Pose Estimation of GIS Pipeline Based on Spatial Transformation Network

The manual flange structure alignment between GIS pipelines in the power system is inefficient and difficult to accurately align. To solve this problem, combined with the research results in the field of deep learning named spatial transformation network, a new pose estimation method based on single camera is proposed. In view of the high similarity between the moving flange and the static flange at the pixel level, the spatial transformation network is used to find the pixel mapping relationship of the two flange images. Thereby establishing the mapping relationship between the pixel coordinates of the two flange images and then using multiple points. In the perspective method, the pixel coordinates are mapped to the world coordinates to obtain the estimation of the position of the key point in the flange, and then the direction vector of the flange is calculated according to the position of the key point. Since there is a pixel coordinate transformation relationship between the static flange and the movable flange. Only the position of the key point in the static flange can be inversely solved by measuring the position of the key point in the static flange. Experiments show that, compared to the traditional method of measuring flange pose based on instrument measurement and linear regression, the method proposed in this paper can accurately measure the pose of the flange structure. And it can rely as little as possible on the measurement of the key points of the moving flange by the instrument.

Pedestrian exposure to black carbon and PM2.5 emissions in urban hot spots: new findings using mobile measurement techniques and flexible Bayesian regression models

Background Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians’ exposure to particulate matter (black carbon (BC) and PM2.5 mass concentrations). Objective We present a distributional regression model in a Bayesian framework that estimates the effects of spatiotemporal factors on the pollutant concentrations influencing pedestrian exposure. Methods We modeled the mean and variance of the pollutant concentrations obtained from MM in two cities and extended commonly used lognormal models with a lognormal-normal convolution (logNNC) extension for BC to account for instrument measurement error. Results The logNNC extension significantly improved the BC model. From these model results, we found local sources and, hence, local mitigation efforts to improve air quality, have more impact on the ambient levels of BC mass concentrations than on the regulated PM2.5. Significance Firstly, this model (logNNC in bamlss package available in R) could be used for the statistical analysis of MM data from various study areas and pollutants with the potential for predicting pollutant concentrations in urban areas. Secondly, with respect to pedestrian exposure, it is crucial for BC mass concentration to be monitored and regulated in areas dominated by traffic-related air pollution.

Apnea Monitor Using Pulse Oxymetry with Tactile Stimulation to Reduce Respiration Failure

Respiratory failure (apnea) often occurs in premature babies, this should be avoided because it causes low oxygen concentrations in the blood so that it can damage brain function and lead to death. Apnea is characterized by a decrease in oxygen saturation (SpO2). The purpose of this study was to design an apnea monitor that was detected with SpO2 parameters, alarms, and vibrating stimulation. This study uses infrared and red LEDs that emit light through the surface of the finger and is detected by a photodiode sensor, this light signal will be converted into an electrical signal and calculated by Arduino to determine the patient's SpO2 and BPM values. If the SpO2 value drops 5% within 5 seconds from the baseline, the device will indicate apnea has occurred and the vibrating motor is working. SpO2 signals and alarms are sent to the nurse station computer via Bluetooth HC-05. The instrument was calibrated with an SpO2 calibrator and the measurement results were compared with a BION pulse oximetry brand. The results of the instrument measurement on two subjects on the SpO2 parameter showed an error value of 2% and the BPM parameter obtained an error value of 4.54%. Testing the BPM parameter using a calibrator at the 30 and 60 BPM settings shows an error value of 0% and at the 120 BPM setting the error value is 0.01%. The vibrating motor to stimulate the baby's body when apnea occurs is functioning properly. The results showed that measurements using subjects tended to have high error values ​​due to several factors. This research can be implemented on patient monitors to improve patient safety and reduce the workload of nurses or doctors

A Tool for Energy Consumption Monitoring and Analysis of the Android Terminal

With the rapid development of communication technology, the intelligent mobile terminal brings about great convenience to people’s life with rich applications, while its power consumption has become a great concern to researchers and consumers. Power modeling is the basis to understand and analyze the power consumption characteristics of the terminal. In this paper, we analyze the Bluetooth and hidden power consumption of the android platform and fix the power model of open-source Android platform. Then, a power consumption monitoring tool is implemented based on the model; the tool is divided into three layers, which are original information monitor layer, power consumption calculation layer, and application layer. The original monitor layer gets the power consumption data and running time of the different components under different states, the calculation layer calculates the power consumption of each hardware and each application based on the power model of each component, and the application layer displays the real-time power consumption of the software and hardware. Finally, we test our tool in real environment by using Xiaomi 9 Pro and perform comparison with actual instrument measurement; the error between the monitored value and the measured value is less than 5%.

Analysis on Food Crispness Based on Time and Frequency Domain Features of Acoustic Signal

Crispness is an important indicator of crunchy food. However, it cannot be easily quantified by sensory evaluation, due to the high subjectivity of evaluators; instrument measurement of this indicator requires much manpower and time. To improve the efficiency of food crispness prediction, this paper attempts to build a rapid, convenient, and accurate crispness analysis model. Starting with the fracturing sound of crunchy food, the authors collected the fracturing acoustic signal, conducted wavelet denoising, analyzed the eigenvalues in time and frequency domains, and constructed crispness prediction models based on multiple linear regression (MLR) and neural network, respectively. Through fracturing test and acoustic test, cluster analysis was adopted to select the typical eigenvalues of acoustic signal, including the peak amplitude of power spectral density (PSD) curve, amplitude difference, and waveform index. Based on these eigenvalues, a crispness analysis model was established, and used to predict the crispness of four kinds of food, namely, potato, sweet potato, carrot, and turnip. The results show that the BP neural network had a smaller relative error than the MLR; when the threshold was 5%, the BP neural network maintained a prediction accuracy of >90%, and achieved 100% prediction accuracy on two types of food. To sum up, this paper reveals the relationship between the food chewing sound features and food quality, laying the theoretical basis for the research of food chewing sound mechanism.

