Research and Discussion on Flipped Classroom Combined with Case-Based Learning in Pharmacoeconomics Teaching

Objective:To explore the application effect of flipped classroom combined with case-based learning teaching methods in pharmacoeconomics teaching.Methods:The students majoring in clinical pharmacy in 2019 were selected as the study subjects,and the cost-effectiveness analysis of different dosage forms of Yinzhihuang in the treatment of neonatal jaundice was selected as the teaching case.The flipped classroom combined with case-based learning teaching method was used to carry out theoretical teaching to the students.After the course,questionnaires were distributed through the Sojump platform to evaluate the teaching effect.Results:The results of the questionnaire showed that 85.71%of the students believed that the flipped classroom combined with case-based learning teaching method was helpful in mobilizing the learning enthusiasm and initiative,and improving the comprehensive application ability of the knowledge of pharmacoeconomics.92.86%of the students think that it is conducive to the understanding and memorization of learning content,as well as the cultivation of teamwork,communication,etc.Conclusion:Flipped classroom combined with case-based learning teaching method can improve students’knowledge mastery,thinking skills,and practical application skills,as well as optimize and improve teachers’teaching levels.

Integration of Theory and Practice in Medical Morphology Curriculum in Postgraduate Training:A Flipped Classroom and Case-based Learning Exercise

Objective:The integration of training in theory and practice across the medical education spectrum is being encouraged to increase student understanding and skills in the sciences.This study aimed to determine the deciding factors that drive students'perceived advantages in class to improve precision education and the teaching model.Methods:A mixed strategy of an existing flipped classroom(FC)and a case-based learning(CBL)model was conducted in a medical morphology curriculum for 575 postgraduate students.The subjective learning evaluation of the individuals(learning time,engagement,study interest and concentration,and professional integration)was collected and analyzed after FC-CBL model learning.Results:The results from the general evaluation showed promising results of the medical morphology in the FC-CBL model.Students felt more engaged by instructors in person and benefited in terms of time-saving,flexible arrangements,and professional improvement.Our study contributed to the FC-CBL model in Research Design in postgraduate training in 4 categories:1)advancing a guideline of precision teaching according to individual characteristics;2)revealing whether a learning background is needed for a Research Design course to guide setting up a preliminary course;3)understanding the perceived advantages and their interfaces;and 4)barriers and/or improvement to implement the FC-CBL model in the Research Design class,such as a richer description of e-learning and hands-on practice.Conclusion:Undertaking a FC-CBL combined model could be a useful addition to pedagogy for medical morphology learning in postgraduate training.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Fuzzy Entropy Based Combined Learning Algorithm for Neural Networks

Learning is one of key problems of artificial neural networks. In this paper, we present a kind of combined learning algorithm based on fuzzy entropy criterion for neural networks. The basic idea is to simulate the learning mechanism of human brain and overcome the limitations of monocrifsterion learning. The comparison is made between the given learning algorithm and the typical BP algorithm in order to show the characteristics of the new algorithm.

Effects of ginsenoside of stem and leaf combined with choline on learning and memory ability of rat models with Alzheimer diseases

