Current Measurement Methods Research Articles

Measuring Performance of Digital Projects: Case of Swiss Medium-sized Enterprises

Abstract. The research objective is to understand the challenges of digitalisation of Swiss medium-sized enterprises and their satisfaction with current measurement methods, and to develop a measurement model to evaluate the performance of their digital projects. The research methodology involved a comprehensive literature review, followed by interviews with experts from 15 medium sized companies. The analysis revealed that Swiss medium sized enterprises not only lack the appropriate tools to measure their digital projects’ performance but are also facing challenges caused by the lack of financial resources and time to implement digital projects; and the difficulty of finding the right people to execute digital projects. The results from the research include a measurement model for digital projects. This model supports managers in making effective decisions regarding their digital projects, allocating resources more effectively, and enhancing overall organizational performance. The research outcomes are not necessarily generalizable to other enterprises (other size, other nations). However, they show that in the context of digital projects, Swiss medium-sized companies need a measurement model to evaluate the performance and success since such technologies/projects are associated with financial costs. For future research, the developed model should be tested with different firms in an operational handling where effective past or future projects would be measured. This could further explore the applicability of the model to different sized enterprises or those in other nations.

Up-to-the-date practices of geodetic measurements for build-up area expansion: a case study from Uzbekistan

This paper seeks to contribute to continuing efforts to better understand and control the effects of urbanization on our environment and society by offering a thorough review of the most recent geodetic measuring techniques for build-up area growth. In this article, we give a case study from Uzbekistan that looks at current geodetic measuring techniques for expanding build-up areas. Uzbekistan is a fast urbanizing nation in Central Asia, and politicians and experts are both deeply concerned about the growth of the country's built-up regions. The case study, which offers a thorough investigation of the geodetic measuring methods and tools used to gauge and track the growth of the built environment, is focused on a particular metropolitan region in Uzbekistan. Therefore, this article gives a summary of current geodetic measurement methods, including remote sensing methods like LiDAR and satellite imaging as well as surveying methods like total station and GNSS. The case study also emphasizes some of the difficulties and restrictions related to these methods, such as the requirement for precise geodetic control points and the inadequacies of satellite imaging in regions with thick vegetation or cloud cover.

A Temperature Monitoring Method For Power Electronic Converter Based on Infrared Image and Object Detection Algorithm

Power electronic converters are more and more widely used, and abnormal temperature of converter components is the most important factor in converter failure. To improve the reliability of the converter design, it is necessary to monitor the temperature of key components in the converter during the prototype test stage. The temperature measurement method of infrared thermal images has rich temperature information, wide detection range, and does not affect the original circuit design. However, in the current automatic temperature measurement methods, it is necessary to manually establish a standard matching template for the infrared thermal image of the circuit to be tested, which indicates a large workload and poor versatility. This paper proposes a method for fully automatic temperature monitoring of converter components. This method is based on a deep learning object detection algorithm, which can automatically identify the type of converter components, obtain partial infrared thermal images of components through heterogeneous image registration, achieve accurate component temperature monitoring and facilitate the converter state monitoring and fault detection. The advantages of this method are: 1) there is no need to manually establish a standard template for each converter; 2) it can monitor the converter temperature without manual intervention; 3) combining the temperature information and circuit prior knowledge, the state monitoring and fault diagnosis can further be realized. The experimental results also verify the feasibility and accuracy of this method.

A High-Bandwidth and Easy-to-Integrate Parasitics-Based Switching Current Measurement Method for Fast GaN Devices

While the very high switching speed of the gallium nitride devices can easily enable power electronics converters to operate at multi megahertz, it also brings huge challenges to switching current measurement, which is very important for the evaluation of switching performance. The conventional switching current measurement methods either have low bandwidth or have influence on layout inductance. To address these issues, this article proposes a high-bandwidth and easy-to-integrate switching current measurement method, with almost no influence on power loop layout. The proposed method utilizes the power loop trace from the source terminal of bottom switch to circuit ground as the sensing trace. By measuring the sensing voltage and considering the frequency-dependent parasitic inductance and resistance of the sensing trace, the switching current is derived based on the Fourier series theory. The simulation verification is firstly performed by using LTspice, and shows consistent results. Then, to accurately measure the sensing voltage, the used voltage probe TPP1000 is carefully modeled for measurement compensation and correction. Finally, two double pulse test (DPT) boards are designed, one with coaxial current shunt and the other without coaxial current shunt. Through DPT experiment, the accuracy of the proposed method with almost no insertion inductance is verified.

