Dynamic scheduling for flexible job shop based on MachineRank algorithm and reinforcement learning.

This paper investigates the Dynamic Flexible Job Shop Scheduling Problem (DFJSP), which is based on new job insertion, machine breakdowns, changes in processing time, and considering the state of Automated Guided Vehicles (AGVs). The objective is to minimize the maximum completion time and improve on-time completion rates. To address the continuous production status and learn the most suitable actions (scheduling rules) at each rescheduling point, a Dueling Double Deep Q Network (D3QN) is developed to solve this problem.

A LiDAR-Camera Joint Calibration Algorithm Based on Deep Learning.

Multisensor (MS) data fusion is important for improving the stability of vehicle environmental perception systems. MS joint calibration is a prerequisite for the fusion of multimodality sensors. Traditional calibration methods based on calibration boards require the manual extraction of many features and manual registration, resulting in a cumbersome calibration process and significant errors.

Sustained release of hydrophilic drug from polyphosphazenes/poly(methyl methacrylate) based microspheres and their degradation study.

Drug delivery system is referred as an approach to deliver the therapeutic agents to the target site safely in order to achieve the maximum therapeutic effects. In this perspective, synthesis of three new polyphosphazenes and their blend fabrication system with poly(methyl methacrylate) is described and characterized with (1)H NMR, (31)P NMR, GPC and DSC. Furthermore, these novel blends were used to fabricate microspheres and evaluated for sustain release of hydrophilic drug (aspirin as model drug).