Boxplot Method Research Articles

Prediction Model for Cutterhead Rotation Speed Based on Dimensional Analysis and Elastic Net Regression

The development and maturation of TBM (tunnel boring machine) technology have significantly improved the accuracy and richness of excavation data, driving advancements in intelligent tunneling research. However, challenges remain in managing data noise and parameter coupling, limiting the interpretability of traditional machine learning models regarding TBM parameter relationships. This study proposes a cutterhead rotation speed prediction model based on dimensional analysis. By utilizing boxplot methods and low-pass filtering techniques, excavation data were preprocessed to select appropriate operational and mechanical parameters. A dimensionless model was established and integrated with elastic net regression to quantify parameters. Using TBM cluster data from a water diversion tunnel project in Xinjiang, the accuracy and generalizability of the model were validated. Results indicate that the proposed model achieves high prediction accuracy, effectively capturing trends in cutterhead rotation speed while demonstrating strong generalizability.

Classification-Based Parameter Optimization Approach of the Turning Process

The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line.

Recognition and Characterization of Nanoscale Phases: Modulus Mapping of Asphalt Film in Pavement Mixture Cores.

The objective of this study is to recognize and characterize the nanoscale phase modulus mapping of the asphalt film in pavement mixture cores using atomic force microscopy quantitative nanomechanical technology. The pavement core samples from the upper and middle layers of four highways and laboratory samples were taken as the research object. The phase modulus-macro property correlation of recovered asphalt was analyzed using mathematical statistics. The results showed that the pavement core samples had more significant multi-phase and diversified phase characteristics compared to lab samples. This indicated that the asphalt in the pavement core had an obvious phase separation phenomenon due to aging. The phase modulus of each sample was distributed across a relatively wide numerical range, and there were also many numerical points with large fluctuations. Especially for the mixture sample containing SBS (Styrene-Butadiene-Styrene)-modified asphalt, the phase modulus distribution mappings presented a multi-peak phenomenon. Hence, considering the distribution characteristics of the data, the box plot method was introduced. Compared with quantified results from laboratory samples, the phase modulus of SBS-modified asphalt increased by 0.96 times, 1.18 times and 1.15 times, and that of base asphalt increased by 0.59 times, 0.56 times, 0.42 times, 1.24 times and 0.39 times, respectively. This indicates that the aging degree of asphalt in the upper layer was generally greater than that of the asphalt in the middle layer and that there was an aging gradient in the direction of pavement depth. All points were within the 95% confidence band in terms of correlation fitting, indicating a better fitting effect between phase modulus and complex shear modulus, as well as between phase modulus and penetration. This research provides innovative ideas for future multi-scale numerical simulation and cross-scale performance model development of asphalt binders.

Research on the framework and meteorological parameter optimization method of dynamic heating load prediction model for heat-exchange stations

Heat-exchange station is the key mid-link between heat source and heat users in district heating system, playing a role in distributing heat and regulating supply and demand. Accurate load prediction at the level of heat-exchange stations and then regulating the heat supply is the important step to optimize district heating system. Based on real and limited data, the framework of heat-exchange station load prediction method is established in this study, which includes 5 procedures ‘data preprocessing method, input variable selection, solar radiation substitution, prediction model establishment and model transplantation application’. The sliding box plot method was proposed for data preprocessing, and the effects of data preprocessing under the widths of 24 h, 48 h, 72 h, 96 h and 120 h were compared, with 48 h as the optimal sliding window width. Support Vector Regression (SVR) algorithm was used to establish a heating load prediction model for heat-exchange station in residential buildings. And parameter substitution was made for solar radiation data that cannot be obtained in weather forecasts. High accuracy was obtained with a MAPE of 5.80 % and RMSE of 0.18 GJ. The above model was transplanted and applied in three other heat-exchange stations of different building types. The MAPEs were 5.51 %, 7.79 % and 6.54 %, with corresponding RMSEs of 0.017 GJ, 0.04 GJ and 0.04 GJ, respectively, which all achieved good prediction results. The research results provide certain technical support for the operation and regulation of district heating systems.

