Fuzzy logic data analysis of environmental data for traffic control

2002 ◽  
Author(s):  
B. Krause ◽  
C. von Altrock ◽  
M. Pozybill
Keyword(s):  
Fuzzy Logic ◽  
Data Analysis ◽  
Traffic Control ◽  
Environmental Data

scholarly journals An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

2019 ◽  
Vol 9 (1S6) ◽  
pp. 52-61
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Energy Efficient ◽  
Residual Energy ◽  
Environmental Data ◽  
Energy Balancing ◽  
Control Centre

WSN consist of set of Sensing points which are responsible for collecting the detected information and then send the packets towards control centre which is responsible for processing of data. The applications of WSN include environmental data analysis, defence data collection and information. The survey of algorithms is done for the improvement of lifetime ratio. Four different algorithms namely Random, Random-CGT, EGT-Random and GTEB algorithms. The four algorithms are compared and then it is proved GTEB exhibits best behaviour with respect to energy consumed, number of non-holes, number of holes, Non-Hole to Hole ratio, residual energy, overhead and throughput.

scholarly journals Pemetaan Tingkat Kerawanan Longsor Berdasarkan Curah Hujan dan Geologi Menggunakan Metode Fuzzy Logic Di Kecamatan Leupung Kabupaten Aceh Besar

2018 ◽  
Vol 3 (2) ◽  
pp. 434-441
Author(s):  
Rasyid Alkhoir Lubis ◽  
Muhammad Rusdi ◽  
Hairul Basri
Keyword(s):  
Fuzzy Logic ◽  
Data Analysis ◽  
Analysis Data ◽  
Fuzzy Logic Method ◽  
Input Variables ◽  
Logic Method

Abstrak. Penelitian ini bertujuan untuk mengetahui tingkat kerawanan longsor di Kecamatan Leupung Kabupaten Aceh Besar. Penelitian ini dilakukan menggunakan SIG dengan Metode Fuzzy Logic. Curah Hujan dan Geologi sebagai variabel input dan tingkat kerawanan longsor sebagai variabel output metode fuzzy logic. Beberapa tahapan yang dilakukan dalam metode ini antara lain : fuzzyfication, inferensi dan defuzzyfication. Secara umum, tahapan penelitian persiapan, pra analisis data, analisis data dan output.. Penelitian ini dilakukan karena Kecamatan Leupung berbukit, berlereng, tersusun dari material sedimen termasuk batuan pegunungan dan memiliki curah hujan yang lebih tinggi dibandingkan dengan kecamatan lainnya di lingkup Kabupaten Aceh Besar.Hasil penelitian memperoleh hasil bahwa Kecamatan Leupung didominasi dengan tingkat kerawanan longsor kategori rendah dan sedang. Tingkat kerawanan longsor rendah seluas 16.486,01 ha (97,97 %) dan tingkat kerawanan longsor sedang seluas 342,37 ha (2,03 %). Kedua faktor yaitu curah hujan dan geologi saling mempengaruhi sehingga membedakan nilai defuzzyfication serta kelas kerawanan longsor. Abstract. This study aims to determine the level of landslide vulnerability in Leupung District, Aceh Besar District. This research was conducted using GIS with Fuzzy Logic Method. Rainfall and Geology as input variables and landslide vulnerability as output variables fuzzy logic method. Some of the steps performed in this method include: fuzzyfication, inference and defuzzyfication. In general, the stages of preparatory research, pre-data analysis, data analysis and output. This research was conducted because the hilly Leupung District, the slopes, composed of sedimentary materials including mountainous rocks and had higher rainfall compared to other sub-districts in Aceh Besar .The result of this research is that Leupung District is dominated by low and medium category avalanche vulnerability. Low landslide vulnerability of 16,486.01 ha (97.97%) and moderate landslide vulnerability of 342.37 ha (2.03%). Both factors are rainfall and geology influence each other so as to distinguish the defuzzyfication value and the class of landslide vulnerability.

