STUDY ON THE COAL-ROCK INTERFACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.

Online composite shape recognition based on relevance feedback

This paper describes a novel method of online composite shape recognition interms of the relevance feedback technology to capture a user's intentions incrementally, and adynamic user modeling method to adapt to various users' styles. First, the relevance feedback isadapted to refine the recognition results and reduce the ambiguity incrementally based on theestablishment of a feature-based vector model of a user's sketches. Secondly, a dynamic usermodeling is introduced to model the user's sketching habits based on recording and analyzinghistorical information incrementally. A model-based matching strategy is also employed in the methodto recognize sketches dynamically. Experiments prove that the proposed method is both effective andefficient.

Speak-Correct: A Computerized Interface for the Analysis of Mispronounced Errors

Any natural language may have dozens of accents.Even though the equivalent phonemic formation of the word,if it is properly called in different accents,humans do have audio signals that are distinct from one another.Among the most common issues with speech,the processing is discrepancies in pronunciation,accent,and enunciation.This research study examines the issues of detecting,fixing,and summarising accent defects of average Arabic individuals in English-speaking speech.The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues.The proposed SpeakCorrect computerized interface employs a cuttingedge speech recognition system and analyses pronunciation errors with a speech decoder.As a result,some of the most essential types of changes in pronunciation that are significant for speech recognition are performed,and accent defects defining such differences are presented.Consequently,the suggested technique increases the Speaker’s accuracy.SpeakCorrect uses 100 h of phonetically prepared individuals to construct a pronunciation instruction repository.These prerecorded sets are used to train Hidden Markov Models(HMM)as well as weighted graph systems.Their speeches are quite clear and might be considered natural.The proposed interface is optimized for use with an integrated phonetic pronounced dataset,as well as for analyzing and identifying speech faults in Saudi and Egyptian dialects.The proposed interface detects,analyses,and assists English learners in correcting utterance faults,overcoming problems,and improving their pronunciations.

基于PSD特征的FBCCA脑电信号识别方法

当前基于稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)的脑机接口(brain-computer interfaces,BCIs)使用的都是单一识别算法,针对不同时间长度的识别准确率较低。提出了一种基于滤波器组的典型相关分析(filter bank canonical correlation analysis,FBCCA)与功率谱密度(power spectral density,PSD)分析相结合的SSVEP识别算法,可以提高SSVEP识别的普适性与准确率。该方法使用FBCCA寻找高相似度的参考频率信号,再通过多组PSD分析来锁定最终的响应频率,完成频率识别。该方法无需经过训练就能得到较高的识别准确率。实验结果表明:在刺激时长为1 s时,该方法能达到86.61%的准确率,比PSD分析方法提升了5.44%,比典型相关性分析方法(canonical correlation analysis,CCA)提升了10.38%的准确率,比FBCCA提升了8.86%的准确率。

基于CCA融合FFT的SSVEP脑机接口分类算法

为解决多目标刺激范式的稳态视觉诱发电位脑电信号识别准确率低和信息传输率低的问题,提出了一种快速傅里叶变换同典型相关分析相结合的方法,通过快速傅里叶变换将信号训练成对应频率的训练模板,并作为参考信号与实时采集的信号进行典型相关分析来计算频率的识别准确率。6名受试者参与并完成了180组实验,在时间窗口长度为1.5 s的条件下,基于快速傅里叶变换-典型相关分析的稳态视觉诱发电位信号识别算法的平均识别准确率为93.98%,比典型相关分析算法提升了14.75%,信息传输率为62.30 bit·min^(-1),比典型相关分析算法提升了55.63%。实验结果表明,快速傅里叶变换-典型相关分析算法性能更优。

采用多任务特征融合的脑电情绪识别方法

特征选择与融合是提升脑电信号情绪解码精度的重要手段之一。然而,当前脑电情绪解码中的特征选择方法常忽略了脑电信号内在数据结构的隐含信息。该文提出一种基于近邻传播聚类的多任务特征融合方法,通过L_(2,1)范数约束实现稀疏特征选择,同时利用图拉普拉斯正则化保持不同子类间的潜在关系。该算法在不揭示真实样本标签的情况下,在子任务空间有效融合脑网络空间拓扑结构信息和微分熵信息,为高精度脑电信号情绪解码提供具有更高情绪表征能力的特征。DEAP和SEED数据集以及本实验室数据集的分析结果表明,该文提出的方法能显著提高脑电情绪解码的精度。

