Narrow Results

With the increasing complexity and scale of tunnel engineering construction, accurately assessing the stability of tunnel rocks has become particularly important. This work proposes a data-driven Multi-Kernel Support Vector Regression (MK-SVR) model for evaluating the stability of tunnel rocks. First, after considering the unique properties of the kernel function, the data characteristics of the variation sequence of tunnel rock stability parameters, and the complexity of algorithm implementation, this paper selects the polynomial kernel function and Gaussian radial basis kernel function as the basis for constructing a multi-kernel support vector regression model. Then, by weighting and combining into a composite kernel, an MK-SVR model is established to enable the model to more flexibly adapt to the nonlinear and multimodal characteristics of the data. Finally, the adaptive Artificial Fish Swarm Algorithm (AFSOA) was used to optimize the kernel parameters and improve the prediction accuracy of the model, resulting in the construction of the MK-SVR-AFSOA model for accurate evaluation of tunnel rock stability in this paper. The results indicate that the MK-SVR-AFSOA model has high accuracy and robustness in tunnel rock stability evaluation, and can effectively reduce errors and uncertainties in traditional methods.

A new method based on eye-tracking data — visual momentum (VM) — was introduced to quantitatively evaluate a dynamic interactive visualization interface. We extracted the dimensionless factors from the raw eye-tracking data, including the fixation time factor $T$, the saccade amplitude factor D and the fixation number factor $N$. A predictive regression model of VM was deduced by eye movement factors and the performance response time (RT). In Experiment 1, the experimental visualization materials were designed with six effectiveness levels according to design techniques proposed by Woods to improve VM (total replacement, fixed format data replacement, long shot, perceptual landmark and spatial representation) and were tested in six parallel subject groups. The coefficients of the regression model were calculated from the data of 42 valid subjects in Experiment 1. The mean VM of each group exhibited an increasing trend with an increase in design techniques. The data of the performance and eye tracking among the combined high VM group, middle VM group and low VM group indicated significant differences. The data analysis indicates that the results were consistent with the previous qualitative research of VM. We tested and verified this regression model in Experiment 2 with another dynamic interactive visualization. The results indicated that the VM calculated by the regression model was significantly correlated with the performance data. Therefore, the virtual parameter VM can be a quantitative indicator for evaluating dynamic visualization. It could be a useful evaluation method for the dynamic visualization in general working environments.

User Experience (UX) is an important attribute for the success and quality of a software application. UX explores how an application is used and the emotional and behavioral consequences of such use. Although several UX evaluation methods allow understanding the reasons for a poor UX, some of them are tedious or too intrusive, making the evaluation unpleasant. This paper presents the Method for the Assessment of eXperience (MAX), which through cards and a board assists software engineers in gathering UX data while motivating users to report their experience. We conducted two pilot studies to verify the feasibility of MAX, which showed that the method is useful for evaluating the UX of finished/prototyped applications from the point of view of users and software engineers.

OWA operators and related aggregation techniques generally focus on input vector with a linear ordering. However, in commonly faced multi-criteria and multi-sources evaluation and decision making, the inputs involved form an evaluation matrix. Considering the fact that the data under evaluation are all with two dimensional meanings, this study explores and proposes four novel preference involved aggregation techniques by using RIM quantifiers and OWA operators. The first two models are both with two steps to carry out the aggregation processes, with one using two times of OWA operators and another considering evaluation matrix as a vector lattice. The last two models come from a whole perspective to direct the aggregation processes, with one arising from a global magnitude view and another based on staggered ordering using two specially defined collections of permutations. Illustrative examples and remarks are also spotted immediately following the proposed models or at suitable positions.

English for Science and Technology (EST), as a special language style, is widely used in the field of Science and Technology. For this kind of articles, the requirements of translation quality are relatively high. Therefore, this paper studies a quality evaluation method of Sci-Tech English translation for cross-cultural communication. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This paper also describes the evaluation of machine translation quality with and automatic evaluation process with machine learning technology. The evaluation index of EST translation quality is selected according to the selection principle and expert consultation method. Then, the weight of the index is calculated by using the analytic hierarchy process. Finally, the translation quality evaluation is given by using the fuzzy comprehensive evaluation, glass-box and black-box evaluation with machine learning method. The results show that under the application of the research method, the evaluation results are completely corresponding to the actual competition results of four competitors, which proves the effectiveness of the research method.

