Automatic Modification Research Articles

Decision Support System for Diagnosing Underwater Electrical Cables

The object of this study is the process of generating an appropriate response by an intelligent agent when detecting and tracking an underwater electrical cable using a decision support system. The goal of the work is to develop the architecture of a decision support system for diagnosing underwater electrical cables and the main algorithms for its operation. To achieve the research goal, intelligent agents with a complex IPK architecture, which includes information, preferences (rules), and knowledge, were used. The combination of the structure of intelligent agents and the advantages of hierarchical knowledge bases allowed for the natural language representation of knowledge. The functional core of the system consists of four main agents, one of which facilitates the interaction between the user and the surrounding environment. To address the uncertainty in the cable's position on the seabed, the capabilities of fuzzy sets, describing its feature space with membership functions, were employed. The most significant results involve planning for the detection and tracking of underwater electrical cables, taking into account past decision-making experiences in similar cases and adapting them to the current situation through case-based reasoning. The importance of the obtained results lies in providing the decision-maker with a possible option for deploying an underwater vehicle based on accepted logic and rules for detecting underwater electrical cables. Further research is focused on implementing automatic target selection for intervention and developing a method for automatic modification of reasoning methods, preference rule bases, and operational knowledge. Keywords: electrical cable, diagnostics, autonomous underwater vehicle, navigation, motion control, fuzzy sets, intelligent agent, decision support system.

An extension of NASCA for SDDR analysis of mobile activated components with global variance reduction

During the plasma operation period of fusion reactor facilities, neutrons generated by DT reaction can penetrate deeply into structures and induce strong activation. Highly activated components have to be transferred from the on-load position to the hot cell for the purpose of maintenance. This movement produces a high-dose field, originating from the mobile radiation source, which could affect the functionality of important devices or pose risks to the workers. Therefore, it is imperative to compute the shutdown dose rate (SDDR) maps while transporting activated components in the facility and develop an optimized solution for this calculation process. In this study, we established a new extension of the NASCA method combined with the OTF GVR method to compute SDDR maps. NASCA-OTF is a method developed based on the shutdown dose simulation code NASCA to ensure automatic modification of the geometry structure on the fly. In the meantime, it uses OTF method for speeding up the Calculation. Thus, this modified method produces convergent SDDR maps corresponding to different positions of activated components within a single Monte Carlo (MC) simulation. The NASCA-OTF was verified by comparison with R2S method through benchmark analysis. The method was also used for SDDR analysis of the mobile blanket sector for the China Fusion Engineering Test Reactor (CFETR), and the results indicated that the NASCA-OTF is feasible for computing the SDDR resulting from the movement of activated components.

Automatic Code Review by Learning the Structure Information of Code Graph

At present, the explosive growth of software code volume and quantity makes the code review process very labor-intensive and time-consuming. An automated code review model can assist in improving the efficiency of the process. Tufano et al., designed two automated tasks to help improve the efficiency of code review based on the deep learning approach, from two different perspectives, namely, the developer submitting the code and the code reviewer. However, they only used code sequence information and did not explore the logical structure information with a richer meaning of the code. To improve the learning of code structure information, a program dependency graph serialization algorithm PDG2Seq algorithm is proposed, which converts the program dependency graph into a unique graph code sequence in a lossless manner, while retaining the program structure information and semantic information. We then designed an automated code review model based on the pre-trained model CodeBERT architecture, which strengthens the learning of code information by fusing program structure information and code sequence information, and then fine-tuned the model according to the code review activity scene to complete the automatic modification of the code. To verify the efficiency of the algorithm, the two tasks in the experiment were compared with the best Algorithm 1-encoder/2-encoder. The experimental results show that the model we proposed has a significant improvement under the BLEU, Lewinshtein distance and ROUGE-L metrics.

