零位校准在标定在线红外水分仪中的应用

介绍零位校准的方法,通过试验对其在标定在线红外水分仪的实际应用中的可行性进行验证,并与烘箱法标定在线红外水分仪进行比较,通过对标定方式、精度控制以及实际生产情况等因素,探讨零位校准作为标定在线红外水分仪依据的可行性。     

电阳应变式传感器的温度误差及其补偿

系统分析了电阻应变式传感器在实际应用中由于会受周围环境温度等因素的影响产生的附加误差,为解决此类问题,文章给出了几种温度补偿的方法来提高测量精度,保证测量的准确性.     

电阻应变式传感器的温度误差及其补偿

系统分析了电阻应变式传感器在实际应用中由于会受周围环境温度等因素的影响产生的附加误差,为解决此类问题,文章给出了几种温度补偿的方法来提高测量精度,保证测量的准确性。     

关于柔性版印刷CTP校准及补偿的若干情况

数字柔性版印刷计算机到印版(CTP)的图像照排机早已出现在北美市场上,它不仅是一种新产品,也是一项新技术。商店和纸制品加工厂接受去除印版或套筒的理念,他们也知道制作柔印版的技术和工作肯定会发生变化。 商店购买柔版数字图像照排机的原因是看到了通过校准、控制和补偿,其生产过程的可能性,而使用模拟印版是不可能的。纸制品加工厂在印刷机上看到了标准的设置,使其终端用户,消费品公司得到可以预测的,一致的和可以重复的效果。     

温度设定实验装置校准方法

本文简要介绍了温度设定装置的定义与工作原理,重点解释温度设定装置在校准过程中传感器的放置方法,以及数据的采集规则和数据的处理方式。对检测此类特殊的温度设备、装置有一定的指导作用。     

低温黑体辐射源亮度温度的校准方法及数据处理

主要介绍了黑体辐射源亮度温度的校准原理、校准前的详细准备步骤、具体校准方法及数据处理方法,并给出了有关校准的建议。     

基于LabVIEW的校准实验室温度控制系统

针对我院检测校准实验室控温系统中标准水槽、标准油槽及卧式高温检定炉等主设备存在手动控温差,控温稳定时间长,且每测一个点都需要对温控系统重新进行设置的传统温度控温系统,已不适应当前我院科研生产的检测校准需求;为提高控温精度和效率,分层分步提升温度校准实验室数字化技术水平,本项目开展了采用美国NI公司的LabVIEW图形化编程语言的温度控制系统开发,包括系统硬件和软件的设计。它使用一种新型模糊PID控制器,可以很好地克服温控系统中参数的变化和负载扰动引起的冲击和突变,可实现温度自动控制数、据记录、数据查看、数据打印、远程网络监控及报警等多种功能,经测试取得了满意的控制效果,能够更快更精准的实现校准实验室的温度控制要求。     

论温度试验设备的校准对食品质量检验结果的影响

讨论食品质量检验对温度试验设备的要求,分析温度试验设备的温度技术性能,从而对食品质量检验中温度试验设备的使用进行指导,提出温度试验设备计量确认的重要性,以保证食品质量检验结果的可靠性。     

太阳能热水器温度补偿控制器的研制

利用模糊控制技术，研制出了太阳能热水器补偿控制器。该控制器的中央处理器选用高性能的ＳＴ６２Ｔ１５单片机，为方便程序的使用和维护，软件采用模块化结构设计。该控制器外围电路简化、功能多样。模糊控制技术的应用实现了太阳能热水器的智能化控制，满足了用户全天候的使用要求。     

微波消解仪温度校准方法的探讨

通过介绍微波消解仪的工作原理,明确了测量标准的选取原则;选择符合要求的无线微波专用温度校准标准器对微波消解仪进行温度校准.提出温度校准的计量特性参数,并结合校准工作实际,明确测量点的数量与位置选择方法以及校准过程和校准数据的处理.从测量标准的选取和校准计量特性的确定两个方面,给出了微波消解仪的温度校准方法;同时结合实际...     

苹果糖度的光谱模型温度补偿设计

目的 为减少温度对便携仪器近红外光谱模型预测的影响, 尝试构建局部温度混合校正模型, 结合温度信息来预测不同温度下的苹果内部品质。方法 以16、24、32 ℃贮藏温度下红富士苹果为原料, 分别用内置微型光谱仪的自制便携式水果分析仪器获得其透射光谱, 结合温度传感器获取环境温度, 用阿贝折射仪测定苹果糖度。建立单一温度校正模型、全局温度混合校正模型和局部温度混合校正模型对不同温度的样本进行预测。结果 单一温度校正模型对不同温度下苹果糖度预测均方根误差为0.474~3.125% Brix; 当采用全局温度混合校正模型时能降低温度对光谱的影响, 预测均方根误差分布在0.488~0.533% Brix。根据待测样本的温度来构建多个局部温度混合校正模型, 对不同温度下苹果糖度的预测均方根误差为 0.462~0.500% Brix。结论 局部温度混合校正模型可以结合样本温度信息预测苹果糖度, 降低温度对模型的影响, 同时能减少初期建模成本。     

丝光机碱液浓度检测温度补偿方法的研究

探讨电磁法碱液浓度检测中的温度补偿问题.通过实验方法测量不同浓度在不同温度下的电导率数据,利用最小二乘原理拟合浓度-温度-电导率曲线,从而得到具有温度补偿的不同浓度下电磁浓度变送器的输出函数关系表达式.实现了丝光机碱液浓度检测过程中温度补偿,使检测精度达到丝光工艺要求.     