Current Measurement Method of Multiple Chips Using Rectangular PCB Rogowski Coils Integrated in Press Pack IGBT Device

Unbalance of transient currents of multiple chips inside press-pack insulated-gate bipolar transistor (PP IGBT) results in degrading operation and even device damage. Chip currents measurement plays an essential role to understand and solve this problem. Printed-circuit board Rogowski coil (PCB RC), as a small, noninvasive, and high bandwidth current sensor, is very suitable for this application. However, PCB RC integrated with multiple Rogowski coils (RCs) is still difficult to be applied to measure the current of multiple chips in press pack IGBT device because of the low measurement accuracy due to the large interference. In this letter, a high accuracy PCB RC with ten rectangular RCs inside PP IGBT is proposed to measure ten chip currents at the same time. Different from single RC measuring single chip current, the interference of adjacent chip currents has to be considered, and is reduced to 0.6% by optimizing the turns arrangement and lead wires of RCs. The noise of cross talk of adjacent RCs is 1.7% by decreasing the capacitance coupling between RCs. Shielding layers and grounding patter of lead wires for ten RCs are proposed to eliminate the interference of collector voltage. The experiment of transient process of ten chips verifies the effectiveness of proposed PCB RC, and measurement error is only 1.8%.

실차 운행 조건에서 전기자동차 배터리 소모 전류 측정 방법 및 전비 비교

As the number of electric vehicles is increasing, various studies such as driving motors, batteries, and charging systems have been conducted. To perform these studies, battery pack current measurement is a prerequisite. In general, battery pack current can be measured by installing an ammeter. If we access to OBD via CAN communication, the current offered by the BMS can be read. The first method is cumbersome and risky to install the ammeter and measure current due to the usage of the high-voltage cable. For the second method, the current data is output from the BMS and thus easy to acquire it if available to access CAN DB. In this study the current consumption was compared based on these two current measurement methods. To compare the current consumption, an electric vehicle was run on the chassis dynamometer at three driving modes such as ASM2525, UDDS and US06 reflecting various driving conditions. Furthermore, the fuel economy was calculated with such test modes. The difference of current consumption between two measurement methods was comparable and the fuel economy was the highest in the order of ASM2525, UDDS and US06.

무기체계 국산화 평가방안의 한계와 발전방안

Currently, overseas exports of domestic defense products are gaining momentum starting with the Ukraine-Russia war. The way to continuously improve the competitiveness of domestic defense products is to utilize localization evaluation measures as a key tool to promote substantial localization advancement. This study aims to contribute to innovation in the way of working in the defense industry by inducing the level of localization to continuously upgrade by improving the evaluation plan for localization. First, the scope of the study is to understand the limitations of the current localization evaluation. Second, the effect on the total life cycle is analyzed when the localization evaluation is distorted. Third, finally, it seeks development solitions that can practically carry out localization. Qualitative research methodology is applied for the effective performance of this study. The opinions of various interest groups are collected and analyzed through the f irst and second Delphi surveys and in-depth interviews. Conflicting opinions are agreed through expert discussions in in-depth interviews. As a result of the performance of this study, it was analyzed that the current localization rate calculation method has a high possibility of abuse or distortion. In order to solve this problem, an agreement was reached that it was necessary to expand the scope of calculation to the 1st or 2nd partner companies. It was partially confirmed that when the result of calculating the localization rate is distorted, the effect is increased as the total life cycle progresses. However, in order to reduce the distortion effect of the localization rate at the operation maintenance stage, it was confirmed that it is not reasonable to convert the operational maintenance cost into the present value and reflect it in the localization rate. In summary, the current localization evaluation relies only on measuring the simple cost performance of calculating the localization rate, and there is a limitation that the possibility of abuse or distortion is high due to the limitation of the scope of calculating the localization rate to primary suppliers. In order to improve this, a model that comprehensively evaluates not only cost performance but also various performance is needed, but in the short term, it is realistic to maintain the current cost-related performance measurement method and expand the scope of localization rate calculation. The expected effect of this study is to seek ways to continuously promote localization by raising the need for improvement of localization evaluation measures, and furthermore, localization evaluation is used as a key tool to secure the external competitiveness of domestic defense products. In addition, various stakeholders may have conflicting opinions in various fields in the defense sector, and the introduction of qualitative research methodology can help each other share various perspectives and come up with solutions through discussions and agreements.

