Syntactic Pattern Recognition Techniques Research Articles

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.

Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images.

In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. Thereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.

A novel approach to recognize interacting features for manufacturability evaluation of prismatic parts with orthogonal features

This paper describes a computer-aided tool for the quantitative evaluation of manufacturability of prismatic machining parts. A feature recognition approach, which uses volume subtraction and syntactic pattern recognition techniques, is proposed to identify machining features on a prismatic part from B-Rep data extracted from 3D model in STEP AP203 format. The methodology presented is also capable of identifying features in variety cases of feature interactions. The manufacturability of a part is expressed in terms of relative manufacturability indices of its constituting features. The present work considers the geometrical aspects of the designed product along with manufacturing issues at a very early stage of design. Geometrical and technological complexities of the design are established using several parameters such as feature intricacy, tool access direction, feature face orientation, feature accessibility, approach direction depth, feature neighborhood, feature hierarchy, parent and child feature complexities, tolerances, surface finish, and tooling complexities which affect the manufacturability of a feature on the part directly or indirectly. The best worst method (BWM) is used to assign weights to manufacturability parameters to reflect their relative importance. A case study is presented to show the capability of the system to generate sound indices that could make designs easier to manufacture without compromising on the functional requirements.

Transmission lines’ fault detection using syntactic pattern recognition

In this paper, an efficient hardware relay is presented that is implemented based on syntactic pattern recognition techniques. The proposed system is capable of detecting faults existing at power system waveforms in \(\upmu \)s, after reading each peak of the examined signal, aiming to reduce or even to totally prevent safety problems and economic losses. In order syntactic pattern recognition methods to be utilized as a recognition tool of waveforms at transmission lines, the tasks of selecting appropriate primitive patterns, determining the linguistic representation and forming a suitable grammar, should be developed. In this study, attribute grammars have been selected to model the examined signals due to their power to describe syntactic and semantic knowledge. The hardware implementation of the suggested relay, that stands on Earley’s parsing algorithm, is developed using Verilog hardware description language, downloaded on a Virtex 7 XILINX FPGA board and evaluated through real waveforms and data received from IPTO. The obtained results have shown that the presented system could be an efficient alternative tool in the field of transmission lines’ fault detection.

Similarity measure of sedimentary successions and its application in inverse stratigraphic modeling

This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application in inverse stratigraphic modeling. A sedimentary facies succession is represented with a string of symbols, or facies codes in its natural vertical order, in which each symbol brings with it one attribute such as thickness for the facies. These strings are called attributed strings. A similarity measure is defined between the attributed strings based on a syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. Inverse stratigraphic modeling aims to generate quantitative 3D facies models based on forward stratigraphic modeling that honors observed datasets. One of the key techniques in inverse stratigraphic modeling is how to quantify the similarity or distance between simulated and observed sedimentary facies successions at data locations in order for the forward model to condition the simulation results to the observed dataset such as measured sections or drilled wells. This quantification technique comparing sedimentary successions is demonstrated in the form of a cost function based on the defined distance in our inverse stratigraphic modeling implemented with forward modeling optimization.

Development of a feature recognition module for tapered and curved base features

This paper presents a feature recognition module which uses the B-rep details extracted from the STandard for Exchange of Product (STEP) format to recognize the manufacturing features on a prismatic part. It adopts the syntactic pattern recognition technique to recognize five classes of features considered in this research by matching the standard pattern strings developed for every class of feature. Using these strings, initially, the types of faces on the prismatic part are determined and edge loops are constructed. The edge loops describe a feature by the information implicit in the details of the edges, vertices, coordinate points, and directions. Using the edge loops and by checking the presence of similar edge loops on parallel faces and the connectivity of faces between parallel edge loops, the final shape of a feature is identified. The developed methodology identifies interacting, tapering, interacting–tapering, curved base features, and tapering cross-sections. Methodologies are presented here to calculate the full dimensional details of all of the features. A case study dealing with complex geometries is presented in the validation of the proposed feature recognizer.

Simulating Sedimentary Successions Using Syntactic Pattern Recognition Techniques

Sections from a sedimentary succession can be simulated using a process which includes both probabilistic and deterministic information. The inclusion of both of these types of information allows the production of geologically realistic simulations which contain the required level of heterogeneity. The process uses syntactic pattern recognition techniques and is based on the formal description of a geological model using a grammar. The simulations can be conditioned on well data.

Automatic extraction of manufacturable features from CADD models using syntactic pattern recognition techniques

This paper reports a system for automatic extraction of manufacturable features from 'engineering drawings' created in CADD format. It employs the techniques of string based pattern recognition and natural language processing. Drawing entities are processed to derive feature string patterns, which are syntactically analyzed to detect the feature topology and geometry. The information of the features so detected is augmented by non-geometric data like dimensions, position, tolerances and the relevant drawing callouts. The system is capable of detecting generic classes of features like holes, pockets, steps, slots, bosses, etc. The derived information can be easily linked to the downline CAD/CAM activities in the product cycle. The system, thus, demonstrates the feasibility of design automation from CADD models.

