Using laboratory measurement data to improve acoustic simulations and evaluate performance

Historically, in dipmeter analysis, depositional patterns are delineated for environmental, structural, and stratigraphic interpretations. The proposed method is a more fundamental approach, which uses raw data measurements from the dipmeter sonde to aid the geologist or log analyst in describing subsurface structures on a stratigraphic scale. Raw data are available at the well site, require only a basic understanding of sedimentary features and facies, and can be combined with computed results. A case study illustrates the reconstruction of sedimentary features from a raw data log recorded by a six-arm dipmeter. The dipmeter is a wireline tool with a series of evenly spaced focused electrodes applied to the circumference of the borehole wall. The raw data are presented as curves representing the electrode response and tool orientation. In outcrop, the geologist can often see an entire sedimentary feature in 3 dimensions relative to its depositional environment. Therefore, a large range of features can be resolved. However, in the borehole environment the perspective is reduced to the borehole diameter, thus reducing the range of recognizable features. In this study, a table was assembled that identifies the features distinguished by the proposed method as a function of borehole diameter. End_of_Record - Last_Page 163-------

The numbers of hard disk drive heads manufactured in Thailand have increased rapidly in the past few years, and one of the most important components of the hard disk drive head is the hard disk drive arm. This component has been produced in large amounts and has been a major income source for a case study company. The manufacturing process of the hard disk drive arm, especially the machining process, is highly complicated and also a main factor of defining the usability of the final product. However, during dimension inspections, many defected parts were detected, resulting in an overall decrease in productivity, sales and profit. Normally, parts are randomly chosen from each CNC machine to be inspected. If there is a defective product, that machine would be shut down and the tool settings will be reset. If the machine that has been producing defective products is inspected late into the inspection shifts, it would result in a considerable amount of defective parts produced before any corrective actions can be made. Therefore, this study presents an application of the integration between Multiple Attribute Decision Making (MADM) and Data Mining (DM) to define the inspection order for tooling adjustment of which the machines with higher risk of producing defective parts can be inspected and corrected before those with lower risk. The methodology is as follows. First, raw measurement data from each machine was collected and noise elimination was performed using the anomaly clustering method. Secondly, K-means clustering, after a machine performance hypothesis test to confirm that the performance of each machine is not equal, was opted for dividing the raw data into three clusters, consisting of good, normal, and bad machines. Finally, a daily CNC machine priority assessment model (CNC-MPAM) is developed based on the Simple Additive Weight (SAW) technique, resulting in a machine performance score for optimal ordering of machine tooling adjustment. Experimental results suggested that this proposed method is capable of machine adjustment ordering so that defective prone machines can be serviced sooner, reducing defective parts produced and improving overall productivity.

The thruster fault diagnosis problem of the satellite attitude control system is investigated in this article. This challenging problem is first changed into the binary image classification issue. A deep learning based data-driven fault diagnosis approach is then presented. It benefits from this approach that the stuck-open and the stuck-close faults of thruster are detected, diagnosed, and located online with high accuracy. The proposed method is purely data-driven and directly implemented by using raw measurement data only. It is independent of the dynamics of the thruster and the mathematical model of the satellite attitude control system. The effectiveness of the proposed approach is finally demonstrated on a satellite example.

Acoustic source localization based on time difference of arrival (TDOA) measurements from spatially separated sensors is an important problem in wireless sensor networks (WSNs). While extensive research works have been performed on algorithm development, limited attention has been paid in how to form the sensor pairs. In the literature, most of the works adopt a centralized sensor pairing strategy, where only one common sensor node is chosen as the reference. However, due to the multi-hop nature of WSNs, it is well known that this kind of centralized signal processing method is power consuming since raw measurement data is involved in the transmissions. To reduce the requirements for both network bandwidth and power consumptions, we propose an in-network sensor pairing method to collect the TDOA measurements while guaranteeing the quality of source localization. The solution involves finding a minimal sized dominating set (MSDS) for a graph of the muti-hop network. It has been proved that in-network sensor pairing can result in the same Cramer-Rao-Bound (CRB) as the centralized one but at a far more less communication cost. Furthermore, the structure of the proposed in-network sensor pairing coincides with the decentralized source localization, which is an important application of our method.

Improvement of the Microwave Imaging System by Deconvolution of the Antenna Pulse ResponseIn the paper a microwave imaging system and its algorithm of image reconstruction from the raw measurement data were briefly introduced. Authors observed that pulse responses of the antennas used in the system have similar duration to the measurement pulse and therefore also contribute to the limits of system resolution. Paper presents a simple method that improves the system performance by additional deconvolution of the antenna pulse responses. Method was verified experimentally.

