食堂售饭系统窗口机设计     

邹继军  饶运涛 《华东地质学院学报》2004,27(3):293-297

叙述了传统食堂售饭系统的不足，介绍了CAN总线与非接触式IC卡技术的特点和优势，并对以这两种技术为基础的新型食堂售饭系统窗口机的软硬件设计原理进行了较详细的分析。窗口机硬件系统主要由CAN总线通信接口、IC卡读写器、键盘、显示和存储器等功能模块组成，软件系统主要由窗口机初始化模块、窗口机监控模块、窗口机按键处理模块等组成。论文在对窗口机原理进行分析的同时，也对设计中遇到的关键问题进行了讨论，对于今后食堂售饭系统的设计提供了一个新的思路。  相似文献   

Expert System for Applicability of Tunnel Boring Machines in Japan   总被引：6，自引：0，他引：6  

S. Okubo  K. Fukui  W. Chen 《Rock Mechanics and Rock Engineering》2003,36(4):305-322

Summary ¶This paper outlines the development of a new expert system for assessing the applicability of tunnel boring machine (TBM) tunneling in Japan. Although a great deal of research on TBMs has been published, and the applicability of TBMs has been discussed, considerable differences in opinion still exist between authors. In this paper, we review previous studies and outline the present situation with particular focus on disc-cutter TBMs. Based on the knowledge acquired, we present an expert system for the applicability of TBMs, for use in pre-feasibility studies. Originally, we planned to construct the expert system on the basis of unified knowledge or rules without contradictions. However, it was found after several attempts that it is very difficult to unify knowledge because opinions vary considerably and TBMs are under continuous development. As a result, the expert system was divided into three stages. In stage A, the fulfillment of fundamental requirements is checked. Stage B is a simple expert system consisting of a minimal set of suitable rules as judged by the authors. Stage C incorporates the opinions of various other experts and the over-simplified and omitted points in stage B. The system is applied to 18 tunnels in Japan, and while the results provided by the expert system can certainly be improved, the method for accumulating knowledge and rules makes the system simple and easy to use, with very large scope for improvement and expansion.Received July 1, 2001; accepted December 9, 2002 Published online April 29, 2003  相似文献   

In-situ Measurements of Cutter Forces on Boring Machine at Äspö Hard Rock Laboratory Part I. Laboratory Calibration and In-situ Measurements     

Z. X. Zhang  S. Q. Kou  X. C. Tan  P.-A. Lindqvist 《Rock Mechanics and Rock Engineering》2003,36(1):39-61

Summary This paper presents the testing methods used and the results obtained in an investigation of the cutter forces on a Boretec DS 1.6 boring machine during field boring in ?sp? Hard Rock Laboratory. Two button cutters, one front cutter and one gauge cutter, were used in the field measurements. A total of 6 strain gauges were bonded on the shaft of each cutter. And each group of two gauges was used to measure a one-orthogonal cutter force component, i.e. the normal force, tangential force, and side force, respectively. In order to measure the cutter forces successfully, a telemetry system composed of a transmitter and a receiver was employed to transfer the signals from the strain gauges to a computer.  A three-direction-loading system was employed in the laboratory calibration so as to solve the force-coupling problem appearing in the cutter force measurements. Correspondingly, a mathematical treatment of the force-coupling problem was performed. Then, by means of the established testing system, which was proved successful in the laboratory, the normal force, tangential force, and side force of the two button cutters on the boring machine were measured in the field. In addition, the penetration rate, thrust, and rotation speed of the boring machine were also recorded in the field. The results show the following. (1) A force-coupling phenomenon really exists and it should be considered. (2) All three directional force components always show quite a high peak value every few seconds. (3) The cutter forces of the front cutter are always much larger than the respective cutter forces of the gauge cutter. Moreover, as expected, the normal force of each cutter is much larger than the tangential force and side force of the cutter in question. Received October 5, 2001; accepted June 25, 2002; Published online November 19, 2002  相似文献   

