无线传感系统在石油井架承载能力计量中的应用研究

为了解决现有石油井架实际测试中安装繁琐、测试精度低等不足,提出基于Wifi的无线应力测试方法进行在役石油井架承载能力评价。根据力学和电学理论,对应变传感器的工作原理进行了分析,并讨论了测试系统所使用的网络系统,结构承载性能评价时以无线测试获得的实测应力和结构理论分析结果之间的残差构建优化修正函数,并以某油田使用的在役井架为例进行分析,结果表明应用此方法可以取得满意的结果,为有损伤在役石油井架的安全评定及状态监测提供了有效的方法和依据。     

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers’ water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (), correlation coefficient (), Root Mean Square Error (), Mean Absolute Percentage Error (), and are provided.     

Highway Bridge Assessment Using an Adaptive Real-Time Wireless Sensor Network

A real-time wireless sensor network platform capable of maintaining lossless data transmission over several minutes of continuous, high-rate sampling is presented in this paper. The platform was designed specifically to provide the capability to enable expeditious system identification, as well as load rating of highway bridges without compromising the typical data acquisition parameters employed in comparable cable-based tests. Consequently, the hardware signal conditioning interface permits data collection from a variety of sensors typical to structural health monitoring, including accelerometers, strain transducers, and temperature sensors. The embedded software features a proprietary network transmission protocol capable of lossless, real-time delivery of up to 40 measurement channels at an effective sampling rate of 128 samples per second per channel. Documented in this paper is a field study on an end-of-service highway bridge in which ambient vibration monitoring was performed using 60 accelerometers interfaced with 30 wireless sensor nodes operating within one of two simultaneously operating star topology networks. In addition, an experimental load rating of the entire structure was performed through large-scale strain measurement facilitated by the same wireless sensor network platform.     

Remaining Capacity Measurement and Analysis of Alkaline Batteries for Wireless Sensor Nodes

In most applications, wireless sensor networks (WSNs) will deploy a large number of distributed sensor nodes in remote or inhospitable places, making batteries their main source of energy; thus, the stored energy is a key resource of a WSN. Sensor nodes should balance their limited resources to increase the lifetime of the network. The knowledge of the available amount of energy becomes an important requirement for the maintenance, implementation of self-management techniques, and viability of the WSN. Therefore, the research of the State-of-Charge (SoC), or the remaining capacity estimation, is of key importance. This paper presents an energy-efficient battery-remaining capacity-estimation technique. The experiments were conducted using the MICA2 wireless sensor node platform, which shows that the voltage-only-based estimation presented an available 18% of the battery maximum capacity, although the battery had been fully discharged, and a current-based estimation technique is presented with minimal hardware intervention.      

A Wearable and Wireless Sensor System for Real-Time Monitoring of Toxic Environmental Volatile Organic Compounds

An integrated volatile organic toxicants sensor with a Bluetooth device interface has been developed. The device is based on novel tuning fork sensor platform along with a wireless communication/interface technology taken in an integrated system approach. It features high sensitivity and selectivity. The sensitivity and selectivity are accomplished through the use of novel tuning fork sensor modified by design (molecularly imprinted) polymers and selective filtering. Experiments have shown that the device can detect toxic volatile organic compounds (VOCs) under high concentrations of common interferents from flavors and fragrances. Applications of the device for detection of BTEX in real-world situations such as outdoor and gas station VOCs have also been demonstrated. All these features make the device a promising candidate to be deployed in real-world applications, particularly in environmental health and air pollution studies.      

钢水连续测温传感器的准确度提高和结构优化

为提高钢水连续测温传感器的准确度和灵敏度,以传感器所形成的在线黑体空腔的实际结构模型、带盖半球-圆筒形腔体为研究对象,建立了结构参数为二次关系的积分发射率的计算公式,并推导出全照区、半照区和不可见区域的自动划分计算公式.详细分析了传感器结构,即黑体空腔的长度、半径、孔径、腔口与探测器之间的距离,以及探测器接收面半径对积分发射率的影响,并在此基础上给出了传感器的优化参数.     

