Manual Matching Research Articles

SimpliScopeX: Enhanced Deep Learning Model for Identification of Microscopic Image of Simplicia Fragments of Medicinal Plant Leaves

As the "Back to Nature" trend progresses, people are switching from chemical medicine to herbal medicine or traditional medicine derived from nature. One form of traditional medicine is the simplicia of medicinal plant leaves. The authenticity of dried simplicia powder of medicinal plants can be determined through a microscopic test by looking at the identifier fragments. However, this remains difficult for humans to identify due to the need for more information on standard references. The dataset from microscopic images of simplicia fragments of medicinal plant leaves still needs to be improved. In addition, manual matching of microscopic test results with standard reference books requires quite a long time. It allows for human error, so it is necessary to apply artificial intelligence that can assist researchers in quickly and accurately predicting the species of medicinal plants and their fragments based on microscopic images. Deep learning performance has shown promising results in computer vision in recent years. Inspired by sophisticated deep learning techniques, the proposed work presents a deep learning method to identify and classify images of microscopic fragments of simple medicinal plants and their enhanced fragments using data augmentation techniques, modified EfficientNetB0 architecture, and the use of the ReduceLROnPlateau function in the training process, which is referred to as "SimpliScopeX". The SimpliScopeX model can also automatically extract microscopic image features of simplicia fragments of medicinal plant leaves. Experimental results using the new dataset show that our proposed model can produce the highest accuracy value of 80.25% for the test data for microscopic image problems of medicinal leaf simplicia. The implications of this research are petrified in the pharmaceutical world in fast and accurate microscopic identification.

Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation

Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such processes by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective by looking at ontology matching techniques. As the manual matching of different sources of information becomes unrealistic once the system scales up, the automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual non-semantically enriched relational data with the support of existing tools (pre-LLM technology) for automatic ontology matching from the scientific community. Even considering a relatively simple case study—i.e., the spatio–temporal alignment of macro indicators—outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for more generalized application.

Art Design and Interior Color Selection for New Energy Vehicles using sustainable AI algorithm

New energy vehicles (NEVs) are becoming increasingly popular in the automotive industry as a sustainable solution to reduce carbon emissions. In order to attract and retain customers, NEVS needs to have visually appealing exterior designs and interior color schemes. The traditional process of selecting art design and interior colors for vehicles can take time and effort. To address this challenge, our team has developed a sustainable AI algorithm for art design and interior color selection for NEVs. This algorithm utilizes machine learning techniques to analyze market trends, customer preferences, and sustainable color choices. The algorithm uses a database of color options and design elements to generate customizable options for NEV manufacturers. It reduces the need for physical prototypes and manual color matching, leading to cost and time savings. The algorithm also takes into consideration the sustainable aspect of NEVs by suggesting color options that are environmentally friendly and promote eco-consciousness.

The effectiveness of the semi‐automated Identifin software to accurately fast‐track dorsal fin photographic identifications of sharks

Abstract For some marine species, photo‐identification (photo‐ID) data can be used to generate population estimates critical for conservation efforts. For the white shark (Carcharadon carcharias), different photo‐ID methods have been proposed: (1) the traditional manual matching method based on non‐organized databases, (2) the notches code method based on organized databases and (3) the semi‐automatic software, Identifin. However, the effectiveness of these different methods and the applicability to other shark species have not been adequately explored. Eleven inexperienced and two experienced investigators used the three different photo‐ID methods to record the accuracy and time taken to identify individual white sharks from photo‐ID images. Both the traditional and notches code methods allowed for the accurate matching of individuals to the databases provided. For all investigators, the notches code method used significantly less time (14 min ± 0.14 min) than the traditional matching method (19 min ± 1.34 min). The semi‐automated method, Identifin, further shortened the identification time to 2 min (± 0.10 min) and provided a significantly higher matching accuracy (96% correct matches) compared with traditional manual matching (78% correct matches) and the notches code method (70% correct matches). The use of Identifin was tested to identify bronze whaler sharks (Carcharhinus brachyurus), a species with finer notches patterns when compared with white sharks. Identifin showed less efficiency in matching individual photographs to the database, but when very‐high‐quality (VHQ) photographs were available, matching improved substantially. Identifin software shows great potential in assisting researchers to accurately identify individual sharks in large databases, and this can contribute significantly to the conservation of shark species. The software and automated process need to be adapted to be functional on a variety of different shark species, but it proves to be an effective tool in managing large photo‐ID databases.

