Processing Algorithms Research Articles

A-182 Applications of artificial intelligence optical character recognition in laboratory clerical functions offsetting staffing shortages and error reduction

Abstract Background The clinical laboratory profession has been dealing with a decade long, and now critical retention and staffing problem. The average national turnover rate for laboratory technicians is 17%, and the highest turnover rate exhibited for phlebotomists is 36%. While turnover rates have an impact on a laboratory, or hospitals budget, more importantly they have a substantial impact on quality and error rates, particularly in the pre-analytical stage of testing; with studies showing up to 65.09% of errors impacting the pre-analytical phase. Methods In an effort to remedy this high error rate, a meta-analysis was performed on paper order entry and clerical pre-analytical error, in parallel with performance and accuracy data of advanced optical character recognition driven by artificial intelligence and machine learning (AI OCR). Results AI OCR studies demonstrated an untrained accuracy percentage, to include handwriting of 91.08%, and when trained utilizing both relational and hierarchal logic data bases accuracy increased to 98.4%. The rate at which AI OCR processed and classified medical information was 3-4 pages per second. It is estimated that 99.9%-100% accuracy can be achieved with additional form field training and mapping. The level of accuracy and efficiency for laboratory paper order-entry utilizing AI OCR can also be increased with the addition of natural language processing and data recognition algorithms when combined with logic databases containing information such as medical record numbers, ICD-10 codes, and laboratory test compendiums. Conclusions The performance of this technology demonstrates a strong application to reduce the burden of clerical paper order entry, while addressing the 30.8% dissatisfaction rate regarding manual entry processes among lab professionals surveyed. Moreover, AI OCR clerical data classification, extraction and entry processes augmented with human verification steps examined, demonstrated overall accuracy of 99.99%. The application of AI OCR in laboratory pre-analytical processes has the potential to improve test order accuracy and efficiency, ultimately impacting the quality of patient care and operational costs for healthcare facilities. When examined through the lens of staff retention, studies show that 80% of laboratory employees feel that a workplace with the necessary tools and technology positively impacts their job satisfaction. While organizations investing in technologies that drive both job efficiency and satisfaction see a 125% return on investment through productivity. Demonstrating that the application of sophisticated technologies that both increase productivity and decrease error rates have a substantial impact on job satisfaction and ultimately retention. Although laboratory clerical data and order entry processes were examined for this meta-analysis, the aggregate data compiled showed that the application of AI OCR in clerical processes could also have a substantial impact on the 25.9% annual turnover rate for hospitals as a whole in the United States.

Thermal testing of corundum ceramics: pulse heating and optimized data processing algorithms

A review of the types of defects in the production of corundum ceramic tiles and traditional methods of integrity testing of products made of this material is carried out. The integrity of tiles containing artificial defects was investigated by the method of active thermal testing using pulsed optical heating. A scheme of two-way thermal testing with software processing of the initial thermograms using the Parker method is applied. It has been established that the best results in detecting internal defects in ceramic tiles with a thickness of 10 mm during thermal stimulation using xenon lamps are obtained by the method of two-way thermal testing with the construction of thermal conductivity maps.

Research of ways to improve the security of corporate networks

Objective. The article explores a way to improve the security of a corporate network by using the technique of spoofing sender addresses when forwarding packets between computers. Method. Network traffic analysis methods, network protocol level programming and data packet processing algorithms are used. Computers on the network are connected to one server. When sending packets between computers in the network, the sender's address is replaced, and when a packet is received by another computer, the packet is disassembled, and the real sender's address is inside it. Result. The method allows for effective protection of a corporate network from attacks associated with the forgery of addresses of data packet senders. Conclusion. Using the sender address substitution technique when transmitting data in a corporate network is an effective way to increase security and protection from external threats. Further research is aimed at developing complex and reliable methods for protecting networks.

A Novel Method for Aircraft Structural Dynamic Strain Trend Signal Processing via Optimized Parallel Computing

In this study, we investigate the underlying causes of drift in the time history curves of measured parameters obtained through strain electrical measurements and assess their impacts on load measurements. To address the challenge of efficiently processing large volumes of aircraft load data, we propose and analyze a multi-level parallel algorithm specifically designed for the data processing of aircraft load measurements. To achieve this objective, we discuss parallel processing at both medium- and fine-grained levels and develop two distinct parallel processing algorithms: one for coarse- and medium-grained aircraft-type data streams, and another for medium- and fine-grained takeoff and landing data streams. The efficacy of these algorithms is validated through the processing of load data measured on a specific aircraft wing. The results demonstrate that the proposed approach offers a novel technical pathway for large-scale scientific computations and enhances data processing efficiency in the domain of aircraft load spectrum analysis.