BACKGROUND： Central adrenergic nerve and 5-serotonergic nerve can influence central cholinergic nerve on learning and memory and make easy for study; however, ginsenoside of stem and leaf （GSL） can improve functions of central adrenergic nerve; moreover, 5-serotonergic nerve and the combination with choline can produce synergistic effect and enhance learning and memory ability so as to improve learning and memory disorder of patients with Alzheimer disease （AD）. OBJECTIVE ： To observe the effects of GSL combining with choline on learning and memory of AD model rats DESIGN ： Randomized grouping design and controlled animal study SETIING ： Department of Pharmacology, Taishan Medical College MATERIALS ： The experiment was carried out in the Pharmacological Department of Medical College of Jilin University from October 1996 to January 1997. Forty healthy male Wistar rats of clean grade were randomly divided into 5 groups, including sham-injury group, model group, GSL group, choline group and combination group, with 8 rats in each group. Main medications： GSL with the volume more than 92.8% was provided by Department of Chemistry, Norman Bethune Medical College of Jilin University. Panaxatriol, the main component, was detected with thin layer scanning technique and regarded as the index of GSL quality [（55±1）%, CV= 2%, n = 5]. Choline was provided by the Third Shanghai Laboratory Factory. METHODS ： 150 nmol quinolinic acid was used to damage bilateral Meynert basal nuclei of adult rats so as to establish AD models. Rats in GSL, choline and combination groups were intragastric administrated with 400 mg/kg GSL, 200 mg/kg choline （20 mL/kg）, and both respectively last for 17 days starting from two days before operation. Rats in sham-injury group and model group were perfused with the same volume of distilled water once in each morning for the same days. （1） Passive avoidance step-down test： Five minutes later, rats jumped up safe platform when they were shocked with 36 V alternating current. If rats jumped down from the platform and the feet touched railings, the response was wrong. Numbers of wrong response were recorded within 3 minutes, and then the test was redone after 24 hours. （2） Morris water-maze spatial localization task： Swimming from jumping-off to platform directly was regarded as right response. Additionally, 4 successively right responses were regarded as the standard. Each rat was trained 10 times a day with 120 s per time for 3 successive days. The interval was 30 s. Three days later, numbers of right response were recorded. The training times were increased to 30 for unlearned rats. （3） Measurement of activity of choline acetylase in cerebral cortex： Rats were sacrificed at 17 days after operation to obtain cerebral cortex to measure activity of choline acetylase with radiochemistry technique. （4） Synergistic effect： It was expressed as Q value： Q value = factual incorporative effect/anticipant incorporative effect; Q ≥ 1 was regarded as synergistic effect. Anticipant incorporative effect = （EA＋EB-EA·EB）, EA and EB were single timing effect, respectively in GSL group and choline group. E（step-down test and Morris water maze test） = （x in model group - factual value in medicine groups）/x in model group; E （activity of choline acetylase） = （factual value in medicine groups -xin model group）/xin model group. MAIN OUTCOME MEASURES ： （1） Passive avoidance step-down test and Morris water-maze spatial localization task in the study of learning and memory; （2） activity of choline acetylase. RESULTS ： All 40 rats were involved in the final analysis. （1） Passive avoidance response： At learning phase on first day and retesting phase on the next day, numbers of wrong responses within 3 minutes were more in model group than sham operation group, and there was significant difference [（5.88±1.46）, （2.25±0.87） times; （2.63±1.06）, （0.50±0.53） times; P 〈 0.01]; numbers of wrong responses within 3 minutes were less in combination group than model group, and there was significant difference [learning phase： （1.12±0.83）, （5.88±1.46） times; retesting phase： （0.38±0.74）, （2.63±1.06）times, P 〈 0.01]; moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.07 and 1.59, respectively and it showed synergistic effect. Spatial localization task： Training times were more in model group than sham operation group, and there was significant difference [（2.9±2.5）, （12.6±3.5） times; P 〈 0.01]. Training times were less in combination group than model group, and there was significant difference [（11.8±2.4）, （27.9±2.5） times, P 〈 0.01]; moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.07 and it showed synergistic effect. （3） Activity of choline acetylase： Activity was lower in model group than sham operation group, and there was significant difference [（30.56±8.33）, （61.11 ±8.33） nkat/g; P 〈 0.01]. Activity was higher in combination group than model group and there was significant difference [（50.00±8.33）, （30.56±8.33） nkat/g, P 〈 0.01];moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.5 and it showed synergistic effect. CONCLUSZON： GSL in combination with choline can synergically improve the disorder of learning and memory of AD model rats. Its mechanism may be involved in enhancing the function of central cholinergic system.

Hydraulic Circuit Design and Dynamic Learning Using Case-based Reasoning

This paper describes the design and implementation of a hydraulic circuit design system using case-based reasoning (CBR) paradigm from AI community The domain of hydraulic circuit design and case-based reasoning are briefly reviewed Then a proposed methodology in compuer-aided circuit design and dynamic leaning with the use of CBR is described Finally an application example is selected to illustrate the ussfulness of applying CBR in hydraulic circuit design with leaming.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

The application of machine learning under supervision in identification of shale lamina combination types——A case study of Chang 7_(3)sub-member organic-rich shales in the Triassic Yanchang Formation,Ordos Basin,NW China

Organic rich laminated shale is one type of favorable reservoirs for exploration and development of continental shale oil in China.However,with limited geological data,it is difficult to predict the spatial distribution of laminated shale with great vertical heterogeneity.To solve this problem,taking Chang 73 sub-member in Yanchang Formation of Ordos Basin as an example,an idea of predicting lamina combinations by combining'conventional log data-mineral composition prediction-lamina combination type identification'has been worked out based on machine learning under supervision on the premise of adequate knowledge of characteristics of lamina mineral components.First,the main mineral components of the work area were figured out by analyzing core data,and the log data sensitive to changes of the mineral components was extracted;then machine learning was used to construct the mapping relationship between the two;based on the variations in mineral composition,the lamina combination types in typical wells of the research area were identified to verify the method.The results show the approach of'conventional log data-mineral composition prediction-lamina combination type identification'works well in identifying the types of shale lamina combinations.The approach was applied to Chang 73 sub-member in Yanchang Formation of Ordos Basin to find out planar distribution characteristics of the laminae.