ASSESSMENT OF SUBSOIL CORROSIVITY USING GEOELECTRIC LAYER PARAMETERS AT ILERE COMMUNITY NEAR AKURE, SOUTHWESTERN NIGERIA

ASSESSMENT OF SUBSOIL CORROSIVITY USING GEOELECTRIC LAYER PARAMETERS AT ILERE COMMUNITY NEAR AKURE, SOUTHWESTERN NIGERIA

Automated Clinical Trial Cohort Definition and Evaluation with CQL and CDS-Hooks.

Patient recruitment for clinical trials faces major challenges with current methods being costly and often requiring time-consuming acquisition of medical histories and manual matching of potential subjects. Designing and implementing an Electronic Health Record (EHR) and domain-independent automation architecture using Clinical Decision Support (CDS) standards that allows researchers to effortlessly enter standardized trial criteria to retrieve eligibility statistics and integration into a clinician workflow to automatically trigger evaluation without added clinician workload. Cohort criteria are translated into the Clinical Quality Language (CQL) and integrated into Measures and CDS-Hooks for patient- and population-level evaluation. Successful application of simplified real-world trial criteria to Fast Healthcare Interoperability Resources (FHIR®) test data shows the feasibility of obtaining individual patient eligibility and trial details as well as population eligibility statistics and a list of qualifying patients. Employing CDS standards for automating cohort definition and evaluation shows promise in streamlining patient selection, aligning with increasing legislative demands for standardized healthcare data.

Automatic Depth Shifting by Identifying and Matching Events on Well Logs Chicheng Xu

Depth matching or depth shifting between well logs acquired from different runs or between core scans and well logs is a critical data quality control task to ensure subsequent accurate petrophysical interpretation and modeling. Conventional depth-shifting workflow heavily relies on human expertise to manually match a series of peaks and troughs between log curves, which is often subjective, error-prone, and cumbersome. Therefore, it is necessary to establish an automatic depth-shifting workflow to perform this routine yet important task accurately in a consistent and efficient manner. We implemented an automatic workflow to emulate human expertise to identify important “events” such as peaks, troughs, and bed boundaries on log curves and then intelligently match the identified series of events between log curves with local maximum correlation criteria to generate a depth shift table. We applied the automatic workflow in a field case to shift the well log and core gamma ray and delivered a depth shift table comparable to manual depth matching. The final shifted log achieved a significantly enhanced correlation with the reference log. The events identifying and matching method presents a white-box solution that still follows the conventional petrophysical wisdom and allows more user interaction to fine-tune the results. The users have full control of the parameters for optimal results.

Novel linkage approach to join community-acquired and national data

BackgroundCommunity optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools.MethodsAs the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices.ResultsA sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records.ConclusionsThe potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.

Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data

Developing automatic history matching (AHM) methods to replace the traditional manual history matching (MHM) approach in adjusting the permeability distribution of the reservoir simulation model has been studied by many authors. Because permeability values need to be evaluated at hundreds of thousands of grid cells in a typical reservoir simulation model, it is necessary to apply a reparameterization technique to allow the optimization algorithms to be implemented with fewer variables. In basic reparameterization techniques including zonation and pilot point methods, the calibrations are usually based solely on the production data with no systematic link to the geological and geophysical data, and therefore, the obtained permeability distribution may be not geologically consistent. Several other reparameterization techniques have attempted to preserve geological consistency by incorporating 4D seismic data; however, these techniques cannot be applied to our fractured basement reservoirs (FBRs) as they do not have 4D seismic data. Taking into account these challenges, in this study, an AHM methodology and workflow have been developed using a new reparameterization technique. This approach attempts to minimize the potential for geological nonconsistency of the calibrated results by linking the permeability to geophysical data. The proposed methodology can be applied to fields with only traditional geophysical data (3D seismic and conventional well logs). In the proposed workflow, the spatial distributions of seismic attributes and geomechanical properties were calculated and estimated from 3D seismic data and well logs, respectively. After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. Then, the calibration of the permeability distribution is performed by adjustment of the ANN model. Modification of the ANN model is performed using the simultaneous perturbation stochastic approximation (SPSA) algorithm to calibrate transmission coefficients in the ANN model to minimize the discrepancy between the simulated results and observed data. The developed methodology is applied to calibrate the permeability distribution of a simulation model of Bach Ho FBR in Vietnam. The effectiveness of the methodology is evident by comparing the historical matches with an available manually history-matched simulation model. The application shows that the proposed methodology could be considered as a suitable practical approach for adjusting the permeability distribution for FBR reservoir simulation models.

Research on Automated Choreography and Real-time Adjustment of Cheerleading Performance Based on Artificial Intelligence

Abstract The automated choreography of dance movements is a new field combining artificial intelligence and dance performance, which has important research value. In this paper, a Transformer-based cheerleading automatic choreography and real-time adjustment algorithm are proposed, which generates cheerleading movements consistent with the music rhythm by stacking multi-layer bidirectional cross-attention layers and introduces an algorithm for real-time adjustment according to the music phrases and the emotions of the dance phrases. The experimental results show that the new algorithm has a dance matching accuracy score of 4.33, which is 0.95 points higher than the accuracy score of manual matching, and 80.76% of the judges think that the overall effect of the cheerleading exercise generated by this algorithm is better than that of the choreography results of the comparison algorithm. This paper’s algorithm has good results, as evidenced by the results.

Evaluation of geophysical parameters for groundwater productivity in the Commune of Jesse, Delta State Nigeria


 Aquifer geohydraulic properties in Jesse were studied by the application of vertical electrical sounding (VES) and pumping test. The VES data was collected using ABEM SAS 1000 Terrameter and results processed using manual matching of curves and computer iteration, and interpreted in the forms of geologic models and geoelectric layers. The aquiferous unit was found at a depth of 5.30 m to 30.30 m with a range of resistivity from 87.30 Ω-m to 1945.40 Ω-m. Dar-Zarrouk parameters estimation and results of borehole pumping test produced the aquifer hydraulic parameters. The hydraulic conductivity values range from8.171 - 8.279 m/day, transmissivity from 43.8770 - 517.7480 m2/day, and formation factor from 0.369 - 8.501. These results are indications of a prolific and high yielding aquifer capable of withstanding long duration of pumping. Results of groundwater electrical conductivity and total dissolved solids ranged from 11.65 - 198.80 μs/cm, and 29.40 - 35.40 mg/dm3 respectively, signifying very moderate mineralization of the groundwater. The average electrical conductivity of 68.04 μS /cm and total dissolved solids of 32.58 mg/L were lesser than the acceptable limits of the Nigerian Standard for Drinking Water Quality (NSDWQ) of 1000 μS/cm and 500 mg/L respectively. This study affirmed that the aquifer in Jesse is suitable for groundwater exploitation for local and regional purposes.

