Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

Artificial-intelligent-powered safety and efficiency improvement for controlling and scheduling in integrated railway systems

The multi-mode integrated railway system,anchored by the high-speed railway,caters to the diverse travel requirements both within and between cities,offering safe,comfortable,punctual,and eco-friendly transportation services.With the expansion of the railway networks,enhancing the efficiency and safety of the comprehensive system has become a crucial issue in the advanced development of railway transportation.In light of the prevailing application of artificial intelligence technologies within railway systems,this study leverages large model technology characterized by robust learning capabilities,efficient associative abilities,and linkage analysis to propose an Artificial-intelligent(AI)-powered railway control and dispatching system.This system is elaborately designed with four core functions,including global optimum unattended dispatching,synergetic transportation in multiple modes,high-speed automatic control,and precise maintenance decision and execution.The deployment pathway and essential tasks of the system are further delineated,alongside the challenges and obstacles encountered.The AI-powered system promises a significant enhancement in the operational efficiency and safety of the composite railway system,ensuring a more effective alignment between transportation services and passenger demands.

Design of a Novel Multi-Mode LED Dimming and Color Control System

Aiming at the needs of different lighting applications, combined with the characteristics of LED dimming and color control, and using MSP430 digital control technology, a novel LED intelligent dimming and color control system was designed. The system integrated Bluetooth remote, human infrared sensor, voice/gesture control and other control modes, which could achieve LED multi-mode dimming and color intelligent control. System hardware and software were designed to develop a prototype to experimental verification. The designed method proposes new ideas and information for LED control.

Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.

智能化时代雷达导引头信号处理关键技术展望

随着电磁及干扰技术的飞速发展,战场环境日益复杂,尤其是网络化技术以及人工智能技术的大量应用,现代战争越来越呈现出信息化、网络化、智能化特点。对雷达型空空导弹而言,智能空战时代智能组网、协同攻击将会成为雷达弹的主要作战模式,此时传统的基于个体的雷达探测、识别、抗干扰方法局限性愈加明显;协同智能化探测、智能抗干扰将逐步成为雷达导引头发展的重要方向,开发高效的反隐身、智能化抗干扰抗杂波方法将直接关系到智能时代雷达型空空导弹的成败。从目前欧美的发展趋势来看,将人工智能技术用于雷达导引技术可进一步提升雷达导引头智能化水平,有助于提升其抗干扰、抗杂波、反隐身能力。

多模式组合诊断技术在导弹导引头上的应用研究

阐明了四种类型的多模式组合诊断方法及其故障诊断的核心思想。以某导弹导引头的故障诊断为实例,详细描述了基于模糊理论、神经网络和粗糙集理论的三组合模式的诊断过程,并给出了诊断结果;介绍了我们研制的基于不确定性理论和专家系统组合的智能化故障诊断专家系统的应用。研究和应用实例表明:多模式组合诊断技术通用性和鲁棒性强,能够加快诊断速度,降低误诊率,尤其适合于复杂系统的故障诊断,具有很大的工程实用价值。

空空导弹雷达导引头信息处理智能化思考

以未来空战的发展趋势为背景,本文分析了空空导弹雷达导引头在新形势下所面临的挑战,如反隐身、抗新型干扰、抗强杂波等。为提高雷达导引头面对挑战的能力,对当前初级人工智能阶段的导引头信息处理技术进行了分析,在此基础上,对中级人工智能阶段的信息处理技术,如微弱特征目标智能检测技术、智能抗干扰技术、智能杂波抑制技术等展开思考。

基于SiP的低成本微小型GNC系统技术

随着微机电系统(MEMS)技术及微惯性器件的发展,大量小型化、低成本、高性能的导航、制导与控制(GNC)产品正越来越多地应用于小型无人飞行器、地面无人系统以及精确制导弹药等领域。针对各类应用需求,基于MEMS惯性测量单元(IMU)、GNSS接收模块、全捷联红外/可见光/激光多模智能导引头、信息处理器(DSP)与数据链通信模块,采用SiP技术研制出GNC芯片。基于GNC芯片构建一体化微小型GNC系统,突破了基于SiP一体化微小型GNC系统集成、全捷联红外/可见光/激光多模智能感知、嵌入式深组合导航、全捷联多模智能导引头/导航/制导与控制一体化设计等关键技术,并对其性能进行了评估。微小型GNC系统技术为低成本小型无人系统和精确制导弹药的发展夯实技术基础。

搜寻者优化算法与卡尔曼滤波相结合的PID控制

针对传统PID控制器的参数整定依靠经验调整,且在控制过程中,系统易受多种噪声影响的问题,提出搜寻者优化算法与卡尔曼滤波相结合的PID控制方法。利用搜寻者优化算法对PID参数进行智能优化,利用卡尔曼滤波器实现对控制噪声和测量噪声的有效抑制。对二阶延迟系统进行仿真试验,结果表明,与传统PID控制方法相比,该方法可以智能地获得较优的PID参数,有效剔除了控制过程中的噪声干扰,实现了调节速度快、稳态误差低、抗干扰能力强的智能PID控制。