Improved Parkinsonian tremor quantification based on automatic label modification and SVM with RBF kernel

Objective. The quantitative assessment of Parkinsonian tremor, e.g. (0, 1, 2, 3, 4) according to the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale, is crucial for treating Parkinson’s disease. However, the tremor amplitude constantly fluctuates due to environmental and psychological effects on the patient. In clinical practice, clinicians assess the tremor severity for a short duration, whereas manual tremor labeling relies on the clinician’s physician experience. Therefore, automatic tremor quantification based on wearable inertial sensors and machine learning algorithms is affected by the manual labels of clinicians. In this study, an automatic modification method for the labels judged by clinicians is presented to improve Parkinsonian tremor quantitation. Approach. For the severe overlapping of dynamic feature range between different severities, an outlier modification algorithm (PCA-IQR) based on the combination of principal component analysis and interquartile range statistic rule is proposed to learn the blurred borders between different severity scores, thereby optimizing the labels. Afterward, according to the modified feature vectors, a support vector machine (SVM) with a radial basis function (RBF) kernel is proposed to classify the tremor severity. The classifier models of SVM with RBF kernel, k-nearest neighbors, and SVM with the linear kernel are compared. Main results. Experimental results show that the proposed method has high classification performance and excellent model generalization ability for tremor quantitation (accuracy: 97.93%, precision: 97.96%, sensitivity: 97.93%, F1-score: 97.94%). Significance. The proposed method may not only provide valuable assistance for clinicians to assess the tremor severity accurately, but also provides self-monitoring for patients at home and improve the assessment skills of clinicians.

URBAN GROWTH SIMULATION USING URBAN DYNAMICS AND CITYGML: A USE CASE FROM THE CITY OF MUNICH

Abstract. Urban dynamics modelling using system dynamic (SD) approaches aims to provide an understanding of the major internal forces within an urban area, such as population development. SD models provide valuable information for decision and policy making. Urban systems are strongly related to the urban space, which is well described by geospatial data. Therefore, the connection of SD and geospatial data is advantageous, both for feeding spatial information into SD models and for further spatial analyses and for visualizing the results of SD in geographic context. This paper describes a new approach to combine an SD model with a semantic 3D city model. Our approach shows that a bidirectional data exchange between semantic city models and SD models improves the predictions generated by the SD models. Furthermore, we show that automatic modification of the semantic city model by the output of the SD model allows for 3D visualization and further analysis of future scenarios.Since semantic 3D city models and SD models have complex data structures, and since the models have evolved in very different domains, integrating the models is a complex task. In order to facilitate the integration process, we developed a conceptual model, which describes the data structures of the semantic city model and of the SD model as well as the bidirectional relations between the models using the concept of model weaving. The approach was tested using the SD tool Vensim and a CityGML data set from the city of Munich for an urban densification use case.

Nonhyperbolic normal moveout stretch correction with deep learning automation

Normal-moveout (NMO) correction is a fundamental step in seismic data processing. It consists of mapping seismic data from recorded traveltimes to corresponding zero-offset times. This process produces wavelet stretching as an undesired by-product. We have addressed the NMO stretching problem with two methods: (1) an exact stretch-free NMO correction that prevents the stretching of primary reflections and (2) an approximate post-NMO stretch correction. Our stretch-free NMO produces parallel moveout trajectories for primary reflections. Our post-NMO stretch correction calculates the moveout of stretched wavelets as a function of offset. Both methods are based on the generalized moveout approximation and are suitable for application in complex anisotropic or heterogeneous environments. We use new moveout equations, modify the original parameter functions to be a constant over the primary reflections, and then interpolate the seismogram amplitudes at the calculated traveltimes. For fast and automatic modification of the parameter functions, we use deep learning. We design a deep neural network (DNN) using convolutional layers and residual blocks. To train the DNN, we generate a set of 40,000 synthetic NMO-corrected common-midpoint gathers and the corresponding desired outputs of the DNN. The data set is generated using different velocity profiles, wavelets, and offset vectors, and it includes multiples, ground roll, and band-limited random noise. The simplicity of the DNN task — a 1D identification of primary reflections — improves the generalization in practice. We use the trained DNN and find successful applications of our stretch-correction method on synthetic and different real data sets.

A TECHNIQUE OF PANELS CUTTING FOR MODIFICATION OF HULL GEOMETRY HYDRODYNAMIC MODELS

A panel cutting technique is developed for automatic modification of an initial mesh of a ship hull used for hydrodynamic computations leading to improved meshes for the prediction of wave induced vertical load effects. The technique can provide a model with divided panels in any defined position regardless of the initial discretization of the body. The applications of the provided technique include panel distinction and division in predetermined positions, generation of finer mesh based on the initial coarser model of meshes and improvement of vertical load prediction in predetermined positions. The method is applied for case studies of a barge, shuttle tanker and frigate to depict various applications. Finally, the hydrostatic and hydrodynamic vertical shear forces are calculated for two models of initial and modified panels of well-known frigate 5415. The results are compared for the sections alongside the ship and accuracy of load integration is shown for predetermined sections.