Remote Sensing of Seawater Temperature and Salinity Profiles by the Brillouin Lidar Based on a Fizeau Interferometer and Multichannel Photomultiplier Tube

Brillouin spectroscopy is a powerful tool to measure the water temperature and salinity profiles of seawater. Considering the insufficiency of the current spectral measurement methods in real-time, spectral integrity, continuity, and stability, we developed a new lidar system for spectrum measurement on an airborne platform that is based on a Fizeau interferometer and multichannel photomultiplier tube. In this approach, the lidar system uses time-of-flight information to measure the depth and relies on Brillouin spectroscopy as the temperature and salinity indicator. In this study, the system parameters were first optimized and analyzed. Based on the analysis results, the performance of the system in terms of detection depth and accuracy was evaluated. The results showed that this method has strong anti-interference ability, and under a temperature measurement accuracy of 0.5 °C and a salinity measurement accuracy of 1‱, the effective detection depth exceeds 40.51 m. Therefore, the proposed method performs well and will be a good choice for achieving Brillouin lidar application in seawater remote sensing.

A Machine Learning-Based Correction Method for High-Frequency Surface Wave Radar Current Measurements

An algorithm based on a long short-term memory (LSTM) network is proposed to reduce errors from high-frequency surface wave radar current measurements. In traditional inversion algorithms, the radar velocities are derived from electromagnetic echo signals, with no constraints imposed by physical oceanographic processes. In this study, sea surface winds and tides are included in the LSTM algorithm to improve radar data. These physical factors provide the LSTM network with more oceanic information by which to constrain and improve its training efficiency. The results show that the domain-averaged root-mean-square errors of the radar-derived velocities are reduced from 0.22 to 0.09 m/s for the whole radar observation area. The overall correlation coefficient increases from 0.37 to 0.88. To provide a practical strategy for future field work, we conduct a set of sensitivity experiments, showing that the LSTM network based on one single point can be applied to other data points within a sub-domain.

Fault Diagnosis for Power Converter in SRM Drives Based on Current Prediction

This article proposes an online diagnosis scheme for transistor faults of asymmetric half-bridge converter (AHBC) in switched reluctance motor (SRM) drives. In order to extract more information from the phase current to identify faults, based on the conventional current measurement method, two novel current measurement methods are presented by reconstructing current sensors. According to the novel current measurement methods, a current prediction method is raised. The phase current at the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> th sampling time is available so that the estimated current can be calculated. The fault can be detected and located by analyzing the given and actual switching states, which are deduced by comparing the measured and estimated currents. Compared with existing methods, the proposed scheme can be applied to diagnosis of multiple faults in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -phase AHBC, which can operate at various control strategies and chopping modes. With a single current sensor in each phase, the scheme will not increase the cost and complexity of the SRM drive. Furthermore, the scheme does not require complicated computation, making it easy to implement online. The experimental results confirm the effectiveness and flexibility of the proposed scheme.

Ultrafast Current Shunt (UFCS): A Gigahertz Bandwidth Ultra-Low-Inductance Current Sensor

In an effort to reduce switching loss, increase switching frequencies, and shrink passive component sizes, power device switching times are being pushed into nanosecond and subnanosecond intervals. However, these fast switching times require high rates of change of current making the power devices susceptible to overvoltage damage from parasitic inductance in the power loop. This presents challenges in measuring the device switching current accurately and, in turn, makes accurate measurements of device switching energy and stored charge difficult. Traditional current measurement methods either do not have the required frequency response to view the switching edge accurately or have a high parasitic inductance that can significantly alter switching performance and cause damage to the power device. In this article, an active current measurement system is developed. This circuit achieves up to 1.6-GHz bandwidth with zero overshoot. By utilizing mutual inductance cancellation, an insertion inductance down to 20 pH has been achieved. The research performed in this article thereby allows for an accurate measurement of device switching loss with minimal power circuit disturbance. The performance of this current measurement is experimentally verified in the frequency domain using a vector network analyzer, and time-domain switching measurements are compared with state-of-the-art current measurement.

Irisin and Bone in Sickness and in Health: A Narrative Review of the Literature.

Irisin is a hormone-like myokine produced by the skeletal muscle in response to exercise. Upon its release into the circulation, it is involved in the browning process and thermogenesis, but recent evidence indicates that this myokine could also regulate the functions of osteoblasts, osteoclasts, and osteocytes. Most human studies have reported that serum irisin levels decrease with age and in conditions involving bone diseases, including both primary and secondary osteoporosis. However, it should be emphasized that recent findings have called into question the importance of circulating irisin, as well as the validity and reproducibility of current methods of irisin measurement. In this review, we summarize data pertaining to the role of irisin in the bone homeostasis of healthy children and adults, as well as in the context of primary and secondary osteoporosis. Additional research is required to address methodological issues, and functional studies are required to clarify whether muscle and bone damage per se affect circulating levels of irisin or whether the modulation of this myokine is caused by the inherent mechanisms of underlying diseases, such as genetic or inflammatory causes. These investigations would shed further light on the effects of irisin on bone homeostasis and bone disease.