Simulation has become an essential way and sometimes the only way to study complex systems (e.g., system of systems, SoS). Simulation is the model-based activity. How to build a high-quality model is the first consideration in simulation. Fidelity and credibility are the two mostly used metrics to evaluate the quality of a model. However, the definitions and evaluation methods of fidelity and credibility vary from one research to another and it’s hard to evaluate the metrics precisely. More importantly, the evolution process of a model in use cannot be directly reflected by the two metrics. Therefore, this paper introduces the model maturity to track the status of a model during its life cycle, especially in the use and management phases, which will be an important supplement to the quality evaluation system of models. The concept of model maturity is given and a framework of index system for model maturity evaluation is established. Then, a hierarchical evaluation method based on qualitative and quantitative analysis (HEQQ) for model maturity is proposed. Finally, a case study is used to validate the feasibility and effectiveness of the proposed method.

In the era of Industrial Revolution 4.0, digital transformation has become an important strategic choice for the development of logistics. In order to comprehensively evaluate the digital transformation capability of logistics, this paper analyzes the influencing factors and constructs an evaluation index system from five aspects. Then, the index weight is calculated by the structure entropy weight method, the evaluation score is obtained by the Delphi method, and the evaluation results are divided into five categories. Finally, an example is given to illustrate how the proposed evaluation method can effectively support digital transformation logistics decisions.

An evaluation index system is based on combining the operability of indicators and the trends of distribution network; also taking reactive power compensation device into consideration. In order to avoid being too subjective or objective, Analytic Hierarchy Process and Entropy Weight Coefficient method are combined together to give a new and comprehensive weighting method. Finally, Grey Relational Analysis is used to calculate the level which the case is related to the ideal sample. Three methods are integrated to turn into a new comprehensive evaluation method, in terms of the new index system, making a difference to distribution network.

In this study, a predictive Transit Signal Priority (TSP) strategy with advanced detection of a transit vehicle is developed, and an analytical evaluation method was proposed to examine the potential impact on the performance of the TSP system. The stochastic effects of the vehicle actuated (V-A) system is considered and the stochastic arrival and departure process is modeled. The evaluation results demonstrate that the proposed TSP strategy is more effective than the immediate control

Anti-icing effects of different nano coatings on a rectangle test model have been investigated experimentally in an icing wind tunnel under different icing weather conditions. The shape of ice accretion on the test model are measured and compared respectively. A quantitative method of evaluating anti-icing effect is established on the basis of nine geometric characteristic parameters of ice profile. The nondimensional deviation of the nine parameters are calculated and analyzed on the coated model and the clean model respectively. The results demonstrate that the anti-icing nano coatings can effectively reduce the icing velocity and intensity, but not eliminating the icing phenomena completely. The silicone rubber coating has a little anti-icing capability, while better performance can be achieved once different proportion of nano molecules is added in, which is a valuable way of fabricating anti-icing coatings. The best coating tested in this study for anti-icing is the silicone rubber coating added in 10% cetane and 10% octadecane. The conjugation of cetane and octadecane in the silicone rubber makes this coating better ice-repellent capacity. But the dimethyl silicones added in the coating decrease or delay the releasing of the nanoparticles and hence make the anti-icing capacity unstable.

Rapid and accurate fault sealing evaluation is indispensible for high-efficient exploration and exploitation of offshore complex fault block oilfields. Firstly, necessary conditions for fault sealing evaluation of offshore complex fault block oilfields have been put forward. Then, shale gouge ratio method that are suitable for sealing quantitative evaluation have been screened out. Afterwards, the technical procedure has been formulated by integrating static and dynamic data. Finally, case study of the multi-order faults around well A1 in a complex fault block oilfield in eastern Andaman sea of Burma fully demonstrates the practicability, reliability and flexibility of the methods.