A New Tool for Building Energy Optimization: First Round of Successful Dynamic Model Simulations

Several tools and pieces of software support building energy modelling for optimization, certification and comparisons of different scenarios and usages. Nevertheless, the consistent rise in accessible computational power and the expansion of ICT are pushing the development of new software functionalities and tools able to support cross-disciplinary work on smart building optimization. This paper introduces a new platform (under development) that combines the EnergyPlus dynamic simulation tool with extra-functionalities and pre-defined usage scenarios based on automatic actions to manage massive simulations and correlation analyses. The tool’s utility was tested in three experiments, with goals that we consider to be fundamental requirements: comparing simple retrofit actions to reduce net energy needs; analyzing the free-running potential of a demo building and the impacts of different low-energy technologies in terms of increasing thermal comfort (shading and ventilative cooling); and comparing measured sensor data indicators with simulated ones under real weather conditions for model verification. The results demonstrate the ability of the tool to automatically generate hundreds of EnergyPlus input building models by acting on building geometry; we focused on the most common uses for parametric dynamic simulations. Additionally, the way in which the tool combines the automatic modification of the building’s design and the parallel launching of multiple simulations allows the labor to be reduced. The user can execute complex tasks without spending any time working with model editing software and aggregating the results from multiple simulations.

A Technique of Panels Cutting for Modification of Hull Geometry Hydrodynamic Models

A panel cutting technique is developed for automatic modification of an initial mesh of a ship hull used for hydrodynamic computations leading to improved meshes for the prediction of wave induced vertical load effects. The technique can provide a model with divided panels in any defined position regardless of the initial discretization of the body. The applications of the provided technique include panel distinction and division in predetermined positions, generation of finer mesh based on the initial coarser model of meshes and improvement of vertical load prediction in predetermined positions. The method is applied for case studies of a barge, shuttle tanker and frigate to depict various applications. Finally, the hydrostatic and hydrodynamic vertical shear forces are calculated for two models of initial and modified panels of well-known frigate 5415. The results are compared for is shown for predetermined sections.

Three-parameter normal moveout correction in layered anisotropic media: A stretch-free approach

In conventional normal moveout (NMO) correction, some parts of the recorded data at larger offsets are discarded because of NMO distortions. Deviation from the true traveltime of reflections due to the anisotropy and heterogeneity of the earth, and wavelet stretching are two reasons of these distortions. The magnitudes of both problems increase with increasing the offset to depth ratio. Therefore, to be able to keep larger offsets of shallower reflections, both problems should be obviated. Accordingly, first, we have studied different traveltime approximations being in use, alongside new parameterizations for two classical functional equations, to select suitable equations for NMO correction. We numerically quantify the fitting accuracy and uncertainty of known nonhyperbolic traveltime approximations for P-waves in transversely isotropic media with vertical symmetry axis (VTI). We select three suitable three-parameter approximations for NMO in layered VTI media as the VTI generalized moveout approximation, a double-square-root approximation, and a perturbation-based approximation. Second, we have developed an extension of the earlier proposed stretch-free NMO method, using the selected moveout approximations. This method involves an automatic modification of the input parameters in anisotropic NMO correction, for selected reflections. Our anisotropic stretch-free NMO method is tested on synthetic and three real data sets from Gulf of Mexico and Iranian oil fields. The results verify the success of the method in extending the usable offsets, by generating flat and stretch-free NMO corrected reflections.

Consensus-Based Track Association with Multistatic Sensors under a Nested Probabilistic-Numerical Linguistic Environment.

Track association is an important technology in military and civilian fields. Due to the increasingly complex environment and the diversity of the sensors, it is a key factor to separate the corresponding track from multiple maneuvering targets by multisensors with a consensus. In this paper, we first transform the track association problem to multiattribute group decision making (MAGDM), and describe the MAGDM with nested probabilistic-numerical linguistic term sets (NPNLTSs). Then, a consensus model with NPNLTSs is constructed which has two key processes. One is a consensus checking process, and the other is a consensus modifying process. Based on which, a track association algorithm with automatic modification is put forward based on the consensus model. After that, the solution of a case study in practice is given to obtain the corresponding track by the proposed method, and it provides technical support for the track association problems. Finally, we make comparisons with other methods from three aspects, and the results show that the proposed method is effective, feasible, and applicable. Moreover, some discussions about the situation where there is only one echo point at a time are provided, and we give a discriminant analysis method.

