Quantitative Evaluation Index Research Articles

Formal modeling and analysis of cyber-physical cross-space attacks in power grid

In this paper, we apply the formal theory to explain the cascading propagation and feedback mechanism of cross-space attacks in Cyber-Physical Power System (CPPS). The cyber-physical cross-space attack model is constructed using Markov processes, and quantitative evaluation indexes on attack impact are proposed. Based on the Linear Temporal Logic (LTL), the formal axiom architecture is proposed to describe the hybrid processes triggered by the attack event, in which the Labelled Transition Semantic is applied to create formal specifications for system state transitions. The 160-node CPPS test system from an actual engineering project is selected as the study case. A typical cross-space attack scenario is constructed using the event-driven model, the reachable regions and the evolution vector field are obtained as the formal analysis results of system state transitions. The proposed method guarantees the effectiveness and reliability of the modeling and analysis of CPPS under cyber-physical cross-space attacks.

Research and Implementation of Intelligent Evaluation System of Teaching Quality in Universities Based on Artificial Intelligence Neural Network Model

Scientific and objective education quality assessment is an important demand in the current education industry. Artificial intelligence empowering various industries has become an inevitable trend for future social development. The education quality assessment system combines big data and artificial intelligence technology. The system uses various human intelligence algorithms to analyze the collected image. Text data are related to teachers’ teaching quality and can give objective results for education quality evaluation. This paper introduces the research and implementation methods of constructing the quantitative evaluation index system of university teachers’ teaching quality and its quantitative processing method as well as the intelligent quantitative evaluation system of teaching quality and explores the technical ways to discover the effectiveness of measurable factors on the evaluation of teaching quality, and the obtained results have important practical significance for the scientific evaluation of teaching quality.

A Machine Learning Study of Predicting Mixing and Segregation Behaviors in a Bidisperse Solid–Liquid Fluidized Bed

In this work, a convolutional neural network combined with a long short-term memory model (CNN-LSTM) is employed to predict the mixing and segregation behaviors in a bidisperse solid–liquid fluidized bed (SLFB). The data set comes from the CFD-DEM simulations under a range of superficial inlet velocities vl and size ratios dl/ds and includes detailed particle information in different temporal and spatial dimensions. The CNN-LSTM model uses CNN to preprocess the original data, and then the output of CNN is regarded as the input of the LSTM model for model training. Two scenarios are considered: (1) varied vl under the same dl/ds; (2) varied dl/ds under the same vl. The training effects of LSTM and CNN-LSTM models are compared using loss function, random model tests, and several quantitative error evaluation indices (R2 score, MAE (mean absolute error) and RMSE (root mean squared error)). The results show that the CNN-LSTM model has better training effects than the LSTM model. Further, the prediction results are used to characterize the mixing and segregation behaviors by a series of indices and compared with the CFD-DEM simulations. The comparisons indicate that the CNN-LSTM model can obtain more reliable predictions than the LSTM model, because the CNN-LSTM model can capture more complex features after convolutional processing even though in the less occupied grids. The comparison of computation time cost indicates the high efficiency and accuracy of the CNN-LSTM model. This work provides a fast and accurate prediction method of the granular flow behaviors on temporal and spatial scales.

A Simulation Experimental Study on the Advance Support Mechanism of a Roadway Used with the Longwall Coal Mining Method

Aiming to advance a support problem for roadways used with the longwall coal mining method, the S1202 working face of the Ningtiaota Coal Mine is taken as the engineering background. The monitoring and analysis of bolt force, anchor cable force and surrounding rock deformation of two types of roadways within the whole advance pressure influence range are carried out in the present paper. Based on this, a numerical calculation model consistent with the field is established, and numerical comparison tests under different influencing factors are carried out. The rationality of the numerical test results is verified by using the field monitoring data. At the same time, quantitative evaluation indexes, such as characterization deformation, are established, and the deformation law of roadways surrounding rocks under different advance passive support forces is analyzed. The advance support mechanism of the roadway used with the longwall coal mining method is clarified. The test shows that under the condition of no advance passive support, the maximum characteristic deformation of surrounding rock in the haulage roadway and ventilation roadway is 7.1 cm and 10.1 cm, respectively. The above surrounding rock deformation still meets the requirements of on-site safety production. The research results can provide experimental support for the advance support parameters of the roadway used with the longwall coal mining method.

