Jiongzhi Zheng — Page 3

arXiv2021

Giant and bidirectionally tunable thermopower in non-aqueous ionogels enabled by selective ion doping

Ionic thermoelectrics show great potential in low-grade heat harvesting and thermal sensing owing to their ultrahigh thermopower, low cost and ease in production. However, the lack of effective n-type ionic thermoelectric materials seriously hinders their applications. Here, we report giant and bidirectionally tunable thermopowers within an ultrawide range from -23 to +32 mV K-1 at 90% RH in solid ionic-liquid-based ionogels, rendering it among the best n- and p-type ionic thermoelectric materials. A novel thermopower regulation strategy through ion doping to selectively induce ion aggregates via strong ion-ion interactions is proposed. These charged aggregates are found decisive in modulating the sign and enlarging the magnitude of the thermopower in the ionogels. A prototype wearable device integrated with 12 p-n pairs is demonstrated with a total thermopower of 0.358 V K-1 in general indoor conditions, showing promise for ultrasensitive body heat detection.

physics.app-ph↗

arXiv2021

Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem

We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, which combines three reinforcement learning methods (Q-learning, Sarsa and Monte Carlo) with the well-known TSP algorithm, called Lin-Kernighan-Helsgaun (LKH). VSR-LKH replaces the inflexible traversal operation in LKH, and lets the program learn to make choice at each search step by reinforcement learning. Experimental results on 111 TSP benchmarks from the TSPLIB with up to 85,900 cities demonstrate the excellent performance of the proposed method.

cs.AI↗