scholarly journals An Ontology-based Visual Analytics for Apple Variety Testing

2021 ◽  
Author(s):  
Ekaterina Chuprikova ◽  
Abraham Mejia Aguilar ◽  
Roberto Monsorno
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Integration ◽  
Visual Analytics ◽  
Agricultural Sector ◽  
Environmental Data ◽  
Data Sources ◽  
Apple Variety ◽  
Testing Program ◽  
Variety Testing

<p>Increasing agricultural production challenges, such as climate change, environmental concerns, energy demands, and growing expectations from consumers triggered the necessity for innovation using data-driven approaches such as visual analytics. Although the visual analytics concept was introduced more than a decade ago, the latest developments in the data mining capacities made it possible to fully exploit the potential of this approach and gain insights into high complexity datasets (multi-source, multi-scale, and different stages). The current study focuses on developing prototypical visual analytics for an apple variety testing program in South Tyrol, Italy. Thus, the work aims (1) to establish a visual analytics interface enabled to integrate and harmonize information about apple variety testing and its interaction with climate by designing a semantic model; and (2) to create a single visual analytics user interface that can turn the data into knowledge for domain experts. </p><p>This study extends the visual analytics approach with a structural way of data organization (ontologies), data mining, and visualization techniques to retrieve knowledge from an extensive collection of apple variety testing program and environmental data. The prototype stands on three main components: ontology, data analysis, and data visualization. Ontologies provide a representation of expert knowledge and create standard concepts for data integration, opening the possibility to share the knowledge using a unified terminology and allowing for inference. Building upon relevant semantic models (e.g., agri-food experiment ontology, plant trait ontology, GeoSPARQL), we propose to extend them based on the apple variety testing and climate data. Data integration and harmonization through developing an ontology-based model provides a framework for integrating relevant concepts and relationships between them, data sources from different repositories, and defining a precise specification for the knowledge retrieval. Besides, as the variety testing is performed on different locations, the geospatial component can enrich the analysis with spatial properties. Furthermore, the visual narratives designed within this study will give a better-integrated view of data entities' relations and the meaningful patterns and clustering based on semantic concepts.</p><p>Therefore, the proposed approach is designed to improve decision-making about variety management through an interactive visual analytics system that can answer "what" and "why" about fruit-growing activities. Thus, the prototype has the potential to go beyond the traditional ways of organizing data by creating an advanced information system enabled to manage heterogeneous data sources and to provide a framework for more collaborative scientific data analysis. This study unites various interdisciplinary aspects and, in particular: Big Data analytics in the agricultural sector and visual methods; thus, the findings will contribute to the EU priority program in digital transformation in the European agricultural sector.</p><p>This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 894215.</p>

Vehicle Detection and Classification Using Model-Based and Fuzzy Logic Approaches

2005 ◽  
Vol 1935 (1) ◽  
pp. 154-162
Author(s):  
Heng-Da Cheng ◽  
Haining Du ◽  
Liming Hu ◽  
Chris Glazier
Keyword(s):  
Fuzzy Logic ◽  
Traffic Control ◽  
Vehicle Detection ◽  
Classification Systems ◽  
Vehicle Classification ◽  
Highway Traffic ◽  
Control Center ◽  
Model Based ◽  
Fixed Orientation ◽  
Classification Information

Vehicle detection and classification information is invaluable in many transportation issues. Vehicle feature extraction and detection are the preprocesses required for vehicle classification. Current automatic vehicle classification systems have deficiencies: low accuracy, special requirements, fixed orientation of the camera, or additional hardware and devices. This paper discusses a vehicle detection and classification system using model-based and fuzzy logic approaches. The system was tested with the use of a variety of images captured by the highway traffic control center of the Utah Department of Transportation. In comparison with existing systems, major advantages of the proposed system are ( a) no special orientation of the camera is required, ( b) no additional devices are needed, and ( c) high classification accuracy is provided. Experimental results show that the performance of the proposed system exceeds that of the existing video-based vehicle classification systems.

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