岩样单轴压缩的失稳破坏和试验机加载性能   总被引：9，自引：2，他引：7  

尤明庆 《岩土力学》1998,19(3):43-49

岩样单轴压缩过程的应力-应变曲线是特定岩样与试验机共同作用的结果,并非岩石材料的力学特性;以此观点研究了不同形状岩样峰后强度降低的规律．给出了岩样与试验机联合作用模型,得到了简单而明确的岩样失稳破坏准则;讨论了电液伺服试验机的加载特性和获取全程曲线的方法,并对Ⅱ类全程曲线作出了新的解释。  相似文献   

专设图案环境中自动定位与定向的一种新算法   总被引：1，自引：0，他引：1  

林宗坚  张继贤 《武汉大学学报(信息科学版)》1998,(4)

通过在房间的天花板和四壁上喷画专门设计的图案,实现在屋内自由运动物体的定位和定向。自由运动物体被装上ＣＣＤ相机用以摄取壁板上的图案,并通过无线电将信号传输到屋内固定的计算机上。从影像提取图案特征以及从中解算定位参数（ＨＸ、ＨＹ、ＨＺ）和定向参数（φ、ω、κ）的过程是全自动的,不需要任何人工操作。支持这种处理的算法是基于传统摄影测量理论的,但一系列新的算式是专门推导出来的。  相似文献   

Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data   总被引：1，自引：0，他引：1  

Abdulhakim Mohamed Abdi 《地理信息系统科学与遥感》2020,57(1):1-20

ABSTRACT

In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency’s Sentinel missions have been used in various remote sensing applications. However, there is a lack of studies that utilize these data to assess the performance of machine learning algorithms in complex boreal landscapes. In this article, I compare the classification performance of four non-parametric algorithms: support vector machines (SVM), random forests (RF), extreme gradient boosting (Xgboost), and deep learning (DL). The study area chosen is a complex mixed-use landscape in south-central Sweden with eight land-cover and land-use (LCLU) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 covering spring, summer, autumn and winter conditions. Using stratified random sampling, each LCLU class was allocated 1477 samples, which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, but primarily overall accuracy was used in allocating algorithm hierarchy. A two-proportion Z-test was used to compare the proportions of correctly classified pixels of the algorithms and a McNemar’s chi-square test was used to compare class-wise predictions. The results show that the highest overall accuracy was produced by support vector machines (0.758 ± 0.017), closely followed by extreme gradient boosting (0.751 ± 0.017), random forests (0.739 ± 0.018), and finally deep learning (0.733 ± 0.0023). The Z-test comparison of classifiers showed that a third of algorithm pairings were statistically different. On a class-wise basis, McNemar’s test results showed that 62% of class-wise predictions were significant from one another at the 5% level or less. Variable importance metrics show that nearly half of the top twenty Sentinel-2 bands belonged to the red edge (25%) and shortwave infrared (23%) portions of the electromagnetic spectrum, and were dominated by scenes from spring (38%) and summer (40%). The results are discussed within the scope of recent studies involving machine learning and Sentinel-2 data and key knowledge gaps identified. The article concludes with recommendations for future research.  相似文献   

Forest structural diversity characterization in Mediterranean landscapes affected by fires using Airborne Laser Scanning data     

