provide vast spatial degrees of freedom and abundant bandwidth ...

Abstract: Next Point-of-interest recommendation involves modeling user interactions with Point-of-interests (PoIs) to analyze user behavior patterns and suggest future scenarios. Data sparsity ...

Abstract: Modern day cellular mobile networks use Massive MIMO technology to extend range and service multiple devices within a cell. This has brought tremendous improvements in the high peak data ...

Abstract: The survey and monitoring of natural resources in complex and hazardous areas with rugged terrain and insufficient infrastructure is a challenging issue in the industry. Currently, ground ...

Abstract: With the continuous development of the power system, in the face of the frequency deviation caused by the randomness and volatility of renewable energy sources such as photovoltaic and wind ...

Abstract: As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization ...

Abstract: This paper introduces a novel optimized hybrid model combining Long Short-Term Memory (LSTM) and Transformer deep learning architectures designed for power load forecasting. It leverages the ...