Using Genetic Algorithm for DNA Profile Matching

The DNA is used in forensic investigations to identify suspects and victims at crime scenes. However, manual matching of DNA profiles is difficult and error-prone, especially in large databases. In Iraq, technology for DNA matching is limited, making manual matching the only option. Regenerate. In this work, we propose a Genetic Algorithm (GA) for DNA dataset matching to provide simple and user-friendly software to be used by law enforcement agencies in Iraq. The genetic algorithm is a type of heuristic search method used in computing science and artificial intelligence. It is based on the theory of natural selection and evolutionary biology and is used to find the best solutions to search problems. Genetic algorithm is robust for searching through big, complicated datasets. Thus, in this paper, the GA is the algorithm of choice to achieve the goal of DNA matching search. The used dataset is actual data that have been collected from the Ministry of Interior at the Basra Investigation Center. Finally, the python simulation results show 100% accuracy where the proposed method managed to find the DNAs under consideration precisely.

Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm

Reservoir simulation is known as perhaps the most widely used, accurate, and reliable method for field development in the petroleum industry. An integral part of a reliable reservoir simulation process is to consider robust and rigorous tuned EOS models. Traditionally, EOS models are tuned iteratively through arduous workflows against experimental PVT data. However, this comes with a number of drawbacks such as forcingly using weight factors, which upon alteration adversely affects the optimization process. The objective of the current work is thus to introduce an auto-tune PVT matching tool using NSGA-II multi-objective optimization. In order to illustrate the robustness of the presented technique, three different PVT samples are used, including two black-oil and one gas condensate sample. We utilize Peng-Robinson EOS during all the manual and auto-tuning processes. Comparison of auto-tuned EOS-generated results with those of experimental and computed statistical error values for these samples clearly show that the proposed method is robust. In addition, the proposed method, contrary to the manual matching process, provides the engineer with several matched solutions, which allows them to select a match based on the engineering background to be best amenable to the problem at hand. In addition, the proposed technique is fast, and can output several solutions within less time compared to the traditional manual matching method.

Assessment of Setup Errors in Gynecological Malignancies Treated With Radiotherapy Using Onboard Imaging.

Introduction Radiotherapy plays a vital role in the management of gynecological malignancies. However, maintaining patient position poses a challenge during daily radiotherapy treatment of these patients. This study identifies and calculates setup errors in interfraction radiotherapy and optimum clinical target volume-planning target volume (CTV-PTV) margins in patients with gynecological malignancies. Material and methods A total of 38 patients with gynecological malignancies were included in the study. They were treated with a dose of 50 Gy in 25 fractions for five weeks, followed by brachytherapy. All patients were immobilized using a 4-point thermoplastic cast. Anteroposterior and lateral images were taken thrice weekly for five weeks. Setup verification was done using kilovoltage images obtained using Varian On-board Imager (Varian Medical System, Inc., Palo Alto, CA). Manual matching was done utilizing bony landmarks such as the widest portion of the pelvic brim, anterior border of S1 vertebrae, and pubic symphysis in the X, Y, and Z axes, respectively. Results A total of 1140 images were taken. The individual systematic errors ranged from -0.24 to 0.17 cm (LR), -0.15 to 0.19 cm (AP) and -0.36 to 0.29 cm (CC) while the individual random errors ranged from 0.04 to 0.36 cm (LR), 0.06 to 0.33 cm (AP) and 0.10 to 0.29 cm (CC). The calculated CTV-PTV margins in LR, AP and CC directions were 0.17, 0.18, and 0.25 cm (ICRU-62); 0.28, 0.31 and 0.47 cm in LR, AP and CC directions (Stroom's), and 0.32, 0.36 and 0.55 cm (Van Herk) respectively. Conclusion Based on this study, the calculated CTV-PTV margin is 6 mm in gynecological malignancies, and the present protocol of 7 mm of PTV margin is optimum.

