Strong Association Rules Research Articles

Mining text for causality: a new perspective on food safety crisis management

The aim of the present study was to quantitatively analyze the importance of each risk factor in a food safety event, so as to fully elucidate the correlation between different risk factors and provide a reference for food safety governance. Text mining and complex network analysis methods were utilized to explore the causal mechanism of food safety incidents. By performing text mining on food safety event news reports, 15 major risk factors were identified based on high-frequency words. A causal network for food safety accidents was then constructed using strong association rules among these factors. Through network centrality analysis, the five core factors of food safety incidents and their associated sets were clarified. Based on text mining of 6,282 cases of food safety incidents reported by online media, 168 keywords related to food risk factors were extracted and further categorized into 15 types of food safety risk factors. Network analysis results revealed that microbial infection emerged as the most critical risk factor, with its associated sets including biotoxins and parasites, counterfeiting or fraud, processing process issues, and non-compliance with quality indicators.

Optimized evaluation of the quality of sensor video internet of things (VIOT) by the integration of big data and artificial intelligence

AbstractThe application of sensor video internet of things technology to large-scale integrated work can significantly improve the working quality of employees. However, the degree of improvement in working quality is still difficult to measure in a systematic, intelligent, stable, and accurate manner. local optimization and adjustment after evaluation are still relatively challenging, To address these issues, the study proposes a method of optimizing the evaluation of sensor video quality through the integration of big data and AI techniques. A large-scale integrated distance education system in the field of education and training with a certain application basis is adopted as a case. Including big data and AI techniques such as integrated intelligent agent modules, recommendation algorithms, and transaction optimization algorithms, a new agent-oriented system design with fast response speed, strong scalability, convenient local optimization, and greater stability is achieved. According to the network topology structure of the distance education system in colleges and universities, this paper uses queuing theory to analyze the system performance of the system. The focus of this paper is the quantitative relationship between system communication intensity ρ, user arrival rate λ, system channel capacity n and system waiting delay, blocking probability, average queue length, system throughput and other important performance indicators. In teaching evaluation, the key factor that affects the quality of classroom teaching, that is, Developing a comprehensive system for evaluating classroom instruction is crucial. By incorporating student feedback, leveraging data mining techniques, and harnessing computer technology, a holistic framework for gathering, analyzing, and generating actionable insights on teaching performance is established. This approach makes the evaluation process more systematic and evidence-based, identifying 12 key elements that influence classroom education standards. In the experimental section, the student assessment data sets I1 and I2 exhibit experimental values (statistics) that significantly exceed the thresholds, with a minimum support of 0.32 and a confidence level of 0.61. Moreover, the Boolean matrix is divided into 90 points. The rule U1Ua ≥ U2 is identified as a subset of {U1U2Ua} within the large item set, signifying a strong association rule. These findings confirm the robustness of the artificial intelligence model proposed in this paper for video quality prediction. The optimized sensor video quality evaluation method not only meets a satisfactory confidence level and matching value but also demonstrates good reliability and relevance in the evaluation criteria.

Path analysis of coal mine accident risk factors based on the 24Model.

Coal mine accidents seriously affect China's coal safety production and sustainable development. The present study aimed to reveal the risk factors in coal mine accidents and explore the causal relationship among risk factors. This study utilized text mining to analyse 450 coal mine accident reports, identifying 50 risk factors and efficiently mapping them into the 24Model. The association rule algorithm was then used to mine the strong association rules among the risk factors within the 24Model, establishing the interaction mechanism among them. Based on the strong association rules, related hypotheses were proposed. Finally, the hierarchical and logical relationships of risk factors within the 24Model were analysed, and the causal and mediating effects were tested by path analysis. The safety management system has a direct effect on unsafe acts, unsafe conditions, habitual behaviour and organizational safety culture. Moreover, external influence has an effect on unsafe acts, organizational safety culture and habitual behaviour through the mediating effect of the safety management system. Based on the results obtained, this study proposes a series of specific measures to prevent risks in coal mines, providing a new perspective for the analysis and prevention of accidents.

Identifying Factors of Dynamic Positioning Incidents through Association Rule Mining

Accidents in the offshore industry can have severe repercussions for people, cargo, vessels, and the environment, making maritime safety a crucial concern. Dynamic positioning incidents, particularly those involving loss of position, represent a significant risk. This study employs association rule mining to analyze DP incident data, leveraging its strength in discovering robust associations. Using the Apriori algorithm, the analysis identifies strong association rules for loss of position (drift-off, drive-off) and loss of redundancy situations. The findings reveal event-related variables and potential causal relationships, providing insights and guidance for reducing the risk and occurrence of future DP incidents through stringent and targeted safety measures.