Advancements in Shoreline Variation Prediction Considering Mixed Grain Sizes

Shoreline variations occurring in sea areas are caused by the combined effect of sediment transport due to the inflow and outflow of sediment caused by waves at the left and right boundaries of the sea area and the depth of movement limit. Although many studies have been conducted to understand the characteristics of sediment transport, assessment on valid sediment run-off supplied to the sea area from mountains and rivers in sea areas with river inflow and research on the sediment budget analysis considering the topographical characteristics of South Korean sea areas are insufficient. Therefore, the establishing mitigation measures considering comprehensive viewpoints is necessary to systematically respond to various natural and anthropogenic causes. Moreover, further research related to the setting and analysis of major parameters must be conducted for improving the prediction performance as well as the evaluation system for analyzing the sediment budget, which is a quantitative evaluation index for coastline management optimized for domestic waters. This study aimed to establish a comprehensive analysis system for analyzing the sediment run-off generated in domestic sea areas and develop a shoreline model (In-MPaS model) considering mixed grain sizes for advancing sediment run-off analysis. With the observation data, the parameters necessary for the numerical analysis were set, the sensitivity of the In-MPaS model according to the particle size distribution of the sediment constituting the sea area was evaluated, and the major parameters for improving the prediction performance were analyzed. Consequently, it was found that the prediction performance of the In-MPaS model improved in terms of valid sediment run-off as well as the sediments constituting the sea area (which was assumed to have mixed grain sizes rather than a single grain size(<i>D</i><sub>50</sub>). In particular, considering the topographical properties that regulate sediment transport due to the island (Deokbong Mountain) at the foreside of the river mouth bar, the shoreline prediction performance was significantly improved with the application of groins, which exhibit structurally similar performance.

Quantitative Evaluation of Post-Lunch Dip Using Event-Related Potential

The sense of sleepiness and fatigue that occurs at around 2 p.m. is known as the “post-lunch dip (PLD).” It causes a transient decline in brain function, including cognitive function, attentiveness, and arousal level. Various research hypotheses have been proposed for the mechanism of occurrence of PLD, including explanations involving blood sugar spikes or the inhibition of neuropeptides. However, the evidence for these hypotheses is poorly constructed, and none of them is widely recognized as an explanation for the mechanism. The establishment of quantitative evaluation indicators for the decline in brain function caused by PLD is essential to clarify the mechanism of occurrence of PLD. In this study, a demonstration experiment was conducted focusing on P300 and contingent negative variation (CNV), which are types of event-related potentials (ERP), as evaluation indicators of PLD. The subjects were 14 healthy young people, and the meal load used was two slices of white bread and 285 mL of water. In the experiment, measurements were taken four times in total (preprandial, immediately postprandial, 40 min postprandial, and 80 min postprandial). The Stanford sleepiness scale (SSS) and a subjective questionnaire about fatigue using a visual analog scale (VAS) were administered before each measurement. The results confirmed that, at 40 min postprandial, when a significant increase in SSS was observed, there was a reduction in the area of early CNV and late CNV and a prolongation of P300 latency (p<0.05). An evaluation using late CNV also confirmed a reduction in area immediately postprandial that could not be confirmed in the SSS.

Indirect method for the quantitative identification of unstable rock

Under the influence of continuous external factors (rainfall, earthquakes, construction, etc.), slope rock masses in a stable state can gradually transition to unstable rock, which can then collapse. A safety factor can identify the occurrence of failure but cannot identify the transition of stable rock to unstable rock; thus, it cannot realise the quantitative identification of the latter. In this study, a cohesive safety factor (CSF) and a relatively objective analysis method are proposed to effectively identify unstable rock. The CSF can be calculated by the natural vibration frequency and applied as a mechanical index to characterise unstable rock; when CSF is less than 1, the rock is defined as unstable. Compared with the traditional method, the new method has the merits of simple operation, low cost and high efficiency and provides a relatively complete quantitative evaluation index and evaluation criteria for quantitative identification of unstable rock for engineers engaged in early warning and prevention of rock collapse.

Hyperspectral Image Denoising via Low-Rank Representation and CNN Denoiser

Hyperspectral images (HSIs) are widely used in various tasks such as earth observation and target detection. However, during the imaging process, HSIs are often corrupted by various noises. In this article, we firstly investigate the advantages of traditional physical restoration models and the denoising convolutional neural networks (CNN) for HSIs denoising tasks. The sparse based low-rank representation can explore the global correlations in both the spatial and spectral domains, and the CNN-based denoiser can represent the deep prior which cannot be designed by traditional restoration models. Then, we propose a HSI denoising model with low-rank representation and CNN denoiser prior in the flexible and extensible plug-and-play framework by combining the advantages of the two methods. The proposed model is user-friendly, requiring no retraining. Simulated data experiments show that, compared with competitive methods, the proposed one achieves better denoising results for both additive Gaussian noise and Poissonian noise in various quantitative evaluation indicators. Real data experiments show that the proposed model yields the best performance.

