航空发动机进气总温传感器测温误差研究

航空发动机进口总温测量的准确性是优化发动机控制率、保障发动机飞行安全的关键因素之一。针对影响传感器测量准确性的响应时间、恢复系数，通过空气动力学和热力学理论，分析出具体的影响因素，设计了多种结构模型，并通过热风洞试验、FloEFD软件建模和热流体仿真，从材料及结构上系统地提出了优化途径和改善办法并进行了验证，尤其是分析了双余度传感器的响应一致性误差并提出改善方法。研究结果为同类传感器的设计提供了参考和借鉴。     

Deploy Efficiency Driven k-Barrier Construction Scheme Based on Target Circle in Directional Sensor Network

Journal of Computer Science and Technology - With the increasing demand for security, building strong barrier coverage in directional sensor networks is important for effectively detecting...     

Development of LTCC micro-hotplate with PTC temperature sensor for gas-sensing applications

Low temperature co-fired ceramic (LTCC) micro-hotplates show wide applications in gas sensors and micro-fluidic devices. It is easily structured in three-dimensional structures. This paper presents the low power consumption micro-hotplates which were developed with PTC (positive temperature coefficient) temperature sensor and inter-digitated electrodes. The paper presents two different structures for micro-hotplate with platinum as a heating element. The PTC temperature sensor using two different materials viz. PdAg and platinum paste are developed with micro-hotplates. The simulation has been achieved through COMSOL for LTCC and alumina micro-hotplates. The temperature variation with power consumption has been measured for the developed LTCC micro-hotplates. The change in resistance of PTC temperature sensors was measured with micro-hotplate temperature. The aim of this study was to place a temperature sensor with the gas sensor module to measure and control the temperature of micro-hotplate. A SnO₂ sensing layer is coated on LTCC micro-hotplate using screen printing and characterized for the sensing of carbon monoxide gas (CO). This study will be beneficial for designing hotplates based on LTCC technology with low power consumption and better stability of temperature for gas-sensing applications.     

Environmentally friendly CsPbBr3 QDs multicomponent glass with super-stability for optoelectronic devices and up-converted lasing

Ultra-stable CsPbBr₃ perovskite quantum dots (QDs) multicomponent glass with high transmittance was prepared by melt-quenching heat treatment. The average diameter of the CsPbBr₃ QDs was ∼1.96 nm. The resulting glass displayed a high exciton binding energy of 362 ± 18 meV. Notably, these glass-encapsulated materials exhibited excellent resistance to heat, light, and water, superior to that of previously reported perovskite-based materials, and underwent an extremely low rate of Pb leaching during water immersion. Based on the glass, a high-performance white light-emitting diode (WLED) device was fabricated with Commission Internationale de L’Eclairage (CIE) coordinates of (0.3156, 0.3326) and color gamut of ∼113 % National Television Standards Committee (NTSC). The CsPbBr₃ QDs glass without rare earth elements further acted as an optical gain medium, realizing up-conversion lasing with 980-nm laser excitation for the first time. The reversible linear fluorescence response indicates that the glass could be a potential candidate for temperature sensors.     

改进Elman神经网络在短期热负荷预测中的应用

热电厂的短期热负荷预测在城市集中供暖中起着至关重要的作用,直接影响热电厂的经济效益和热能利用率。电厂的短期热负荷一般采用神经网络预测模型进行预测,而BP神经网络应用最为广泛。Elman神经网络算法在BP神经网络基础上加入了承接层,作为一步延时算子,实现记忆能力,使系统具备适应时变能力,增强系统全局稳定性。但Elman神经网络算法模型的构造依然需要大量样本的支撑,而且输入层的变量多,导致预测时间依然很长,收敛速度慢。该文在Elman神经网络预测前,进行了相关系数预处理和对样本中异常值的平均化预处理,通过数据归一化运算,使Elman神经网络输入层变量大幅减少。仿真实验表明,改进的Elman神经网络算法使预测模型快速寻优,减少预测时间的同时明显提高预测精度。     

Extensive evaluation and classification of low-cost dust sensors in laboratory using a newly developed test method

An extensive evaluation of low-cost dust sensors was performed using an exponentially decaying particle concentration. A total of 264 sensors including 27 sensors with light-emitting diodes (LEDs) and 237 sensors with laser lighting sources were tested. Those tested sensors were classified into 4 groups based on the deviation from the reference data obtained by a reference instrument. The response linearities of all the tested samples for PM₁, PM_2.5, and PM₁₀ were in excellent agreement with the reference instrument, except a few samples. For the measurements of PM₁ and PM_2.5, the lighting source, that is, LED or laser, did not show any significant difference in overall sensor performance. However, LED-based sensors did not perform well for PM₁₀ measurements. The 32, 24, and 16% of all the tested sensors for PM₁, PM_2.5, and PM₁₀ measurement, respectively, are in the category of Class 1 (reference instrument reading ± 20%) requirement. The performance of the low-cost dust sensors for PM₁₀ measurement was relatively less satisfactory.     

人工监督神经网络在煤层裂缝发育区识别中的应用

针对在地震属性应用中难以精细识别煤层裂缝发育区边界的难题,开展了基于人工监督神经网络技术的煤层裂缝发育区应用研究。从地震资料中获取倾角导向体以提取高质量的地震属性,以多属性为指导进行人工拾取样点,并基于多层感知器进行神经网络机器训练学习,建立裂缝的最优属性集,拓展整体数据后获得裂缝概率体,从而识别划分出煤层裂缝发育区。该技术在山西阳泉新元矿区进行了应用,煤层裂缝发育区的识别结果明显优于属性直接识别,但勘探区内暂无钻井资料,预测效果还有待进一步验证。     

基于双层修正无迹卡尔曼的水下节点定位算法* .txt

摘要：针对水下无线传感网络中运动节点定位精度低的问题，提出了一种新的基于双层修正无迹卡尔曼的水下节点定位算法(DLMUKF)。该算法利用下层无迹卡尔曼滤波算法对节点状态进行预测，根据各信标节点的测距传播时延对预测的节点状态进行修正。运用上层无迹卡尔曼滤波算法对修正后的状态进行新的预测与修正。仿真实验中，DLMUKF算法的平均定位误差约为传统多边定位算法的15%，约为基于无迹卡尔曼滤波（UKF）定位算法的16%，受节点运动时间与速度的影响最小。通过实验证明DLMUKF算法能更充分利用实际距离值，可以有效减小运动节点的定位误差。 .txt     

基于循环一致性对抗网络的室内火焰图像场景迁移

基于深度学习的视频火灾探测模型的训练依赖于大量的正负样本数据,即火灾视频和带有干扰的场景视频。由于很多室内场合禁止点火,导致该场景下的火灾视频样本不足。本文基于生成对抗网络,将其他相似场景下录制的火焰迁移到指定场景,以此增广限制性场合下的火灾视频数据。文中提出将火焰内核预先植入场景使之具备完整的内容信息,再通过添加烟雾和地面反射等风格信息,完成场景与火焰的融合。该方法克服了现有多模态图像转换方法在图像转换过程中因丢失信息而造成的背景失真问题。同时为减少数据采集工作量,采用循环一致性生成对抗网络以解除训练图像必须严格匹配的限制。实验表明,与现有多模态图像转换相比,本文方法可以保证场景中火焰形态的多样性,迁移后的场景具有较高的视觉真实性,所得结果的FID与LPIPS值最小,分别为119.6和0.134 2。