A personalized mail re-filtering system based on the client

Email is an essential communication tool, but a large number of spam emails can seriously affect the work and life of users and can even cause property damage. Due to different interests and hobbies, there may be huge differences in the definition of spam by users; the realization of personalized spam filtering has become an important issue in the field of spam filtering. When emails are misjudged, the user has to manually modify it, which brings great inconvenience to the user experience. In order to effectively solve the above problems and realize the functions of personalized email filtering and automatic correction of mis-filtered emails, this paper combined with rules and statistical methods presents a personalized email re-filtering system based on the client (PRFC) and implements the automatic modification of the mis-filtered emails. A large part of existing spam filters do not consider the difference between class prior probability and class imbalance problem; they only filter the mail online. Firstly, the proposed filter system processes the mails entering the inbox and the garbage and then designs two mutually learned filters based on the multi-task learning principle to be used for the automatic modification of the mis-filtered emails in inbox and garbage. To ensure the performance of the filter based on the interests of users and data distribution of mails varying with time, a multi-window learning framework that combines important weights to effectively implement the dynamic adaptation of the filter was designed. Finally, our proposed filtering system on the TREC 2006c and 2007p data sets that gets a significant filtering efficiency was verified.

Synchronization in nonlinear complex networks with multiple time‐varying delays via adaptive aperiodically intermittent control

SummaryThis article is concerned with the problem of synchronization in nonlinear complex networks with multiple time‐varying delays via adaptive aperiodically intermittent control. The couplings inside nodes are assumed to be nonlinear and subject to multiple time‐varying delays. Meanwhile, the connection topology among the nodes can be directed and weighted. Then, the adaptive aperiodically intermittent control method is employed to realize synchronization and automatic modification to compensate the changes in dynamic errors. In addition, several synchronization criteria are rigorously induced based on the Lyapunov stability theory. Finally, the proposed control method is evaluated by utilizing numerical simulation. The results can be also applied to linear complex networks with delays.

The campfire storytelling system – automatic creation and modification of a narrative

ABSTRACTThis paper presents and evaluates a method for automatically generating a narrative that includes initial input from the listener, without forcing him/her to become an actual character in the story. More importantly, the story can be modified by the listener during the course of the story being narrated. This latter feature works without pre-defining alternative paths, thereby permitting the changes to take place at any time throughout the course of the story. This system, called the Campfire Storytelling System, employs a contextual approach to narrative generation called the Cooperating Context Method (CCM). Campfire STS was assessed for its functionality and its user acceptance. Thirty-eight human test subjects evaluated stories generated by Campfire STS. They responded to a survey stating their opinions about the generated stories, their entertainment value for adults, their creativity, and other similar issues. Furthermore, 20 of these subjects who self-reported non-trivial experience with children were additionally asked to evaluate the generated stories with respect to how much children would enjoy them. The results of these evaluations suggest that Campfire STS is able to create interesting and novel stories, as well as change them seamlessly in mid-stream while still maintaining interesting and entertaining stories.

Surgical soundtracks: automatic acoustic augmentation of surgical procedures.

Advances in sensing and digitalization enable us to acquire and present various heterogeneous datasets to enhance clinical decisions. Visual feedback is the dominant way of conveying such information. However, environments rich with many sources of information all presented through the same channel pose the risk of over stimulation and missing crucial information. The augmentation of the cognitive field by additional perceptual modalities such as sound is a workaround to this problem. A major challenge in auditory augmentation is the automatic generation of pleasant and ergonomic audio in complex routines, as opposed to overly simplistic feedback, to avoid alarm fatigue. In this work, without loss of generality to other procedures, we propose a method for aural augmentation of medical procedures via automatic modification of musical pieces. Evaluations of this concept regarding recognizability of the conveyed information along with qualitative aesthetics show the potential of our method. In this paper, we proposed a novel sonification method for automatic musical augmentation of tasks within surgical procedures. Our experimental results suggest that these augmentations are aesthetically pleasing and have the potential to successfully convey useful information. This work opens a path for advanced sonification techniques in the operating room, in order to complement traditional visual displays and convey information more efficiently.

