Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization

Solving large-scale multistage stochastic programming (MSP) problems poses a significant challenge as commonly used stagewise decomposition algorithms, including stochastic dual dynamic programming (SDDP), face growing time complexity as the subproblem size and problem count increase. Traditional approaches approximate the value functions as piecewise linear convex functions by incrementally accumulating subgradient cutting planes from the primal and dual solutions of stagewise subproblems. Recognizing these limitations, we introduce TranSDDP, a novel Transformer-based stagewise decomposition algorithm. This innovative approach leverages the structural advantages of the Transformer model, implementing a sequential method for integrating subgradient cutting planes to approximate the value function. Through our numerical experiments, we affirm TranSDDP's effectiveness in addressing MSP problems. It efficiently generates a piecewise linear approximation for the value function, significantly reducing computation time while preserving solution quality, thus marking a promising progression in the treatment of large-scale multistage stochastic programming problems.

Development of AI-cloud based high-sensitivity wireless smart sensor for port structure monitoring

Regular structural monitoring of port structure is crucial to cope with rapid degeneration owing to its exposure to saline and collisional environment. However, most of the inspections are being done visually by human in irregular-basis. To overcome the complication, lots of research related to vibration-based monitoring system with sensor has been devised. Nonetheless, it was difficult to measure ambient vibration due to port's diminutive amplitude and specify the exact timing of berthing, which is the major excitation source. This study developed a novel cloud-AI based wireless sensor system with high-sensitivity accelerometer M-A352, which has 0.2uG/sqrt(Hz) noise density, 0.003mg of ultra-low noise feature, and 1000Hz of sampling frequency. The sensor is triggered based on either predefined schedule or long rangefinder. After that, the detection of ship is done by AI object detection technique called Faster R-CNN with backbone network of ResNet for the convolution part. Coordinate and size of the detected anchor box is further processed to certify the berthing ship. Collected data are automatically sent to the cloud server through LTE CAT 1 modem within 10Mbps. The system was installed in the actual port field in Korea for few days as a preliminary investigation of proposed system. Additionally, acceleration, slope, and temperature data are analyzed to suggest the possibility of vibration-based port condition assessment.