A Wireless, Passive Embedded Sensor for Real-Time Monitoring of Water Content in Civil Engineering Materials

A wireless, passive embedded sensor was applied for real-time monitoring of water content in civil engineering materials such as sands, subgrade soils, and concrete materials. The sensor, which comprised of a planar inductor-capacitor (LC) circuit, was embedded in test samples so that the internal water content of the samples could be remotely measured with a loop antenna by tracking the changes in the sensor's resonant frequency. Since the dielectric constant of water was much higher compared with that of the test samples, the presence of water in the samples increased the capacitance of the LC circuit (capacitance of the capacitor was proportional to the dielectric constant of the medium between its electrodes), thus decreasing the sensor's resonant frequency. Using the described sensor, a study was conducted to investigate the drying rate of sand samples of different grain sizes. A study was also conducted to measure the curing rate of a portland cement concrete slab during casting, and its drying rate after it has been soaked in water. The described sensor technology can be applied for long-term monitoring of localized water content inside soils and sands to understand the environmental health in these media. In addition, this sensor will be useful for monitoring water content within concrete supports and road pavements. The measurement of water content is important for civil engineering infrastructure since excess water may hasten their degradation.      

Battery-Free,Wireless, Cuff-Type,Multimodal Physical Sensor for Continuous Temperature and Strain Monitoring of Nerve

Peripheral nerve injuries cause various disabilities related to loss of motor and sensory functions. The treatment of these injuries typically requires surgical operations for improving functional recovery of the nerve. However, capabilities for continuous nerve monitoring remain a challenge. Herein, a battery-free, wireless, cuff-type, implantable, multimodal physical sensing platform for continuous in vivo monitoring of temperature and strain from the injured nerve is introduced. The thin, soft temperature, and strain sensors wrapped around the nerve exhibit good sensitivity, excellent stability, high linearity, and minimum hysteresis in relevant ranges. In particular, the strain sensor integrated with circuits for temperature compensation provides reliable, accurate strain monitoring with negligible temperature dependence. The system enables power harvesting and data communication to wireless, multiple implanted devices wrapped around the nerve. Experimental evaluations, verified by numerical simulations, with animal tests, demonstrate the feasibility and stability of the sensor system, which has great potential for continuous in vivo nerve monitoring from an early stage to complete regeneration.     

Real-Time Recognition and Location of Indoor Objects

Object recognition and location has always been one of the research hotspots in machine vision. It is of great value and significance to the development and application of current service robots, industrial automation, unmanned driving and other fields. In order to realize the real-time recognition and location of indoor scene objects, this article proposes an improved YOLOv3 neural network model, which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network, which is applied to the detection and recognition of objects in indoor scenes. In this article, RealSense D415 RGB-D camera is used to obtain the RGB map and depth map, the actual distance value is calculated after each pixel in the scene image is mapped to the real scene. Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene. More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement. The running time of objects detection and recognition is reduced to less than half of the original. This improved network has a certain reference value for practical engineering application.     

6061铝合金飞机溢油口拉深成形研究

目的 掌握深腔薄壁溢油口拉深成形工艺,解决拉深成形过程中起皱和破裂问题,研究各参数对拉深成形的影响规律,最终成形出合格产品。方法 以6061铝合金飞机溢油口为主要研究对象,采用Dynaform有限元数值模拟软件建立有限元模型,分析成对拉深成形对材料流动的影响并与单一拉深成形进行对比,通过改变毛坯外形尺寸、凹凸模间隙和压边力等参数进行拉深成形模拟试验研究,最终通过拉深成形试验验证成形方法及各参数设置的合理性。结果 成对拉深成形能限制单一拉深成形圆弧开口位置材料收缩,改善开口处起皱和变形缺料的现象。随着凹凸模间隙的增大,最大减薄率逐渐降低而后升高,最佳成形凹凸模间隙为1.05t;随着压边力的增大,最大减薄率逐渐升高,最大增厚率逐渐降低,最佳成形压边力为50 kN;凹模圆角半径小于7 mm 时,板料最大减薄率逐渐减小,半径为4 mm时,最大减薄率下降最快,半径在7~8 mm范围内,板料最大减薄率趋于平稳,最佳成形凹模圆角半径为7 mm。结论 模拟得到了毛坯外形尺寸、凹凸模间隙和压边力等参数对拉深成形的影响规律,最终制造出了满足设计要求的产品,验证了各参数设置的合理性。     

