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International Conference on Internet of Things and Machine Learning (IML)

作者： Gupta, Aparana Garg, Anshul Rawat, Namrata Chigurupati, Sandeep Kumar, Dinesh U. Indian Inst Management Bangalore Karnataka India

ISBN: (纸本)9781450352437

Water is at the heart of 'Sustainable Development Goals (SDGs)'set by United Nations- with an objective to balance the three dimensions of sustainable development: Environment, Social and Economic - and is indirectly associated with the success of all the other Goals. But, with changing climatic patterns, untimely rains, prolonged dry spells, depleting ground water and drought making every drop of water extremely precious, the need of the hour is to gauge and work towards the major aspects of water harvesting- 'Catchment'. Water Harvesting must be a key element of any strategy to bring an end to India's perennial swings between drought and flood and to meet the following SDGs for sustained development. This study presents a structured and meticulous approach, wielding `Geospatial Analytics'to identify the prospective locations for Water Harvesting in arid and semi-arid parts of the country for sustainable *** paper is structured as follows. Section 1 describes the background and motivation for this idea. Section 2 details out the objective. In section 3 we present the 'Literature Survey 'on the work that has already been carried out in this field. While section 4 discerns our area of study, Section 5 provides process flow starting from Data gathering, Data extraction,Data pre-processing, Model selection and Multi Criteria Decision Making (Model Application). In Section 6, we present and validate our experimental results achieved using the proposed methodology. Section 7 concludes our study followed by Section 8 on Recommendations for future enhancements and next steps.

关键词： Geospatial Analytics AHP Analytical Hierarchy Process LAND SAT-8 Digital Elevation Model Image Processing Rain Water Harvesting Water Tanks RWH optimum location selection GIS sliding window algorithm Flood Fill Model Smart Water

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A Vision-based Navigation System for an Agricultural Autonomous Tractor 7th

A Vision-based Navigation System for an Agricultural Autonom...

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7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)

作者： Saha, Sristi Morita, Tsuyoshi Ospina, Ricardo Noguchi, Noboru Hokkaido Univ Grad Sch Agr Sapporo Hokkaido Japan Hokkaido Univ Res Fac Agr Sapporo Hokkaido Japan

RTK-GNSS (Real-time Kinematic-Global Navigation Satellite System) signal is necessary for the safe navigation of an autonomous tractor. However, the main problem associated with this type of precise GNSS is that the RTK correction signal might not be available at all geographical locations. Tree canopies present close to the tractors' vicinity obstruct satellite signals and degrade the position accuracy. This can lead the autonomous tractor to travel outside of its predetermined path, which is unsafe. To avoid such situations, this study aims to develop a vision-based road detection system, which does not rely on RTK-GNSS. This system aims to detect both paved and unpaved roads in rural farm areas. It takes the RGB images obtained from an HD onboard camera and segments the road surface and road edges. The segmentation is performed by using several image filters, the sliding window method, and a set of rules that are determined manually Finally, the lateral error obtained from the machine vision system is transmitted to the tractor's automatic navigation system. Experimental runs performed at the experimental farm of Hokkaido University showed that the lateral error calculated by this system is less than 0.2 m for unpaved roads and 0.4 m for paved roads. This shows that the system can detect both paved and unpaved roads. Future work addresses considerations to make the system more robust. Copyright (C) 2022 The Authors.

关键词： Image segmentation sliding window algorithm lateral error robot navigation robot vision road edge detection precision agriculture

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Event-triggered Relearning Modeling Method for System with Non-stationary Variable Operating Conditions 36

Event-triggered Relearning Modeling Method for System with N...

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36th Chinese Control and Decision Conference (CCDC)

作者： Liu, Jiyan Sun, Qinjiang Zhou, Yuyang Zhang, Yong Inner Mongolia Univ Sci & Technol Sch Informat Engn Baotou 014010 Peoples R China CNOOC China Ltd Tianjin Branch Tianjin 300459 Peoples R China Edinburgh Napier Univ Sch Comp Engn & Built Environm Edinburgh EH10 5DT Midlothian Scotland

ISBN: (纸本)9798350387780;9798350387797

This paper proposes an innovative event-triggered relearning strategy for neural network modeling, to enhance prediction accuracy under the non-stationary and variable conditions frequently encountered in complex industrial environments. Firstly, the proposed approach divides the input and output datasets across different operational conditions, by applying a sliding window algorithm along with entropy to identify the boundaries of the input data. Following this, we utilize the prediction error from the current model, which is relevant to variable operational states, to construct an array of event-triggered conditions. Once these conditions are triggered, we employ the corresponding dataset to re-establish the model and predict the data under this operating condition. Compared with the traditional neural network model, the proposed model can produce a better estimation accuracy. Finally, numerical simulation tests are provided to verify the effectiveness of the proposed method. The results show that the proposed method can effectively improve the prediction accuracy of neural network model under non-stationary variable working conditions.