The Influence of Particle Swarm Optimization‐Back Propagation Neural Network Hyperparameter Selection on the Prediction Accuracy of Converter Endpoint Temperature

The converter is a complex, high temperature, high pressure reactor with limited internal moitoring. At present, data‐driven models mainly focus on the prediction differences between algorithms, and there is relatively little analysis of the impact of different hyperparameters on prediction accuracy. Taking a 120 t converter in a Chinese steel plant as an example, this paper explores the application of particle swarm optimization‐back propagation neural network (PSO‐BP) in converter temperature prediction. First, the Pauta criterion or Box plot method was used to preprocess the data by prescreening. Subsequently, the influence of the activation function, learning rate, and number of hidden layer nodes of BP on the prediction accuracy of the endpoint temperature were explored. Then we investigated the influence of PSO parameters on the optimal result of BP initial value. Comparing the temperature prediction hit rate before and after optimization, the BP model has hit rates of 63.64%, 79.22%, and 87.45% within ±10, ±15, and ±20 °C, respectively, and the PSO‐BP model has hit rates of 68.40%, 84.85%, and 94.81%, respectively. In comparison, PSO‐BP extracts data features more accurately, has higher stability, and has better accuracy in predicting the endpoint temperature of the converter.

A Novel Tool for the Rapid and Transparent Verification of Reference Intervals in Clinical Laboratories.

Background/Objectives: We present a software package called reflimR (Version 1.0.6), which enables rapid and transparent verification of reference intervals from routine laboratory measurements. Our method makes it easy to compare the results with specified target values and facilitates the interpretation of deviations using traffic light colors. Methods: The algorithm includes three procedural steps: (a) definition of an appropriate distribution model, based on Bowley's quartile skewness, (b) iterative truncation, based on a modified boxplot method to obtain the central 95% of presumably inconspicuous results, and (c) extrapolation of reference limits from a truncated normal quantile-quantile plot. Results: All algorithms have been combined into one consolidated library, which can be called in the R environment with a single command reflim (x). Using an example dataset included in the package, we demonstrate that our method can be applied to mixed data containing a substantial proportion of pathological values. It leads to similar results as the direct guideline approach as well as the more sophisticated indirect refineR software package. As compared to the latter, reflimR works much faster and needs smaller datasets for robust estimates. For the interpretation of the results, we present an intuitive color scheme based on tolerance ranges (permissible uncertainty of laboratory results). We show that a relatively high number of published reference limits require careful reevaluation. Conclusions: The reflimR package closes the gap between direct guideline methods and the more sophisticated indirect refineR method. We recommend reflimR for the rapid routine verification of large amounts of reference limits and refineR for a careful analysis of unclear or doubtful results from this check.

Underwater weak spectral line extraction scheme based on improved HMM

To address the difficulty of extracting weak spectral lines from signals received by passive sonar, we propose a spectral line extraction scheme based on an improved hidden Markov model (HMM). A new state transition probability based on spectral line features is proposed that solves the problem of state transition probability relying on prior information in traditional HMMs. Using a peak detection algorithm and a parallel processing framework reduces computation. We employ the boxplot method to remove the outliers from the spectral lines caused by strong noise and compensate for them. By improving the forward–backward probability calculation method through a peak penalty factor, we manage adjacent spectral lines prone to be missed by traditional HMMs. Lastly, we use dynamic sliding windows to determine a spectral line’s birth and death. Data verification by simulations and sea tests show that our algorithm extracts spectral lines better and with a smaller error, accurately detects the birth and death of spectral lines, and is faster than traditional HMM algorithms.

Online boxplot derived outlier detection

Outlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset’s distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

Reliability-driven time-of-use tariffs for efficient plug-in electric vehicle integration

Demand response (DR) plays a crucial role in balancing electricity supply and demand, but existing studies on price-based DR for plug-in electric vehicle (PEV) load management lack comprehensive considerations. Therefore, this study designs reliability-driven time-of-use (TOU) tariffs for the integration of PEVs, considering various factors such as random component failures, the stochastic characteristics of non-dispatchable components, and fluctuations in load patterns and uncertainties. The proposed framework employs advanced stochastic simulation models to assess generation facility reliability amidst extensive PEV deployment in three steps. First, individual components, including conventional generation capacity, renewable energy sources, and demand, as well as PEV load models, are developed. Then, a system reserve margin assessment is carried out via the generation of seasonal synthetic time series data. During this step, innovative techniques, including box-plot methods and expert rules, are used to simplify the representation of the system reserve margin at each hour. Finally, optimized TOU pricing schedules for PEVs are generated. These pricing hours are initially categorized as off-peak, mid-peak, or on-peak, streamlining scenario analysis and aligning PEV charging profiles with predefined pricing schedules for grid reliability evaluation. Case studies have shown that the proposed method can improve the reliability indices by effectively mitigating the impacts.