DPT‐tracker:Dual pooling transformer for efficient visual tracking

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input images.To alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time dimensions.MCPT aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking prediction.DPT tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking methods.Extensive experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

脑电情绪识别研究现状及展望

脑电的研究正在不断突破并取得成果,在情绪识别领域,包含脑电信号采集、数据预处理、提取时频特征、构建脑网络特征和分类识别等步骤。本文综述了脑电特征计算方法的现状,脑电情绪识别的步骤、方法,归纳总结了应用方式并对今后的发展进行展望。脑电情绪识别的应用展现出巨大的潜力,如心理健康评估、情绪感知计算和人机交互。随着机器学习和人工智能的进步,以及脑电图信号处理的改进,有望进一步提高基于脑电图的情绪识别的准确性和可靠性。本文对脑电情绪识别特征计算方法,情绪识别的步骤和临床应用方面的具体事例进行了阐述,并对未来的方向进行了展望,对于研究人员系统地了解脑电情绪识别具有积极意义。

基于BP神经网络的智慧车牌识别系统

车牌识别系统在我国智慧交通系统中起着重要作用。为解决识别不精准、易受环境影响等问题,本文设计一种基于BP神经网络的智慧车牌识别系统,该系统主要包含车牌图像预处理模块、BP神经网络模块和GUI用户界面模块。通过MATLAB仿真,得出该系统可以在较为复杂的环境中识别车牌,并实现了在可视化用户界面进行数字图像的可视化操作。

三维地震全波形反演技术在煤矿综采工作面的应用

煤岩界面的识别是煤矿智能化建设的关键一环,为解决此难点,以三维地震全波形反演技术为研究基础,采用建立目标函数和寻找最优化方法进行分析,对某矿工作面煤岩界面进行了详细探查。结合实际工作面地震资料,通过理论分析与实际计算,得到研究区全波形反演三维速度结构模型,并与巷道揭露情况进行误差对比分析,其结果表明:1)三维地震全波形反演可在井工煤矿下观测系统进行实际应用,可较为准确地反演出煤层顶底板界面三维空间形态特征;2)相较于地面三维地震的垂向精度5~30 m,该方法实际揭露计算误差纵波速度模型结果垂向平均误差为±1.3 m左右,横波速度模型结果垂向平均误差为±1.8 m左右,精度明显提升;3)三维纵波速度模型对煤层顶底板界面识别效果优于三维横波速度模型结果,对于横波结果,纵波结果识别精度平均提升27.78%;4)通过三维纵波速度模型可最终构建煤层顶底板三维空间特征,为煤层顶底板界面识别提供一种思路。

微动技术在登科地区深部找矿中的应用效果研究

为开拓深部找矿新方法,特引入地球物理探测新技术-微动,并探索分析其应用效果。首先介绍微动探测方法原理和数据采集与处理,其次在CSAMT推测的建议钻孔位置开展微动勘探工作,分析单点频散曲线特征,识别深部地层界面;最后根据钻孔验证结果,综合对比CSAMT和微动勘探在深部重点地层界面识别效果。结果显示,微动勘探识别深部层界面效果明显,CSAMT联合微动技术可以提高物探解译的精准度。

Analysis and Numerical Simulation of Hydrofracture Crack Propagation in Coal-Rock Bed

In underground coal mines,hydrofracture can cause the increase of breathability in the fractured coal bed.When the hydrofracture crack propagates to the interface between the coal bed and the roof-floor stratum,the crack may enter roof-floor lithology,thus posing a limit on the scope of breathability increase and making it difficult to support the roof and floor board for subsequent coal mining.In this work,a two-dimensional model of coal rock bed that contains hydrofracture crack was constructed.Then an investigation that combines the fracture mechanics and the system of flow and solid in rock failure process analysis(RFPA2D-Flow)were carried out to study the failure mechanism at the interface between rocks and coals,and critical water pressure that hydrofracture crack propagates.The results indicated that the main factors that affect the direction of hydrofracture crack propagation are the angle of intersection between coal-rock interface and horizontal section,horizontal crustal stress difference,tension-shear mixed crack fracture toughness in coal-rock interface and differences in elasticity modulus of coal-rock bed.The possibility of crack directly entering coal-rock interface would increase with the increase in angle of intersection or horizontal crustal stress difference.The trend that crack propagates along the coal-rock interface will become stronger with the decrease of the fracture toughness at the coal-rock interface and the increase of the elasticity modulus difference between the coal bed and the roof strata.The results of this study was to put forward a method of controlling hydrofracture crack,optimize the fracturing well location provides a certain theoretical basis.