Intelligent Identification of Hidden Dangers in Hydrogen Pipeline Transmission Station Using GWO-Optimized Apriori Algorithm

This work proposes an intelligent grey-wolf-optimizer-improved Apriori algorithm (GWO-Apriori) to mine the association rules of hidden dangers in hydrogen pipeline transmission stations. The optimal minimum support and minimum confidence are determined by GWO instead of the time-consuming trial approach. Experiments show that the average support and average confidence of association rules using GWO-Apriori increase by 29.8% and 21.3%, respectively, when compared with traditional Apriori. Overall, 59 ineffective association rules out of the total 105 rules are filtered by GWO, which dramatically improves data mining effectiveness. Moreover, 23 illogical association rules are excluded, and 12 new strong association rules ignored by the traditional Apriori are successfully mined. Compared with the inefficient and labor-intensive manual investigation, the intelligent GWO-Apriori algorithm dramatically improves pertinency and efficiency of hidden danger identification in hydrogen pipeline transmission stations.

Analysis on typical characteristics and causes of coal mine gas explosion accidents in China.

Large-scale coal mine gas explosion (CMGE) accidents have occurred occasionally and exerted a devastating effect on society. Therefore, it is essential to systematically identify the characteristics and association rules of causes of CMGE accidents through analysis on large-scale CMGE accident reports. In this study, 298 large-scale CMGE accidents in China from 2000 to 2021 were taken as the data sample, and mathematical statistical methods were adopted to analyze their general characteristics, coupling cross characteristics, and characteristics of gas accumulation and ignition sources. Moreover, the text mining technology and the Apriori algorithm were used for exploring the formation mechanism of CMGE accidents, during which 46 main causal factors were identified and 59 strong association rules were obtained. Furthermore, an accident causation network was constructed based on the co-occurrence matrix. The key causal items and sets of CMGE accidents were clarified through network centrality analysis. According to the research results, electrical equipment failure, cable short circuit, mine lamp misfire, hot-line work, and blasting spark are the key ignition sources of CMGE. Fan failure, airflow short circuit, and local ventilation fan damage are the main causes of gas accumulation. Besides, the confidence levels of two association rules of "static spark-fan failure" and "blasting spark-airflow short circuit" are higher than 70%, indicating that they are the two dominant risk-coupling paths of gas explosions. In addition, six causes appear frequently in the shortest risk paths of gas explosion and are closely related to other causes, i.e., fan failure, local ventilation fan damage, static sparks, electrical equipment failure, self-heating ignition, and friction impact sparks. This study provides a new perspective on identifying causes of accidents and their complex association mechanisms from accident report data for practical guidance in risk assessment and accident prevention.

Forecasting Customer Actions: Exploring Machine Learning Techniques for Behavior Analysis and Prediction

Modern modelling of today’s consumer behaviour is highly dependent on data mining which mines customers’ data in order to answer specific questions in time. This complexity and uncertainty relating to the consumer be-haviour makes it difficult for us to predict. Therefore, selection of appropri-ate methods is very important. Predictive models can be designed so as to identify specific group or individual actions and thus making them useful marketing tools. However, many models are overly simplistic for they leave out some of the essential factors hence leading to inaccurate forecasting out-comes. Consequently, instead of concentrating on customer behaviour-firm capital structure relationships only a strong association rule mining model should be created using online store data that effectively estimates customer response. Knowing their business clients is crucial when it comes to profitability by aligning the company’s earnings with their concerns. Artificial intelligence (AI) optimizes product location by clustering techniques targeting the right customers. It examines customer behaviour and buying trends utilizing Kaggle’s client membership card records such as Customer ID, age, gender, annual income, spending score among others. This includes basic market analytics based on demographic information like age or income split into groups, however big data superiority over traditional methods cannot be underestimated including machine learning applications such as Customer Seg-mentation Automated Methodology (CSAM), k-means clustering for user segmentation among others. This approach facilitates focused advertising campaigns and development and launch of new products aimed at particular consumer sections.

Association rules forecasting for the foreign exchange market

Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.