Illumimote: Multimodal and High-Fidelity Light Sensor Module for Wireless Sensor Networks

We describe the system requirements, design, system integration, and performance evaluation of the Illumimote, a new light-sensing module for wireless sensor networks. The Illumimote supports three different light-sensing modalities: incident light intensity, color intensities, and incident light angle (the angle of ray arrival from the strongest source); and two situational sensing modalities: attitude and temperature. The Illumimote achieves high performance, comparable to commercial light meters, while conforming to the size and energy constraints imposed by its application in wireless sensor networks. We evaluated the performance of our Illumimote for light intensity, color temperature, and incident light angle measurements and verified the function of the attitude sensor. The Illumimote consumes about 90 mW when all features on board are activated. We describe our design and the experiment design for the performance evaluation.     

Hybrid Micropower Source for Wireless Sensor Network

Wireless sensor networks have become a very significant enabling technology in many applications and the use of environmental energy is a feasible source for low-power wireless sensor networks. The challenges of developing a power supply including generation or conversion, storage, and power management are manifold to extend the lifetime of a wireless sensor network. The objective of this research is to develop an intelligent hybrid power system to realize a self-sustaining wireless sensor node. The photovoltaic and thermoelectric generators are adopted as energy converters. The lithium ion battery and ultracapacitor are used as reservoirs. An intelligent power management system has been developed to control the power distribution. The design data and experimental results show that the hybrid micropower source can extend the lifetime of a sensor network.     

A Wireless Sensor Network for Cold-Chain Monitoring

This paper deals with a wireless sensor network that was specifically designed to monitor temperature-sensitive products during their distribution with the aim of conforming to the cold-chain assurance requirements. The measurement problems and the constraints that have been encountered in this application are initially highlighted, and then, an architecture that takes such problems into account is proposed. The proposed architecture is based on specifically designed measuring nodes that are inserted into the products to identify their behavior under real operating conditions, e.g., during a typical distribution. Such product nodes communicate through a wireless channel with a base station, which collects and processes the data sent by all the nodes. A peculiarity of the product nodes is the low cost, which allows the information on the cold-chain integrity to be provided to the final customer. The results that refer to the functional tests of the proposed system and to the experimental tests performed on a refrigerated vehicle during a distribution are reported.     

金属镀层膜厚量值溯源方法的研究

对金属镀层膜厚的不同测量方法进行了比较分析,提出通过设计台阶镀层和测量台阶高度的方法,实现镀层量值的直接溯源,并与X荧光分析测量法进行大量的比对测量试验,验证台阶镀层测量方法的正确性.     

Hybrid Sensor Selection Technique for Lifetime Extension of Wireless Sensor Networks

Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks (WSNs) where the battery capacity and energy sources are very restricted. Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features. This paper suggests a Hybrid Sensor Selection (HSS) technique that combines filter-wrapper method to acquire a rich-informational subset of sensors in a reasonable time. HSS aims to increase the lifetime of WSNs by using the optimal number of sensors. At the same time, HSS maintains the desired level of accuracy and manages sensor failures with the most suitable number of sensors without compromising the accuracy. The evaluation of the HSS technique has adopted four experiments by using four different datasets. These experiments show that HSS can extend the WSNs lifetime and increase the accuracy using a sufficient number of sensors without affecting the WSN functionality. Furthermore, to ensure HSS credibility and reliability, the proposed HSS technique has been compared to other corresponding methodologies and shows its superiority in energy conservation at premium accuracy measures.

