检索结果-内蒙古大学图书馆

Sustainability of Horticulture in Agriculture Era 4.0 in Lembang Sub-district, West Java

IOP conference Series: Earth and Environmental Science 2021年第1期694卷

作者： Y A Stanny B Barus A E Pravitasari Regional Planning Science Study Program Department of Soil Science and Land Resources Faculty of Agriculture IPB University Jl. Meranti IPB Darmaga Campus Bogor West Java Indonesia 16680 Center for Regional System Analysis Planning and Development (P4W/CRESTPENT) IPB University IPB Baranangsiang Campus Jl. Raya Pajajaran Bogor West Java Indonesia 16144

Horticulture in Lembang Sub-district seems to be threatened with unsustainability due to urban expansion in Bandung Metropolitan Area. This urban expansion causes a land conversion and puts farmers at risk of losing their arable land. In Agriculture Era 4.0, horticulture management should be integrated with the Internet of Things (IoT), so it can survive amid the urban expansion. Hence, this research aims to analyze the sustainability of horticulture in the current Agriculture Era 4.0 by knowing the spatial distribution of horticulture and land capability class in Lembang Sub-district. The data was collected through field surveys, questionnaires, and in-depth interviews. While the methods were performed by visual interpretation and overlay using ArcGIS and RAP-FARM/MDS. Results showed that 3,079.03 ha was classified as horticulture with vegetables as the dominant cultivated. Based on the horticultural distribution and land capability class, the sustainability index of horticulture were: 54.27 (horticulture in class III), 54.47 (horticulture in class iv), and 44.11 (horticulture in class VI). Horticulture in class III and class iv has quite sustainable status, while class VI has less sustainable status. This study results can be taken into consideration in determining spatial planning for sustainable horticultural development in Lembang Sub-district.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Framework for Sedimentological data Structure Extraction in Gold Mining Facies Delineation

A Framework for Sedimentological Data Structure Extraction i...

引用

Computer Science and data Engineering (CSDE), IEEE Asia-Pacific conference on

作者： Phumlani Nhlanganiso Khoza Scilinx Research School of Computer Science & Applied Mathematics University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

Quartz-pebble conglomerate (reef) horizons of the Witwatersrand Basin are the most important sources of gold (Au) in South Africa. Concentration of gold to economic ore-grades in the Witwatersrand-type gold deposits has been linked to sedimentary processes having operated as principal driving forces. Therefore, in Witwatersrand-type gold deposits, sedimentological data are often utilized to make qualitative assessment of in-situ gold-grade. This paper presents a framework that prescribes a normative and principled analysis of sedimentological data in order to quantify in-situ gold-grades. Its significance is the provision of a scalable process for extracting correlational structures, regardless of the attribute value-types. The framework applies network analysis, and a bespoke clustering algorithm to support visual analysis of spatial correlation between sedimentological data samples. A key result is the preservation of the semantic interpretation of the resulting cluster assignments via a topologically invariant transformation. This is achieved by applying non-uniform weighting to the attributes to emphasize the influence of non-numeric attributes in cluster assignment. Additionally, sedimentological attributes that are strongly correlated with gold-grade are identified by adopting total variance reduction from decision tree regression as a guiding principle. Using this approach, and data collected from a mining company located in the Witwatersrand Basin, spatial correlation structures defined over sedimentological data attributes are reported. As a byproduct, observations that lack spatial correlation with the rest of the data are identified. These isolated observations can inform quality assurance and quality control departments about potential irregularities in the reporting of sedimentological data within exploration and mining companies.

关键词： Machine learning data mining Gold Uncertainty Mineralization Estimation

来源：评论

学校读者我要写书评

暂无评论

Interesting Recommendations Based on Hierarchical visualizations of Medical data 22nd

Interesting Recommendations Based on Hierarchical Visualizat...

引用

22nd Pacific-Asia conference on Knowledge Discovery and data Mining (PAKDD)

作者： Ibrahim, Ibrahim A. Almars, Abdulqader M. Pokharel, Suresh Zhao, Xin Li, Xue Univ Queensland Brisbane Qld Australia Minia Univ Al Minya Egypt

ISBN: (纸本)9783030045036;9783030045029

Due to the dramatic growth in Electronic Health Records (EHR), many opportunities are arising for discovering new medical knowledge. Those kinds of knowledge are useful for related stakeholders such as hospital planners, data analysts, doctors, insurance companies for better patients' management. However, many challenges need to be addressed while dealing with medical domain such as (1) how to measure the interestingness of information (2) how to visualize such interestingness (3) how to handle high dimensionality of medical data. To address these challenges, we present MedVIS, an interactive tool to visually explore the data space and finds interesting visualizations compared with another dataset. MedVIS structures visualizations into a hierarchical coherent tree to reveal interestingness of dimensions and other measures. We introduce a novel analysis work flow, and discuss various optimization mechanisms to effectively and efficiently explore the data space. Additionally, we discuss various approaches to mitigate the problem of high-dimensional medical data analysis and its visual exploration. In our experiments, we apply MedVIS to a real-world dataset and show promising visualization outcomes in terms of effectiveness and efficiency.

