This work aims to analyze the specific application of sensor environment perception based on the Kalman filter algorithm in intelligent vehicles. Hence, this work proposes a design for a multi-sensor environment perception and adaptive cruise control (ACC) system based on the Kalman filter algorithm. The system utilizes multiple sensors to collect data and employs the Kalman filter algorithm to process the data, enabling obstacle detection and tracking. This provides a new solution for environmental perception in intelligent vehicles. Meanwhile, combined with ACC technology, the vehicle speed is adjusted to achieve a safe and efficient autonomous driving experience. The experimental results indicate that the system using the Kalman filter algorithm performs in various scenarios, including different weather conditions, road conditions, and obstacle detection. This work achieves high detection accuracy and tracking precision, with the highest values reaching 97.5% and 96.3%, respectively. In the tests, the ACC system can maintain an appropriate following distance and control the vehicle speed well, whether it is a car, a large truck, or a motorcycle. This work has crucial reference value and promotion significance for developing intelligent vehicle technology.

Cross-lingual representation is a technique that can both represent different languages in the same latent vector space and enable the knowledge transfer across languages. To learn such representations, most of existing works require parallel sentences with word-level alignments and assume that aligned words have similar Bag-of-Words (BoW) contexts. However, due to differences in grammar structures among different languages, the contexts of aligned words in different languages may appear at different positions of the sentence. To address this issue of different syntactics across different languages, we propose a model of bilingual word embeddings integrating syntactic dependencies (DepBiWE) by producing dependency parse-trees which encode the accurate relative positions for the contexts of aligned words. In addition, a new method is proposed to learn bilingual word embeddings from dependency-based contexts and BoW contexts jointly. Extensive experimental results on a real world dataset clearly validate the superiority of the proposed model DepBiWE on various natural language processing (NLP) tasks.

The task of feature selection is to select a subset of the original features according to certain predefined criterion with the goal to remove irrelevant and redundant features, improve the prediction performance and reduce the computational costs of data mining algorithms. In this paper, we integrate feature relevance and redundancy explicitly in the feature selection criterion. Spectral feature analysis is applied here which can fit into both supervised and unsupervised learning problems. Specifically, we formulate the problem into a combinatorial problem to maximize the relevance and minimize the redundancy of the selected subset of features at the same time. The problem can be relaxed and solved with an efficient extended power method with global convergence guaranteed. Extensive experiments demonstrate the advantages of the proposed technique in terms of improving the prediction performance and reducing redundancy in data.