Use and repeatability of 3D light scanning to measure transverse dorsal profile size and symmetry in the thoracic region in horses

Equine epaxial muscle size, thoracolumbar profile and symmetry in horses is of clinical interest due to relationships with pain and pathology. Flexible-curve rulers have previously been used to gather reliable, objective measures of thoracic profile, however, 3D light-scanning offers a potential non-contact alternative method to estimate cross sectional area (CSA) of the region. 3D light-scans of the thoracic epaxial region were taken from ten endurance horses (7 geldings, 3 mares; 8±2 years). Total CSA of the combined epaxial musculature, using computer software, was calculated at scapula and T18 levels (depth: 15 cm). Intra and inter-rater (n=3) reliability of CSA measurements was assessed using Friedman’s analyses and post-hoc Wilcoxon rank tests (three repeated measures). Intraclass correlation estimates (ICC ± 95% confidence intervals (CI)) were calculated (mean-rating, absolute-agreement, 2-way mixedeffects model). Paired t-tests assessed differences between right and left areas. No significant differences existed for transverse plane-cuts (scapula, T18 P&gt;0.05) between light-scans. Right and left areas were significantly different at the withers (P=0.012) with the left side larger in 70% of scans, but no significant differences were found between sides at T18. No differences existed for different plane-cuts of the same horse (P=0.53; ICC: 0.76; CIs: 0.43-0.92). While reliability was reduced between all raters (P=0.02; ICC: 0.70; CIs: 0.56-0.82), no significant differences occurred between two different assessors experienced in using the software (P=0.88; ICC: 0.90; CIs: 0.82-0.95). Intra-rater reliability for assessing thoracic profile and inter-rater reliability ICC values with experienced analysts was interpreted as good/excellent. The results suggest 3D light-scanning is an objective, non-invasive method to record size and symmetry of the epaxial region in horses and warrants validity testing against current measurement methods such as the flexible-curve ruler.

Virtual Reality Induced Symptoms and Effects: Concerns, Causes, Assessment &amp; Mitigation

The utilization of commercially available virtual reality (VR) environments has increased over the last decade. Motion sickness that is commonly reported while using VR devices is still prevalent and reported at a higher than acceptable rate. The virtual reality induced symptoms and effects (VRISE) are considered the largest barrier to widespread usage. Current measurement methods have uniform use across studies but are subjective and are not designed for VR. VRISE and other motion sickness symptom profiles are similar but not exactly the same. Common objective physiological and biomechanical as well as subjective perception measures correlated with VRISE should be used instead. Many physiological biomechanical and subjective changes evoked by VRISE have been identified. There is a great difficulty in claiming that these changes are directly caused by VRISE due to numerous other factors that are known to alter these variables resting states. Several theories exist regarding the causation of VRISE. Among these is the sensory conflict theory resulting from differences in expected and actual sensory input. Reducing these conflicts has been shown to decrease VRISE. User characteristics contributing to VRISE severity have shown inconsistent results. Guidelines of field of view (FOV), resolution, and frame rate have been developed to prevent VRISE. Motion-to-photons latency movement also contributes to these symptoms and effects. Intensity of content is positively correlated to VRISE, as is the speed of navigation and oscillatory displays. Duration of immersion shows greater VRISE, though adaptation has been shown to occur from multiple immersions. The duration of post immersion VRISE is related to user history of motion sickness and speed of onset. Cognitive changes from VRISE include decreased reaction time and eye hand coordination. Methods to lower VRISE have shown some success. Postural control presents a potential objective variable for predicting and monitoring VRISE intensity. Further research is needed to lower the rate of VRISE symptom occurrence as a limitation of use.

A review on design considerations in polymer and polymer composite solid-state electrolytes for solid Li batteries

The current all-solid-state battery (ASSB) technology must stride through meticulous research work to reap the much-sought benefits of high energy density, stable cycling life and economical fabrication of ASSBs for large scale applications. Among all the prevailing scientific challenges, low room temperature ionic conductivity and interfacial impedance are the major roadblocks which need to be addressed before introducing them in large scale application in the high-power devices such as electric vehicles. Herein, we briefly discuss the background and the advances of polymeric and composite solid electrolyte systems with their fundamental ion transport mechanism, followed by a discussion on understanding the chemistry of various interfaces and interphases phenomena, various types of (in)stability issues, other bottleneck and challenging parameters, and the proposed solution to these in cell design strategies. In addition, this review also critically introspects the current measurement methods for collecting and reporting data on the ionic conductivity and reliability of the acquired data. The insights into these aspects will not only enlighten the readers about the recent trends in polymeric solid electrolytes but also will assist determining the correct type of measurement methods and the right cell design strategies.