Infrared PLUS4501水分传感器的检测原理与标定

QCS通过水分传感器控制造纸生产过程中纸张的水分含量,对提高产品质量,节能降耗尤其重要.本文针对霍尼韦尔公司Infrared PLUS4501水分传感器的原理和标定进行阐述.QCS通过水分传感器可以进行高精度的测量与实时控制,系统运行稳定可靠.     

高集成度热电偶温度变送器的研制

采用Ｌｉｎｅａｒ公司的ＬＴ１０２５和德州仪器公司的ＴＩＬ３００／Ａ研制开发了一种高集成度热电偶温度变送器。该变送器由冷端补偿电路、线性化电路、光电耦合电路和隔离电源等部分组成，具有精度高、体积小等优点，可实现电源、输入、输出三端隔离，且传输非线性度〈０．２５％。     

Microwave nondestructive sensing of moisture content in shelled peanuts independent of bulk density and with temperature compensation

Dielectric methods for rapid and nondestructive sensing of moisture content in shelled peanuts from free-space measurement of attenuation and phase shift, and their corresponding dielectric properties at temperatures ranging from 1 to 38 °C and frequencies ranging from 8 to 14 GHz, are presented. These methods provide moisture content independent of bulk density and compensated for temperature effects. Results of moisture prediction with three density-independent calibration functions (ψ₁, ψ₂, and ψ₃) are compared. For each function, the moisture calibration equation with temperature compensation is given along with corresponding standard errors of performance (SEP). For all three calibration functions, the SEP was less than 1% moisture content. Also, the frequency behavior of each of these calibration functions was examined in the frequency range between 8 and 14 GHz. Among the three density-independent calibration functions, calibration function ψ₃ showed the least variation with frequency.     

酸度计测试原理及其应用

从pH值定义、酸度计测试原理及构造、标准缓冲溶液、温度补偿、校准方式、响应时间、正确使用维护保养及电极性能判定等多方面进行了系统论述。     

Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n = 24,717) or diseased (n = 243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24 h before the CM observation, to a time window from 24 h before to 24 h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100%. This indicates the inability to detect all CM cases based on sensor data alone. Sensitivity levels varied largely when the decision tree was validated per herd. This trend was confirmed when decision trees were trained using data from 8 herds and tested on data from the ninth herd. This indicates that when using the decision tree as a generic CM detection model in practice, some herds will continue having difficulties in detecting CM using mastitis alert lists, whereas others will perform well.     

Technical note: Assessment of milk temperature measured by automatic milking systems as an indicator of body temperature and fever in dairy cows

The objective of this study was to evaluate whether milk temperature (MT) measured by automatic milking system (AMS) is a reliable indicator of body temperature of dairy cows and whether cows with fever could be detected. Data loggers (Minilog 8, Vemco Ltd., Halifax, NS, Canada) measuring body temperature were inserted for 7 ± 1 d into the vaginal cavity of 31 dairy cows and programmed to take 1 reading/min. Milk temperature was recorded at each milking event by the AMS, and values from the vaginal loggers were paired with the corresponding MT. The correlation (r) between vaginal temperature (VT) and MT was 0.52. Vaginal temperature was higher (39.1 ± 0.4°C) than MT (38.6 ± 0.7°C) with a mean difference of 0.5 ± 0.6°C. The ability of MT to identify cows with fever was assessed using 2 approaches. In the first approach, VT could indicate fever at any time of the day, whereas MT could display fever only during the milking events of a given day. Different definitions of fever based on thresholds of VT and duration exceeding these thresholds were constructed. Different thresholds of MT were tested to distinguish between cows with and without fever. The combination of 39.0°C as a threshold for MT and 39.5°C for at least 2 h/d as a threshold for VT resulted in the highest combination of sensitivity (0.65) and specificity (0.65). In the second approach, we evaluated whether MT could identify cows with fever at a given milking event. A threshold of MT >38.7°C delivered the best combination of sensitivity (0.77) and specificity (0.66) when fever was defined as VT ≥39.5°C. Therefore, MT measured by AMS can be indicative of fever in dairy cows to a limited extent.     

Effect of temperature and relative humidity on yellowing rate of paddy

The effects of temperature and relative humidity (or water activity) in storage chambers on yellowing rate of paddy were investigated and then an empirical equation for predicting the yellowing rate was developed. Paddy was conditioned using saturated salt solutions at relative humidities ranging from 0.80 to 0.95 and temperatures of 35, 45, 55, 60 and 65°C. The yellowing rate was found to follow the zero order kinetics. The yellowing constant value (k) increased exponentially with temperature and increased linearly with water activity. The magnitude of apparent activation energy varied from 130–145 kJ/mol. A predictive equation for determining yellowing rate was ln k=−δa_w−/T+(γa_w)/T where a_w was water activity (valid from 0.80 to 0.95), T was absolute temperature (valid from 308 to 338 K) and , δ, and γ were constants. The results of variance analysis showed that temperature, water activity and their interaction significantly influenced the yellowing rate of paddy.