Finger mouse and gesture recognition system as a new human computer interface

In this paper, we propose a new human computer interface which offers an alternative to traditional mouse and direct manipulation interfaces. This interface enables a user to specify commands and additional parameters by drawing single intuitive gestures with his finger. Most gesture-based interfaces researched so far have needed a mouse or stylus pen as a gesture input device. However, we believe that the idea of making gestures with a human finger appeals more to not only experienced users but also novice users who are usually not accustomed to using computer devices. This paper describes the finger mouse system (FMS) and finger gesture recognition system(FGRS). FMS emulates the functions of a mouse by capturing the image of a hand and analyzing its motion and shape. FGRS provides the user with the capabilities of command input and direct manipulation by recognizing the gesture input by FMS. We utilized Kalman filter-based tracking and syntactic pattern recognition techniques for implementing FMS and FGRS. In addition, we introduce an example drawing program in order to demonstrate the practicality of our approach.

Fractals in deep crustal seismic reflection profiles

Application of a syntactic pattern recognition technique, seismic skeletonization, to deep crustal seismic reflection data allows attributes such as energies, lengths and dips to be associated with individual reflection events. Some of these attributes exhibit fractal properties, e.g. the relationship between seismic event lengths and their spatial distribution throughout the crust. This approach provides a new technique to analyse complex geometry on seismic reflection data.

Recognition of Ricker wavelets by syntactic analysis

Syntactic pattern recognition techniques are applied to the analysis of 1-D seismic traces to classify Ricker wavelets. Seismic Ricker wavelets have structural information, and each wavelet can be represented by a string of symbols. To recognize the strings, we use a finite‐state automaton to identify each string. The automaton can accept strings having substitution, insertion, and deletion errors of the symbols. There are two attributes, terminal symbol and weight, in each transition of the automaton. A minimum‐cost, error‐correcting, finite‐state automaton is proposed to parse the input string.

A system for automatic extraction of 3D part features using syntactic pattern recognition techniques

This paper reports a system which employs string based syntactic pattern recognition techniques for feature extraction. A B-rep based solid model of the component is used for processing the face boundaries (loops) to derive boundary strings which are syntactically analysed to detect feature signatures. The detected signatures are geometrically verified, and the positional and dimensional parameters of the features are calculated. The system is capable of detecting features with complex cross sections; compound features which include nested and intersecting features; and various types of holes and pockets. These capabilities of the system make it an effective interface between a geometric solid modeller and various application programs.

A scheme of inference of regular grammars for the syntactic pattern recognition of saccadic eye movements

Saccadic eye movements are measured as digital signals which are then transformed to symbol strings of formal grammars. At first, saccades are identified from a digital signal by a syntactic pattern recognition technique developed earlier. Using strings we automatically infer regular grammars which can be applied to reason different types of saccades as normal, or abnormal and affected by otoneurological disorders. We have tested this scheme with some pathological saccades and found such fully automatic recognition possible at least in principle.

Diagnosis of ECG diagnosable diseases—syntactic pattern recognition approach

A method is presented for the analysis of electrocardiogram (ECG) diagnosable diseases using syntactic pattern recognition techniques. The ECG analysis described here will determine right-bundle branch block, left-bundle branch block, left ventricular hypertrophy, left-anterior hemiblock, and left-atnal hypertrophy. The 'patient data matrix’, obtained through the already developed data acquisition, string generation program, string compression program, wave recognition programs, wave measurement programs, the axis analysis programs and the arrhythmia programs, is used to classify ECG diagnosable diseases of a standard patient ECG. The ECG analysis program together with the above-specified programs can be connected to standard three-channel ECG chart to provide immediate on-site ECG interpretation.

Feature based expert parts assignment in cellular manufacturing

Abstract This paper presents a new approach for ongoing decision making needed to assign parts to flexible manufacturing cells, in support of the dynamic dispatching and scheduling required for day-to-day operation. A feature based design description language (FDDL) and expert analyzer, synthesizer and recognizer have been developed to directly perform the automatic and dynamic assignment of parts to manufacturing cells from a feature based prototype modeling system. The assignment system is based upon knowledge of both the part design features and manufacturing processes. Feature based representation of components structure and related machining operations is presented. The proposed technique represents a new and generative methodology for integrating CAD and CAM. Artificial intelligence techniques such as expert systems, languages, pattern recognition, and finite-state automata are applied in this research. Symbolic strings containing the design and machining attributes of components are analyzed using knowledge rules and syntactic pattern recognition techniques. Cell inference and part assignment are then carried out using domain specific expert rules. A case study is included to illustrate the feasibility of these approaches. It is believed that this research provides some useful contributions for both CAD and CAM integration as well as parts assignment techniques for cellular manufacturing.