On their continuous quest to improve drilling efficiency, operators are reaching more and more towards the sensor and data-streaming technologies and their powerful data analytics capabilities. For this project, an operator partnered with the drilling automation research group at the University of Texas at Austin to develop a workflow for big data analysis and visualization. The objectives were to maximize the value derived from data, establish an analysis toolkit, and train students on data analytics—a necessary job function of any future drilling engineer. The operator provided data sets, business and technical objectives, and guidance for the project, while a multi-disciplinary group of undergraduate and graduate students piloted an analysis workflow. The students developed methods to: 1) understand and clean the data; 2) structure, combine, and condense information; 3) visualize, benchmark, and interpret the data, as well as derive key performance indicators (KPIs); and 4) automate these processes. The operator provided data collected from drilling 16 wells in an US unconventional play. The large data sets comprised of un-organized time and depth based information from surface and downhole sensors, daily drilling reports, geological information, etc. Students were trained on specialized software and subsequently curated data into smaller sizes and standard formats. Students investigated bottom hole assembly (BHA) and directional drilling performance using a combination of auto-generated conventional visuals (e.g., BHA designs, annotated time vs depth curves) and newly developed tools (e.g., tortuosity, 3D well trajectory plots combined with operational data). Methods for ‘push a button' investigations of mechanic specific energy (MSE), vibration, torque and drag were also developed by calculating specific KPIs from the raw measurement data. The analysis work itself coupled with the attempt to improve the workflow processes served as a meaningful and highly effective way to educate students and prepare them to be the "drilling engineers of the future" with proficiency in data analytics.

The space-borne Environment Monitoring Instrument detects the Earth's surface radiation from ultraviolet to visible band, and it could be used to inverse the trace gas concentrations of target area. To obtain the 1b radiation data products, raw measurement data (level 0 data) should be corrected and modulated. Based on CCD detectors and payload’s characteristics, the electronic correction processing flow and data correction algorithm are proposed, and dark current, electronic offset, Smear and PRNU are also corrected. By using this algorithm, the ground level 0 test data are corrected. The result shows that the corrected data can improve the accuracy of the data; it can be used to process the data which are measured in orbit observation; it strongly guarantees the formation of further level 2 data products.

Incoming inspections of battery cells prior to module assembly help to ensure the quality of the battery system and prevent the installation of anomalous cells. Depending on the area of application, identifying deviations in the electrical behavior of the battery cells under test can be essential for downstream assembly processes like cell matching and algorithm adaptations of the battery management software. In this work, the use of a multi‐cell testing procedure involving differential voltage analysis, incremental capacity analysis, direct current internal resistance tests, and electrochemical impedance spectroscopy is investigated to reveal differences in cell properties and identify anomalous cells while economizing on the required cell test channels. The results obtained from 20 model‐identical 21700 cylindrical cells from four different batches demonstrate that this methodology can detect material variations, such as differing silicon and graphite content, which are not disclosed by the supplier or indicated in the data sheet. A teardown with elemental analysis of two cells from different batches is carried out as verification. Finally, prospects for potential application scenarios and raw measurement data are provided.

In machinery and equipment manufacturing industry, screw is an important part. Not only as a precision screw precision transmission components, but also commonly used for precision measuring devices. Precision screw itself directly affects the accuracy of precision machine tools and precision measuring instruments for transmission, positioning precision and accuracy, and thus the processing and inspection of precision screw is higher demanded. Traditionally, screw inspection is usually done by manual and the quality of inspection is not high, with very low inspection efficiency. To solve these problems, optimized system for screw inspection was designed and implemented. Using the Quartile range method in Statistics to remove outliers, and with the help of kalman filter, systemic error is got rid of. With these methods, raw measurement data is analyzed and processed, and inspection report is generated. The efficiency of screw inspection is highly improved.

Convolutional neural network-based power system transient stability assessment and instability mode prediction

