Dynamic EMI sensor platform for digital geophysical mapping and automated clutter rejection for conus and oconus applications

We recently developed a new, man-portable, electromagnetic induction (EMI) sensor designed to detect and classify small, unexploded sub-munitions and discriminate them from non-hazardous debris. The ability to distinguish innocuous metal clutter from potentially hazardous unexploded ordnance (UXO) and other explosive remnants of war (ERW) before excavation can significantly accelerate land reclamation efforts by eliminating time spent removing harmless scrap metal. The EMI sensor employs a multi-axis transmitter and receiver configuration to produce data sufficient for anomaly discrimination. A real-time data inversion routine produces intrinsic and extrinsic anomaly features describing the polarizability, location, and orientation of the anomaly under test. We discuss data acquisition and post-processing software development, and results from laboratory and field tests demonstrating the discrimination capability of the system. Data acquisition and real-time processing emphasize ease-of-use, quality control (QC), and display of discrimination results. Integration of the QC and discrimination methods into the data acquisition software reduces the time required between sensor data collection and the final anomaly discrimination result. The system supports multiple concepts of operations (CONOPs) including: 1) a non-GPS cued configuration in which detected anomalies are discriminated and excavated immediately following the anomaly survey; 2) GPS integration to survey multiple anomalies to produce a prioritized dig list with global anomaly locations; and 3) a dynamic mapping configuration supporting detection followed by discrimination and excavation of targets of interest.

This research work was undertaken to assess the application of quality control concepts in the production of masonry blocks for building projects in Imo State, Nigeria.The study was embarked upon in order to examine the different types of masonry blocks produced in the state, and to assess the level of quality control concepts applied in their production processes, using selected block industries in Owerri Municipality.The study also was aimed at finding out the educational qualifications of the workers at those block industries.The research was an attempt to search for answers to the problem of building and infrastructural collapse that has become rampant in Nigeria recently.Data for the research was collected through the views of the staff and management of the case study block industries and data analysis which was carried out by the use of percentages and frequency tables and chi-square statistical tool, revealed that the problem of low quality masonry blocks used in building projects were due to several latent factors like "time lag" between mixing and placing of mix into mould; mould of placing mix into mould before compaction; type of cement; and inappropriate sand-cement-water mixture ratio.These things were found to have great and decisive effects on the ultimate strength and quality of the building blocks.The research also revealed that poor mixing of cement, sand and water, lack of quality control measures and low quality sand were responsible for low quality blocks in many block producing small and medium scale industries.This situation is contributing to building and infrastructural collapse.The study therefore recommends that quality control concepts should be applied in the production of masonry blocks.The use of acceptance sampling tests for all the raw materials used in masonry block production and application of control-chart for work-in-process should be adopted.The pre-process, inprocess, and post-process inspections and testings must be carried out for the production and standardization of high-quality building blocks before the products are made available to the customer.

The real-time quality control (RTQC) methods applied to Argo profiling float data by the United Kingdom (UK) Met Office, the United States (US) Fleet Numerical Meteorology and Oceanography Centre, the Australian Bureau of Meteorology and the Coriolis Centre are compared and contrasted. Data are taken from the period 2007 to 2011 inclusive and RTQC performance is assessed with respect to Argo delayed-mode quality control (DMQC). An intercomparison of RTQC techniques is performed using a common data set of profiles from 2010 and 2011. The RTQC systems are found to have similar power in identifying faulty Argo profiles but to vary widely in the number of good profiles incorrectly rejected. The efficacy of individual QC tests are inferred from the results of the intercomparison. Techniques to increase QC performance are discussed.

Modern manufacturing processes characterized by short series, complex part geometry and high refinement values often demonstrate conditions when traditional quality control (QC) methods do not work properly. This paper presents a prototype system for real-time QC where the developed methods and applications are integrated and post-process quality control is applied only as a complement and for reference measurements. All activities are supervised and fed with information from a developed active data acquisition system. The proposed concept contributes to bridging the gap between traditional post-process control and realtime QC of machining processes.