基于e-learning平台的“工学结合”教学模式探索

解决身处异地学生的继续学习问题,是"工学结合"教学模式改革成功的保障。通过e-learning教学平台作用的描述,介绍了e-learning教学平台的构建,阐述了通过e-learning教学平台实施教学。

Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm

One of the challenges of face recognition in surveillance is the low resolution of face region. Therefore many superresolution(SR) face reconstruction methods are proposed to produce a high-resolution face image from one or a set of low-resolution face images. However, existing dictionary learning based algorithms are sensitive to noise and very time-consuming.In this paper, we define and prove the multi-scale linear combination consistency. In order to improve the performance of SR, we propose a novel SR face reconstruction method based on nonlocal similarity and multi-scale linear combination consistency(NLS-MLC). We further proposed a new recognition approach for very low resolution face images based on resolution scale invariant feature(RSIF). A series of experiments are conducted on two public face image databases to test feasibility of our proposed methods. Experimental results show that the proposed SR method is more robust and computationally effective in face hallucination, and the recognition accuracy of RSIF is higher than some state-of-art algorithms.

Learning and competence development via clinical cases-what elements should be investigated to best train good medical doctors?

In European higher education,application of information technology,concentration on the learning-processes,consistent implementation,transfer learning,case-based learning,autonomous learning has been extensively studied in the last decade.Educational sciences based on neuroscientific findings use brain-based learning and teaching,including integrated thematic instructions and emotion-theory.Elements essential to this strategy,such as theory and methods for learning,competencies,attitudes,social reality,and a metadiscourse are described herein.Research on learning tends to focus on declarative knowledge,associative learning with conditional stimuli,and procedural knowledge with polythematic/crosslinking thinking.Research on competencies:In research on competencies(e.g.,for clinical reasoning,decision-making),intuitive and analytical components are studied.As repeated presentation and exercising of clinical cases is crucial for an efficient learning process,the implementation of interactive scenarios including affectively involving didactics is considered.For competence-development observational methods,questionnaires/item sets or factors have to be targeted and empirically validated.Attitudes and social reality:Clinical decision-making,identification processes and attitudes(“Hidden curriculum”),as well as secondary socialization processes(integration of social norms,values,preparation of role-acquisition,occupational role)are studied via process research,conceptual research,and observational methods.With respect to social reality research,conscious and unconscious bargaining processes have to be taken into account.Methodology:Neuroscience-memory,neuronal,molecular biology,and computer science(Neurocircuits)are integrated into observational process research(e.g.,affective-cognitive interface,identification processes)and conceptual research is added and studied on the meta-level,including discussion of research paradigms.This discussion provides ongoing feedback to projects in a hermeneutic circle.

Case and Questions Design in Case- based Learning Used in Medical-nursing English Teaching

Case-based learning(CBL) is gradually replacing the traditional lecturing-based learning in nursing English teaching.In the process of CBL, selecting and compiling a good case is key to the success of CBL. In the meantime, designing questions is an important factor for successful CBL. In this article, we discuss how to select and compile cases and how to design questions in CBL used in Medical-nursing English Teaching.

Novel ensemble learning based on multiple section distribution in distributed environment

Because most ensemble learning algorithms use the centralized model, and the training instances must be centralized on a single station, it is difficult to centralize the training data on a station. A distributed ensemble learning algorithm is proposed which has two kinds of weight genes of instances that denote the global distribution and the local distribution. Instead of the repeated sampling method in the standard ensemble learning, non-balance sampling from each station is used to train the base classifier set of each station. The concept of the effective nearby region for local integration classifier is proposed, and is used for the dynamic integration method of multiple classifiers in distributed environment. The experiments show that the ensemble learning algorithm in distributed environment proposed could reduce the time of training the base classifiers effectively, and ensure the classify performance is as same as the centralized learning method.

