Dalian University of Technology

About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.

Insight into Perovskite Solar Cells Based on SnO 2 Compact Electron- Selective Layer

Abstract: Considering the remarkable progress in photovoltaic performance, scholars have focused on perovskite solar cells (PSCs) over the recent two years. TiO2 thin film is a semiconductor with a wide band gap and is usually used as an electron-selective layer (ESL) in PSCs. Although SnO2 exhibits conductivity higher than that of TiO2, its use as a compact ESL in PSCs has not been reported. In this study, nanocrystalline SnO2 thin film was prepared through a sol–gel method and then characterized. The prepared SnO2 thin film was composed of small tetragonal rutile nanocrystals. We applied the SnO2 compact ESL into PSCs and compared their performance with that of PSCs based on a TiO2 thin layer. SnO2-ESL-based PSCs (S-PSCs) showed higher short-circuit current density and lower open-circuit voltage, fill factor, and conversion efficiency than the conventional TiO2-ESL-based PSCs. Furthermore, the photovoltaic performance of S-PSCs was highly dependent on measurement means, and this relationship was investigated and ...

Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

Abstract: In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to learn a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further delved into the adversarial training to strengthen the correlation between the inputs and corresponding outputs. Our approach is generative to learn hash functions, such that the learned hash codes can maximally correlate each input–output correspondence and also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method outperforms the state of the arts.

Autonomous Platoon Control Allowing Range-Limited Sensors

Abstract: This paper investigates control design for the platoon of automated vehicles whose sensors have limited sensing capability. A novel hybrid platoon model is established, in which actuator delay (e.g., the fueling and braking delay) and the effect of sensing range limitation are involved. Based on the new model, a framework of guaranteed-cost controller design is presented, which can robustly stabilize the platoon of vehicles with a given level of disturbance attenuation. The obtained controller is complemented by additional conditions that were established to guarantee string stability and zero steady-state spacing error, yielding a useful string-stable platoon control algorithm. The effectiveness and advantage of the presented methodology is demonstrated by both numerical simulations and experiments with laboratory-scale Arduino cars.