Design of Cold-Junction Compensation and Disconnection Detection Circuits of Various Thermocouples and Implementation of Multi-Channel Interfaces Using Them-A Secondary Publication

Cold-junction compensation(CJC)and disconnection detection circuit design of various thermocouples(TC)and multi-channel TC interface circuits were designed.The CJC and disconnection detection circuit consists of a CJC semiconductor device,an instrumentation amplifier(IA),two resistors,and a diode for disconnection detection.Based on the basic circuit,a multi-channel interface circuit was also implemented.The CJC was implemented using compensation semiconductor and IA,and disconnection detection was detected by using two resistors and a diode so that IA input voltage became-0.42 V.As a result of the experiment using R-type TC,the error of the designed circuit was reduced from 0.14 mV to 3μV after CJC in the temperature range of 0°C to 1400°C.In addition,it was confirmed that the output voltage of IA was saturated from 88 mV to-14.2 V when TC was disconnected from normal.The output voltage of the designed circuit was 0 V to 10 V in the temperature range of 0°C to 1400°C.The results of the 4-channel interface experiment using R-type TC were almost identical to the CJC and disconnection detection results for each channel.The implemented multi-channel interface has a feature that can be applied equally to E,J,K,T,R,and S-type TCs by changing the terminals of CJC semiconductor devices and adjusting the IA gain.

Parametric study on contact sensors for MASW measurement-based interfacial debonding detection for SCCS

Steel-concrete composite structures(SCCS)have been widely used as primary load-bearing components in large-scale civil infrastructures.As the basis of the co-working ability of steel plate and concrete,the bonding status plays an essential role in guaranteeing the structural performance of SCCS.Accordingly,efficient non-destructive testing(NDT)on interfacial debondings in SCCS has become a prominent research area.Multi-channel analysis of surface waves(MASW)has been validated as an effective NDT technique for interfacial debonding detection for SCCS.However,the feasibility of MASW must be validated using experimental measurements.This study establishes a high-frequency data synchronous acquisition system with 32 channels to perform comparative verification experiments in depth.First,the current sensing approaches for high-frequency vibration and stress waves are summarized.Secondly,three types of contact sensors,namely,piezoelectric lead-zirconate-titanate(PZT)patches,accelerometers,and ultrasonic transducers,are selected for MASW measurement.Then,the selection and optimization of the force hammer head are performed.Comparative experiments are carried out for the optimal selection of ultrasonic transducers,PZT patches,and accelerometers for MASW measurement.In addition,the influence of different pasting methods on the output signal of the sensor array is discussed.Experimental results indicate that optimized PZT patches,acceleration sensors,and ultrasonic transducers can provide efficient data acquisition for MASW-based non-destructive experiments.The research findings in this study lay a solid foundation for analyzing the recognition accuracy of contact MASW measurement using different sensor arrays.

Coal–rock interface detection on the basis of image texture features

Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence matrix,twenty-two texture features were extracted from the images of coal and rock.Data dimension of the feature space reduced to four by feature selection,which was according to a separability criterion based on inter-class mean difference and within-class scatter.The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm.In the optimized low-dimensional feature space,the coal–rock classifer was set up using the fsher discriminant method.Using the 10-fold cross-validation technique,the performance of the classifer was evaluated,and an average recognition rate of 94.12%was obtained.The results of comparative experiments show that the identifcation performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.

Application of Brain-Computer-Interface in Awareness Detection Using Machine Learning Methods

The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-computer interface(BCI)to awareness detection with a passive auditory stimulation paradigm.12 subjects with normal hearing were invited to collect electroencephalogram(EEG)based on a BCI communication system,in which EEG signals are transmitted wirelessly.After necessary preprocessing,RBF-SVM and EEGNet were used for algorithm realization and analysis.For a single subject,RBF-SVM can distinguish his(her)name stimuli awareness with classification accuracies ranging from 60-95%.EEGNet was used to learn all subjects'data and improved accuracy to 78.04%for characteristics finding and model generalization.Moreover,we completed the supplementary analysis work from the time domain and time-frequency domain.This study applied BCI communication to human awareness detection,proposed a passive auditory paradigm,and proved the effectiveness,which could be an inspiration for brain,mental or physical diseases diagnosis and detection.