A-169 Artificial Intelligence based Complete Blood Count

Abstract Background The OLO device (Sight diagnostics, Israel) is an artificial intelligence based blood counter. OLO contains a fluorescent microscope and a camera embedded inside it. The pictures are processed by an image processing algorithm based on “machine-vision technology” to identify and quantify cells. The measurement analysis is accompanied by messages (flags) that are used to warn of deviations in the counted parameters, which help the operator refer the sample for microscopic analysis. In this study, we compared the results of 19 complete blood count (CBC) parameters obtained from OLO to the CBC parameters obtained from the ADVIA 2120 device (Siemens, USA). In addition we examined the reliability of OLO alerts (flags) by comparing it to the microscopic analysis findings. Methods 99 blood samples from patients aged >3 months were sampled by the ADVIA and OLO devices. Regression analysis was performed in comparing the results of 19 parameters between the devices. Specificity and sensitivity values ​​of the OLO device alerts were calculated. Results A good correlation was found for CBC parameters between OLO and ADVIA with Pearson correlation (R) of 0.992 (WBC), 0.985 (RBC), 0.991 (HB) and 0.983 (PLT). A fundamental difference in measurement and classification technologies between OLO and ADVIA lead to low correlation measured for monocytes. By comparing OLO alerts against blood smears findings, a total of 14 samples were alerted by OLO but only slight left shift/single pathological cell were found in the smears which is below threshold defined by the manufacturer. These 14 samples were transferred to the true positive category, therefore OLO device alerts showed sensitivity and specificity of 81.8% and 90% (respectively) in identifying pathological cells. Conclusions The comparison findings present a technology based on image processing and artificial intelligence that have a good correlation to the ADVIA reference device. In addition, OLO has the ability to warn of pathological findings even below the threshold defined by the manufacturer. The artificial learning is expected to be improved so that this device can integrate CBC and microscopic differentiation simultaneously, a great advantage to the hematology laboratories workflow.

De Novo Natural Language Processing Algorithm Accurately Identifies Myxofibrosarcoma From Pathology Reports.

Available codes in the ICD-10 do not accurately reflect soft tissue sarcoma diagnoses, and this can result in an underrepresentation of soft tissue sarcoma in databases. The National VA Database provides a unique opportunity for soft tissue sarcoma investigation because of the availability of all clinical results and pathology reports. In the setting of soft tissue sarcoma, natural language processing (NLP) has the potential to be applied to clinical documents such as pathology reports to identify soft tissue sarcoma independent of ICD codes, allowing sarcoma researchers to build more comprehensive databases capable of answering a myriad of research questions. (1) What proportion of patients with myxofibrosarcoma within the National VA Database would be missed by searching only by soft tissue sarcoma ICD codes? (2) Is a de novo NLP algorithm capable of analyzing pathology reports to accurately identify patients with myxofibrosarcoma? All pathology reports (10.7 million) in the national VA corporate data warehouse were identified from 2003 to 2022. Using the word-search functionality, reports from 403 veterans were found to contain the term "myxofibrosarcoma." The resulting pathology reports were manually reviewed to develop a gold-standard cohort that contained only those veterans with pathologist-confirmed myxofibrosarcoma diagnoses. The cohort had a mean ± SD age of 70 ± 12 years, and 96% (287 of 300) were men. Diagnosis codes were abstracted, and differences in appropriate ICD coding were compared. An NLP algorithm was iteratively refined and tested using confounders, negation, and emphasis terms for myxofibrosarcoma. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for the NLP-generated cohorts through comparison with the manually reviewed gold-standard cohorts. The records of 27% (81 of 300) of myxofibrosarcoma patients within the VA database were missing a sarcoma ICD code. A de novo NLP algorithm more accurately (92% [276 of 300]) identified patients with myxofibrosarcoma compared with ICD codes (73% [219 of 300]) or basic word searches (74% [300 of 403]) (p < 0.001). Three final algorithm models were generated with accuracies ranging from 92% to 100%. An NLP algorithm can identify patients with myxofibrosarcoma from pathology reports with high accuracy, which is an improvement over ICD-based cohort creation and simple word search. This algorithm is freely available on GitHub (https://github.com/sarcoma-shark/myxofibrosarcoma-shark) and is available to facilitate external validation and improvement through testing in other cohorts. Level II, diagnostic study.