In this study, we used artificial intelligence (AI) technology to explore for automated medical record quality control methods, standardize the process for medical record documentation, and deal with the drawbacks of manually implemented quality control. In this study, we constructed a medical record quality control system based on AI. We first designed and built, for the system, a quality control rule base based on authoritative standards and expert opinions. Then, medical records data were automatically collected through a data acquisition engine and were converted into structured data through a post-structured engine. Finally, the medical record quality control engine was combined with the rule base to analyze the data, identify quality problems, and realize automated intelligent quality control. This system was applied to the quality control of medical records and five quality control points were selected, including similarities in the history of the present illness, defects in the description of chief complaints, incomplete initial diagnosis, missing in formation in the history of menstruation, marriage, and childbirth, and mismatch between the chief complaints and the history of the present illness. We randomly selected 2 918 medical records of patients discharged in January 2022 to conduct AI quality control. Then we organized medical record quality control experts to conduct an accuracy review, made a comparison with previous manual quality control records, and analyzed the results. The number of quality problems that were verified in the accuracy review was taken as the gold standard and receiver operating characteristic (ROC) curves were drawn for the 5 quality control points. According to the accuracy review performed by medical record quality control experts, the accuracy of AI quality control reached 89.57%. For the sampled medical records, the results of AI quality control were compared with those of previous manually performed quality control and only one problem detected by manual quality control of the sampled medical records was not detected by the AI quality control system. The number of medical record quality problems correctly detected by AI quality control was about 2.97 times that of manual quality control. Analysis of the ROC curves showed that the AUC of the five quality control points of the AI quality control system were statistically significant (P<0.05) and all the AUC values approximated or exceeded 0.9. In contrast, results obtained through manually performed quality control found significant AUC (0.797) for only one quality control point-similarities in the history of present illness (P<0.05). Comparison of the AUC values of the two quality control methods showed that AI quality control system had an advantage over manually performed quality control for the five quality control points. Through the application of medical record quality control system based on AI, efficient full quality control of medical record documentation can be achieved and the detection rate of quality problems can be effectively improved. In addition, the system helps save manpower and improve the quality of medical record documentation.

A critical factor in the adoption of any new aeronautical technology or concept of operation is safety. Traditionally, safety verification is accomplished through a rigorous process that involves human factors, low and high fidelity simulations, and flight experiments. As this process is usually performed on final products or functional prototypes, concept modifications resulting from this process are very expensive to implement. This paper describes an approach to system safety that can take place at early stages of a concept design. It is based on a set of mathematical techniques and tools known as formal methods .I n contrast to testing and simulation, formal methods provide the capability of exhaustive state exploration analysis. We present the safety analysis and verification performed for the Small Aircraft Transportation System (SATS) Concept of Operations (ConOps). The concept of operations is modeled using discrete and hybrid mathematical models. These models are then analyzed using formal methods. The objective of the analysis is to show, in a mathematical framework, that the concept of operation complies with a set of safety requirements. It is also shown that the ConOps has some desirable characteristic such as liveness and absence of dead-lock. The analysis and verification is performed in the Prototype Verification System (PVS), which is a computer based specification language and a theorem proving assistant.

With support of the Environmental Security Technology Certification Program (ESTCP), AETC is developing a UXO survey system for use in shallow water (up to 15 feet deep). The sensor platform incorporates both an array of Cs‐vapor magnetometers and a time domain EMI sensor array. A performance modeling study defined the platform operational and deployment concepts. An engineering design study established the performance characteristics of the new time‐domain EMI sensor, which incorporates an extended transmitter, coupled with an array of four receive coils. The system incorporates a pilot guidance survey navigation aid, a dual GPS sensor system to provide positioning and attitude information, and the Data Acquisition (DAQ) and Data Analysis Systems (DAS) adapted from the Airborne Multi‐sensor Towed Array Detection System (MTADS), which incorporate 3‐dimensional sensor position information into a 3‐dimensional, physics‐based target fitting GUI. Additional marine components include an autopilot system to control the depth and attitude of the sensor platform, digital magnetic compasses, a high‐frequency digital imaging sonar, an inertial attitude measurement sensor, depth sensors, and echo sounders. The tow vessel is a pontoon boat. The first full scale demonstration at a former UXO target range will take place in 2005.

In the construction process, real-time quality control and early defects detection are still the most significant approach to reducing project schedule and cost overrun. Current approaches for qual...

The search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of “known” errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control.