     

接触式板材厚度在线测量跟踪系统

介绍了接触式权材厚度在线测量跟踪系统的原理及其结构特点。实践证明该测量跟踪系统能与被测板材的形态同步变化，使测量结果只与板材的厚度有关。实现了板材厚度的在线检测和监控。     

Intelligent Real-Time Adaptation for Power Efficiency in Sensor Networks

This paper presents an intelligent, dynamic power conservation scheme for sensor networks in which the sensor network operation is adaptive to both changes in the objects under measurement and the network itself. The conservation scheme switches sensor nodes between a sleep and an active mode in a manner such that the nodes can maximize the time they spend in a power-efficient sleep state, which corresponds to a nonmeasuring and/or nontransmitting mode, while not missing important events. A switching decision is made based on changes (or their absence) in the signals sensed from the environment by an intelligent agent that has been trained to determine whether or not a special event has occurred. This intelligent agent is based on a novel neural network topology that allows for a significant reduction in the resource consumption required for its training and operation without compromising its change detection performance. The scheme was implemented to control a sensor network built from a number of Telos rev. B motes currently available on the market. A few new utilities including an original neural network-based intelligent agent, a “visualizer,” a communication manager, and a scheduler have been designed, implemented, and tested. Power consumption measurements taken in a laboratory environment confirm that use of the designed system results in a significant extension of sensor network lifetime (versus “always on” systems) from a few days to a few years.      

Optimum Sensor Placement for Impact Location Using Trilateration

A key problem associated with structural health monitoring (SHM) is the placement of sensors upon a structure to detect the existence, location, and the extent of any damage. Because input data coming from the sensors are groups of measurements, it is arguable that the most widely used approach to SHM nowadays is to consider it as a statistical pattern recognition problem. Artificial neural networks have made a great impact on pattern recognition practice. A problem associated with this monitoring strategy is to find a good compromise between the quality of information achieved by the sensor network, increasing with the sensor density, and the need to keep the minimum weight and instrumentation cost. Thus, the number of sensors must be kept under control, and a search of the optimal location of such sensors needs to be performed. All these aspects have been taken into account in the present work, dealing with the problem of optimum sensor placement for impact location on a multilayered composite structure. Multilayered composite structures may suffer particularly relevant trauma when subject to low‐velocity impacts, as they may produce non‐visible or barely visible damage on the structure surface, while remarkable subsurface delaminations may be present. Such hidden damage, when remaining undetected, may grow to catastrophic failure. To overcome this issue, a neural network approach has been used here to predict the impact locations on a composite panel from time‐dependent data recorded on a set of surface‐mounted piezoelectric sensors during an experimental impact test. A genetic algorithm has been used to find the optimal sensor layout that minimised the error in predicting the impact location. A new approach, based on trilateration, is discussed and compared with the traditional one and is shown to provide the same degree of accuracy at reduced computational cost.     

A New Instrument for Real-Time Ethernet Performance Measurement

Ethernet is now becoming a popular physical layer for industrial communications due to its reliability, performance, and widespread use. Its actual cutting edge is a minimum cycle time on the order of 100 mus and a jitter of 1 mus. Usually, high-cost laboratory devices are used to log network traffic, but they are still not suitable for an industrial environment. In this paper, a field-programmable-gate-array-based instrument is proposed to log 100-Mb/s Ethernet frames, assigning them a high-resolution time stamp. Full-duplex full-rate network traffic is duplicated and sent through two auxiliary Ethernet ports to a personal-computer-based monitor station for frame analysis. A prototype has been realized to verify feasibility and performance. Results show that the proposed instrument introduces a maximum delay of about 500 ns. The measurement capabilities are comparable with commercial instruments: The time-stamp assignment has a jitter of 150 ns measured on a test frame set.     

Magnetic Array Assisted Triboelectric Nanogenerator Sensor for Real‑Time Gesture Interaction

In human-machine interaction,robotic hands are useful in many scenarios.To operate robotic hands via gestures instead of handles will greatly improve the convenience and intuition of human-machine interaction.Here,we present a magnetic array assisted sliding triboelectric sensor for achieving a real-time gesture interaction between a human hand and robotic hand.With a finger’s traction movement of flexion or extension,the sensor can induce positive/negative pulse signals.Through counting the pulses in unit time,the degree,speed,and direction of finger motion can be judged in realtime.The magnetic array plays an important role in generating the quantifiable pulses.The designed two parts of magnetic array can transform sliding motion into contact-separation and constrain the sliding pathway,respectively,thus improve the durability,low speed signal amplitude,and stability of the system.This direct quantization approach and optimization of wearable gesture sensor provide a new strategy for achieving a natural,intuitive,and real-time human-robotic interaction.