The diabetes mellitus multimorbidity network in hospitalized patients over 50 years of age in China: data mining of medical records

ObjectiveMany diabetes mellitus (DM) patients suffer from multimorbidity. Understanding the DM multimorbidity network should be given priority. The purpose of this study is characterize the DM multimorbidity network in people over 50 years.MethodsData on 75 non-communicable diseases (NCDs) were extracted from electronic medical records of 309,843 hospitalized patients older than 50 years who had at least one NCD. The association rules analysis was used as a novel classification method and combined with the Chi-square tests to identify associations between NCDs and DM.ResultA total of 12 NCDs were closely related to DM, {cholelithiasis, DM} was an unexpected combination. {dyslipidemia, DM} and {gout, DM} had the largest lift\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{lift}$$\\end{document} in the male and female groups, respectively. The negative related group included 7 NCDs. There were 9 NCDs included in the strong association rules. Most combinations were different by age and sex. In males, the strongest rule was {peripheral vascular disease (PVD), dyslipidemia, DM}, while {hypertension, dyslipidemia, chronic liver disease (CLD), DM} was the strongest in females. In patients younger than 70 years, hypertension, CLD, and dyslipidemia were the most dominant NCDs in the DM multimorbidity network. In patients 70 years or older, chronic kidney disease (CKD), CVD, CHD, and heart disease (HD) frequently co-occurred with DM.ConclusionFuture primary healthcare policies for DM should be formulated based on age and sex. In patients younger than 70 years, more attention to hypertension, CLD, and dyslipidemia is required, while attention to CKD, CVD, CHD and HD is needed in patients older than 70 years.

Rules of prescriptions for heart failure of dilated cardiomyopathy based on latent structure model and association rules

The latent structure model and association rules analysis were employed to explore the compatibility rules of prescriptions for heart failure of dilated cardiomyopathy, with a view to providing theoretical support for the clinical treatment of this disease based on syndrome differentiation and the formulation of guidelines. The articles about the treatment of heart failure of dilated cardiomyopathy were retrieved from CNKI, Wanfang, VIP, and SinoMed. The database was established in Microsoft Excel 2019. Lantern 5.0 and Rstudio were used to analyze the latent structure and association rules of Chinese medicine with the frequency greater than 4.00%. Furthermore, the frequency structure model was used to mine the rules of prescriptions for heart failure of dilated cardiomyopathy. The study included 175 traditional Chinese medicine(TCM) prescriptions, involving 128 Chinese medicines, with the cumulative frequency of 1 847. High-frequency medicines included Astragali Radix, Salviae Miltiorrhizae Radix et Rhizoma, Poria, Cinnamomi Ramulus, Glycyrrhizae Radix et Rhizoma, with the main effects of tonifying, activating blood, resolving stasis, and releasing exterior. A total of 17 hidden variables, 34 hidden categories, and 6 comprehensive cluster models, along with 15 core prescriptions, were obtained. According to the prescriptions, the patients mainly had the syndromes of heart-Yang and Qi deficiency, Qi deficiency and blood stasis, heart-kidney Yang deficiency or Qi-Yin deficiency. Fifty-four strong association rules were obtained through association rule analysis. The highest degree of support was observed for the combination of Salviae Miltiorrhizae Radix et Rhizoma-Astragali Radix, while the highest degree of confidence was found for the combination of Salviae Miltiorrhizae Radix et Rhizoma-Cinnamomi Ramulus-Ophiopogonis Radix-Astragali Radix. The heart failure of dilated cardiomyopathy, characterized by internal deficiency and excess manifestations, is attributed to deficiency, stasis, and water. These factors are closely associated with the heart, lung, and spleen. The treatment should follow the principle of invigorating Qi and warming Yang, and meanwhile the method of activating blood and resolve stasis or moving Qi and promoting urination can be adopted according to the specific syndrome of patients.

Interaction mechanisms of interface management risks in complex systems of high-speed rail construction projects: an association rule mining-based modeling framework

PurposeThis study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.Design/methodology/approachThis paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.FindingsThe results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.Originality/valueThe interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

A patent text-based product conceptual design decision-making approach considering the fusion of incomplete evaluation semantic and scheme beliefs