DEVELOPMENT OF AN IN VIVO ANKLE AND HINDFOOT KINEMATIC PROTOCOL TO INVESTIGATE ACTIVITIES OF DAILY LIVING

To be able to assess the biomechanical and functional effects of ankle injury and disease it is necessary to characterise healthy ankle kinematics. Due to the anatomical complexity of the ankle, it is difficult to accurately measure the Tibiotalar and Subtalar joint angles using traditional marker-based motion capture techniques. Biplane Video X-ray (BVX) is an imaging technique that allows direct measurement of individual bones using high-speed, dynamic X-rays.The objective is to develop an in-vivo protocol for the hindfoot looking at the tibiotalar and subtalar joint during different activities of living.A bespoke raised walkway was manufactured to position the foot and ankle inside the field of view of the BVX system. Three healthy volunteers performed three gait and step-down trials while capturing Biplane Video X-Ray (125Hz, 1.25ms, 80kVp and 160 mA) and underwent MR imaging (Magnetom 3T Prisma, Siemens) which were manually segmented into 3D bone models (Simpleware Scan IP, Synopsis). Bone position and orientation for the Talus, Calcaneus and Tibia were calculated by manual matching of 3D Bone models to X-Rays (DSX Suite, C-Motion, Inc.). Kinematics were calculated using MATLAB (MathWorks, Inc. USA).Pilot results showed that for the subtalar joint there was greater range of motion (ROM) for Inversion and Dorsiflexion angles during stance phase of gait and reduced ROM for Internal Rotation compared with step down. For the tibiotalar joint, Gait had greater inversion and internal rotation ROM and reduced dorsiflexion ROM when compared with step down.The developed protocol successfully calculated the in-vivo kinematics of the tibiotalar and subtalar joints for different dynamic activities of daily living. These pilot results show the different kinematic profiles between two different activities of daily living. Future work will investigate translation kinematics of the two joints to fully characterise healthy kinematics.

A novel sketch based face recognition in unconstrained video for criminal investigation

Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.

A comparative analysis of text representation, classification and clustering methods over real project proposals

PurposeWhen a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.Design/methodology/approachThis study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.FindingsThe results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.Research limitations/implicationsThe proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.Originality/valueThis study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm

Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm’s efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.

Point Cloud-Based Spacecraft Self-Localization for Distant Small Body Exploration

Image-based terrain-relative localization is expected to play an important role in precision navigation in various small-body missions. In the Hayabusa2 mission, accurate navigation was achieved by manual matching between the asteroid image and a set of vertices of a shape model of the target body, called a “point cloud.” The manual process intentionally excludes invisible regions and adopts visible parts for matching. In this paper, an autonomous optical localization using the point cloud, which will take the place of the manually matching process, is proposed. The proposed method performs matching with the asteroid rims derived from the captured image and point cloud, respectively. Furthermore, the proposed method allows selecting the area used for matching by adding 1-bit information, representing the visible or invisible flag, to the point cloud. The proposed method has achieved the position estimation accuracy of 0.23 [px] (1σ) in simulations with various shading conditions. This paper applies the proposed method to the Hayabusa2 flight data and shows that the proposed method bears comparison with the Hayabusa2 team estimation results.

An automated framework for long-range acoustic positioning of autonomous underwater vehicles

An automated method was developed to align underwater acoustic receptions at various depths and ranges to a single reference prediction of long range acoustic arrival structure as it evolves with range in order to determine source-receiver range. Acoustic receptions collected by four autonomous underwater vehicles deployed in the Philippine Sea as part of an ocean acoustic propagation experiment were used to demonstrate the method. The arrivals were measured in the upper 1000 m of the ocean at ranges up to 700 km from five moored, low frequency broadband acoustic tomography sources. Acoustic arrival time structure for pulse compressed signals at long ranges is relatively stable, yet real ocean variability presents challenges in acoustic arrival matching. The automated method takes advantage of simple projections of the measured structure onto the model space that represents all possible pairings of measured peaks to predicted eigenrays and minimizes the average travel-time offset across selected pairings. Compared to ranging results obtained by manual acoustic arrival matching, 93% of the automatically-obtained range estimates were within 75 m of the manually-obtained range estimates. Least squares residuals from positioning estimates using the automatically-obtained ranges with a fault detection scheme were 55 m root-mean-square.