关键词： Big data visualizations visual analytics Knowledge discovery

来源：评论

学校读者我要写书评

暂无评论

2018 IEEE Scientific visualization conference, Scivis 2018 - Proceedings

2018 IEEE Scientific Visualization Conference, SciVis 2018 -...

引用

2018 IEEE Scientific visualization conference, Scivis 2018

ISBN: (纸本)9781538668825

The proceedings contain 22 papers. The topics discussed include: cluster-based visualization for merger tree data: the challenge of missing expectations;interactive visual exploration and comparison on the effect of asteroid impacts;interactive visualization and analysis of high resolution HPC simulation data on a laptop with VTK;color interpolation for non-Euclidean color spaces;a lagrangian method for extracting eddy boundaries in the red sea and the Gulf of Aden;QuFlow: visualizing parameter flow in quantum circuits for understanding quantum computation;visualization of uncertainty for computationally intensive simulations using high fidelity emulators;and using cinematic effects to visualize the deep water impact data set.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interactive data exploration through Multiple visual Contexts with Different data Models and Dimensions 21

Interactive Data Exploration through Multiple Visual Context...

引用

21st International conference on Information visualisation (iv)

作者： Phi Giang Pham Huang, Mao Lin Quang Vinh Nguyen Univ Technol Sydney NSW Australia Cantho Tech Econ Coll Can Tho Vietnam Western Sydney Univ Sydney NSW Australia

ISBN: (纸本)9781538608319

visual analytics plays a key role in bringing insights to audiences who are interested and dedicated in data exploration. In the area of relational data, many advanced visualization tools and frameworks are proposed in order to dealing with such data features. However, the majority of those have not greatly considered the whole process from data-model mining to query utilizing on dimensions and data values, which might cause interruption to exploration activities. This paper presents a new interactive exploration framework for relational data analysis through automatic interconnection of data models, data dimensions and data values. The basic idea is to construct a relative and switchable chain of those context representations by integrating our previous techniques on node-link, parallel coordinate and scatterplot graphics. This approach enables users to flexibly make relative queries on desired contexts at any stage of exploration for deep data understanding. The result from a typical case study for the framework demonstration indicates that our approach is able to handle the addressed challenge.

关键词： visual analytics dynamic visual query interactive user interface

来源：评论

学校读者我要写书评

暂无评论

CASSL: Curriculum Accelerated Self-Supervised Learning

CASSL: Curriculum Accelerated Self-Supervised Learning

引用

IEEE International conference on Robotics and Automation (ICRA)

作者： Murali, Adithyavairavan Pinto, Lerrel Gandhi, Dhiraj Gupta, Abhinav Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781538630815

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for higher-dimensional control requires either scaling up the data collection efforts or using a clever sampling strategy for training. We present a novel approach Curriculum Accelerated Self-Supervised Learning (CASSL) to train policies that map visual information to high-level, higher-dimensional action spaces. CASSL orders the sampling of training data based on control dimensions: the learning and sampling are focused on few control parameters before other parameters. The right curriculum for learning is suggested by variance-based global sensitivity analysis of the control space. We apply our CASSL framework to learning how to grasp using an adaptive, underactuated multi-fingered gripper, a challenging system to control. Our experimental results indicate that CASSL provides significant improvement and generalization compared to baseline methods such as staged curriculum learning (8% increase) and complete end-to-end learning with random exploration (14% improvement) tested on a set of novel objects.

关键词： Aerospace electronics Grasping Training Task analysis Robots Sensitivity analysis"

来源：评论

学校读者我要写书评

暂无评论

Surprise! Predicting Infant visual Attention in a Socially Assistive Robot Contingent Learning Paradigm

Surprise! Predicting Infant Visual Attention in a Socially A...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Lauren Klein Laurent Itti Beth A. Smith Marcelo Rosales Stefanos Nikolaidis Maja J. Matarić Interaction Lab University of Southern California Los Angeles CA USA iLab University of Southern California Infant Neuromotor Control Laboratory Division of Biokinesiology and Physical Therapy University of Southern California Interactive and Collaborative Autonomous Robotic Systems Lab University of Southern California