Efficacy of electroacupuncture stimulating Shenmen(HT7),Baihui(GV20),Sanyinjiao(SP6)on spatial learning and memory deficits in rats with insomnia induced by para-chlorophenylalanine:a single acupoint vs combined acupoints

OBJECTIVE:To investiage the effect of electroacupuncture(EA)at a single acupoint of Shenmen(HT7),Baihui(GV20),Sanyinjiao(SP6)and at combined acupoints of Shenmen(HT7)and Baihui(GV20)and Sanyinjiao(SP6)on the PKA/CREB and BDNF/TrkB signaling,as well as neuroapoptosis and neurogenesis in hippocampus and elucidate the underlying mechanism of single and combined acupoints on ameliorating spatial learning and memory deficits in a rat model of primary insomnia.METHODS:Primary insomnia was modeled by intraperitoneal injection of para-chlorophenylalanine(PCPA)once daily for 2 d.EA was applied at Shenmen(HT7),Baihui(GV20),Sanyinjiao(SP6),or Shenmen(HT7)+Baihui(GV20)+Sanyinjiao(SP6)(combined)for 30 min daily for 4 d.Spatial learning and memory function was evaluated by the Morris water maze(MWM)test.Protein expressions of hippocampal cAMP-dependent protein kinase(PKA)-Cβ,phosphorylated cAMP-responsive element-binding protein(p-CREB),brainderived neurotrophic factor(BDNF),and tyrosine kinase receptor B(TrkB)were evaluated by Western blotting.Neuronal apoptosis in the hippocampus was detected with the transferase-mediated dUTP-X nick end labeling assay.Endogenous neurogenesis was examined with bromodeoxyuridine staining.The MWM test and hippocampal p-CREB,BDNF,and TrkB protein levels in the combined acupoints group were evaluated after the administration of a PKA-selective inhibitor(H89).RESULTS:Spatial learning and memory were significantly impaired in rats with insomnia.The spatial learning deficits were ameliorated in the Shenmen(HT7),Baihui(GV20),Sanyinjiao(SP6),and combined groups;this improvement was significantly greater in the combined group than the single acupoint groups.The spatial memory impairment was improved in the combined,Baihui(GV20),and Shenmen(HT7)groups,but not the Sanyinjiao(SP6)group.The expressions of PKA-Cβ,p-CREB,BDNF,and TrkB were decreased in rats with insomnia.All these proteins were significantly upregulated in the combined group.PKA/p-CREB protein levels were elevated in the Baihui(GV20)and Shenmen(HT7)groups,whereas BDNF/TrkB expression was upregulated in the Sanyinjiao(SP6)group.The staining results showed significant attenuation of hippocampal cell apoptosis and increased numbers of proliferating cells in the combined group,whereas the single acupoint groups only showed decreased numbers of apoptotic cells.In the combined group,the PKA inhibitor reversed the improvement of spatial memory and upregulation of pCREB expression caused by EA,but did not affect its activation of BDNF/TrkB signaling.CONCLUSIONS:EA at the single acupoints Baihui(GV20),Shenmen(HT7),or Sanyinjiao(SP6)had an ameliorating effect on the spatial learning and memory deficits induced by insomnia.EA at combined acupoints exerted a synergistic effect on the improvements in spatial learning and memory impairment in rats with insomnia by upregulating the hippocampal PKA/CREB and BDNF/TrkB signaling,facilitating neurogenesis,and inhibiting neuronal apoptosis.These findings indicate that EA at combined acupoints[(Baihui(GV20),Shenmen(HT7),and Sanyinjiao(SP6)]achieves a more pronounced regulation of hippocampal neuroplasticity than EA at single acupoints,which may partly explain the underlying mechanisms by which EA at combined acupoints exerts a better ameliorative effect on the cognitive dysfunction caused by insomnia.

A Requirement Driven Learning Management Architecture Based on BPEL

With the prosperity of the Internet,e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity,the IMS Learning Design (LD) specification provides a collaborative scenario for participants. However,IMS LD provides insufficient support for interaction among learning activities and can not dynamically integrate learning resources to meet the continually changing service requirements. In this paper,a Business Process Execution Language (BPEL) enhanced requirement driven learning management architecture to address the issues of personalize adaptive learning was proposed. It models the learning activity by combining IMS LD with BPEL and matches optimal learning sequence based on Case-based reasoning (CBR) method. By providing expandable secure learning sequences flexibly,it satisfies the different actual demands for personalize learning.

Estimation of Potato Biomass and Yield Based on Machine Learning from Hyperspectral Remote Sensing Data

The estimation of potato biomass and yield can optimize the planting pattern and tap the production potential.Based on partial least square(PLSR),multiple linear regression(MLR),support vector machine(SVM),random forest(RF),BP neural network and other machine learning algorithms,the biomass estimation model of potato in different growth stages is constructed by using single variables such as original spectrum,first-order differential spectrum,combined spectrum index and vegetation index(VI)and their coupled combination variables.The accuracy of the models is compared and analyzed,and the best modeling method of biomass in different growth stages is selected.Based on the optimized modeling method,the biomass of each growth stage is estimated,and the yield estimation model of different growth stages is constructed based on the estimation results and the linear regression analysis method,and the accuracy of the model is verified.The results showed that in tuber formation stage,starch accumulation stage and maturity stage,the biomass estimation accuracy based on combination variable was the highest,the best modeling method was MLR and SVM,in tuber growth stage,the best modeling method was MLR,the effect of yield estimation is good.It provides a reference for the algorithm selection of crop biomass and yield models based on machine learning.