Conceptual design evaluation is a significant challenge in product development to select suitable conceptual schemes (CS). Relying on decision makers' (DMs) personal experience for design information may result in incomplete and ambiguous evaluation semantics, creating an incomplete decision matrix. Previous approaches use attributes based on candidate schemes to complete the missing information, but neglect the mining of associated design information from external resources such as patents, reducing the beliefs objectivity after missing semantic completion. Besides, non-compensatory nature between DMs’ interactivity and criteria have not been well considered. To fill these issues, a patent text-based CS decision approach considering the fusion of incomplete semantics and scheme beliefs is proposed. First, using the beneficial effects text from patents as historical design data, constructing general evaluation criteria using the Latent Dirichlet Allocation topic model, and introducing the Apriori model to extract strong association rules between criteria to complete the incomplete semantics. Second, a fuzzy measure model for DMs is constructed based on intuitionistic fuzzy cross-entropy and Shapley value method, supporting the allocation of DMs' interactive weights. Then, the fuzzy Dempster-Shafer evidence theory is used to transform the multi-criteria decision model into a belief fusion problem of CS that avoids aggregating the evaluation semantics of non-compensatory criteria and outputting the optimal CS with interval credibility. A practical case study of an in-pipe inspection robot will be employed to validate the proposed approach, sensitivity analysis and comparison results confirmed that the complement data have the reliability to avoid continuous investment in the evaluation process.

Unveiling the Secrets of Obesity with Machine Learning (ML) Techniques/Algorithms

Obesity is the most significant health threat all over the world as it is considered the mother of all diseases. Recent decades have witnessed an extensive shift towards sedentary lifestyles including adaptation to luxury modes of transportation, processed food, lack of physical activities, and prolonged screen times. The trend of using cars and motorcycles to travel even short distances has emerged as a critical component in Pakistan's obesity pandemic. Whenever the causes of obesity are discussed, the focus remains always on processed food and genetic factors. This study is an effort to exploit a machine-learning-based pathway to predict the major causes of obesity using machine learning (ML) algorithms. For this research, a dataset of 2,111 instances of various age groups and lifestyles was employed. For analysis, three efficient ML algorithms known as k-nearest neighbor (k-NN or KNN), support vector machine (SVM), and decision tree (DT) were utilized. It was observed that the excessive use of personal transport and comparatively limited availability and use of public transport has been a significant cause of obesity in Class II and III obese individuals. From the experimental results, strong association rules with minimum confidence value of 25% and maximum support value of 80% were obtained. Also, DT with random forest technique was found to have the highest accuracy of 90.36%, with the recall factor value of 98.73%. This signifies the fact that sedentary lifestyle and mode of transportation have a significant impact on rising obesity level.

Clinical research and the rules of acupoint selection on acupuncture treatment of psoriasis in the past 20 years

To sum up clinical researches and the rule of acupoint selection on the treatment of psoriasis by acupuncture and moxibustion in the past 20 years, so as to provide new ideas and reference for improving the clinical effect of acupuncture for psoriasis. Research papers, published from January 1, 2002 to December 31, 2022 in both Chinese and English, were searched from databases of China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform (WanFang), and Chinese Science and Technology Journal (CSTJ), PubMed, Embase and Web of Science by using key words of psoriasis, psoriases, psoriasis vulgaris, acupuncture, needle, acupoint and moxibustion. Then, Microsoft Excel 2021 was used for data entry, extraction and sorting of the included literature, data extraction mainly includes diagnostic criteria, treatment methods, treatment frequency, total course of treatment, selection of acupoints, efficacy evaluation and outcomes. SPSS Modeler 18.1 software was used to make analysis on the association rules of acupoints. A total of 26 papers were included, and the diagnostic criteria of clinical studies on acupuncture treatment of psoriasis are constantly updated. Of the 26 clinical studies, 23 had both treatment group and control group, 21 were randomized controlled trials. The main methods for treatment of psoriasis included fire needle, acupuncture, electroacupuncture, acupuncture combined with moxibustion or fire needle, fire needle plus western medicines, etc. with the acupuncture combined with moxibustion, and fire needle combined with western medicines being most frequently used. The observation index and clinical efficacy evaluation were effective rate, psoriasis area and severity index, dermatology life quality index, Traditional Chinese Medicine syndrome score, etc., with the effective rate being about 40% to 100%. Comparison of clinical efficacy of acupuncture in treating psoriasis showed that the simple fire needle, simple acupuncture, acupuncture combined with moxibustion, fire needle or acupuncture combined with western medicines were superior to simple western medicines, etc. In regard to acupoint selection, a total of 35 acupoints were found in the 26 studies included, among which the top 5 frequently used were Ashi points (skin lesion region), Geshu (BL17), Feishu (BL13), Ganshu (BL18) and Quchi (LI11), and there were strong association rules among BL17, BL13, BL18 and Shenshu (BL23). Acupuncture is safe and effective in the treatment of psoriasis, and acupuncture combined with moxibustion and fire acupuncture needle combined with western medicines are recommended as the main intervention method, with Ashi-point, BL13, BL17, BL18 and BL23 as the main acupoints.

