Neural Network-Based Intelligent Animation Plot Generation and Visualization

As technology continues to advance, there is a growing interest in personalized customization with diverse styles and a high degree of fit. To address challenges such as long production cycles and high labor and material costs associated with personalized customization, there has been significant research on automatic pattern generation. However, most of these studies focus on relatively single garment styles. Therefore, this paper proposes a method for automatically generating multi-style collar patterns. First, by analyzing the characteristics of stand collar styles, the modules of stand collar are determined, and the control attributes and methods of each module are determined according to the actual needs. Based on this, a modular design method for stand collar is constructed; the neck parameters and stand collar structure are statistically analyzed, and a mapping model between neck parameters and stand collar structure is established; then, the relationship between stand collar modules and paper patterns is analyzed, and a relationship model between numerical control modules and paper pattern parameters is established to achieve the purpose of driving stand collar structure parameters by stand collar styles; then, according to the stand collar structure design method, the key points of the pattern are parameterized, thus realizing the parametric design of the stand collar pattern; finally, using Matlab software, different components are coordinated, and the modular design method and parametric design of stand collar are comprehensively applied to realize the automatic generation of different styles of stand collar patterns. The research shows that the automatic pattern generation method established in this paper can meet the automatic pattern generation of different stand collar styles, and reduce some human and material costs for the pattern making process in the clothing industry.

An ANN-based element extraction method for automatic mesh generation

Aim: Automatic item generation is "a process of using models to generate items using computer technology". The use of automatic item generation typically involves one of three primary methods: syntax-based, semantic-based, and template-based. Non-template automatic item generation approaches leverage natural language processing techniques. A study showed the potential of using template-based automatic item generation to create high-quality multiple-choice questions for assessing clinical reasoning in Turkish, marking a first in the field. However, the findings of the study were based only on expert opinions, necessitating further research to examine the psychometric qualities of Turkish items. The aim of this study was to reveal psychometric characteristics of the first Turkish case-based multiple-choice questions generated by using automatic item generation in medical education. Methods: This was a psychometric study. Three Turkish case-based multiple-choice questions generated using template-based automatic item generation on essential hypertension were included in an exam that 281 fourth-year medical students participate in. This examination was carried out in-person in classroom settings under proctor supervision. Item difficulty and item discrimination (point-biserial correlation) were calculated, and non-functioning distractors were determined. Results: All three items had acceptable levels (higher than 0.20) of point-biserial correlation (p<0.001). The item difficulty levels indicated the presence of one easy, one moderate, and one difficult question. Each item had 2-3 non-functioning options among five options. All three items had acceptable levels (higher than 0.20) of point-biserial correlation (p<0.001). The item difficulty levels indicated the presence of one easy, one moderate, and one difficult question. Each item had 2-3 non-functioning options among five options. Conclusions: The results indicated that the items successfully discriminate between high and low performers, providing validity evidence on the quality of the questions in evaluating students' comprehension of the subject. Additionally, the findings suggest that it is feasible to create multiple-choice questions with different difficulty levels in Turkish using a single automatic item generation model. This study demonstrated for the first time that automatic generation of case-based multiple-choice questions in Turkish produces acceptable psychometric characteristics in an authentic assessment setting in medical education. The ability to automatically generate effective multiple-choice questions in Turkish holds promise for enhancing the efficiency of written assessment in Turkish medical education.

In order to better standardize the function of intelligent alarm in substation, it needs to be standardized to test it. The existing method of alarm data generation is to manually pick up points. According to the application scenarios of typical faults in substation, the corresponding serialization alarm signals are selected, and the sending order and time interval of the alarm signals are manually configured. This method is not only wastes time and energy, but also easy to make mistakes. In view of the above problems, this paper proposes an automatic generation and injection method for alarm test data in smart substation. Based on the typical fault criterion of intelligent alarm, the fault template file is designed. The test files are automatically generated by a series of options such as lED mapping, alarm basic data mapping, alarm sequence data mapping, and the correction of fault templates, and the test files are loaded into the IED simulation service device. Finally, through the IED simulation device, the serialized warning signals contained in the test files are injected into the integrated monitoring system. The automatic generation and injection method avoids the complex and complicated process of manual pick up and configuration, and realizes automatic testing of intelligent alarm reasoning function.

Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not ideal for the creation of items for non-cognitive constructs. Progress in this area has been made recently by employing long short-term memory recurrent neural networks to produce word sequences that syntactically resemble items typically found in personality questionnaires. To date, such items have been produced unconditionally, without the possibility of selectively targeting personality domains. In this article, we offer a brief synopsis on past developments in natural language processing and explain why the automatic generation of construct-specific items has become attainable only due to recent technological progress. We propose that pre-trained causal transformer models can be fine-tuned to achieve this task using implicit parameterization in conjunction with conditional generation. We demonstrate this method in a tutorial-like fashion and finally compare aspects of validity in human- and machine-authored items using empirical data. Our study finds that approximately two-thirds of the automatically generated items show good psychometric properties (factor loadings above .40) and that one-third even have properties equivalent to established and highly curated human-authored items. Our work thus demonstrates the practical use of deep neural networks for non-cognitive automatic item generation.

The flexibility of Commercial-Off-The-Shelf (COTS) SRAM based FPGAs is an attractive option for the design of artificial satellites, however, the functional verification of HDL-based designs is required and is of fundamental importance. Formal verification using model checking represents a system as formal model that are automatically generated by synthesis tools. On the other hand, the properties are represented by temporal logic expressions and are traditionally manually elaborated, which is susceptible to human errors increasing the costs and time of the verification. This work presents a new method for automatic property generation for formal verification of Hardware Description Language (HDL) based systems. The industrial case study is a communication subsystem of an artificial satellite, which was developed in cooperation with the Brazilian Institute of Space Research (INPE).

This paper presents a method for automatic test case generation for PLC software following the IEC61131-3 standard. The core component is a model checker that iteratively creates program traces, each of them covering a part of the program in terms of a coverage metric. These test cases are translated into Structured Text, a programming languages defined in the IEC61131-3, to allow the execution on a soft-PLC or the actual hardware. Our approach is evaluated on a set of function blocks that are used in industry. We demonstrate that test cases can be automatically generated within few seconds in most cases.

Embedded real-time systems are widely used in avionics, spacecraft, automotive automation, robotics, mobile communications and other fields. In order to detect errors in the development of embedded real-time systems, the development method of model-driven is widely applied. Model-driven finds the potential problems as early as possible by modeling and validating the system at the early stage of design. In the implementation phase of the coding, the code is automatically generated from the validated model to improve the automation of the system development, reduce R & D costs and the possibility of errors in coding process. The research of code generation technology based on architecture analysis and design language (AADL) is an important research content of embedded software development. AADL is a language that models the graphical representation of modeling elements and models in textual form. The C language can compile and process low-level memory in a simple way, generate a small number of machine codes, and run without any support from running environment. Aiming at the characteristics of the above two languages, so we design an automatic code generation tool that automatically converts AADL model into C codes.

The numerous interfaces of spacecraft OBDH software and frequent changes in requirements, resulting in the low efficiency and reliability of the manual coding of OBDH software. An automatic code generation method based on electronic data sheet (EDS) is proposed. The EDS system is introduced, and the output of the EDS system can be used to generate OBDH software code automatically, which improves the efficiency of software development. An structure of OBDH software is designed, which separates the logical code from the parameter code. Due to the EDS system data source is unique, and software code is automatically generated by tools, which avoids the mistakes of coding manually and promotes the reliability of OBDH software and even the reliability of spacecraft is improved.

Using psychometric technology in educational assessment: The case of a schema-based isomorphic approach to the automatic generation of quantitative reasoning items

In this paper, an accumulated method for automatic terrain generation was proposed which was based on the fractal theory. The Fault formation algorithm is used to generate an initial model of terrain. By controlling the iteration and amplitude of Fault formation, we can get diversified Contoured Terrain. Then the MPD (Middle Point Displacement) method of fractal theory is used to iterate the initial model, which results in more terrain details. The main advantage of the method is the combination of automaticity and controllability. On the one hand, Terrain Feature Templates can be automatically generated by the various values of the parameters of Fault formation. On the other hand, arbitrary level detail can be produced by the various values of the Fractal parameters. The experiments prove that our method is feasible and valid and the realistic terrain can be generated through the method.