P. J. Gelabert  A.L. Montealegre  M.T. Lamelas  D. Domingo 《地理信息系统科学与遥感》2020,57(4):497-509

ABSTRACT

Forest fires can change forest structure and composition, and low-density Airborne Laser Scanning (ALS) can be a valuable tool for evaluating post-fire vegetation response. The aim of this study is to analyze the structural diversity differences in Mediterranean Pinus halepensis Mill. forests affected by wildfires on different dates from 1986 to 2009. Several types of ALS metrics, such as the Light Detection and Ranging (LiDAR) Height Diversity Index (LHDI), the LiDAR Height Evenness Index (LHEI), and vertical and horizontal continuity of vegetation, as well as topographic metrics, were obtained in raster format from low point density data. In order to map burned and unburned areas, differentiate fire occurrence dates, and distinguish between old and more recent fires, a sample of pixels was previously selected to assess the existence of differences in forest structure using the Kruskal–Wallis test. Then, k-nearest neighbors algorithm (k-NN), support vector machine (SVM) and random forest (RF) classifiers were compared to select the most accurate technique. The results showed that, in more recent fires, around 70% of the laser returns came from grass and shrub layers, yielding low LHDI and LHEI values (0.37–0.65 and 0.28–0.46, respectively). In contrast, the areas burned more than 20 years ago had higher LHDI and LHEI values due to the growth of the shrub and tree strata. The classification of burned and unburned areas yielded an overall accuracy of 89.64% using the RF method. SVM was the best classifier for identifying the structural differences between fires occurring on different dates, with an overall accuracy of 68.79%. Furthermore, SVM yielded an overall accuracy of 75.49% for the classification between old and more recent fires.  相似文献   

Application of machine learning techniques in groundwater potential mapping along the west coast of India     

Pankaj Prasad  Victor Joseph Loveson  Mahender Kotha  Ramanand Yadav 《地理信息系统科学与遥感》2020,57(6):735-752

ABSTRACT

Groundwater potential mapping (GWPM) in the coastal zone is crucial for the planning and development of society and the environment. The current study is aimed to map the groundwater potential zones of Sindhudurg coastal stretch on the west coast of India, using three machine learning models: random forest (RF), boosted regression tree (BRT), and the ensemble of RF and support vector machine (SVM). In order to achieve the objective, 15 groundwater influencing factors including elevation, slope, aspect, slope length (LS), profile curvature, plan curvature, topographical wetness index (TWI), distance from streams, distance from lineaments, lithology, geomorphology, soil, land use, normalized difference vegetation index (NDVI), and rainfall were considered for inter-thematic correlations and overlaid with spring and well occurrences in a spatial database. A total of 165 spring and well locations were identified, which had been divided into two classes: training and validation, at the ratio of 70:30, respectively. The RF, BRT, and RF-SVM ensemble models have been applied to delineate the groundwater potential zones and categorized into five classes, namely very high, high, moderate, low, and very low. RF, BRT, and ensemble model results showed that 33.3%, 35.6%, and 36.8% of the research area had a very high groundwater potential zone. These models were validated with area under the receiver operating characteristics (AUROC) curve. The accuracy of RF (94%) and hybrid model (93.4%) was more efficient than BRT (89.8%) model. In order to further evaluate and validate, four different sites were subsequently chosen, and we obtained similar results, ensuring the validity of the applied models. Additionally, ground-penetrating radar (GPR) technique was applied to predict the groundwater table and validated by measured wells. The mean difference between measured and GPR predicted groundwater table was 14 cm, which reflected the importance of GPR to guide the location of new wells in the study region. The outcomes of the study will help the decision-makers, government agencies, and private sectors for sustainable planning of groundwater in the area. Overall, the present study provides a comprehensive high-precision machine learning and GPR-based groundwater potential mapping.  相似文献   

Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery     

Lamin R. Mansaray  Adam Sheka Kanu  Lingbo Yang  Jingfeng Huang  Fumin Wang 《地理信息系统科学与遥感》2020,57(6):785-796

ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R² of 0.82 and RMSE of 191.8 g/m²) followed by the RF model (R² of 0.79 and RMSE of 197.8 g/m²). After heading, the k-NN model performed best (R² of 0.43 and RMSE of 452.1 g/m²) followed by the RF model (R² of 0.42 and RMSE of 464.7 g/m²). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.  相似文献   

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis     

Ahmad Sharafati  Seyed Babak Haji Seyed Asadollah  Davide Motta 《水文科学杂志》2020,65(12):2022-2042

ABSTRACT

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.  相似文献