Research on the Path of Civic-Political Construction of Music Appreciation Courses in Public Art Education in Colleges and Universities under the Perspective of the Internet

Abstract In this paper, the association rule algorithm is used to mine all the strong association rules in the public art education music appreciation course, and the course Civics, and the Inter-transaction algorithm is utilized to decompose the mining task into two parts of long and short itemsets according to the time-consuming time, so as to reduce the number of transactional intersection operations. A new pruning strategy has been adopted to effectively limit the number of candidate sets and improve the performance of the algorithm. Finally, based on the model of this paper, we analyzed the relevance of the music appreciation courses in public art education in colleges and universities to Civics and the change in students’ Civics literacy after integrated teaching. The results show that the mean value of students’ moral quality after teaching increased by 3.24 from 18.07±2.08, and the p-value of each index except music literacy is 0.000, which is significantly different. And sup=0.05, conf=0.622, lift=4.499 for Music Art A and Introduction to Basic Marxist Principles A. There is a strong correlation between teaching music and Civic and Political Science courses.

Research on key risk chain mining method for urban rail transit operations: A new approach to risk management

To ensure the safety of urban rail transit operations and uncover the transmission dynamics of risk sources, a key risk chain mining method for urban rail transit operation is proposed. Firstly, the H-Apriori association rule algorithm is proposed for the characteristics of low frequency but high riskiness of high hazard degree risk sources in urban rail transit operation, which adds a new hazard degree evaluation index to the traditional Apriori algorithm and couples with support degree two-dimensionally to mine the strong association rules among risk sources. Secondly, we construct a weighted risk network with risk sources as network nodes and strong association rules as network edges, and propose a key risk chain mining method for urban rail transit operation based on path search theory to mine key risk chains from the weighted risk network. Finally, using the actual urban rail transit operation data of a city in China as an example, a total of 17 key risk chains are mined, and then 5 key risk sources and 8 key chain break locations are obtained by riskiness and frequency analysis of key risk chains, and control plans are proposed. The research outcomes introduce a novel approach to mining risk chains in urban rail transit operations, shedding light on the propagation mechanisms, triggering probabilities, and degrees of unsafety associated with risk sources. The results not only provide theoretical support but also offer methodological guidance for pinpointing locations of risk chain breaks and refining the control of risk sources.

Regularity of prescriptions for sick sinus syndrome based on latent structure combined with association rules

This study aims to mine the regularity of traditional Chinese medicine(TCM) prescriptions for sick sinus syndrome(SSS) and provide a reference for clinical syndrome differentiation and treatment. The relevant papers were retrieved from CNKI, Wanfang, VIP, and SinoMed with the time interval from inception to January 31, 2023. The relevant information from qualified papers was extracted to establish a library. Lantern 5.0 and Rstudio were used to analyze the latent structure and association rules of TCMs with the frequency ≥3%, which combined with frequency descriptions, were used to explore the rules of TCM prescriptions for SSS. A total of 192 TCM prescriptions were included, involving 115 TCMs with the cumulative frequency of 1 816. High-frequency TCMs include Aconiti Lateralis Radix Praeparata, Ginseng Radix et Rhizoma, Glycyrrhizae Radix et Rhizoma, Astragali Radix, and Salviae Miltiorrhizae Radix et Rhizoma. The high-frequency medicines mainly had the effects of tonifying, releasing exterior with pungent-warm, and activating blood and resolving stasis. The analysis of the latent structure model yielded 13 hidden variables, 26 hidden classes, 8 comprehensive cluster models, and 21 core prescriptions. Accordingly, the common syndromes of SSS were inferred as heart-Yang Qi deficiency, heart-spleen Yang deficiency, heart-kidney Yang deficiency, Yang deficiency and blood stasis, both Qi and Yin deficiency and blood stasis, and Yin and Yang deficiency. The analysis of association rules predicted 30 strong association rules, among which Ginseng Radix et Rhizoma-Aconiti Lateralis Radix Praeparata had the highest support. SSS is a syndrome with Yang deficiency and Qi deficiency as the root causes and cold, phlegm, and stasis as the manifestations. The clinical treatment of SSS should focus on warming Yang and replenishing Qi, which should be supplemented with the therapies of activating blood and resolving stasis, warming interior and dissipating cold, or regulating Qi movement for resolving phlegm according to the patients' syndromes.

Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

Regarding the generation and integration of typical scenes of PV and loads in urban photovoltaic distribution networks, as well as the insufficient consideration of the spatiotemporal correlation between PV and loads, this paper proposes a typical scene extraction method based on local linear embedding, kernel density estimation, and a joint PV–load typical scene extraction method based on the FP-growth algorithm. Firstly, the daily operation matrices of PV and load are constructed by using the historical operation data of PV and load. Then, the typical scenes are extracted by the dimensionality reduction of local linear embedding and the kernel density estimation method. Finally, the strong association rules of PV–meteorological conditions and load–meteorological conditions are mined based on the FP-growth algorithm, respectively. The association of PV–load typical daily operation scenarios is completed using meteorological conditions as a link. This experiment involved one year of operation data of a distribution network containing PV in Qingyuan, Guangdong Province. The typical scene extraction joint method, Latin hypercube sampling method, and k-means clustering-based scene generation method proposed in this paper are used for comparison, respectively. The results show that compared to the other two scenario generation methods, the error between the typical scenario obtained by this method and the actual operating scenario of the distribution network is smaller. The extracted typical PV and load scenarios can better fit the actual PV and load operation scenarios, which have more reference value for the operation planning of actual distribution networks containing PV.

Analyzing the Risk Factors of Traffic Accident Severity Using a Combination of Random Forest and Association Rules

This study explores risk factors influencing the at-fault party in traffic accidents and analyzes their impact on traffic accident severity. Based on the traffic accident data of Shenyang City, Liaoning Province, China, from 2018 to 2020, 19 attribute variables including road attributes, time attributes, environmental attributes, and characteristics of the at-fault parties with either full responsibility, primary responsibility, or equal responsibility of the traffic accidents were extracted and analyzed in conjunction with the built environment attributes, such as road network density and POI (points of interest) density at the sites of traffic accidents. Using the RF-SHAP method to determine the relative importance of risk factors influencing the severity of traffic accidents with either motor vehicles or vulnerable groups at-fault, the top ten risk factors influencing the severity of traffic accidents with vulnerable road users as the at-fault parties are: functional zone, density of shopping POI, density of services POI, cause of accident, travel mode, collision type, season, road type, age of driver, and physical isolation. Travel mode, season, and road speed limit are more important risk factors for traffic accidents, with motor vehicle drivers as the at-fault parties. The density of service POI and cause of the accident are less critical for traffic accidents with motor vehicle drivers than traffic accidents with vulnerable road users who are at-fault. Subsequently, the Apriori algorithm based on association rules is used to analyze the important causal factors of traffic accidents, so as to explore the influence mechanism of multiple causal factors and their implied strong association rules. Our results show that most combined factors are associated with the matched Service and Shopping POI features. This study provides valuable information on the perceived risk of fatal accidents and highlights the built environment’s significant influence on fatal traffic accidents. Management strategies targeting the most typical combinations of accident risk factors are proposed for preventing fatalities and injuries in serious traffic accidents.

Discriminating Feed Rate of Combine Harvester by Using Association Rule Mining

The feed rate is an important evaluation index of combine harvester performance. The quick identification of the amount of feed rate that enters the combine during harvesting is of great significance for the efficiency and operational quality of the combine harvester. To address this issue, this study proposes a feed rate discrimination method based on association rule mining. A self-designed data acquisition system was designed, taking the wheat combine harvester as object, and collected seven speed signals and three torque signals when the feed rate was 6 kg/s~8 kg/s, 8 kg/s~10 kg/s, and 10 kg/s~11 kg/s, respectively. The collected time series data were discretized so as to facilitate the construction of transaction sets. Then, the association rules in the constructed transaction set were mined by FP-Growth, and the rules with weak or no correlation with the increase in feed rate were filtered using min-support, min-confidence, and min-lift of 1.3, 0.8, and 3, respectively, to obtain strong association rules. Then, the strong association rules were constructed as classifiers. The test results showed that the accuracy of the constructed classifier for the identification of 6 kg/s~8 kg/s, 8 kg/s~10 kg/s, and 10 kg/s~11 kg/s feed rates was 100 %, 96 %, and 98.7 %, respectively. Research results can provide a basis for the adjustment of the working state of the combine harvester.