Coordinated control of an intelligent wheelchair based on a brain-computer interface and speech recognition

An intelligent wheelchair is devised, which is controlled by a coordinated mechanism based on a brain-computer interface(BCI) and speech recognition. By performing appropriate activities, users can navigate the wheelchair with four steering behaviors(start, stop, turn left, and turn right). Five healthy subjects participated in an indoor experiment. The results demonstrate the efficiency of the coordinated control mechanism with satisfactory path and time optimality ratios, and show that speech recognition is a fast and accurate supplement for BCI-based control systems. The proposed intelligent wheelchair is especially suitable for patients suffering from paralysis(especially those with aphasia) who can learn to pronounce only a single sound(e.g., ‘ah').

Molecular recognition of sulfonatocalixarene with organic cations at the self-assembled interface：a thermodynamic investigation

A microcalorimetric study on molecular recognition of p-sulfonatocalix[4]arene derivatives at selfassembled interface in comparison with in bulk water was performed,inspired by the dramatic change in physicochemical characteristics from bulk water to interface.A total of six cationic molecules were screened as model guests,including ammonium（NH_4~＋）,guanidinium（Gdm~＋）.N,N＇-dimethyl-1,4-diazabicyclo[2.2.2]octane（DMDABCO^（2＋））,tropylium（Tpm~＋）,N-methyl pyridinium（N-mPY＊） and methyl viologen（MV^（2＋））.The complexation with NH_4~＋.Gdm~＋ and DMDABCO2＊ is pronouncedly enhanced when the recognition process moved from bulk water to interface,whereas the complexation stabilities with Tpm~＋,N-mPY~＋ and MV2＊ increase slightly or even decrease to some extent.A more interesting phenomenon arises from the NH_4~＋/Gdm~＋ pair that the thermodynamic origin at interface differs definitely from each other although with similar association constants.The results were discussed in terms of differential driving forces,electrostatic,hydrogen bond as well as π-stacking interactions,originating from the unique physicochemical features of interfaces,mainly the polarity and dielectric constant.

A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detection and control state(CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.

脑机接口技术支持学习情感识别的应用框架及反思

人工智能技术支持下的学习情感识别在教育教学研究中至关重要,能够促进教育教学策略的改进。脑机接口技术作为人工智能技术的一项重要应用,已成为情感识别领域研究热点之一。文章分析了脑机接口技术支持学习情感识别的发展动因,进而构建包括数据处理、情感表达与教学调控三个过程的脑机接口技术支持学习情感识别的应用框架,探讨了脑机接口技术在学习情感识别领域仍面临的技术鸿沟、伦理挑战与学习者的主观偏见等局限。最后提出未来脑机接口支持下的学习情感识别可从强化技术融合、发展以人为本为导向的设计,以及尊重学习者的主观需求等方面进行改进,从而保证脑机接口技术支持学习情感识别应用的可行性,并综合衡量学习状态,拓展其在教育领域的应用与创新。

基于视觉检测的机器人柔性调节抓取系统设计

该文提出一种基于视觉检测工件形状、位置特征进行柔性调节的多功能机器人抓取系统,以提升不同长度、位置的小型细长薄片工件的抓取效率和通用性。采用带视觉系统的六轴机器人作为抓取装置载体,可自动调节吸盘位置间距的柔性抓取装置作为机器人抓取工具。采用PC Interface通信实现机器人与视觉系统的交互,视觉系统随机器人运动自动识别待抓取工件的形状、位置及个数信息。通过分析抓取工件的形状、位置和数量等特征信息,实现机器人对工件的自动分类、精准定位和准确抓取的功能。实践证明,该方法使得机器人抓取工件的可靠性得到提升,降低工件运送过程的丢失率,生产成本得到节省。

基于图像处理技术的人机交互界面控制研究

不同光照条件会对指令图像质量产生干扰,导致人机交互界面控制经常出现错误,为此提出基于图像处理技术的人机交互界面控制方法。该方法先是利用Kinect深度摄像头采集指令图像,然后进行光线补偿、灰度化、滤波等处理,降低光照对指令图像质量的干扰,最后对图像中的交互指令进行识别,实现对人机交互界面的控制,具体包括图像分割、轮廓检测、特征提取、指令识别以及控制等步骤。结果表明:文中方法的人机交互界面控制精度较高,抗干扰性较强,能够提高人机交互界面控制效果。