Implement the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) Algorithms for Sales Classification

The Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) algorithms are two algorithms that have proven efficient in various classification and prediction applications. This research examines the application of these two algorithms in the context of selling goods in PIM supermarkets. In this research, AHP and KNN are used to classify goods sold based on various criteria such as price, number of stock items sold, total sales amount. The research results show that KNN outperforms AHP in predicting the best-selling, best-selling and least-selling items based on sales in 2022 at PIM supermarkets. Based on this research, it can be concluded that the KNN algorithm is suitable for predicting the classification of goods sales in PIM Supermarkets. This research classifies sales of goods using the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) methods. This research uses 3 criteria. By using the value K=1, the experimental results show that the highest KNN has an accuracy of 38%, while AHP has an accuracy of 32%. Differences in accuracy results can be influenced by parameter settings and characteristics of the dataset used. Therefore, further analysis of these factors is needed to understand the performance differences between the two methods.

Development and validation of a natural language processing algorithm using electronic health record data to identify patients with breast cancer with low social support.

421 Background: Social support is important to the management of breast cancer treatment. Our team has developed data from electronic health record (EHR) data into structured ‘concept groups’ that will form the basis for the development of EHRsupport, a computable, EHR-based measure of social support. We report the evaluation of these concept groups against chart review as a part of our validation. Methods: We built a natural language processing (NLP) algorithm on clinical notes in 7,989 women diagnosed from January 2006 to September 2021 with invasive breast cancer. We identified and developed 10 concept groups from unstructured data: 1) living situation, 2) marital/partner status, 3) parenthood status, 4) visit support (accompanied patient to ≥1 visit, patient attended alone at ≥1 visit), 5) friends/other support, 6) explicit positive or negative mentions of social support, 7) mention of a deceased person, 8) transportation issues, 9) relationship conflict or stress, and 10) social isolation. We validated concept groups against the charts of 100 patients randomly drawn from the broader patient population (nonoverlapping with the training data set) also around the time (-1 to +3 months) of diagnosis. Results: Concept group data availability ranged from 1.3% social isolation to 98.3% for living situation. Specificity and negative predictive values were moderate to high for all concept groups. Sensitivity and positive predictive values were moderate to high for concept groups with high data availability (Table) and lower for concept groups with low data availability. Conclusions: Data on social support have been available since the advent of Epic in 2006 and our NLP-based algorithm accurately captured data within the EHR that were systematically collected supporting the development of a clinical tool that can be used to identify patients at risk of low social support. EHRsupport concept groups, data availability (n=7,989), and evaluation vs. chart review. Availability (%) Sensitivity Specificity PPV NPV Living situation (e.g., alone or not) 98.3 100 92 61 100 Partner/spouse 92.0 85 100 100 81 Parenthood status 88.8 95 80 93 83 Visit support 82.9 92 77 96 59 Positive mentions social support *46.9 75 100 100 70 Negative mentions social support 33 99 67 96 Friend/other support 46.8 81 81 79 83 Deceased person 39.9 86 97 95 90 Transportation issues 14.6 80 85 22 99 Relationship conflict/stress 7.7 25 95 29 94 *Availability of positive and negative mentions of social support.

Research of signal processing and detection of satellite telemetry data based on EMD and MBSE

Abstract Based on the characteristics of abnormal data and high Signal-to-Noise Ratio in satellite in-orbit telemetry data, the empirical mode decomposition algorithm is used to remove the abnormal data, and the method is effective. By using the empirical mode decomposition algorithm, trend prediction of telemetry data can also be achieved, and its practicality can be demonstrated through signal construction and development. The combination of the empirical mode decomposition algorithm for telemetry data processing with the satellite’s digital model constructed by MBSE software has expanded the application scope of the digital satellite model.