Exploring the correlation between temperature and crime: A case-crossover study of eight cities in America

Recent years have seen increasing academic interest in exploring the correlation between temperature and crime. However, it is uncertain whether similar long-term trends or seasonality (rather than causal effect) of temperature and crime is the major reason for the observed correlation between them. To explore whether there is still a correlation between temperature and crime when long-term trends and seasonal cycles are filtered out, we use the Kalman filter to decompose the time series of temperature and crimes, and then the fast Fourier transform is used to calculate the exact circle of their seasonality separately. Based on that, the box-plot method and linear regression are used to explore the correlation between temperature residuals and crime residuals. The results show that more than half of the crime types have similar seasonal cycles (approximately 1 year) to that of temperature. Moreover, the daily residual analyses show that temperature residuals have a positive correlation with assault and robbery residuals in all cities, whose average slopes are more than 0.1. The other four types of crimes vary greatly from case to case. The temperature residuals show a weak correlation with the residuals of some crime types.

Determination of uncertainties of geomechanical parameters of metamorphic rocks using petrographic analyses

Geomechanical parameters of intact metamorphic rocks determined from laboratory testing remain highly uncertain because of the great intrinsic variability associated with the degrees of metamorphism. The aim of this paper is to develop a proper methodology to analyze the uncertainties of geomechanical characteristics by focusing on three domains, i.e. data treatment process, schistosity angle, and mineralogy. First, the variabilities of the geomechanical laboratory data of Westwood Mine (Quebec, Canada) were examined statistically by applying different data treatment techniques, through which the most suitable outlier methods were selected for each parameter using multiple decision-making criteria and engineering judgment. Results indicated that some methods exhibited better performance in identifying the possible outliers, although several others were unsuccessful because of their limitation in large sample size. The well-known boxplot method might not be the best outlier method for most geomechanical parameters because its calculated confidence range was not acceptable according to engineering judgment. However, several approaches, including adjusted boxplot, 2MADe, and 2SD, worked very well in the detection of true outliers. Also, the statistical tests indicate that the best-fitting probability distribution function for geomechanical intact parameters might not be the normal distribution, unlike what is assumed in most geomechanical studies. Moreover, the negative effects of schistosity angle on the uniaxial compressive strength (UCS) variabilities were reduced by excluding the samples within a specific angle range where the UCS data present the highest variation. Finally, a petrographic analysis was conducted to assess the associated uncertainties such that a logical link was found between the dispersion and the variabilities of hard and soft minerals.

Determination of Molybdenum in Geological Ores by Laser-Induced Breakdown Spectroscopy (LIBS) with Support Vector Machine Regression (SVMR) and Data Preprocessing

A support vector machine regression (SVMR) model that integrates data preprocessing was devised for the determination of Mo in molybdenum ores. To eliminate the negative effects of spectral fluctuations and improve the computational efficiency, the model processes original measurements through the following steps: denoising via wavelet transform smoothing (WTS), debaselining via adaptive iteratively reweighted penalized least squares (airPLS), main characteristic peak extraction, elimination of abnormal spectral data via the box plot method, and normalization via min-max scaling. In this study, 255 characteristic peaks were selected from 17,916 spectral datasets, and the total number of datasets after removing the outliers was 491. The calibration curve approach was used to establish a univariate model. The limit of detection of Mo was 0.0080 wt%. The R2 value of the calibration curve was 0.6675. Linear regression (LR) and SVMR were used to establish a multivariate analysis model. Compared to that of the calibration curve approach, the determination coefficients (R P 2) of LR and SVMR increased from 0.8034 to 0.9859 and 0.9941, respectively. The range of relative errors (REP) decreased from 0.21%–67.66% to 0.48%–18.46% and 2.65%–7.44%, respectively; the mean absolute error (MAEP) was decreased from 0.0173 wt% to 0.0048 and 0.0039 wt%, respectively; and the root mean square error (RMSEP) decreased from 0.0243 wt% to 0.0065 and 0.0042 wt%, respectively. These results indicate that the integration of SVMR with data preprocessing is suitable for the determination of Mo in molybdenum ores.

A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively.