Unsupervised Hyperspectral Pansharpening by Ratio Estimation and Residual Attention Network

Most deep learning-based hyperspectral pansharpening methods use the hyperspectral images (HSIs) as the ground truth. Training samples are usually obtained by blurring and downsampling the panchromatic image and HSI. However, the blurring and downsampling operation lose much spatial and spectral information. As a result, the model parameters trained by these reduced-resolution samples are unsuitable for fusing full-resolution images. To tackle this problem, we propose an unsupervised hyperspectral pansharpening method via ratio estimation (RE) and residual attention network (RE-RANet). The spatial and spectral information of the fused image are derived from the original panchromatic and HSI rather than reduced-resolution images. At first, we generate the initial ratio image using the ratio enhancement method. The initial ratio image is fine-tuned by the residual attention network (RANet) to generate a multichannel ratio image. Then, we inject the multichannel ratio image that contains spatial detail information into the HSI. Finally, the generated hyperspectral image is constrained by the spatial constraint loss and the spectral constraint loss. Experiments on the EO-1 and Chikusei datasets verify the effectiveness of the proposed method. Compared with other state-of-the-art approaches, our method performs well in qualitative visual effects and quantitative evaluation indicators.

Intelligent Gait Analysis and Evaluation System Based on Cane Robot.

Gait analysis and evaluation are vital for disease diagnosis and rehabilitation. Current gait analysis technologies require wearable devices or high-resolution vision systems within a limited usage space. To facilitate gait analysis and quantitative walking-ability evaluation in daily environments without using wearable devices, a mobile gait analysis and evaluation system is proposed based on a cane robot. Two laser range finders (LRFs) are mounted to obtain the leg motion data. An effective high-dimensional Takagi-Sugeno-Kang (HTSK) fuzzy system, which is suitable for high-dimensional data by solving the saturation problem caused by softmax function in defuzzification, is proposed to recognize the walking states using only the motion data acquired from LRFs. The gait spatial-temporal parameters are then extracted based on the gait cycle segmented by different walking states. Besides, a quantitative walking-ability evaluation index is proposed in terms of the conventional Tinetti scale. The plantar pressure sensing system records the walking states to label training data sets. Experiments were conducted with seven healthy subjects and four patients. Compared with five classical classification algorithms, the proposed method achieves the average accuracy rate of 96.57%, which is improved more than 10%, compared with conventional Takagi-Sugeno-Kang (TSK) fuzzy system. Compared with the gait parameters extracted by the motion capture system OptiTrack, the average errors of step length and gait cycle are only 0.02 m and 1.23 s, respectively. The comparison between the evaluation results of the robot system and the scores given by the physician also validates that the proposed method can effectively evaluate the walking ability.

Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams.

ObjectiveTo develop an approach for automatically analyzing bone metastases (BMs) on bone scintigrams based on deep learning technology.MethodsThis research included a bone scan classification model, a regional segmentation model, an assessment model for tumor burden and a diagnostic report generation model. Two hundred eighty patients with BMs and 341 patients with non-BMs were involved. Eighty percent of cases were randomly extracted from two groups as training set. Remaining cases were as testing set. A deep residual convolutional neural network with different structures was used to determine whether metastatic bone lesions existed, regions of lesions were automatically segmented. Bone scan tumor burden index (BSTBI) was calculated; finally, diagnostic report could be automatically generated. The sensitivity, specificity and accuracy of classification model were compared with three physicians with different clinical experience. The Dice coefficient evaluated the effect of segmentation model and compared to the result of nnU-Net model. The correlation between BSTBI and blood alkaline phosphatase (ALP) level was analyzed to verify the efficiency of BSTBI. The performance of report generation model was evaluated by the accuracy of interpretation of report.ResultsIn testing set, the sensitivity, specificity and accuracy of classification model were 92.59%, 85.51% and 88.62%, respectively. The accuracy showed no statistical difference with moderately and experienced physicians and obviously outperformed the inexperienced. The Dice coefficient of BMs area was 0.7387 in segmentation stage. Based on the whole model frame, our segmentation model outperformed the nnU-Net. BSTBI value changed as the BMs changed. There was a positive correlation between BSTBI and ALP level. The accuracy of report generation model was 78.05%.ConclusionDeep learning based on automatic analysis frameworks for BMs can accurately identify BMs, preliminarily realize a fully automatic analysis process from raw data to report generation. BSTBI can be used as a quantitative evaluation indicator to assess the effect of therapy on BMs in different patients or in the same patient before and after treatment.