关键词： Neural network Non-stationary and variable conditions Event-triggered conditions sliding window algorithm Information entropy

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Efficient Lane Detection and Keeping for Autonomous Vehicles in Real-World Scenarios 26

Efficient Lane Detection and Keeping for Autonomous Vehicles...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Azad, Farhana Islam, Yumna Ruslan, Ch Zakauddin Md Aye Mong Marma, C. Kalpoma, Kazi A. Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

In this study, we developed an auto-lane-keeping assist system to contribute to autonomous driving. The system will keep the vehicle in its designated lane to reduce road accidents. The main challenge of this research is to make the system suitable for real-world road conditions. To achieve an efficient lane-keeping system, we detected the lane lines automatically using canny edge detection and a sliding window algorithm. The proposed lane-keeping system has another stage: lane-keeping. The lane-keeping task includes the calculation of the offset value, meaning how much the vehicle deviates from its lane or if the vehicle is in the center line of the lane, and the steering angle correction of the vehicle to be in the lane. We use the sliding window algorithm for the offset value calculation, which makes the detection of curved lane lines more accurate than the previous works and also performs well in various weather and lighting conditions. The proposed system's performance is evaluated using the IROADS dataset in this work. We implemented lane keeping in both software and hardware models, introducing the AURIX TC-375 microcontroller for hardware integration, which enhances overall lane-keeping system output through the integration of detection and keeping processes. © 2023 IEEE.

关键词： AURIX TC-375 Micro-controller Automotive ECU Lane Detection Lane Keeping sliding window algorithm

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A New Approach for the Development of Grid Models Calculating Tropospheric Key Parameters over China

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REMOTE SENSING 2021年第17期13卷 3546-3546页

作者： Zhu, Ge Huang, Liangke Liu, Lilong Li, Chen Li, Junyu Huang, Ling Zhou, Lv He, Hongchang Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541004 Peoples R China Nat Resources Informat Ctr Guangxi Zhuang Autonom Beidou Nav Dept Nanning 530028 Peoples R China

Pressure, water vapor pressure, temperature, and weighted mean temperature (T-m) are tropospheric parameters that play an important role in high-precision global navigation satellite system navigation (GNSS). As accurate tropospheric parameters are obligatory in GNSS navigation and GNSS water vapor detection, high-precision modeling of tropospheric parameters has gained widespread attention in recent years. A new approach is introduced to develop an empirical tropospheric delay model named the China Tropospheric (CTrop) model, providing meteorological parameters based on the sliding window algorithm. The radiosonde data in 2017 are treated as reference values to validate the performance of the CTrop model, which is compared to the canonical Global Pressure and Temperature 3 (GPT3) model. The accuracy of the CTrop model in regards to pressure, water vapor pressure, temperature, and weighted mean temperature are 5.51 hPa, 2.60 hPa, 3.09 K, and 3.35 K, respectively, achieving an improvement of 6%, 9%, 10%, and 13%, respectively, when compared to the GPT3 model. Moreover, three different resolutions of the CTrop model based on the sliding window algorithm are also developed to reduce the amount of gridded data provided to the users, as well as to speed up the troposphere delay computation process, for which users can access model parameters of different resolutions for their requirements. With better accuracy of estimating the tropospheric parameters than that of the GPT3 model, the CTrop model is recommended to improve the performance of GNSS positioning and navigation.

关键词： GNSS positioning GNSS meteorology MERRA-2 sliding window algorithm tropospheric parameters GNSS

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Ship collision avoidance behaviour recognition and analysis based on AIS data

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OCEAN ENGINEERING 2022年 245卷 110479-110479页

作者： Rong, H. Teixeira, A. P. Soares, C. Guedes Univ Lisbon Ctr Marine Technol & Ocean Engn CENTEC Inst Super Tecn Av Rovisco Pais P-1049001 Lisbon Portugal

A novel approach is proposed to automatically identify the ship collision avoidance behaviour from ship trajectories based on an improved sliding window algorithm. The approach has three main stages: (1) determining the ships' obligations according to the Convention on the International Regulations for Preventing Collision at Sea (COLREGs);(2) assessing the rudder angle according to the true bearing of the target ship;(3) identifying the corresponding ship handling behaviour from the ship trajectory taking into account the Rate of Turn and its derivative based on the sliding window algorithm. Four typical encounter scenarios are studied to demonstrate the feasibility and effectiveness of the proposed method. The approach is then applied to near collision scenarios identified in the maritime traffic off the continental coast of Portugal, which allowed the characterization of the relative and spatial distributions of the locations at which the ships take evasive manoeuvres. The results show that the approach can be applied to accurately detect the ship collision avoidance behaviour from Automatic Identification System trajectory data, and the characterization of the collision avoidance behaviour can potentially be used by situational awareness systems and as the basis for ship collision avoidance decision-making.