STATNet:One-stage coal-gangue detector based on deep learning algorithm for real industrial application

Coal-gangue object detection has attracted substantial attention because it is the core of realizing vision-based intelligent and green coal separation. However, most existing studies have been focused on laboratory datasets and prioritized model lightweight. This makes the coal-gangue object detection challenging to adapt to the complex and harsh scenes of real production environments. Therefore, our project collected and labeled image datasets of coal and gangue under real production conditions from a coal preparation plant. We then designed a one-stage object model, named STATNet, following the “backbone-neck-head” architecture with the aim of enhancing the detection accuracy under industrial coal preparation scenarios. The proposed model utilizes Swin Transformer as backbone module to extract multi-scale features, improved path augmentation feature pyramid network (iPAFPN) as neck module to enrich feature fusion, and task-aligned head (TAH) as head module to mitigate conflicts and misalignments between classification and localization tasks. Experimental results on a real-world industrial dataset demonstrate that the proposed STATNet model achieves an impressive AP50 of 89.27 %, significantly surpassing several state-of-the-art baseline models by 2.02 % to 5.58 %. Additionally, it exhibits stronger robustness in resisting image corruption and perturbation. These findings demonstrate its promising prospects in practical coal and gangue separation applications.

EEG processing and its application in brain-computer interface

Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.

Human Friendly Interface Design for Virtual Fitting Room Applications on Android Based Mobile Devices

This paper presents an image processing design flow for virtual fitting room (VFR) applications, targeting both personal computers and mobile devices. The proposed human friendly interface is implemented by a three-stage algorithm: Detection and sizing of the user's body, detection of reference points based on face detection and augmented reality markers, and superimposition of the clothing over the user's image. Compared to other existing VFR systems, key difference is the lack of any proprietary hardware components or peripherals. Proposed VFR is software based and designed to be universally compatible as long as the device has a camera. Furthermore, JAVA implementation on Android based mobile systems is computationally efficient and it can run in real-time on existing mobile devices.

Giant magneto-impedance sensor with working point selfadaptation for unshielded human bio-magnetic detection

Background Compared with traditional biomagnetic field detection devices,such as superconducting quantum interference devices(SQUIDs)and atomic magnetometers,only giant magneto impedance(GMI)sensors can be applied for unshielded human brain biomagnetic detection,and they have the potential for application in next-generation wearable equipment for brain-computer interfaces(BCIs).Achieving a better GMI sensor without magnetic shielding requires the stimulation of the GMI effect to be maximized and environmental noise interference to be minimized.Moreover,the GMI effect stimulated in an amorphous filament is closely related to its working point,which is sensitive to both the external magnetic field and the drive current of the filament.Methods In this paper,we propose a new noise reducing GMI gradiometer with a dual-loop self-adapting structure.Noise reduction is realized by a direction-flexible differential probe,and the dual-loop structure optimizes and stabilizes the working point by automatically controlling the external magnetic field and drive current.This dual-loop structure is fully program controlled by a micro control unit(MCU),which not only simplifies the traditional constant parameter sensor circuit,saving the time required to adjust the circuit component parameters,but also improves the sensor performance and environmental adaptation.Results In the performance test,within 2 min of self-adaptation,our sensor showed a better sensitivity and signal-to-noise ratio(SNR)than those of the traditional designs and achieved a background noise of 12 pT/√Hz at 10 Hz and 7pT/√Hz at 200 Hz.Conclusion To the best of our knowledge,our sensor is the first to realize self-adaptation of both the external magnetic field and the drive current.

Influence of roughness on the detection of mechanical characteristics of low-k film by the surface acoustic waves

The surface acoustic wave （SAW） technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant （low-k） film by matching the theoretical dispersion curve with the experimental dispersion curve. In this paper, the influence of sample roughness on the precision of SAW mechanical detection is inves- tigated in detail. Random roughness values at the surface of low-k film and at the interface between this low-k film and the substrate are obtained by the Monte Carlo method. The dispersive characteristic of SAW on the layered structure with rough surface and rough interface is modeled by numerical simulation of finite element method. The Young＇s moduli of the Black DiamondTM samples with different roughness values are determined by SAWs in the experiment. The results show that the influence of sample roughness is very small when the root-mean-square （RMS） of roughness is smaller than 50 nm and correlation length is smaller than 20 μm. This study indicates that the SAW technique is reliable and precise in the nondestructive mechanical detection for low-k films.