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

Utilization of Acupuncture and Herbal Ointment Instead of Skin Graft Surgery for the Treatment of Burn Injuries: A Case Series and Literature Review.

Skin graft surgery is a standard treatment that increases the survival rate of patients with burn injuries; however, it leaves many sequelae. Conventional external preparations for the treatment of burns also have various side effects. In this retrospective case study, we assessed the cases of four patients with topical third-degree burns who did not wish to undergo the skin graft surgeries recommended by medical doctors and were thus treated using traditional medicine alone. A Korean medicine doctor administered integrative traditional medicine treatment to the patients using acupuncture and herbal ointments. Analysis of the patients' treatment photographs, quantitative evaluation indices, and vivid narratives suggested that their skin recovered well without adverse effects. Traditional Korean medicine treatment, including acupuncture and herbal ointment treatment, can be a new alternative therapeutic strategy for the treatment of patients with topical burns who do not want to undergo skin graft surgery or who have higher risks for poor surgical outcomes.

Research on Ordering and Transportation Strategy of Raw Materials in Manufacturing Enterprises

This paper mainly studies the impact of upstream suppliers on downstream manufacturing enterprises. Firstly, this paper makes an abstract qualitative analysis on the supply characteristics of suppliers from the four aspects of supplier quality, supplier scale, supply stability and supply continuity, and then quantifies these four qualitative aspects layer by layer into five indicators: supply and demand matching degree, weekly average supply, supply weeks, supply law and the longest continuous supply weeks, The quantitative evaluation index is constructed, the weight is given by entropy weight method, and the supply characteristic measurement index system based on TOPSIS evaluation model is established by Python software to compare the advantages and disadvantages of different suppliers, and the 5 most important suppliers are sorted by scoring.

An Index for Quantitative Evaluation of the Mixing in Ethanol Precipitation of Traditional Chinese Medicine

(1) Background: Ethanol precipitation is widely used in the manufacturing traditional Chinese medicines (TCMs). Insufficient mixing of ethanol solution and concentrate usually results in the coating loss of active ingredients. However, there is no index for quantitative evaluation of the mixing in ethanol precipitation. Therefore, this study aimed to define an index for quantitative evaluation of the mixing effect in ethanol precipitation of TCMs. (2) Methods: The concept and requirements of a mixing indicator were proposed. The mass percentage of concentrate fully mixed with ethanol solution (well-mixing ratio, WMR) was used as an index to evaluate the mixing effect. The formula for calculation of WMR was derived. The utility of the WMR was evaluated on stirring devices and a micromesh mixer. (3) Results: Increasing stirring speed, decreasing total solid content of the concentrate, and decreasing the diameter of the ethanol solution droplets all resulted in higher retention rates for lobetyolin and higher WMR. The WMR increased with the increasing flow rate of the concentrate and ethanol solution in the micromesh mixer. The mixing of ethanol solution and concentrate was better when using a micromesh mixer with a smaller internal mixing zone. The results revealed that WMR could be used to quantitatively characterize the mixing of concentrate and ethanol solution, although it has some limitations. (4) Conclusions: The proposed index WMR could guide quality control of the TCM ethanol precipitation process. This study represents a new contribution to improving ethanol precipitation equipment, optimizing process parameters, and enhanced properties of concentrate for TCM enterprises.

A New Normalized Groundwater Age-Based Index for Quantitative Evaluation of the Vulnerability to Seawater Intrusion in Coastal Aquifers: Implications for Management and Risk Assessments

The vulnerability of coastal aquifers to seawater intrusion has been largely relying on data-driven indexing approaches despite their shortcomings to depict the complex processes of groundwater flow and mass transport under variable velocity conditions. This paper introduces a modelling-based alternative technique relying on a normalized saltwater age vulnerability index post-processed from results of a variable density flow simulation. This distributed index is obtained from the steady-state distribution of the salinity and a restriction of the mean groundwater age to a mean saltwater age distribution. This approach provides a novel way to shift from the concentration space into a vulnerability assessment space to evaluate the threats to coastal aquifers. The method requires only a sequential numerical solution of two steady state sets of equations. Several variants of the hypothetical Henry problem and a case study in Lebanon are selected for demonstration. Results highlight this approach ability to rank, compare, and validate different scenarios for coastal water resources management. A novel concept of zero-vulnerability line/surface delineating the coastal area threatened by seawater intrusion has shown to be relevant for optimal management of coastal aquifers and risk assessments. Hence, this work provides a new tool to sustainably manage and protect coastal groundwater resources.