Knowledge-based system for assignment of parts to machine cells

This paper presents a decision tool for assignment of parts to machining cells. A modelling system and associated expert modules: analyser, synthesiser and recogniser have been developed to perform the automatic and dynamic assignment of parts directly from a feature-based CAD modelling system to manufacturing cells. It is based on the knowledge acquired from design features of parts and manufacturing processes. Feature-based representation of components structure and related machining operations is presented. The proposed algorithm allows the integration of CAD and CAM. Some elements of expert systems, pattern recognition and finite-state automata are applied in this research. Symbolic strings containing the design and machining attributes of components are analysed using knowledge rules and syntactic pattern recognition techniques, then cell inference and part assignment are carried out. A case study is included to illustrate the feasibility of the above approaches. This research, provides some useful contributions for both CAD and CAM integration as well as parts assignment for cellular manufacturing.

Expert parts assignment in cellular manufacturing using pattern recognition

This paper presents a new tool for on-going decision making needed to assign parts to flexible manufacturing cells, in support of the dynamic dispatching and scheduling required for day-to-day operation. A CAM-oriented modelling system and associated expert modules: Analyzer, Synthesizer and Recognizer have been developed to perform the automatic and dynamic assignment of parts to manufacturing cells directly from a feature-based CAD modelling system. It is based upon knowledge of both the part design features and manufacturing processes. Feature-based representation of components structure and related machining operations is presented. The proposed technique represents a new and generic methodology for integrating CAD and CAM. Artificial intelligence techniques such as expert systems, pattern recognition and finite-state automata are applied in this research. Symbolic strings containing the design and machining attributes of components are analyzed using knowledge of rules and syntactic pattern recognition techniques, then cell inference and part assignment are carried out using domain specific expert rules. Two case studies are included to illustrate the feasibility of these approaches for rotational and prismatic parts. It is believed that this research provides some useful contributions for both CAD and CAM integration as well as parts assignment techniques for cellular manufacturing.

Expert System for Inspection Planning

An expert inspection task planning system for Coordinate Measuring Machines(CMM) is developed in PROLOG. It is generative in nature and is bused on a feature-oriented computer-aided modelling system which has been developed in association with the planning system. The planning system takes into consideration the CMM characteristics, inspected part function and geometric properties, geometric tolerancing theory as well as the data extracted from the modelling system. New ideas regarding feature accessibility, feature representation in terms of the geometric elements used by CMM, inspection priority of datum features, measurement grouping according to the nature of geometric elements, checking all possible accessible features before changing a probe and/or part orientation, grouping tolerance checking tasks together based on corresponding datums, etc. and associated algorithms are proposed and implemented in this reseach. These concepts were verified and found to be feasible. Syntactic Pattern Recognition technique is used to identify a part directly from a feature-based CAD database from inspection planning point of view. The developed Feature-Oriented Modelling and Planning System, using artificial intelligence techniques, is discussed. Inspection knowledge representation and planning logic are described and illustrated with examples. Sample planning rules and inspection plan are also included. It is believed that this research can provide some useful contributions towards the development of automated inspection task planning, systems especially for coordinate measuring machines.

HERMeS: A second generation approach to the automatic analysis of two‐dimensional electrophoresis gels. Part III: Spot list matching

AbstractOne of the most critical steps in two‐dimensional gel analysis is the comparison of spot lists. In this paper, we describe our approach to the matching problem. We propose a method based on syntactic pattern recognition techniques, which differs from previously used methods and offers a major improvement to cope with discontinuous geometric distortions. The method is based on non‐metric considerations and describes the pattern observed on a given gel in a non‐geometric way. This approach leads to the definition of finite automatons which can be used to automatically find the presence of a given pattern on a gel. The program uses a technique of clause generation related to artificial intelligence techniques. A set of rules is applied to the definition of homologousspots, and probability coefficients are computed to compare and to chose between different pairing hypotheses. Once matching has been achieved between a set of gels, the construction of a master gel which summarizes the information obtained is allowed. Two types of masters can be used. The first one summarizes the information between spots lists belonging to the same experiment and includes intensity considerations, while the second one summarizes the information resulting from different gels and does not include spot intensity and volume. Masters are written and known by the system as a spot list. This ability allows them to be handled just like spot lists and, using them, to perform all the actions available to the system. This approach is compared with a more classical one based on geometric corrections. The use of probability coefficients is also discussed and extended to their application to multiple matching and further utilization of masters.

Detection of abnormalities on carotid angiograms using syntactic techniques

Syntactic pattern recognition techniques are used to classify a small number of normal and abnormal bifurcations in carotid angiograms. The approach seems successful but necessitates the development of a sophisticated preprocessor if it is to be of practical value.