The strong relationship between music and health has helped prove that soft and peaceful classical music can significantly reduce people’s stress; however, it is difficult to identify and collect examples of such music to build a library. Therefore, a system is required that can automatically generate similar classical music selections from a small amount of input music. Melody is the main element that reflects the rhythms and emotions of musical works; therefore, most automatic music generation research is based on melody. Given that melody varies frequently within musical bars, the latter are used as the basic units of composition. As such, there is a requirement for melody extraction techniques and bar-based encoding methods for automatic generation of bar-based music using melodies. This paper proposes a method that handles melody track extraction and bar encoding. First, the melody track is extracted using a pitch-based term frequency–inverse document frequency (TFIDF) algorithm and a feature-based filter. Subsequently, four specific features of the notes within a bar are encoded into a fixed-size matrix during bar encoding. We conduct experiments to determine the accuracy of track extraction based on verification data obtained with the TFIDF algorithm and the filter; an accuracy of 94.7% was calculated based on whether the extracted track was a melody track. The estimated value demonstrates that the proposed method can accurately extract melody tracks. This paper discusses methods for automatically extracting melody tracks from MIDI files and encoding based on bars. The possibility of generating music through deep learning neural networks is facilitated by the methods we examine within this work. To help the neural networks generate higher quality music, which is good for human health, the data preprocessing methods contained herein should be improved in future works.

Model-based design is an effective means for rapid development of embedded software, and automatic code generation is an important technology for model-based development. Combining the automatic code generation method of Matlab and STM32 with the operation and control of the autonomous underwater robot makes the design of the system more convenient. Use tools such as the Simulink library STM32 MAT/Target and STM32 CubeMX of the STM32 microcontroller to realize the automatic generation of readable and portable C code project files. At the same time, based on the design of the model, the control code of the autonomous underwater robot(AUV) is automatically generated, and the control code is added to the automatically generated C code project file. With Matlab/Simulink as the basic software platform, the motion controller of AUV is mounted on the STM32F407, and a real-time simulation system for the closed-loop control of AUV manipulation motion is constructed. The results of the semiphysical real-time simulation test show that the AUV motion controller has good heading depth control performance, realizes the manipulation and control of AUV, and verifies the practicability of the automatically generated code.

The test case generation technique from formal specifications called the Vibration Testing Method has been put forward. This technique is aimed at gaining coverage of program paths and detecting bugs, even though the test cases are generated only based on specifications. Since it lacks a supporting tool currently, its application is inefficient and errorprone. In this paper, we tackle this problem by describing a supporting tool for the method that we have developed over the last two years. The tool does not only automatically generate test cases based on the Vibration Method, but also can automatically analyze test results. Further, it can also automatically “prove” theorems to support practical formal verification of program properties. During the development of the tool, we have made some important improvements to the techniques of the method for automatic test case generation. We have conducted a small experiment to evaluate our tool and the improved Vibration Method. The experiment result shows that a 12% improvement on the previous method is made.

Automatic Digital Orthophoto Map (DOM) generation plays an important role in many downstream works such as land use and cover detection, urban planning, and disaster assessment. Existing DOM generation methods can generate promising results but always need ground object filtered DEM generation before otho-rectification; this can consume much time and produce building facade contained results. To address this problem, a pixel-by-pixel digital differential rectification-based automatic DOM generation method is proposed in this paper. Firstly, 3D point clouds with texture are generated by dense image matching based on an optical flow field for a stereo pair of images, respectively. Then, the grayscale of the digital differential rectification image is extracted directly from the point clouds element by element according to the nearest neighbor method for matched points. Subsequently, the elevation is repaired grid-by-grid using the multi-layer Locally Refined B-spline (LR-B) interpolation method with triangular mesh constraint for the point clouds void area, and the grayscale is obtained by the indirect scheme of digital differential rectification to generate the pixel-by-pixel digital differentially rectified image of a single image slice. Finally, a seamline network is automatically searched using a disparity map optimization algorithm, and DOM is smartly mosaicked. The qualitative and quantitative experimental results on three datasets were produced and evaluated, which confirmed the feasibility of the proposed method, and the DOM accuracy can reach 1 Ground Sample Distance (GSD) level. The comparison experiment with the state-of-the-art commercial softwares showed that the proposed method generated DOM has a better visual effect on building boundaries and roof completeness with comparable accuracy and computational efficiency.