Autonomous Reconfiguration Strategy Based on Reconfigurability Evaluation

This paper aims to improve the autonomous operational capabilities of spacecraft. For a complex system with physical coupling and flexible attachments, an autonomous reconfiguration strategy based on reconfigurability evaluation is proposed. First, a quantitative evaluation index for reconfigurability is given based on operator theory to fully exploit the potential of system reconfigurability under resource constraints. Then, specific requirements and evaluation methods for autonomous reconfiguration are proposed in accordance with the results, thereby providing a theoretical reference for the design of reconfiguration strategy methods. Thus, reconfigurability is maximized from the design point of view. Finally, the validity and correctness of the method are verified by simulation.

Environmental Impact of District Heating System Retrofitting

Retrofitting of district heating systems is a comprehensive process which covers all stages of district heating (DH) systems: production, distribution and consumption. This study quantitatively shows the effect of retrofitting measures and represents strengths and weaknesses of different development scenarios. Improvements in production units show improvements in fuel use efficiency and thus indirectly reduce CO2 emissions due to unburned fuel. For this purpose, validated district planning tools have been used. Tool uses mathematical model for calculation and evaluation of all three main components of the DH system. For the quantitative evaluation, nine efficiency and balance indicators were used. For each indicator, recommended boundary values were proposed. In total, six simulation scenarios were simulated, and the last scenario have shown significant reduction in CO2 emissions by 40% (from 3376 to 2000 t CO2 compared to the actual state), while share of biomass has reached 47%.

Construction and application of quality evaluation index system for remote-sensing image fusion

To objectively and justly compare the results of remote-sensing image fusion, evaluate the fusion algorithm, and optimize the fusion process, a quality evaluation index system is constructed by considering both qualitative and quantitative evaluations. The quantitative evaluation indexes are divided into three types based on the analysis of evaluation methods of the fusion quality of primary remote-sensing images. The applicability and effectiveness of six pixel-level fusion methods of optical remote-sensing image fusion were verified based on the constructed quality evaluation index system used to evaluate those methods in offshore and archipelagic areas. The experimental results indicate that the constructed quality evaluation index system evaluates the fusion algorithm objectively, comprehensively, and accurately. The intensity–hue–saturation transformation-based method distorts spectral features of the original image and easily causes spectrum degradation, whereas the wavelet transformation-based method has an advantage in the preservation of spectral features but efficiently produces blockiness and image penumbrae. The principal component analysis transformation-based method preserves more detailed textures and structural characteristics of original images but loses some physical properties. The Brovey transformation-based method results in spectral distortion, the high pass filter-based method shows a clear boundary of surface features after image fusion, and the Gram–Schmidt-based method shows a high ability of spectral information retention, but the latter performs poorly in the preservation of spatial information. The proposed quality evaluation index system of remote-sensing image fusion can be used as the objective effect evaluation criteria for remote-sensing image fusion and offers a reference for future applications of fusion results.

An Adaptive Weighted Phase Optimization Algorithm Based on the Sigmoid Model for Distributed Scatterers

Distributed scatterers (DSs) have been widely used in the time series interferometric synthetic aperture radar technique, which compensates for the insufficient density of persistent scatterers (PSs) in nonurban areas. In contrast to PS, DS is vulnerable to temporal and geometric decorrelation effects. Thus, phase optimization processing for DS is essential for reliable deformation parameter estimation. Advanced research has revealed that the application of all possible interferometric pairs will be more conducive to the reduction in phase biases. However, the low-coherence pixels will inevitably increase the difficulty of phase optimization and introduce unpredictable negative effects, which will reduce the effect of phase optimization. Therefore, this study proposed an advanced adaptive weighted phase optimization algorithm (AWPOA). In the AWPOA, the adaptive weighting strategy based on the sigmoid model was first proposed to assign more reasonable weights to pixels of different quality, which can efficiently reduce the negative influence of low-coherence pixels and improve the optimization performance. Moreover, coherence bias correction based on the second-kind statistics and an efficient solution strategy based on eigenvalue decomposition were derived and applied to achieve optimal phase series retrieval. The experimental results validated against both simulated and two sets of TerraSAR-X data demonstrated the overall superiority of the AWPOA over traditional phase optimization algorithms (POAs). Specifically, the processing efficiency of the eigenvalue decomposition solution strategy used in AWPOA was nearly 20 times faster than that of the PTA iterative solution strategy under the case without bias correction. Although bias correction increased the processing time, the optimization effect was significantly improved. Moreover, in terms of the quantitative evaluation indexes with the residual and the sum of the phase difference, the mean value of the improvement percentage of the AWPOA was increased by more than 12%, and the standard deviation was reduced by more than 1% over the traditional POAs, indicating its superior optimization performance and noise robustness.