Detection of steel bars embedded inside concrete for reinforcement has been one of the major goals of nondestructive testing methods for concrete. The use of radar in detecting steel bars is advantageous due to the sensitivity of electromagnetic waves to metallic objects and the versatility in using appropriate electromagnetic wave polarization waves to metallic objects and the versatility in using appropriate electromagnetic wave polarization with respect to the orientation of the bars. However, obtaining imagery of steel bars inside a lossy medium such as concrete requires sophisticated problem solving approaches including understanding of the electromagnetic properties of concrete, identification of proper radar measurement parameters as center frequency and bandwidth, and development of image reconstruction algorithms. This paper presents experimental results of radar measurements of laboratory size concrete specimens with or without steel bars. Sample radar imagery processed from raw measurement data is provided with discussion.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper focuses on integrity analysis on position service of GPS/DGPS signals in port environment in order to support fail-safe navigation of ships. After installation of the developed GPS/DGPS monitoring system at a port, it calculates the quality monitoring and horizontal protection level of navigational signals at port aspect, and also analyzes the integrity using the relation of alert limit and position error. The system can provide the results of reliability level determination based on the quality monitoring and the integrity assessment on Port GPS/DGPS signals to user navigation receiver. This paper presents the comparison results with position accuracy and integrity monitor on the port approach and restricted waters requirement of IMO based on the results of performance assessment using the raw measurement data in a real port environment.

Android has been providing global navigation satellite system (GNSS) raw measurement data since the release of the Android operating system Nougat (N) in 2016. Users can use the GNSS measurements to directly estimate their accurate location. Studies are being actively conducted to improve the accuracy of user locations down to the decimeter level using GNSS raw measurements in Android smartphones and by applying methods such as Kalman filter (KF) and real-time kinematics (RTK). Android smartphones use planar inverted-F antennas (PIFAs) owing to hardware limitations. The PIFA structure is susceptible to multipath errors; therefore, significant noise is introduced into the raw GNSS measurements. In addition, there are software limitations. Further, the Duty Cycle functions in Android smartphones prioritize communication, which is the primary function of smartphones, over navigation during processor allocations. When a Duty Cycle is enabled, the antenna tracking the satellite to receive carrier measurements is forcibly disconnected and repeatedly reconnected. However, studies have shown that cycle slips still occur, even after limiting the Duty Cycle function, owing to the limitations of the smartphone antenna, and the utilization of carrier measurements is still challenging. In addition, not every smartphone manufacturer offers seamless support for carrier measurement. Therefore, Conducting RTK research using carrier measurements of smartphones is difficult. Previous studies have addressed the limitations of smartphones by receiving satellite signals using high-performance antennas on roofs of high-rise buildings, in ideal environments, and rebroadcasting the signals to smartphones from rebroadcasting rooms. In contrast to carrier measurements, Doppler measurements are not affected by cycle slips. As shown in Fig. 1, Doppler measurements in Android smartphones are less accurate than carrier measurements in the absence of cycle slips. However, the accuracy of Doppler measurements is excellent compared to the accuracy of code measurements. In this study, we applied a code–Doppler smoothing method that combines the advantages of code and Doppler measurements in Android smartphones. Fields such as flight formation, autonomous driving, and car navigation require accurate distance between objects. Therefore, sensors that estimate distance, such as LiDAR and radar, are typically required. The relative positioning method can be used to accurately estimate the distance between objects using only satellite measurements without these sensors. In this method, the difference between the two pseudoranges is determined. Subsequently, an accurate relative distance between two receivers can be estimated using this baseline vector and the position of the satellite. In our previous study, we improved the distance estimation accuracy using raw GNSS measurements from an Android smartphone and the two methods previously mentioned. The methods were verified by conducting an experiment using receivers of different performance levels in static and dynamic environments, but not in an ideal environment such as the rooftop of a high-rise building. In this study, relative positioning accuracy was improved by using the extended KF and GNSS raw measurements from an Android smartphone. Furthermore, the code–Doppler smoothing method was applied based on the characteristics of smartphone measurements. This method of precision relative positioning using Android smartphones is anticipated to be useful because of its simple and convenient usage without the need for additional equipment.

Correlation-based damage detection method using convolutional neural network for civil infrastructure

Repeatable and accurate tests are important when designing hardware and algorithms for solar-powered wireless sensor networks (WSNs). Since no two days are exactly alike with regard to energy harvesting, tests must be carried out indoors. Solar simulators are traditionally used in replicating the effects of sunlight indoors; however, solar simulators are expensive, have lighting elements that have short lifetimes, and are usually not designed to carry out the types of tests that hardware and algorithm designers require. As a result, hardware and algorithm designers use tests that are inaccurate and not repeatable (both for others and also for the designers themselves). In this article, we propose an indoor test methodology that does not rely on solar simulators. The test methodology has its basis in astronomy and photovoltaic cell design. We present a generic design for a test apparatus that can be used in carrying out the test methodology. We also present a specific design that we use in implementing an actual test apparatus. We test the efficacy of our test apparatus and, to demonstrate the usefulness of the test methodology, perform experiments akin to those required in projects involving solar-powered WSNs. Results of the said tests and experiments demonstrate that the test methodology is an invaluable tool for hardware and algorithm designers working with solar-powered WSNs.