The quality control of observation data is an important link in the entire meteorological observation service system chain. The reactive gas observation is different from the observation of other meteorological elements in general, especially the special operations such as periodic zero / span inspection and multi-point calibration involved in instrument observation, and the quality control corresponding to its data is also special character. The main purpose of this article is to find the most effective quality control method for quality control of reactive gas observation data. Six types of quality control methods are used to perform quality control on reactive gas observation data, including extreme value check, triple standard deviation method, zero-span identification method, internal consistency check, time change check, and comprehensive identification check. By comprehensively identifying the data quality control result identification code given by each method, the quality control status of the data is finally determined. The quality control data used are the observation data of Beijing Shangdianzi atmospheric background station in 2018. Conclusion: A comparison of longsequence, multi-site analysis of the data after quality control using 6 methods and the data processed by a single method can draw conclusions. The multi-method comprehensive quality control results are superior to a single traditional meteorological observation data quality control method, which will cause fewer false judgments on the data and ensure the best quality control results of the observation data

Clutter rejection is a key technique for high-frequency passive radar (HFPR). To solve this problem, the traditional signal processing methods have been used, which mainly depend on prior information of the feature differences between target and clutter in time, space, or frequency domain. As a new attempt to deep-mine the clutter feature automatically and reject it by only data-driven processing, a novel clutter rejection method based on graph-relational mapping using a deep learning network is proposed in this letter. In this method, the clutter rejection problem is turned into an image-to-image translation problem between the range-Doppler (RD) spectrograms before and after clutter rejection. A deep-learning-enabled image translation network (CycleGAN) is exploited to learn from training data of RD spectrograms and to establish the mapping relationship. When processing clutter rejection tasks, the trained network can automatically extract clutter features without prior information and save manpower. The performance evaluations of the novel clutter rejection method are also investigated, and the experimental results confirm that the proposed method can effectively reject clutter in HFPR.

Effective detection of bias and variability using R-value statistics in the even-check method for real-time quality control.

Focusing on the real-time quality control in machining process, sensitivity analysis method based on LS-SVM was proposed in this paper to control the quality of products. The relationship between machining quality and influence factors is built by LS-SVM (least squares support vector machine). Sensitivity and contribution rate are calculated in sensitivity analysis. The quality of products can be controlled by controllable factors according to the result of sensitivity analysis, and the control effect can be evaluated by quality loss function. The method verification was conducted by a simulation example. At the end of this paper, a case about globoidal cam was presented to verify the feasibility and accuracy.

In response to the significant clinical challenges associated with current Electronic Nasopharyngolaryngoscopy examinations-including considerable inter-observer variability in physician interpretation, high rates of missed diagnoses for early-stage lesions, and inefficiencies in dynamic functional assessment-this consensus document aims to establish a comprehensive technical framework for Artificial Intelligence（AI） -enabled data acquisition and application. The framework introduces tiered data acquisition standards, comprising a foundational level to ensure clinical accessibility and an advanced level designed for research purposes. It systematically standardizes a dual-mode imaging protocol utilizing both white light and electronic chromoendoscopy. Furthermore, it innovatively proposes a three-tier annotation framework-encompassing regional, characteristic, and diagnostic levels-and establishes rigorous quality control and multi-center validation mechanisms. This document delineates detailed application pathways for AI across five core scenarios: auxiliary detection, auxiliary diagnosis, real-time quality control, automated report generation, and simulation training. Concurrently, it defines ethically compliant data security standards. These technical specifications provide crucial support for enhancing the standardization of nasopharyngeal and laryngeal disease management, facilitate multi-center research collaboration, and hold significant importance for promoting the standardized application of AI within the field of Otorhinolaryngology.

Introduction The extremely small fiber size and low mass of nanofiber composites makes quality control measurement very difficult. Most commercially available contact measurement equipment cannot work with the nanofiber composite without altering its characteristics. Generally, non-contacting optical methods are not effective because the wavelength of the light used is greater than the diameter of the nanofibers. Non-contact Gamma or Beta gauges lack the necessary resolution. Research methods such as Scanning Electron Microscopy (SEM) are not practical in a production setting. Large-scale production of nanofiber composites requires unique quality control (QC) methods. Figures 1a, 1b, and 1c show examples of nanofiber composites manufactured by Donaldson. As Donaldson Company’s nanofiber production has increased to over 10,000 m per day during the last 20 years, QC methods have been developed to address these needs. Four evaluation tools that have been developed for commercial production of nanofibers will be described. These evaluation tools focus on: uniformity of nanofiber distribution, fiber size distribution, durability of fiber layer in the composite, and environmental resistance.