The Contribution of High-Frequency Wind-Generated Surface Waves to the Stokes Drift

The effects of nonbreaking surface waves on upper-ocean dynamics enter the wave-averaged primitive equations through the Stokes drift. Through the resulting upper-ocean dynamics, Stokes drift is a catalyst for the fluxes of heat and trace gases between the atmosphere and ocean. However, estimates of the Stokes drift rely crucially on properly resolving the wave spectrum. In this paper, using state-of-the-art spatial measurements (in situ and airborne remote sensing) from a number of different field campaigns, with environmental conditions ranging from 2 to 13 m s−1 wind speed and significant wave height of up to 4 m, we characterize the properties of the surface wave field across the equilibrium and saturation ranges and provide a simple parameterization of the transition between the two regimes that can easily be implemented in numerical wave models. We quantify the error associated with instrument measurement limitations, or incomplete numerical parameterizations, and propose forms for the continuation of these spectra to properly estimate the Stokes drift. Depending on the instrument and the sea state, predictions of surface Stokes drift may be underestimated by more than 50%.

Entrepreneurial Strategies and Innovative Strategies: systematic review, concepts, models and future investigations

The research centred on mapping an international production about entrepreneurial and innovative strategies, in the time search period from 2008 to 2018. It comprises a bibliometric research performed in the Web of Science database. By analysing such works, concepts of entrepreneurial strategy and models, types, trends and gaps related to the entrepreneurial strategy and the innovative strategy were observed. The following study trends can be emphasised: innovation, internationalisation, family companies, entrepreneurship, innovative strategies, social responsibility, environmental innovation, among others; and gaps such as the investigation on the relationship between the attributes of top management and the development of entrepreneurial strategies; the verification of a new scale in order to improve the instrument measurement between cultures; the collection of macroeconomic data in order to develop a broad model of evaluation to develop effective strategies in the current market and environmental innovation in the company’s global strategy.

Use of radar plots to compare the analytical performance of five blood glucose monitoring systems

Aim: The radar plot is a relatively new way of communicating blood glucose monitoring system (BGMS) accuracy and precision: data points positioned within concentric circles represent the magnitude (increasing with distance from center) and direction (relative to horizontal) of BGMS-error (center = equivalency with reference instrument measurement). This manuscript aims to demonstrate the utility of radar plots as visual tools for interpretation of BGMS analytical performance. Results & methodology: Radar plots were constructed for five BGMSs, to compare BGMS blood glucose results with reference instrument measurements. Conclusion: Radar plots are a useful tool for the visualization of BGMS analytical performance, communicating accuracy, precision and the satisfaction of certain regulatory criteria at a glance.

Pengujian Reliabilitas Instrumen Terhadap Variabel Kontinu Untuk Pengukuran Konsentrasi Klorofil- A Perairan

Instrumen penelitian adalah alat ukur yang digunakan secara sistematis untuk mengumpulkan data penelitian. Hasil penelitian yang baik diperoleh jika instrumen yang digunakan valid dan reliabel. Reliabilitas instrumen menunjukkan ketepatan atau sejauh mana hasil pengukuran instrumen dapat direplikasi. Reliabilitas menjadi salah satu parameter penting dalam menentukan kualitas suatu instrumen. Penelitian ini bertujuan untuk memilih statistik uji yang paling tepat terhadap pengujian reliabilitas terhadap instrumen untuk konsentrasi klorofil-a perairan. Data konsentrasi klorofil-a perairan diperoleh dari dua instrumen yaitu : metode spektrofotometri dari 14 stasiun pengamatan di Teluk Semarang Jawa Tengah dan penginderaan jauh. Statistik uji yang paling tepat digunakan adalah Intraclass Correlation Coefficients (ICC). Nilai ICC adalah rasio antara varians objek terhadap varians total. Hasil penelitian diperoleh nilai ICC = 0,83 artinya 83 % keragaman data disebabkan keragaman objek dalam hal ini stasiun penelitian. Tidak terdapat bias pengukuran konsentrasi klorofil-a dari kedua instrumen, karena reliabilitas kedua instrumen mendekati sempurna. A research instrument is a measuring tool used systematically to collect the data. A good result of the research is obtained if the instrument is reliable. The reliability of the instrument showed accuracy on instrument measurement could be replicated. Reliability is one of the important parameters in determining the quality of the device. This study is aimed at selecting the most appropriate test of statistics in the reliability of the instrument for chlorophyll-a water concentrations. The data of chlorophyll-a waters concentration is gained from two instruments called spectrophotometric method from 14 observation stations in Semarang Bay, Central Java, and remote sensing. The best test statistic is the Intraclass Correlation Coefficients (ICC). The value on ICC is the ratio between the variance of the object at total variance. The results showed that ICC = 0.83, meaning that 83% of a variety of data is due to the diversity of objects in these research stations. There is no bias in measuring the chlorofil-a concentration using the two instruments because the reliability of the two devices is closely perfect.