A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving

FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events

Traditional visual place recognition (VPR), usually using standard cameras, is easy to fail due to glare or high-speed motion. By contrast, event cameras have the advantages of low latency, high temporal resolution, and high dynamic range, which can deal with the above issues. Nevertheless, event cameras are prone to failure in weakly textured or motionless scenes, while standard cameras can still provide appearance information in this case. Thus, exploiting the complementarity of standard cameras and event cameras can effectively improve the performance of VPR algorithms. In the paper, we propose FE-Fusion-VPR, an attention-based multi-scale network architecture for VPR by fusing frames and events. First, the intensity frame and event volume are fed into the two-stream feature extraction network for shallow feature fusion. Next, the three-scale features are obtained through the multi-scale fusion network and aggregated into three sub-descriptors using the VLAD layer. Finally, the weight of each sub-descriptor is learned through the descriptor re-weighting network to obtain the final refined descriptor. Experimental results show that on the Brisbane-Event-VPR and DDD20 datasets, the Recall@1 of our FE-Fusion-VPR is 29.26% and 33.59% higher than Event-VPR and Ensemble-EventVPR, and is 7.00% and 14.15% higher than MultiRes-NetVLAD and NetVLAD. To our knowledge, this is the first end-to-end network that goes beyond the existing event-based and frame-based SOTA methods to fuse frame and events directly for VPR

Interactive influence of self and other language behaviors: Evidence from switching between bilingual production and comprehension

The neural mechanisms underlying one's own language production and the comprehension of language produced by other speakers in daily communication remain elusive. Here, we assessed how self-language production and other-language comprehension interact within a language switching context using event-related functional Magnetic Resonance Imaging (er-fMRI) in 32 unbalanced Chinese-English bilinguals. We assessed within-modality language interference during language production and comprehension as well as cross-modality interference when switching from production to comprehension and vice versa. Results revealed that the overall effect of production (across switch and repeat trials) was larger in the cross-modality than within-modality condition in a series of attentional control areas, namely the left dorsolateral prefrontal cortex, anterior cingulate cortex and left precuneus. Furthermore, the left precuneus was recruited more strongly in switch trials compared to repeat trials (i.e., switching costs) in within-production conditions but not in the cross-modality condition. These findings suggest that switching from production to comprehension recruits cognitive control areas to successfully implement switches between modalities. However, cross-language interference (in the form of language switching costs) mainly stems from the self-language production system

Binaural Rendering of Ambisonic Signals by Neural Networks

Binaural rendering of ambisonic signals is of broad interest to virtual reality and immersive media. Conventional methods often require manually measured Head-Related Transfer Functions (HRTFs). To address this issue, we collect a paired ambisonic-binaural dataset and propose a deep learning framework in an end-to-end manner. Experimental results show that neural networks outperform the conventional method in objective metrics and achieve comparable subjective metrics. To validate the proposed framework, we experimentally explore different settings of the input features, model structures, output features, and loss functions. Our proposed system achieves an SDR of 7.32 and MOSs of 3.83, 3.58, 3.87, 3.58 in quality, timbre, localization, and immersion dimensions

Fabrication of B doped g-C3N4/TiO2 heterojunction for efficient photoelectrochemical water oxidation

With the development of clean and renewable energy, hydrogen produced via photoelectrochemical (PEC) water splitting has attracted considerable attention. However, to develop the photoanodes with stable and excellent PEC ability is still a big challenge. In our work, TiO2 nanorods decorated with boron doped g-C3N4 (BCN/TiO2) is fabricated via thermal polymerization method to improve the PEC performance. The BCN/TiO2 displays 4-fold increase of the photocurrent density (1.01 mA cm−2) at 1.23 V vs. RHE under irradiation (100 mW cm−2, AM 1.5 G). And the onset potential of BCN/TiO2 exhibits a negative shift with 100 mV. Attributed to the broad light absorption of BCN and hetero-junction forming between BCN and TiO2, the IPCE value is increased to 87.8% in 380 nm, and the charge separation and transfer efficiency are both increased. Doping metal-free inorganic material with heteroatoms is a simple and efficient strategy to increase the light absorption within visible light and charge transfer efficiency in PEC and photocatalytic applications

Spike-EVPR: Deep Spiking Residual Network with Cross-Representation Aggregation for Event-Based Visual Place Recognition

Event cameras have been successfully applied to visual place recognition (VPR) tasks by using deep artificial neural networks (ANNs) in recent years. However, previously proposed deep ANN architectures are often unable to harness the abundant temporal information presented in event streams. In contrast, deep spiking networks exhibit more intricate spatiotemporal dynamics and are inherently well-suited to process sparse asynchronous event streams. Unfortunately, directly inputting temporal-dense event volumes into the spiking network introduces excessive time steps, resulting in prohibitively high training costs for large-scale VPR tasks. To address the aforementioned issues, we propose a novel deep spiking network architecture called Spike-EVPR for event-based VPR tasks. First, we introduce two novel event representations tailored for SNN to fully exploit the spatio-temporal information from the event streams, and reduce the video memory occupation during training as much as possible. Then, to exploit the full potential of these two representations, we construct a Bifurcated Spike Residual Encoder (BSR-Encoder) with powerful representational capabilities to better extract the high-level features from the two event representations. Next, we introduce a Shared & Specific Descriptor Extractor (SSD-Extractor). This module is designed to extract features shared between the two representations and features specific to each. Finally, we propose a Cross-Descriptor Aggregation Module (CDA-Module) that fuses the above three features to generate a refined, robust global descriptor of the scene. Our experimental results indicate the superior performance of our Spike-EVPR compared to several existing EVPR pipelines on Brisbane-Event-VPR and DDD20 datasets, with the average Recall@1 increased by 7.61% on Brisbane and 13.20% on DDD20.Comment: 14 pages, 10 figure