Dynamic Energy-Efficient Path Planning for Electric Vehicles Using an Enhanced Ant Colony Algorithm

Electric vehicles (EVs) energy efficient path planning is crucial for maximizing the range of EVs. However, existing path planning algorithms often prioritize least time or shortest path without considering energy efficiency, leading to issues such as long computation time, slow convergence, and suboptimal solutions in complex environments. To address these challenges, this study proposes an improved ant colony optimization (E-ACO) algorithm for dynamic energy efficient path planning of EVs. The E-ACO algorithm incorporates a traffic flow prediction model and an energy consumption model specific to EVs. By redesigning heuristic factors and state transition rules, the algorithm enhances the efficiency and accuracy of path planning. Moreover, to address the challenge of selecting optimal charging station locations based on existing battery levels, a charging path planning method is introduced. This method utilizes the E-ACO algorithm and employs charging station pre-screening strategies to identify the most suitable charging station for completing the charging process. Experimental results show that the E-ACO algorithm reduces energy consumption by approximately 7% compared to the traditional ant colony optimization (ACO) algorithm. Additionally, through data analysis, a pre-screening threshold of 10 charging stations is determined based on the relationship between distance and energy consumption. To provide a visual representation of the path planning results, software is used to display the optimized paths. This allows users to easily interpret and analyze the recommended routes. Overall, the proposed E-ACO algorithm offers an effective and efficient solution for energy-efficient path planning in EVs. The incorporation of charging station pre-screening strategies further enhances the charging process. The study\u27s findings contribute to the development of more sustainable and efficient EV routing strategies, benefiting both EV users and the environment

Adaptive design of experiment via normalizing flows for failure probability estimation

Failure probability estimation problem is an crucial task in engineering. In this work we consider this problem in the situation that the underlying computer models are extremely expensive, which often arises in the practice, and in this setting, reducing the calls of computer model is of essential importance. We formulate the problem of estimating the failure probability with expensive computer models as an sequential experimental design for the limit state (i.e., the failure boundary) and propose a series of efficient adaptive design criteria to solve the design of experiment (DOE). In particular, the proposed method employs the deep neural network (DNN) as the surrogate of limit state function for efficiently reducing the calls of expensive computer experiment. A map from the Gaussian distribution to the posterior approximation of the limit state is learned by the normalizing flows for the ease of experimental design. Three normalizing-flows-based design criteria are proposed in this work for deciding the design locations based on the different assumption of generalization error. The accuracy and performance of the proposed method is demonstrated by both theory and practical examples.Comment: failure probability, normalizing flows, adaptive design of experiment. arXiv admin note: text overlap with arXiv:1509.0461

Distributed Active Noise Control System Based on a Block Diffusion FxLMS Algorithm with Bidirectional Communication

Recently, distributed active noise control systems based on diffusion adaptation have attracted significant research interest due to their balance between computational complexity and stability compared to conventional centralized and decentralized adaptation schemes. However, the existing diffusion FxLMS algorithm employs node-specific adaptation and neighborhood-wide combination, and assumes that the control filters of neighbor nodes are similar to each other. This assumption is not true in practical applications, and it leads to inferior performance to the centralized controller approach. In contrast, this paper proposes a Block Diffusion FxLMS algorithm with bidirectional communication, which uses neighborhood-wide adaptation and node-specific combination to update the control filters. Simulation results validate that the proposed algorithm converges to the solution of the centralized controller with reduced computational burden

Joint Training or Not: An Exploration of Pre-trained Speech Models in Audio-Visual Speaker Diarization

The scarcity of labeled audio-visual datasets is a constraint for training superior audio-visual speaker diarization systems. To improve the performance of audio-visual speaker diarization, we leverage pre-trained supervised and self-supervised speech models for audio-visual speaker diarization. Specifically, we adopt supervised~(ResNet and ECAPA-TDNN) and self-supervised pre-trained models~(WavLM and HuBERT) as the speaker and audio embedding extractors in an end-to-end audio-visual speaker diarization~(AVSD) system. Then we explore the effectiveness of different frameworks, including Transformer, Conformer, and cross-attention mechanism, in the audio-visual decoder. To mitigate the degradation of performance caused by separate training, we jointly train the audio encoder, speaker encoder, and audio-visual decoder in the AVSD system. Experiments on the MISP dataset demonstrate that the proposed method achieves superior performance and obtained third place in MISP Challenge 2022

Prompt-based Alignment of Headlines and Images Using OpenCLIP

In this paper, we describe how we leverage OpenCLIP to generate automated image recommendations for online news articles for the MediaEval 2023 NewsImages task. By exploring different text prompting techniques, a total of five retrieval approaches were devised. Results show, however, that the best performing approach is an unmodified CLIP version with the raw article headline as input. We reflect on this finding and its implication for future NewsImages tasks

The FlySpeech Audio-Visual Speaker Diarization System for MISP Challenge 2022

This paper describes the FlySpeech speaker diarization system submitted to the second \textbf{M}ultimodal \textbf{I}nformation Based \textbf{S}peech \textbf{P}rocessing~(\textbf{MISP}) Challenge held in ICASSP 2022. We develop an end-to-end audio-visual speaker diarization~(AVSD) system, which consists of a lip encoder, a speaker encoder, and an audio-visual decoder. Specifically, to mitigate the degradation of diarization performance caused by separate training, we jointly train the speaker encoder and the audio-visual decoder. In addition, we leverage the large-data pretrained speaker extractor to initialize the speaker encoder