Exploration of Applicability of Diatom Indices to Evaluate Water Ecosystem Quality in Tangwang River in Northeast China

The diatom index has been widely used in the evaluation of water ecological quality, but the applicability of the diatom index often varies in different study areas. The accuracy of the evaluation results depends on the applicability of the diatom index, especially when it is not applied to the place where it is created. In order to screen out the diatom index suitable for the evaluation of the water ecological quality of Tangwang River in northeast China, and to identify the factors affecting the accuracy of the diatom index, the community structure and water environment characteristics of 24 sample sites were investigated in Tangwang River in August 2018, and 18 diatom indices were calculated. The discriminative ability of diatom indices was analyzed using the box plot method, and the factors affecting the accuracy of the diatom index were identified by combining Pearson and Spearman correlation analyses. The results show that the discriminability of the Biological Diatom Index (BDI), Specific Pollution Sensitivity Index (IPS), Idse Leclercq (IDSE), Indice Diatomique Artois Picardie (IDAP), Diatom Eutrophication Pollution Index (EPI-D), Trophic Index (Rott TI), European Economic Community Index (CEE), and Watanabe Index (WAT) was the strongest, which could reasonably distinguish the reference group from the lightly damaged group. In general, the water ecological condition of Tangwang River Basin is good in the wet season, and the water ecological quality of about 80% of the sample sites was “moderate” or better. The main factors affecting the evaluation accuracy of the diatom index in Tangwang River Basin are the correlation strength between the diatom index and habitat quality, organic pollution, and nutrients. The coverage of diatom index species had no significant effect on the accuracy of evaluation. In order to reasonably evaluate the aquatic ecological status, it is recommended to use the diatom index, which has a good correlation with the environmental factors in the study area, or to establish a new diatom index based on the diatom community and environmental factors in the study area.

Prediction of Sleep Health Status, Visualization and Analysis of Data

Sleep, as an indispensable element of human life, is accepted as one of the main sources of health, vitality and productivity. There are many factors that affect sleep health. Stress level, irregularity of sleep patterns and excessive use of technological devices can be given as examples. Sleep health can be determined by analyzing various variables about sleep. Sleep health can be determined by using these variables with machine learning methods. For this purpose, a dataset containing 374 rows of data and 13 features was used in this study. Sleep disorder conditions can be classified as None, Sleep Apnea, and Insomnia using 12 features. Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k Nearest Neighbor (kNN) methods were used for classification. Classification success was 91.66% from the RF model, 90.27% from the SVM model, 90.27% from the LR model and 87.50% from the kNN model. In order to analyze which feature is more effective in classification processes, box plot and correlation analysis methods were used. As a result of the analyzes, it was determined that the body mass index has the greatest effect on the determination of sleep disorder.

Analysis and prediction of rockburst intensity using improved D-S evidence theory based on multiple machine learning algorithms

Accurate prediction of rockburst hazards is an effective means to improve the development and utilization of underground space. When using a single machine learning algorithm for rockburst prediction, the reliability of the classification results can hardly be guaranteed. In this paper, a fusion model for rockburst prediction based on multiple machine learning algorithms is proposed in combination with D-S evidence theory. Six characteristic parameters (σθ, σc, σt, SR, BR and Wet) were statistically analyzed for 304 sets of rockburst cases, and the data had outliers and class imbalances. The box plot method was used to detect replacement of a small number of outliers, and the Adasyn oversampling method eliminated the effect of data class imbalance. The visualization of the data by using the t-SNE method showed improved classifiability. SVM, BP, RBF, RF and ELM rockburst prediction models were developed with the six characteristic parameters as model inputs. In addition, the global optimization algorithms were used to find the optimal hyperparameters of the five machine learning algorithms. The accuracy of the optimized models was improved by 2.63%, 5.27%, 2.63%, 6.58% and 7.5% compared to the unoptimized models. Based on the prediction results, the precision was proposed as the fusion index. Additionally, based on improved D-S evidence theory, a fusion model for rockburst prediction was developed with five optimized machine learning algorithm models as base classifiers. Finally, the fusion model was applied to the rockburst prediction of Jiangbian Hydropower Station and Sanshan Island Gold Mine in China with the accuracy of 92.86%, and the prediction was outperformed by a single base classifier. The fusion model compensated for the uncertainty and poor robustness of the prediction results of the single-base classifier, and improved the accuracy and reliability of rockburst prediction.

Determining the critical threshold of meteorological heat damage to tea plants based on MODIS LST products for tea planting areas in China

