What distinguishes a model of systems engineering from other models of designing? An ontological, data-driven analysis

This paper investigates how the core technical processes of the INCOSE model of systems engineering differ from other models of designing used in the domains of mechanical engineering, software engineering and service design. The study is based on fine-grained datasets produced using mappings of the different models onto the function-behaviour-structure (FBS) ontology. By representing every model uniformly, the same statistical analyses can be carried out independently of the domain of the model. Results of correspondence analysis, cumulative occurrence analysis and Markov model analysis show that the INCOSE model differs from the other models in its increased emphasis on requirements and on behaviours derived from structure, in the uniqueness of its verification and validation phases, and in some patterns related to the temporal development and frequency distributions of FBS design issues.

Heart Sound Classification Based on Fractal Dimension and MFCC Features Using Hidden Markov Model

Early diagnosis is crucial in the treatment of heart diseases. Researchers have applied a variety of techniques for cardiovascular disease diagnosis, including the detection of heart sounds. It is an efficient and affordable diagnosis technique. Body organs, including the heart, generate several sounds. These sounds are different in different individuals. A number of methodologies have been recently proposed to detect and diagnose normal/abnormal sounds generated by the heart. The present study proposes a technique on the basis of the Mel-frequency cepstral coefficients, fractal dimension, and hidden Markov model. It uses the fractal dimension to identify sounds S1 and S2. Then, the Mel-frequency cepstral coefficients and the first- and second-order difference Mel-frequency cepstral coefficients are employed to extract the features of the signals. The adaptive Hemming window length is a major advantage of the methodology. The S1-S2 interval determines the adaptive length. Heart sounds are divided into normal and abnormal through the improved hidden Markov model and Baum-Welch and Viterbi algorithms. The proposed framework is evaluated using a number of datasets under various scenarios.

Background Speech Synchronous Recognition Method of E-commerce Platform Based on Hidden Markov Model

In order to improve the effect of e-commerce platform background speech synchronous recognition and solve the problem that traditional methods are vulnerable to sudden noise, resulting in poor recognition effect, this paper proposes a background speech synchronous recognition method based on Hidden Markov model. Combined with the principle of speech recognition, the speech feature is collected. Hidden Markov model is used to input and recognize high fidelity speech filter to ensure the effectiveness of signal processing results. Through the de-noising of e-commerce platform background voice, and the language signal cache and storage recognition, using vector graph buffer audio, through the Ethernet interface transplant related speech recognition sequence, thus realizing background speech synchronization, so as to realize the language recognition, improve the recognition accuracy. Finally, the experimental results show that the background speech synchronous recognition method based on Hidden Markov model is better than the traditional methods.

Spatiotemporal Evolution Analysis and Future Scenario Prediction of Rocky Desertification in a Subtropical Karst Region

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui.

Map matching for circular road via contextual voting

Due to the limitation of positioning devices, there is a certain error between GPS positioning data and the real location on the map, and the positioning data needs to be processed to have better usability. For example, accurate location is needed for traffic flow control, automatic driving navigation, logistics tracking, etc. There are few studies specifically for circular road sections. In addition, many existing map matching methods based on Hidden Markov model (HMM) also have the problem that GPS points are easily to be matched to tangent or non-adjacent road sections at circular road sections. Therefore, the contextual voting map matching method for circular road sections (STDV-matching) is proposed. The method proposes multiple subsequent point direction analysis methods based on STD-matching to determine entry into the circular section, and adds candidate section frequency voting analysis to reduce matching errors. The effectiveness of the proposed method is verified at the circular section by comparing it with three existing HMM methods through experiments using two real map and trajectory datasets.