Variable-length sequential dynamic features-based malware detection

In order to solve the problem that traditional signature-based malware detection systems are inefficacious in detecting new malware,a practical malware detection system is constructed to find out new malware. Application programming interface( API) call sequence is introduced to capture activities of a program in this system. After that,based on variable-length n-gram,API call order can be extracted from API call sequence as the malicious behavior feature of a software. Compared with traditional methods,which use fixed-length n-gram,the solution can find more new malware. The experimental results show that the presented approach improves the accuracy of malware detection.

Ultrafast metal oxide reduction at Pd/PdO_(2) interface enables onesecond hydrogen gas detection under ambient conditions

Here,we report a Pd/PdO_(x) sensing material that achieves 1-s detection of 4% H_(2) gas(i.e.,the lower explosive limit concentration for H_(2))at room temperature in air.The Pd/PdO_(x) material is a network of interconnected nanoscopic domains of Pd,PdO,and PdO_(2).Upon exposure to 4% H_(2),PdO and PdO_(2) in the Pd/PdO_(x) are immediately reduced to metallic Pd,generating over a>90% drop in electrical resistance.The mechanistic study reveals that the Pd/PdO_(2) interface in Pd/PdOx is responsible for the ultrafast PdO_(x) reduction.Metallic Pd at the Pd/PdO_(2) interface enables fast H_(2) dissociation to adsorbed H atoms,significantly lowering the PdO2 reduction barrier.In addition,control experiments suggest that the interconnectivity of Pd,PdO,and PdO2 in our Pd/PdO_(x) sensing material further facilitates the reduction of PdO,which would otherwise not occur.The 1-s response time of Pd/PdO_(x) under ambient conditions makes it an excellent alarm for the timely detection of hydrogen gas leaks.

An Intelligent Robot based on Sound Source Localization and Ultrasound Distance Detection

In both industrial and research areas of electronic engineering,Sound Source Localization for robot control has always been an interesting subject to be further studied.Under some dangerous situation,especially when a special driver is required to implement a particular task,the device should be able to combine robotics control technology with Sound Source Localization,and take actions according to the different response patterns.In this research project,a multifunc-tional model driver,named "Mobile Island",has been designed and built up by integrating the Emulator 8051 micro-controller,Intel 8255 interfaces,some components and other necessary devices.The intelligent Mobile Island imple-mented by C language programs can operate under three control modes.In the sound control Mode 1,the model driver can detect and track a target by Sound Source Localization and then turn and move toward the destination.In the keypad control Mode 2,it can be controlled by a manual keypad.In the free run Mode 3,Mobile Island can move and turn by itself.When finding an object in front,it will turn away before moving forward again,so that it can avoid crashing on the obstacle.

柔性可穿戴湿敏传感器制备及呼吸检测实验

该文以壳聚糖/导电炭黑复合薄膜作为敏感层,构建了高性能湿度传感器,并研究了传感器件微结构薄膜对呼吸检测敏感的机理。该文展开实验测试了传感器的湿度及呼吸响应特性,使用灵敏度、响应恢复特性、重复性等参数评价传感器性能,同时基于LabVIEW技术实现了湿敏/呼吸信号的实时读取与处理、单片机与电脑终端通信、呼吸波形显示等功能。该检测平台成本低、易操作,为传感器检测技术的实际应用提供了借鉴,同时可促进学生对柔性可穿戴传感器检测实验与应用的深度理解,有效提升课堂教学效果和学生的学习积极性。

基于稀疏成像的半导体薄膜材料界面缺陷检测

复杂背景下检测半导体薄膜材料界面微小缺陷具有一定的难度,为了精准检测半导体薄膜材料界面缺陷,提出一种稀疏成像下半导体薄膜材料界面缺陷检测方法。扫描采集半导体薄膜材料界面二维图像,对含有噪声的半导体薄膜材料界面实施小波分解,获取不同频带的子图像。低频图像保持不变,选择对应的模板对高频图像滤波处理,将滤波处理后的高频图像和低频图像两者合成,获取去噪后的图像。通过机器视觉定位薄膜材料界面的缺陷位置,提取缺陷区域特征,采用稀疏成像对特征参数修正,完成半导体薄膜材料界面缺陷检测。仿真结果表明,采用所提方法可以获取更加精准的检测结果,用时比较短,满足高效与高精度检测需求。