Tea (Camellia sinensis (L.) Kuntze) is a daily drink in modern life throughout the world. Global warming increases the exposure of tea plants to elevated heat conditions during the summer picking period. Therefore, it is a fundamental function of the meteorological service to determine the critical temperature threshold that triggers heat damage (HD) to tea plants. In this study, MODIS daytime land surface temperature (LST) products obtained from the Aqua satellite (MYD11A1), annual land cover type products (MCD12Q1), and actual HD records were jointly used to determine the critical LST threshold resulting in HD to tea planting areas in China. An LST dataset of HD and HD-free samples was constructed based on actual HD records. The box-plot method was then used to delineate the range of LST outliers in HD to tea plants. The cumulative frequency distribution (CFD) method was adopted to determine the critical LST threshold by searching for the maximum slope of CFD within the range of LST outliers based on 2901 actual HD samples from 42 agrometeorological stations in 11 provinces in China. Results showed that the critical LST threshold triggering HD to tea plants was 30.1 °C. Two different HD data sources (i.e., 68 counties in eight provinces from the Meteorological Disaster Management System (MDMS) and 37 counties in six cities from a published book) were used as HD validation samples. Validation accuracy analysis revealed that a critical LST threshold of 30.1 °C was reasonable, with an average accuracy of between 60% and 90% for the published-book dataset, and 75.7% in the northern Yangtze River area and 86.3% in the southern Yangtze River area for the MDMS dataset. This study confirmed that using remotely sensed LST products is an effective method for identifying temperature-related agrometeorological disasters owing to the method's ability to intuitively capture the surface temperature distribution of the disaster-affected object. The findings will be helpful for identifying the timing and location of HD to tea plants, and thereby potentially preventing HD to tea plants.

Changes in the social situation in EU countries during COVID‐19 (an alternative approach to the assessment of social indicators)

AbstractThis contribution focuses on research on the social impacts of the COVID‐19 pandemic, more specifically on unemployment, income, and poverty in 2020. The indicators selected to measure these phenomena are suitable for understanding and explaining social changes related to and linked to the COVID‐19 pandemic, and represent scientifically reliable, verified, and suitable indicators for evaluating and monitoring the differentiated impact and impacts of the pandemic on individual EU countries. The goal of the paper was to determine how the pandemic—or measures against the spread of the virus—affected the targeted social phenomena. The boxplot method was used to capture the dynamics of changes in the values of selected indicators, and the synthetic variable construction method was used to classify countries’ overall vulnerability/resilience. The results of the ordered country classification explicitly showed the significantly differentiated impacts of the pandemic on the observed phenomena and the changes in the overall social situation in individual countries.

A Multilayered and Multifactorial Health Assessment Method for Launch Vehicle Engine under Vibration Conditions

Sixty percent of the failures of launch vehicles in the ascending phase occur in the propulsion system. Among them, the vibration generated by the engine is an important factor in the occurrence of failure. At present, health assessment methods in the aerospace field are mostly for specific equipment, and scholars mostly assess the real-time health status of launch vehicle engines which can only reflect the current health status of the launch vehicle. Existing methods cannot be applied to different equipment, and there is a lack of research on health assessments of fuzzy and complex mechanical systems. In this article, we propose a multi-layer and multi-factor predictive evaluation method for a fuzzy and complex system and conduct experiments on real vibration data of rockets. First, we divide the health assessment level according to the vibration data that affect the normal operation of the rocket. Secondly, we obtain the future trend of vibration signals based on five data prediction methods and calculate the health status interval of the rocket engine’s working conditions based on the boxplot method. At the same time, we calculate the single health evaluation set of every vibration signal. We obtain the weights of each level and factor for the health value based on an analytic hierarchy process (AHP). The optimization of this step avoids an over-reliance on expert experience. Finally, we complete a fuzzy comprehensive evaluation of the engine system from the bottom up to obtain the final health value. The minimum evaluation error is 0.0193% on the test data of the Long March series launch vehicle engine, which shows that the proposed method can successfully predict and evaluate the launch vehicle engine.

Evaluating Bicycling Environments with Trajectory Data on Shared Bikes: A Case Study of Beijing

A detailed evaluation of the riding environment can help the government master the urban riding environment, identify problematic road sections, and improve riding quality. However, the current evaluation of riding environment is mainly subjective, lacking big data (e.g., shared bicycle trajectory data) as a data-driven objective evaluation system. The emergence of shared bicycle data has provided data support for data-driven riding environment evaluation, but there are few studies using shared bicycle data for riding evaluation at present. First, according to the characteristics of the data and the riding environment, a boxplot method and Bayesian probabilistic network model are used to exclude abnormal data and to match trajectories to road sections. Second, this paper proposes a data-driven evaluation framework based on riding influencing factors. An evaluation framework, which is composed of node-, link-, and block-level evaluation indicators, was constructed, using an evaluation model combining TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and KNN (K-Nearest Neighbors). The evaluation results can identify parking issues, intersection difficulties, and lane occupancy issues on bicycle sections, and visually reflect the riding environment. The significance of this article is to create an objective evaluation system based on a data-driven technology to accurately identify sections and causes of riding quality problems. The research results can be applied in the future to evaluate the cycling environment around the railway stations for bicycle parking planning and determine the foothold for traffic management.