Visuals to Text:A Comprehensive Review on Automatic Image Captioning

Image captioning refers to automatic generation of descriptive texts according to the visual content of images.It is a technique integrating multiple disciplines including the computer vision(CV),natural language processing(NLP)and artificial intelligence.In recent years,substantial research efforts have been devoted to generate image caption with impressive progress.To summarize the recent advances in image captioning,we present a comprehensive review on image captioning,covering both traditional methods and recent deep learning-based techniques.Specifically,we first briefly review the early traditional works based on the retrieval and template.Then deep learning-based image captioning researches are focused,which is categorized into the encoder-decoder framework,attention mechanism and training strategies on the basis of model structures and training manners for a detailed introduction.After that,we summarize the publicly available datasets,evaluation metrics and those proposed for specific requirements,and then compare the state of the art methods on the MS COCO dataset.Finally,we provide some discussions on open challenges and future research directions.

基于智能算法的动力吸振器多参数优化研究

动力吸振器在改善系统振动与噪声方面应用广泛,优化动力吸振器的参数是提高动力吸振器性能的关键。设计动力吸振器多参数优化目标函数,研究人群搜索算法在动力吸振器参数优化中的应用,并运用人群搜索算法、遗传算法和粒子群算法进行仿真计算,对比三种算法对动力吸振器进行参数优化时的稳定性、计算速度、计算精度。结果表明,所设计的基于人群搜索算法的优化方法具有较好的稳定性及计算精度,但计算速度稍慢于另外两种算法。工程实例证明,基于人群搜索算法优化后的动力吸振器对于改善汽车部件共振、降低车内噪声具有良好效果。

基于人工智能技术的雷达目标检测

强杂波背景下的目标检测是各国争相研究的热点,传统的雷达目标检测技术无法实现,人工智能技术的出现,其在目标识别和特征提取方面的独特优势,使得强杂波背景下的目标检测成为可能,利用人工智能神经网络的强大非线性预测能力对海杂波的非线性动力方程进行逼近,实现了对海杂波的非线性预测,提出了一种基于神经网络技术的强海杂波背景下的脉冲多普勒雷达导引头小目标检测方法,使得强海杂波背景下的目标检测能力有2 dB到6 dB的提升。

基于光电传感的目标寻的智能车设计

随着汽车技术的成熟,有自主驾驶功能的智能车成为今后汽车产业发展的主流。设计将利用MK60FX512VLQ15微控制器作为核心控制单元,并搭载不同传感器进行车体环境感知和图像采集,实现精准高效识别检测目标物,采用多种电机控制方式,控制车体速度和转向,实现智能车目标寻的功能。实验结果表明,设计的目标寻的智能车,具有一定的实用性和安全性,为智能车的设计提供了思路与方案。

Filtering and regret network for spacecraft component segmentation based on gray images and depth maps

Identifying and segmenting spacecraft components is vital in many on-orbit space missions,such as on-orbit maintenance and component recovery.Integrating depth maps with visual images has been proven effective in improving segmentation accuracy.However,existing methods ignore the noise and fallacy in collected depth maps,which interfere with the network to extract representative features,decreasing the final segmentation accuracy.To this end,this paper proposes a Filtering and Regret Network(FRNet)for spacecraft component segmentation.The FRNet incorporates filtering and regret mechanisms to suppress the abnormal depth response in shallow layers and selectively reuses the filtered cues in deep layers,avoiding the detrimental effects of low-quality depth information while preserving the semantic context inherent in depth maps.Furthermore,a two-stage feature fusion module is proposed,which involves information interaction and aggregation.This module effectively explores the feature correlation and unifies the multimodal features into a comprehensive representation.Finally,a large-scale spacecraft component recognition dataset is constructed for training and evaluating spacecraft component segmentation algorithms.Experimental results demonstrate that the FRNet achieves a state-of-the-art performance with a mean Intersection Over Union(mIOU)of 84.13%and an average inference time of 133.2 ms when tested on an NVIDIA RTX 2080 SUPER GPU.