基于压电波动法检测灌浆连接段界面损伤

由于海上恶劣的环境,海上风机灌浆连接段可能会产生损伤,对其受力性能产生不利影响。为了检测灌浆连接段界面损伤,提出了一种基于压电传感器波动法的无损检测方法。该方法通过在灌浆连接段表面粘贴压电传感器来激励和接收应力波,根据接收应力波幅值的变化来判断灌浆连接段的界面损伤程度。将激励压电传感器布置在桩管,接收压电传感器分别布置在套管和桩管。对灌浆连接段进行轴向分级加载,每一级加载完毕后进行一次检测。试验结果表明随着轴向荷载的增加,内钢管上接收传感器接收信号的幅值逐渐减小,外钢管上接收传感器接收信号的幅值逐渐增加。说明轴向加载过程中两条检测路径上灌浆连接段的界面均发生了破坏。该结果与试件割开后观察到的界面损伤状态一致,说明该方法可以用于灌浆连接段界面损伤的检测。

高端航天器连接状态检测技术研究进展

航天飞行器向高性能发展是目前的主流需求,而连接状态退化是高端航天飞器高性能运行的“潜在威胁”,连接状态检测技术是保障航天器高性能运行的“监护保险”。综述了高端航天器连接状态检测技术的最新研究进展,旨在保障航天器的高性能运行与安全性。首先阐述了航天器连接状态检测的重要性,指出其作为确保航天飞行器高效运行的关键“保障措施”,接着从螺栓预紧力检测和接触界面状态检测两个方面,详细分析了国内外在该领域的研究现状与方法,揭示了高端航天器连接状态检测技术理论与方法所面对的挑战,并探讨了应对这些挑战的解决途径和发展趋势。为航天器连接状态检测技术的进一步发展提供了理论基础与实践指导。

基于眼动实验的健康检测产品界面评估及优化

目的 为了优化智能健康检测一体机人机界面,提升自助老人检测界面操作效率。方法 以现有的智能健康检测一体机PRS-X5界面为原型,通过问卷访谈与启发式评估对一体机的操作系统进行整体评估,以用户调研与专家评估结果为导向,设计出两种不同的界面方案;利用眼动追踪技术与SPSS数据分析,对两种界面方案进行评估,并根据试验后用户满意度调查,对一体机界面进行迭代优化。结果 将原界面与优化后界面再次进行眼动实验与t检验数据对比分析,实验结果发现,自助老人对优化后的界面操作效率和准确性均更高。结论 结合眼动实验,综合运用定性与定量分析法进行优化设计,有利于提高自助老人对智能健康检测产品界面操作效率,同时,此方法可为智能健康检测类产品界面评估与优化提供可借鉴思路。

基于MSP技术的掘进迎头前方断层识别及其应用

针对山西某矿21采区胶带运输下山迎头超前探测需求,利用本安型KDZ1114—6A30矿井巷道地质探测仪在21采区胶带运输下山2胶2位置开展了地质异常体(断层)超前探测与识别研究。研究内容包括:矿井震波超前探测技术(MSP)原理、震波数据处理流程以及综合地质解释等。探测结果显示,在巷道迎头前方,有3处地震反射异常界面,分别对应着J3、G4、J6、J7以及J8断层的位置,实现了对矿井地质异常体的准确识别,为巷道安全、快速掘进提供了技术支撑。

基于机器视觉的化工多相液态界面智能检测

为解决化工生产过程中多相液体界面接触式检测的难题,研究一种基于机器视觉的智能界面检测系统,可实现多相液体界面的外置非接触式检测,并研究界面检测算法的改进及数据集的构建。结果表明,系统的检测误差率仅为5.3%,模型精度达到86.3%,满足了工业应用的精确度要求和实时检测效果,且模型权重文件大小仅为13.6 MB,可在边缘计算设备上部署,在技术上具备了实时、精确、高效的特点,成功地为化工生产中的多相液体分液问题提供了创新的解决途径。

应用纳米技术改进骨肉瘤诊治的研究现状

骨肉瘤作为一种恶性肿瘤,其早期诊断与有效治疗是医学界高度关注的焦点之一。随着纳米生物材料技术的发展,纳米技术在骨肉瘤的诊疗方面呈现了不可限量的潜力。本文以简略介绍骨肉瘤和纳米技术为基础,综合纳米材料的多种功能特性,详细探讨了纳米材料在骨肉瘤诊治中的研究现状,包括易修饰性、信号放大、高载药性、光热转换、免疫调节等方面。同时,展望了纳米技术在未来癌症检测和治疗领域的应用前景进,旨在为该领域未来的良好发展提供有益的参考。