Simulated hyperspectral data analysis using continuum removal: case study on leaf chlorophyll prediction

A case study ofa third year course in the Department of Economic and Social History in the University of Edinburgh isusedto considerandhighlightaspects of good practice in the teaching of computer-assisted historical data analysis.

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KAJIAN PENYELENGGARAAN PAUD DI KOTA BANJARMASIN DITINJAU DARI ASPEK PERENCANAAN

Jurnal Penelitian Kebijakan Pendidikan ◽

10.24832/jpkp.v2i2.9 ◽

2018 ◽

Vol 2 (2) ◽

pp. 159

Author(s):

Lisna Sulinar Sari

Keyword(s):

Early Childhood ◽

Data Analysis ◽

Early Childhood Education ◽

Educational Needs ◽

Needs Analysis ◽

Case Study Approach ◽

Childhood Education ◽

Study Approach ◽

Early Childhood Institutions

Abstrak: Permasalahan dalam penelitian ini yaitu dari jumlah lembaga PAUD yang ada diKota Banjarmasin belum semuanya memiliki perencanaan khususnya pada analisispeningkatan legalitas kelembagaan PAUD dan analisis kebutuhan pendidikan untuk anak usiadini (AUD). Penelitian ini menggunakan pendekatan studi kasus dengan analisis data deskrtifkuantitatif dan kualitataif. Hasil studi menunjukkan bahwa: i) Disdik Kota Banjarmasin danLembaga PAUD sampel tidak melakukan perencanaan yang baik untuk pendataan analisiskebutuhan pendidikan AUD; ii) Belum semua lembaga PAUD sampel memiliki izinoperasional dikarenakan adanya persyaratan yang belum dapat dipenuhi karena memerlukanbiaya yang cukup besar seperti, pembuatan akta notaris; iii) Belum semua lembaga PAUDmemiliki sarpras sesuai dengan pedoman sarana dan prasarana dari pusat; iv) untuk membantuketersediaan sarpras, Disdik Kota Banjarmasin sudah mengalokasikan dana APBD II berupabantuan RKB, rehab kelas rusak ringan dan berat, serta bantuan APE Dalam dan Luar berupabarang. Abstract: The problem in this study is from the number of early childhood institutions in thecity of Banjarmasin not all have plans in particular to the analysis of institutional legalityincrease early childhood education and educational needs analysis for early childhood (AUD).This study uses a case study approach to data analysis of quantitative and qualitative deskrtif.The study shows that: i) Disdik Banjarmasin and Institutions ECD sample is not doing betterplanning for data analysis AUD educational needs; ii) Not all the samples of early childhoodinstitutions have an operating permit because of the requirements can not be met because itrequires significant costs such as notary deed; iii) Not all early childhood institutions haveinfrastructure accordance with the guidelines of the central infrastructure; iv) to assist theavailability infrastructure, Disdik Banjarmasin already allocated budget II in the form ofclassroom assistance, rehabilitation of damaged light and heavy classes, as well as the In andOut APE assistance in the form of goods.

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Unsupervised Classification System for Hyperspectral Data Analysis

10.21236/ada398803 ◽

2001 ◽

Author(s):

Luis O. Jimenez ◽

Miguel Velez ◽

Shawn Hunt

Keyword(s):

Data Analysis ◽

Classification System ◽

Unsupervised Classification ◽

Hyperspectral Data

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An Evaluation of the Formative Experiences of Students Enrolled in Postgraduate Studies in Education: Case Study in Northern Mexico

Sustainability ◽

10.3390/su13094790 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4790

Author(s):

Brenda Imelda Boroel Cervantes ◽

José Alfonso Jiménez Moreno ◽

Salvador Ponce Ceballos ◽

José Sánchez Santamaría

Keyword(s):

Data Analysis ◽

Focus Groups ◽

Teaching Staff ◽

Training Context ◽

Northern Mexico ◽

Postgraduate Studies ◽

Successful Design ◽

Formative Experiences

The educational journey in postgraduate programs is linked to the actors, processes and results, setting the tone for different approaches from the perspective of characterization, development and evaluation. It is summarized in a sequential manner in four stages: entry to the program, progress within the program, and the final educational stretch, where the instructor/tutor plays an important part and obtaining the diploma or degree. The goal of this research was to evaluate, using the students’ perceptions, formative experiences as a result of their academic journey in postgraduate programs within education in Northern Mexico. We have used a case study based on the focus groups technique, applied to a sample of cases comprised of students enrolled in their final educational stage. The information was analyzed using inductive data analysis. The main results were grouped into three meta categories: (1) development of professional skills for the successful design of the intervention proposal, which unfolded into four categories; (2) the role of the tutor during the formative process, consisting of four analysis categories and (3) contributions of the teaching staff in their profession, consisting of two categories. These trends also evidence the formative abundance in the personal, academic and social training context of the students.

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Geospatial Serverless Computing: Architectures, Tools and Future Directions

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9050311 ◽

2020 ◽

Vol 9 (5) ◽

pp. 311 ◽

Cited By ~ 1

Author(s):

Sujit Bebortta ◽

Saneev Kumar Das ◽

Meenakshi Kandpal ◽

Rabindra Kumar Barik ◽

Harishchandra Dubey

Keyword(s):

Big Data ◽

Forest Cover ◽

Mineral Resources ◽

Hyperspectral Data ◽

Real Time Analysis ◽

First Case ◽

Amazon Web Services ◽

Aws Lambda ◽

Physical Features

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

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Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting

Energies ◽

10.3390/en13174343 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4343

Author(s):

Yunbo Yang ◽

Rongling Li ◽

Tao Huang

Keyword(s):

Data Analysis ◽

Peak Load ◽

Smart Meter ◽

Load Shifting ◽

Load Profile ◽

Transition Season ◽

Morning Peak ◽

Heating Load ◽

Heating Loads

In recent years, many buildings have been fitted with smart meters, from which high-frequency energy data is available. However, extracting useful information efficiently has been imposed as a problem in utilizing these data. In this study, we analyzed district heating smart meter data from 61 buildings in Copenhagen, Denmark, focused on the peak load quantification in a building cluster and a case study on load shifting. The energy consumption data were clustered into three subsets concerning seasonal variation (winter, transition season, and summer), using the agglomerative hierarchical algorithm. The representative load profile obtained from clustering analysis were categorized by their profile features on the peak. The investigation of peak load shifting potentials was then conducted by quantifying peak load concerning their load profile types, which were indicated by the absolute peak power, the peak duration, and the sharpness of the peak. A numerical model was developed for a representative building, to determine peak shaving potentials. The model was calibrated and validated using the time-series measurements of two heating seasons. The heating load profiles of the buildings were classified into five types. The buildings with the hat shape peak type were in the majority during the winter and had the highest load shifting potential in the winter and transition season. The hat shape type’s peak load accounted for 10.7% of the total heating loads in winter, and the morning peak type accounted for 12.6% of total heating loads in the transition season. The case study simulation showed that the morning peak load was reduced by about 70%, by modulating the supply water temperature setpoints based on weather compensation curves. The methods and procedures used in this study can be applied in other cases, for the data analysis of a large number of buildings and the investigation of peak loads.

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