RETRACTED ARTICLE: Analysis of intelligent evaluation algorithm based on english diagnostic system

With the development of computer technology, an increasing number of computer technology is used in English teaching. After studying, designing, implementing and testing the standardized test and non-standardized test diagnostic evaluation algorithms in the English intelligent diagnosis system, as to lack of computer assistance for secondary school English teaching and primary and secondary school teachers and students’ needs cannot be met, the author proposed an online intelligent diagnosis evaluation program based on J2EE. Through the automatic modification of the subjective and objective questions and the personalized design of the diagnosis results, the system has functions of teaching, diagnosis, testing, feedback and many other functions as an auxiliary teaching algorithm.

Hull surface modification for ship resistance performance optimization based on Delaunay triangulation

Combining computational fluid dynamics and optimization techniques provides a significant method for automatic multi-scheme selection in hull form optimization, which can improve the design level of energy-efficient ships. This paper focuses on hull surface automatic modification and its application in hull form optimization. A new hull surface automatic modification method is proposed, based on the radial basis function interpolation method. Furthermore, Delaunay triangulation is applied in order to determine the support radius of the Wendland basic function. The developed hull form optimization tool is applied to the hydrodynamic design optimization of a Series 60 model, and model tests are carried out on the original and an optimised model at WHUT (Wuhan University of Technology) for validation. The numerical optimization and model tests results demonstrate that the proposed method can significantly improve optimization efficiency.

Development of the algorithm of adaptive construction of hierarchical neural network classifiers

This paper presents the development of the algorithm for adaptive construction of hierarchical neural network classifiers based on automatic modification of the desired response of a perceptron with a small number of neurons in a single hidden layer. Improved versions of the algorithm are tested on standard benchmark problems Vowels and MNIST. A discussion of the results, strengths and weaknesses of the algorithm, directions of further work on its testing and improvement, is provided.

Optimizing fiducial visibility on periodically acquired megavoltage and kilovoltage image pairs during prostate volumetric modulated arc therapy.

Robust detection of implanted fiducials is essential for monitoring intrafractional motion during hypofractionated treatment. The authors developed a plan optimization strategy to ensure clear visibility of implanted fiducials and facilitate 3D localization during volumetric modulated arc therapy (VMAT). Periodic kilovoltage (kV) images were acquired at 20° gantry intervals and paired with simultaneously acquired 4.4° short arc megavoltage digital tomosynthesis (MV-DTS) to localize three fiducials during VMAT delivery for hypofractionated prostate cancer treatment. Beginning with the original optimized plan, control point segments where fiducials were consistently blocked by multileaf collimator (MLC) within each 4.4° MV-DTS interval were first identified. For each segment, MLC apertures were edited to expose the fiducial that led to the least increase in the cost function. Subsequently, MLC apertures of all control points not involved with fiducial visualization were reoptimized to compensate for plan quality losses and match the original dose-volume histogram. MV dose for each MV-DTS was also kept above 0.4 MU to ensure acceptable image quality. Different imaging (gantry) intervals and visibility margins around fiducials were also evaluated. Fiducials were consistently blocked by the MLC for, on average, 36% of the imaging control points for five hypofractionated prostate VMAT plans but properly exposed after reoptimization. Reoptimization resulted in negligible dosimetric differences compared with original plans and outperformed simple aperture editing: on average, PTV D98 recovered from 87% to 94% of prescription, and PTV dose homogeneity improved from 9% to 7%. Without violating plan objectives and compromising delivery efficiency, the highest imaging frequency and largest margin that can be achieved are a 10° gantry interval, and 15 mm, respectively. VMAT plans can be made to accommodate MV-kV imaging of fiducials. Fiducial visualization rate and workflow efficiency are significantly improved with an automatic modification and reoptimization approach.

Sample Selection Method for Ship Resistance Performance Optimization Based on Approximated Model

Approximated model instead of computational fluid dynamics tool is utilized for performance analysis in hull form optimization process, which can save time significantly. Sample selection is the central issue of approximated model building. This article focuses on the sample selection method and the application of approximated model in hull form optimization. Latin hypercube sampling and uniform design are compared. The uniform design based on genetic algorithm approach is proposed. The radial basis function interpolation method is used for hull surface automatic modification. An approximated model using a neural network for ship resistance performance is established. The stem profile's optimization for the Korea Research Institute of Ships and Ocean Engineering (KRISO) container ship is completed. The results show that hull form optimization based on the approximated model can significantly improve optimization efficiency and is practical for engineering design.