An improved whale algorithm for real-time boundary processing and its convergence analysis

Abstract To tackle the deficiencies in the whale algorithm, including its weak stability, sluggish convergence rate, and tendency to get trapped in local optima, an adapted whale optimization algorithm has been created, which introduces quasi-opposition learning and a real-time boundary processing mechanism. In the enhanced algorithm, a quasi-opposition learning mechanism is implemented to broaden the search scope and augment the variety of the population during the initial phases of iteration. This approach also helps to guarantee the convergence of the algorithm in the later phases of iteration. The unified boundary processing method of delay concentration is improved to solve the problem that a large amount of convergence damages diversity if a large number of transgressive individuals are unified and centralized. The algorithm flow is given and the asymptotic fitness and the asymptotic global optimal solution are proved. The time complexity is analyzed theoretically. Ultimately, six recognized algorithms were utilized to simulate the CEC 2017 function set across different scales. Findings indicate that the enhanced algorithm significantly boosts convergence rate, optimization precision, and solution robustness, demonstrating strong convergence characteristics.

An adaptive processing model for large superalloy pipeline components

Many large pipeline components in an aerospace engine are made of superalloy. Precision machining of superalloy curved surface components is an important way to achieve the weight reduction of the aerospace engine’s gas pipeline system. However, this type of components often uses the hot forging technique to process blanks, and the components processed with the technique have large deformation and difficulties in cutting, which jointly exacerbate the complexity of mechanical processing. Domestic and foreign scholars have conducted fruitful theoretical and experimental study of adaptive machining theory, curved surface path planning algorithms and model reconstruction methods. At present, the theoretical analysis methods for deformation analysis and error compensation in the adaptive machining of high-temperature alloys are not clear; The precise physical model reconstruction technology is not yet mature and engineering applications are not yet popularized. Therefore, this article proposes a precise registration algorithm for the mechanical processing of superalloy pipeline blanks after forging and forming based on inner curved surface. Combined with digital measurement techniques of curved surfaces, it achieves the adaptive processing model reconstruction of large pipeline components. The technical feasibility is verified through curved surface model reconstruction and adaptive processing experiments.

A model free adaptive control method based on self-adjusting PID algorithm in pH neutralization process

In this paper, a new model free adaptive control method based on self-adjusting PID Algorithm (MFAC-SA-PID) is proposed to solve the problem that the pH process with strong nonlinearity is difficult to control near the neutralization point. The MFAC-SA-PID method also solves the problem that the parameters of the model free adaptive control (MFAC) method are not easy to be adjusted and the effect is not obvious by introducing a fuzzy self-adjusting algorithm to adjust the controller parameters. Then the convergence and stability of the MFAC-SA-PID method are proved in this paper. In the simulation study, the control performance of the MFAC-SA-PID method proposed in this paper is compared with the traditional MFAC method and the improved model free adaptive control (IMFAC) method, respectively. The results show that the proposed MFAC-SA-PID method has better control effect on the pH neutralization process. The MFAC-SA-PID control performance also outperforms the traditional MFAC method and IMFAC method when step input disturbances are added, which indicates that the MFAC-SA-PID method has better robustness and stability.

Algorithm-Enhanced Engineering English Education in the Era of Artificial Intelligence: A Data-Driven Approach

The era of artificial Intelligence (AI), the landscape of language training, especially in the vicinity of Engineering English, is gift technique a transformative shift. This paper offers a unique data-driven technique to Engineering English training, stronger via modern day algorithms, to address the precise stressful situations and opportunities furnished through AI enhancements. Our approach integrates algorithmic answers with conventional language coaching methodologies to create a dynamic, adaptive gaining knowledge of environment sustainable-made to the unique wishes of engineering students. Primary method is the usage of AI-driven analytics to investigate students’ language talent and studying patterns. Using leveraging natural language processing and machine mastering algorithms, we are able to customize the curriculum and offer focused practise that aligns with each student’s linguistic and technical level. This consists of the development of specialised vocabulary, comprehension of technical files, and effective verbal exchange in expert engineering contexts. A tremendous element of this study is the collection and evaluation of data on student performance and engagement. This information-driven feedback loop allows non-prevent refinement of coaching techniques and substances, ensuring that the instructional content material cloth remains relevant and effective in the unexpectedly evolving discipline of engineering.