导弹导引头智能感知抗干扰性能评估方法研究

面向愈加复杂的战场态势和导弹精打干扰对抗形势,人工智能技术正加快赋能导弹装备,但以机器学习为代表的智能技术具有数据驱动、可解释性弱等特点,对于任务关键、安全关键的导弹武器来说,如何有效评估导弹智能感知抗干扰性能并清晰界定装备适用范围是亟待解决的问题。从导弹导引头智能感知面临的特殊问题和完成的任务出发,阐述导弹智能感知抗干扰的基本实现思路和方法,分析智能感知抗干扰性能评估面临的挑战,最后给出导弹导引头智能感知抗干扰评估的展望和建议。

基于不同算法对桂枝茯苓胶囊内容物吸湿性预测建模研究

目的比较不同算法对桂枝茯苓胶囊内容物吸湿性预测模型性能的影响,确定最优建模算法。方法以54个物理性质参数为输入,胶囊内容物吸湿性为输出,对比偏最小二乘算法(partial least squares,PLS)、决策树算法(classification and regression tree,CART)、多元自适应回归样条算法(multivariate adaptive regression splines,MARS)和广义路径追踪算法(generalized path seeker,GPS)对建立吸湿性预测模型性能的影响。结果MARS算法建立的预测模型性能最佳,预测能力最强,模型的校正集决定系数(R2c)为0.843,预测集决定系数(R2p)为0.808,校正集均方根误差(root mean square error of calibration,RMSEC)为0.391,预测集均方根误差(root mean square error of prediction,RMSEP)为0.472,平均相对预测误差为2.69%,小于5%。结论MARS算法建立的吸湿性预测模型更适合桂枝茯苓胶囊的生产应用,该算法可嵌入在线控制系统,为生产过程的质量控制智能化提供技术支持。

π-Conjugated cyanostilbene-based optoelectric functional materials

π-Conjugated organic luminescent materials are essential components for modern optical and photoelectric research.This review mainly probes into the recent work in the progress of smart π-conjugated organic systems in the form of cyanostilbene and its derivatives,which can regulate its excellent features in response to a variety of physical or chemical stimuli（e.g.viscosity,light,magnetism,electric field,polarity,pH and solvent environment）.As a result of its extensive applicability and adaptability,cyanostilbene and its derivatives have been planted into different structural architectures such as polymers,functional nanoparticles,solid membranes,supramolecular systems and so on.This review will first give a general description of the preparation and characterization of cyanostilbenebased optoelectric luminophores and then focus on their peculiar functional properties in the need for advanced material applications,such as AIEE（aggregation-induced enhanced emission effect）,solidstate emission,photovoltaics,photolithography and photochromism to be further processed afterwards.The purpose of this review is to give a platform of practical organic materials,mostly cyanostilbene and its derivatives,based on stable aromatic derivatives,to contribute to the booming of modern π-conjugated photoelectric materials that integrate with contemporary physics,material chemistry,bioengineering,medical science and aerospace altogether.

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

Visual representation learning is ubiquitous in various real-world applications,including visual comprehension,video understanding,multi-modal analysis,human-computer interaction,and urban computing.Due to the emergence of huge amounts of multimodal heterogeneous spatial/temporal/spatial-temporal data in the big data era,the lack of interpretability,robustness,and out-of-distribution generalization are becoming the challenges of the existing visual models.The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge,which lacks unified guidance and analysis about why modern visual representation learning methods easily collapse into data bias and have limited generalization and cognitive abilities.Inspired by the strong inference ability of human-level agents,recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability.In this paper,we conduct a comprehensive review of existing causal reasoning methods for visual representation learning,covering fundamental theories,models,and datasets.The limitations of current methods and datasets are also discussed.Moreover,we propose some prospective challenges,opportunities,and future research directions for benchmarking causal reasoning algorithms in visual representation learning.This paper aims to provide a comprehensive overview of this emerging field,attract attention,encourage discussions,bring to the forefront the urgency of developing novel causal reasoning methods,publicly available benchmarks,and consensus-building standards for reliable visual representation learning and related real-world applications more efficiently.

雷达回波信号的深度学习目标检测识别方法研究

在雷达目标检测识别领域,干扰技术不断升级。针对复杂场景环境下的海面目标检测任务,为解决传统方法迁移适应力不足问题,提出一种基于深度学习的雷达回波信号的目标检测识别方法通过剖析雷达导引头数据特性,对比雷达回波数据同可见光数据差异,并对海面舰船和干扰两类目标雷达回波数据进行试验分析。将原本适用于可见光数据域的深度神经网络目标检测识别模型迁移至雷达回波数据域中,并进行轻量化模型试验,以便后续嵌入式开发工作。最后,在相关雷达回波数据集上,开展了模型训练和算法验证,并取得了较优异的结果,验证了深度神经网络模型在雷达数据领域中的可行性和有效性。

2012年度国外飞航导弹主要技术发展动向

总结了2012年国外飞航导弹型号和技术的发展动态,分析了飞航导弹的主要技术发展重点,包括亚声速隐身、高速动力技术、智能化导引头技术、不依赖GPS的导航定位技术、数据链和电子战技术等型号改型重点技术以及可改变战场规则的高新技术。最后提出飞航导弹技术发展的建议。