Adaptive positioning current measurement method for multi-conductor system based on linear magnetic sensor array

As transmission and distribution networks now span a wider region, the difficulty of monitoring lines and the incidence of faults are both increased, which reduces the stability of the power system. During the troubleshooting procedure, quick problem identification, line selection, and placement are essential, and non-contact current measurement can be quite helpful. Its advantages include the capacity to monitor high-frequency current and line space position, both of which contain a range of fault characteristics, as well as a high-frequency band, compact size, low cost, and simple installation. In this paper, we present an adaptive positioning current measurement method for multi-conductor systems based on a linear magnetic sensor array. The method detects the position shifts of individual conductors when their locations are displaced to varying degrees using a novel conductor positioning calculation algorithm. Besides, it automatically adjusts the measurement matrix to provide precise current measurements. In the simulations, three-phase parallel currents with an amplitude of 100 A are analyzed based on the parameters of a 15-meter cup-type tensile tower. During the different displacements of the three conductors, the new method calculates the conductor displacements and the current in real-time. For all the three conductor positions change with the maximum amplitude of 2 m, the average error of all the conductor position measurements is about 0.1 m, and the root mean square error (RMSE) of the currents drops from 34.18A to 3.72A with correction, which proves the effectiveness of the algorithm.

Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review.

Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers.

Effect of Dynamic Soft Reduction Range and Amount on Central Segregation in Bloom and the Resulting Microstructure in the Rod of GCr15‐Bearing Steel

Composition uniformity is essential to the quality, stability, and fatigue life of bearing materials. In the current study, various calculations and measurement methods are implemented to obtain the high‐temperature physical properties of GCr15‐bearing steel. Combined with a validated transient heat transfer model, two cases with different dynamic soft reduction (DSR) ranges and amounts are designed for industrial trials. The defects, carbon contents, and carbides in the bloom center and the carbon contents and microstructure in the rod center are compared. The results show that the reasonable matching of casting speed and cooling water flux in continuous casting could reduce the total content of enriched carbon at the middle and late stages of solidification. Setting a reasonable DSR range (solid fraction: 0.3–1.0) and applying a heavy DSR amount could effectively improve central segregation in bloom and the resulting microstructure in the rod.

Classification of Droplets of Water-PVP Solutions with Different Viscosity Values Using Artificial Neural Networks

When a liquid flows, it has an internal resistance to flow. Viscosity is the property that measures this resistance, which is a fundamental characteristic parameter of liquids. The monitoring of viscosity is essential for quality control in many industrial areas, such as the pharmaceutical, chemical, and energy-related industries. Several instruments measure the viscosity of a liquid, the most used being the capillary viscometers. These instruments are complex, associated with high cost and expensive prices. This represents a challenge in several industries, where accurate viscosity knowledge is essential in designing various industrial equipment and processes. Using image processing and machine learning algorithms is a promising alternative to the current measurement methods. This work aims to extract characteristic information from videos of droplets of different samples using image processing algorithms. An Artificial Neural Network model utilizes the extracted characteristics to classify the droplets in the correct category, which is correlated with the viscosity of the sample. Different solutions samples were created using different ratios of Water and PVP (Polyvinylpyrrolidone) and videos of their droplets were taken and processed. It was found that for water-PVP solutions, the proposed ANN model was able to successfully classify the droplets using the data extracted from the videos with high accuracy. The results imply that the ANN model can recognize the features that affect the viscosity values.

Non intrusive current measurement method for multi conductor in DC distribution network

At present, the power grid is undergoing digital transformation. In order to realize the digitization of the power grid, the first thing to do is to build a power grid wide sensor network with intelligent sensors, and carry out real-time measurement and upload of current, voltage and other state quantities of routes and equipment in the power grid. Most of the existing current measurement methods are intrusive, and can only measure the current on one line. In the lines and equipment of DC distribution network, there are scenes of multi-conductor current measurement such as separated conductor and multi-core cable. Therefore, a non-invasive multi conductor current measurement method is proposed in this paper. Using tunnel magnetoresistance (TMR) as the sensitive element, the non-contact multi conductor current measurement target is realized by using electromagnetic field theory and genetic algorithm. Finally, the correctness and effectiveness of the proposed method are verified by simulation analysis of an example.