Early intervention to address developmental disability in infants has the potential to promote improved outcomes in neurodevelopmental structure and function [1]. Researchers are starting to explore Socially Assistive Robotics (SAR) as a tool for delivering early interventions that are synergistic with and enhance human-administered therapy. For SAR to be effective, the robot must be able to consistently attract the attention of the infant in order to engage the infant in a desired activity. This work presents the analysis of eye gaze tracking data from five 6-8 month old infants interacting with a Nao robot that kicked its leg as a contingent reward for infant leg movement. We evaluate a Bayesian model of low-level surprise on video data from the infants' head-mounted camera and on the timing of robot behaviors as a predictor of infant visual attention. The results demonstrate that over 67% of infant gaze locations were in areas the model evaluated to be more surprising than average. We also present an initial exploration using surprise to predict the extent to which the robot attracts infant visual attention during specific intervals in the study. This work is the first to validate the surprise model on infants; our results indicate the potential for using surprise to inform robot behaviors that attract infant attention during SAR interactions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interactive Prediction for Multiple, Heterogeneous Traffic Participants with Multi-Agent Hybrid Dynamic Bayesian Network

Interactive Prediction for Multiple, Heterogeneous Traffic P...

引用

International conference on Intelligent Transportation

作者： Lingfeng Sun Wei Zhan Di Wang Masayoshi Tomizuka Department of Mechanical Engineering University of California Berkeley CA USA Visual Cognitive Computing and Intelligent Vehicle (VCC&IV) Lab Xian Jiaotong University Xian P.R. China

ISBN: (纸本)9781538670255

Interactive prediction with multiple traffic participants in highly dynamic scenarios is extremely challenging for autonomous driving, especially when heterogeneous agents such as vehicles and pedestrians are involved. Existing prediction methods encounter problems on interpretability and generalizability to tackle such a complicated task. In this paper, we construct an integrated framework to estimate and predict the behavior of multiple, heterogeneous agents simultaneously. A Multi-agent Hybrid Dynamic Bayesian Network (MHDBN) method is proposed, which can model the state changes of multiple, heterogeneous agents in a variety of scenarios. We incorporate prior knowledge such as map information and traffic rules into the graph structure and use Particle Filter (PF) to track and predict intentions and trajectories of the agents. Motion data with pedestrian-vehicle interactions from a four-way-stop intersection in the real world is used to design the model and verify the effectiveness of the estimation and interactive prediction capability of the proposed framework.

关键词： Predictive models Vehicle dynamics Bayes methods Trajectory data models Estimation Task analysis

来源：评论

学校读者我要写书评

暂无评论

COVIZ: A system for visual formation and exploration of patient cohorts 45th

COVIZ: A system for visual formation and exploration of pati...

引用

45th International conference on Very Large data Bases, VLDB 2019

作者： Pahins, Cícero A.L. Omidvar-Tehrani, Behrooz Amer-Yahia, Sihem Siroux, Valérie Pepin, Jean-Louis Borel, Jean-Christian Comba, Joao L.D. UFRGS Brazil Univ. Grenoble Alpes CNRS LIG France Institute for Advanced Biosciences and Inserm France Grenoble University Hospital France AGIR-à-Dom France

We demonstrate COVIZ, an interactive system to visually form and explore patient cohorts. COVIZ seamlessly integrates visual cohort formation and exploration, making it a single destination for hypothesis generation. COVIZ is easy to use by medical experts and offers many features: (1) It provides the ability to isolate patient demographics (e.g., their age group and location), health markers (e.g., their body mass index), and treatments (e.g., Ventilation for respiratory problems), and hence facilitates cohort formation;(2) It summarizes the evolution of treatments of a cohort into health trajectories, and lets medical experts explore those trajectories;(3) It guides them in examining different facets of a cohort and generating hypotheses for future analysis;(4) Finally, it provides the ability to compare the statistics and health trajectories of multiple cohorts at once. COVIZ relies on QDS, a novel data structure that encodes and indexes various data distributions to enable their efficient retrieval. Additionally, COVIZ visualizes air quality data in the regions where patients live to help with data interpretations. We demonstrate two key scenarios, ecological scenario and case cross-over scenario. A video demonstration of COVIZ is accessible via http://***/video-coviz. © 2019 VLDB Endowment.

关键词： Medical problems

来源：评论

学校读者我要写书评

暂无评论

visually Exploring data Provenance and Quality of Open data

Visually Exploring Data Provenance and Quality of Open Data

引用

20th Eurographics/IEEE VGTC conference on visualization, EuroVis 2018

作者： Bors, C. Gschwandtner, T. Miksch, S. TU Wien Institute of Visual Computing and Human-Centered Technology Austria

ISBN: (纸本)9783038680659

While open data platforms are increasingly popular among end-users as well as data providers, there is a growing problem with inconsistent update frequencies and lack of quality in datasets. Efforts to monitor data quality are currently limited to checking meta-information and creating revisions to allow manual inspection of former datasets. We employ a visual Analytics framework for generating and visualizing data provenance from data quality to facilitate data analysis and help users to understand the impact of updates on the data. data quality metrics are utilized to quantify the development of data quality over time for open data projects. We combine quality metrics, data provenance, and data transformation information in an interactive exploration environment to expedite assessment and selection of appropriate open datasets. © 2018 The Author(s)

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：