Research on Dynamic Forecast of Flowering Period Based on Multivariable LSTM and Ensemble Learning Classification Task

The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. Flowering forecast is not only an important part of the construction of agro-meteorological index system, but also an important part of the meteorological service system. In this paper, by analyzing local meteorological data and phenological data of “Red Fuji” apples in Fen County, Linfen City, Shanxi Province, with the help of machine learning and neural networks, we proposed a method based on the combination of time series forecasting and classification forecasting is proposed to complete the dynamic forecasting model of local flowering in Ji County. Then, we evaluated the effectiveness of the model based on the number of error days and the number of days in advance. The implementation shows that the proposed multivariable LSTM network has a good effect on the prediction of meteorological factors. The model loss is less than 0.2. In the two-category task of flowering judgment, the idea of combining strategies in ensemble learning improves the effect of flowering judgment, and its AUC value increases from 0.81 and 0.80 of single model RF and AdaBoost to 0.82. The proposed model has high applicability and accuracy for flowering forecast. At the same time, the model solves the problem of rounding decimals in the prediction of flowering dates by the regression method.

Analysis of Combining OBE and CBL in Pharmacy Internship

Talent training has been emphasized in China’s national development.Training an excellent pharmaceutical professional has far-reaching significance for promoting rational use of drugs,ensuring safe drug use,and improving the quality and safety of medical treatment.The internship program is an important part of a pharmacy major that serves to develop students’practical skills.In this paper,we propose a combination of outcome-based education(OBE)and case-based learning(CBL)in pharmacy internships to promote critical thinking and independent learning among students and ensure the sustainable development of pharmacy education.

煤矿井下掘进机器人路径规划方法研究

针对煤矿非全断面巷道条件下掘进机器人移机难度大、效率低下等问题,分析了煤矿井下非结构化环境特征及掘进机器人运动特性,提出了基于深度强化学习的掘进机器人机身路径规划方法。利用深度相机将巷道环境实时重建,在虚拟环境中建立掘进机器人与巷道环境的碰撞检测模型,并使用层次包围盒法进行虚拟环境碰撞检测,形成巷道边界受限下的避障策略。考虑到掘进机器人形体大小且路径规划过程目标单一,在传统SAC算法的基础上引入后见经验回放技术,提出HER-SAC算法,该算法通过环境初始目标得到的轨迹扩展目标子集,以增加训练样本、提高训练速度。在此基础上,基于奖惩机制建立智能体,根据掘进机器人运动特性定义其状态空间与动作空间,在同一场景下分别使用3种算法对智能体进行训练,综合平均奖励值、最高奖励值、达到最高奖励值的步数以及鲁棒性4项性能指标进行对比分析。为进一步验证所提方法的可靠性,采用虚实结合的方式,通过调整目标位置设置2种实验场景进行掘进机器人的路径规划,并将传统SAC算法和HER-SAC算法的路径结果进行对比。结果表明:相较于PPO算法和SAC算法,HER-SAC算法收敛速度更快、综合性能达到最优;在2种实验场景下,HER-SAC算法相比传统SAC算法规划出的路径更加平滑、路径长度更短、路径终点与目标位置的误差在3.53 cm以内,能够有效地完成移机路径规划任务。该方法为煤矿掘进机器人的自主移机控制奠定了理论基础,为煤矿掘进设备自动化提供了新方法。

基于组合深度学习的轨道交通短时进站客流预测模型

针对轨道交通短时进站客流考虑不充分和特征学习不全面而导致预测精度不高的问题,选取客流特征、天气、空气质量和道路交通拥堵指数等多个因素,提出了一种基于组合深度学习的轨道交通短时进站客流预测模型(CNN-ResNet-BiLSTM)。基于卷积神经网络(CNN)对多因素客流时间序列进行自动提取,在CNN网络中插入多个残差神经网络(ResNet)来加深网络深度,利用双向长短时记忆神经网络(BiLSTM)捕捉前后两个方向的客流时间序列特征并得到预测结果;以杭州市全网80个站点工作日的进站客流为例,验证了该模型的有效性。研究结果表明:与常用的几种模型相比,多因素CNN-ResNet-BiLSTM组合模型的均方根误差(E RMS)至少降低了8.50%,平均绝对误差(E MA)至少降低了6.74%,平均绝对百分比误差(E MPA)至少降低了6.52%。