关键词： Ship evasive manoeuvring Collision avoidance sliding window algorithm Near collision scenario

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A Vision-based Navigation System for an Agricultural Autonomous Tractor

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IFAC-PapersOnLine 2022年第32期55卷 48-53页

作者： Sristi Saha Tsuyoshi Morita Ricardo Ospina Noboru Noguchi

RTK-GNSS (Real-time Kinematic-Global Navigation Satellite System) signal is necessary for the safe navigation of an autonomous tractor. However, the main problem associated with this type of precise GNSS is that the RTK correction signal might not be available at all geographical locations. Tree canopies present close to the tractors’ vicinity obstruct satellite signals and degrade the position accuracy. This can lead the autonomous tractor to travel outside of its predetermined path, which is unsafe. To avoid such situations, this study aims to develop a vision-based road detection system, which does not rely on RTK-GNSS. This system aims to detect both paved and unpaved roads in rural farm areas. It takes the RGB images obtained from an HD onboard camera and segments the road surface and road edges. The segmentation is performed by using several image filters, the sliding window method, and a set of rules that are determined manually. Finally, the lateral error obtained from the machine vision system is transmitted to the tractor's automatic navigation system. Experimental runs performed at the experimental farm of Hokkaido University showed that the lateral error calculated by this system is less than 0.2 m for unpaved roads and 0.4 m for paved roads. This shows that the system can detect both paved and unpaved roads. Future work addresses considerations to make the system more robust.

关键词： Image segmentation sliding window algorithm lateral error robot navigation robot vision road edge detection precision agriculture

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sliding and Adaptive windows to Improve Change Mining in Process Variability

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INFORMATION 2024年第8期15卷 445页

作者： Hmami, Asmae Sbai, Hanae Baina, Karim Fredj, Mounia Mohammed V Univ Rabat Natl Higher Sch Comp Sci & Syst Anal ENSIAS AlQualsadi Res Team Rabat 8007 Morocco Univ Hassan II Casablanca Fac Sci & Technol Mohammadia 20650 Morocco

A configurable process Change Mining approach can detect changes from a collection of event logs and provide details on the unexpected behavior of all process variants of a configurable process. The strength of Change Mining lies in its ability to serve both conformance checking and enhancement purposes;users can simultaneously detect changes and ensure process conformance using a single, integrated framework. In prior research, a configurable process Change Mining algorithm has been introduced. Combined with our proposed preprocessing and change log generation methods, this algorithm forms a complete framework for detecting and recording changes in a collection of event logs. Testing the framework on synthetic data revealed limitations in detecting changes in different types of variable fragments. Consequently, it is recommended that the preprocessing approach be enhanced by applying a filtering algorithm based on sliding and adaptive windows. Our improved approach has been tested on various types of variable fragments to demonstrate its efficacy in enhancing Change Mining performance.

关键词： adaptive window algorithm configurable business process change mining filtering preprocessing process variability sliding window algorithm

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Integrated video surveillance IC card consumption system research

Integrated video surveillance IC card consumption system res...

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2010 2nd International Conference on Intellectual Technology in Industrial Practice (ITIP2010)

作者： Li-hong Li College of Information Engineering Taiyuan University of Technology Taiyuan ,P.R. China Xue-mei Yin, Fu Duan College of computer Taiyuan University of Technology Taiyuan ,P.R. China

To improve the safety of conventional IC card consumption, this paper presents video monitoring technology applied to IC card consumption. During the IC card spending, the video surveillance records the entire consumptive process, and captures image. Videos shot detection, extract key frame, calculate key frames feature, cluster based on the characteristics of key frames, index with videos that are collected from video surveillance. Contrast the image with the key frame which is stored in database, users can quickly retrieve video clips. To a certain extent, ensure consumption safety. System using the Flex-based RIA technologies can effectively balance client and server computing load.

关键词： video surveillance sliding window algorithm gray level co-occurrence matrix IC card consumption

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