Convolutional-Neural-Network-Based Autonomous Navigation of Hera Mission Around Didymos

The European Space Agency (ESA)’s Hera mission requires autonomous visual-based navigation in order to safely orbit around the target binary asteroid system Didymos and its moon Dimorphos in 2027. Nevertheless, the performance of optical-based navigation systems depends on the intrinsic properties of the image, such as high Sun phase angles, the presence of other bodies, and, especially, the irregular shape of the target. Therefore, to improve the navigation performance, thermal and/or range measurements from additional onboard instruments are usually needed to complement optical measurements. However, this work addresses these challenges by developing a fully visual-based autonomous navigation system using a convolutional-neural-network (CNN)-based image processing (IP) algorithm and applying it to the detailed characterization phase of the proximity operation of the mission. The images taken by the onboard camera are processed by the CNN-based IP algorithm that estimates the position of the geometrical centers of Didymos and Dimorphos, the range from Didymos, and the associated covariances. The results show that the developed algorithm can be used for a fully visual-based navigation for the position of the Hera spacecraft around the target with good robustness and accuracy.

Low-Complexity Neural Network-Based Processing Algorithm for QCM-D Signals

Low-Complexity Neural Network-Based Processing Algorithm for QCM-D Signals

Application of indoor thermal energy cycle and visual communication simulation scene based on Machine vision and optical image enhancement

Traditional thermal analysis methods have limitations in complex environments, and innovative technologies are urgently needed to improve the accuracy of indoor thermal monitoring and optimization. In this paper, machine vision and optical image enhancement technology are combined to simulate the indoor heat energy cycle process to achieve more efficient heat management and visual communication, so as to improve the energy utilization efficiency and residential comfort of buildings. In this paper, a high-resolution camera is used to capture real-time images of indoor environment, image quality is enhanced by image processing algorithm, and heat distribution characteristics are extracted. The machine learning model is used to analyze the dynamic changes of the thermal energy cycle, and the visual model of the indoor thermal energy cycle is constructed by combining the fluid dynamics theory. The experimental results show that the proposed method can effectively identify and display the key areas of indoor heat circulation and their changing trends. The simulation results show that the optical enhanced images can show different temperature regions more clearly, directly reflect the heat flow situation, and have a high agreement with the actual measurement data. Therefore, the indoor thermal energy cycle simulation method based on machine vision and optical image enhancement not only improves the accuracy and real-time performance of thermal energy analysis, but also provides visual support for indoor environmental management.

UWB-Based AOA Indoor Position-Tracking System and Data Processing Algorithm

UWB-Based AOA Indoor Position-Tracking System and Data Processing Algorithm

Optical inspection of stator slots for electric motors

An optical non-contact inspection system was developed for measuring the slots in stator lamination stacks. To avoid passing go/no-go gage blocks through the slots, a machine vision system is instead used to measure the stator core slots and identify the presence of burrs within the slots. Utilizing telecentric optics along with an alignment monitoring system configured to monitor and orient the stator core, the core slots can be oriented relative to the imaging axis for further metrology measurements. Among these measurements, the smallest opening dimensions (slot width and depth) of each slot due to misalignment of laminations and the detection of burrs along the edges of the slots throughout the length of the lamination stack are critical for full stator assembly. Advanced image processing algorithms were developed to obtain sub-pixel accuracy which is required to measure the slots. This, used in conjunction with a robust vision calibration technique, increases the feasibility of building a device that can be implemented as a production inspection system. Experiments show the reliability of the computer vision approach and how it can be used in the inspection of slots in lamination stacks.

Developing flaw sizing methodology in Total Focusing Method (TFM) by EDM calibration blocks

The Total Focusing Method (TFM) represents a significant advancement in ultrasonic inspection, delivering high-resolution imaging by leveraging phased array technology combined with sophisticated data processing algorithms. This synergy enables detailed visualization of flaws in various materials, thereby improving flaw detection and characterization. Despite TFM's capabilities, the lack of a standardized methodology for flaw sizing limits its potential for flaw evaluation. This paper seeks to establish a new paradigm in flaw sizing by introducing a custom methodology specifically designed for TFM, using electrical discharge machined (EDM) calibration blocks that reflect a range of flaw shapes. The research highlights the limitations of conventional side-drilled holes (SDH) for capturing realistic flaw nuances and emphasizes the superior ability of EDM notches to simulate the complex geometries inherent in typical flaws. By investigating the influence of different TFM modes, the study provides insight into their effectiveness in improving the accuracy of flaw characterization. Our approach addresses the challenges of existing TFM practices, with EDM notches serving as an essential tool in methodological advancement. This work contributes to the continued development of best practices in TFM application, paving the way for more accurate, reliable, and versatile nondestructive testing.

Research on Knowledge Discovery and Sharing in AIGC Virtual Teaching and Research Room Empowered by Big Data Analysis and Natural Language Processing Algorithms

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today’s big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies.