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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3339 条记录，以下是691-700 订阅

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Determining the Locations of Devices for Intermittent Voltage Unbalance Compensating in an Electrical Network with Traction Load

Determining the Locations of Devices for Intermittent Voltag...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Mikhail Shish Yaroslav Marinov Department of Theoretical Foundations of Electrical Engineering National Research University “Moscow Power Engineering Institute” Moscow Russia National Research University “Moscow Power Engineering Institute” Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

the paper is devoted to intermittent voltage unbalance (IVU) in electric networks supplying traction load. Dynamic modeling of a real network fragment asymmetric modes is performed in MATLAB Simulink. A method for selecting optimal installation locations and power of IVU compensation devices in a network with one-way supply, supplying power to an electrified railway section, is proposed. A software implementation of this method is developed.

关键词： Performance evaluation Substations software packages Switches Medium voltage Control systems Automatic voltage control Rail transportation Mathematical models Switching circuits

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Enhancing Demand Response Forecasting through Random Forest Regression: A Case Study Analysis

Enhancing Demand Response Forecasting Through Random Forest ...

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international conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Benjamin Chatuanramtharnghaka Subhasish Deb Ksh. Robert Singh Electrical Engineering Department Mizoram University Aizawl India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

Demand response (DR) plays a crucial role in optimizing energy consumption and ensuring grid reliability. this study investigates the efficacy of Random Forest Regression (RFR) as a predictive tool for DR forecasting, focusing on its performance within the context of a specific dataset. Utilizing data on residential energy consumption patterns, we conducted a comparative analysis of RFR against alternative regression algorithms. the results reveal promising outcomes, suggesting that RFR outperforms other models in terms of predictive accuracy and generalizability, particularly within our dataset. Furthermore, the study explores parameter optimization strategies and assesses the impact of varying 'n_estimators' on model performance. the findings underscore the potential of RFR in forecasting load dynamics, thereby facilitating informed decision-making in DR program planning and implementation. this research contributes valuable insights into enhancing DR forecasting methodologies, paving the way for more effective and sustainable energy management practices.

关键词： Energy consumption Renewable energy sources predictive models Demand response Stability analysis Reliability Forecasting

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DASENet:A Detail Aware U-Net with Shuffle Excitation for Retinal Vessel Segmentation

DASENet:A Detail Aware U-Net with Shuffle Excitation for Ret...

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software engineering and Computer Science (CSECS), international conference on

作者： Jiqiang Zhu Zhangli Lan Xinpeng Wen Songbai Cai Yuantong Xu School of Information Science and Engineering Chongqing Jiaotong University Chongqing China

Retinal vessel images serve as valuable tools for early screening and timely diagnosis of ocular disorders. With the advancement of deep learning, a multitude of segmentation models have been utilized for retinal vessel image segmentation tasks. However, the challenge of enhancing feature expression capabilities and refining the accuracy of retinal blood vessel segmentation persists. In this study, we propose an end-to-end retinal vessel segmentation network named DASENet. To capture finer vessel details and enhance feature expression capabilities, a detail aware block (DAB) is proposed for feature extraction and to heighten the network's focus on vessel information. Moreover, shuffle excitation (SE) within skip connections is proposed to capture contextual dependencies between features and reduce differences between feature maps at different depths. Experimental results on the DRIVE, STARE and CHASE_DB1 datasets demonstrate that our DASENet outperforms other related advanced work.

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software Detection and Warning Method for Pitch Angle Deviation of Road Traffic Radar

Software Detection and Warning Method for Pitch Angle Deviat...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Haokai Sun Zheng Zhang JARI Technology Lianyungang JARI Electronics Co. Ltd Lianyungang China Control Science and Engineering Jiangsu Automation Research Institute Lianyungang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Introduced a software detection and warning method for the installation angle of road traffic radar in the pitch direction. Establish a mathematical model M1 based on the directional pattern of the transmitting antenna in the elevation direction. According to the RCS definition, the normalized curve shape of the received target echo signal strength is the same as model M1. the normalization coefficient is determined by on-site calibration methods or empirical value methods, and the model M1 is inversely normalized to obtain the model M2. Substitute the statistical analysis result A of the received signal strength result P ” calculated after real-time and cumulative sampling into model M2 to obtain the current elevation angle information of the radar. If the change in radar pitch angle exceeds the threshold, an alarm message will be output. this method does not require the addition of hardware sensors, and only estimates the current elevation angle of the radar through software calculations based on mathematical models and on-site sampled target echo signal strength, which has good engineering application value.

关键词： Analytical models Radar cross-sections Radar detection Radar software Road traffic Mathematical models Real-time systems Hardware Calibration

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A predictive Model for Early Effort Estimation of Mobile Games

A Predictive Model for Early Effort Estimation of Mobile Gam...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Ahrar Javed Ali Afzal Malik Department of Software Engineering National University of Computer and Emerging Sciences Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Mobile games are a popular, cheap, and convenient source of entertainment. However, the increasing complexity of these games is making it more difficult to thoroughly plan, monitor, and control mobile game development projects. Possessing the ability to accurately estimate the development effort for these projects early in their life cycle is expected to make the jobs of their managers much easier. the objective of this study was to build a calibrated and validated early effort prediction model for mobile games developed using the Unity game engine. First, we identified factors that potentially influence development effort in these projects by conducting an industrial survey. Once these factors were identified, we gathered information about these factors and actual development effort for more than 100 real mobile games developed by different game studios. then, we performed a simple linear regression (SLR) analysis to rank these factors with respect to their individual influence on mobile game development effort. Finally, we used the games data collected earlier to build, assess, and validate a forward step-wise multiple linear regression (FSMLR)-based model for early effort prediction of such mobile games. the MMRE value of this model is 0.136 and its PRED (25) value is $85.98 \%$. these values indicate that this model has reasonably good estimation accuracy. It can be a useful tool in the arsenal of managers interested in effectively planning their mobile game development projects early in the life cycle.

关键词： Surveys Accuracy Linear regression Estimation Entertainment industry Games predictive models Planning Monitoring Land mobile radio

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Mining Haulage Road Maintenance Prediction Dashboard Utilising Machine Learning Techniques

Mining Haulage Road Maintenance Prediction Dashboard Utilisi...

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Advancements in Computing (ICAC), international conference on

作者： Brad Purdon Lasitha Piyathilaka Jung-Hoon Sul Daluwathu Preethichandra School of Engineering and Technology Central Queensland University Rockhampton Australia

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

this paper details the development of a predictive maintenance dashboard designed for haulage roads in surface mining operations using machine learning algorithms. We assessed various machine learning models, including Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN), to determine their effectiveness in predicting the timing and location of necessary road maintenance. the research identifies the Logistic Regression model as the most effective, which was trained on data including vibration measurements, equipment counts, and speed metrics to evaluate road surface conditions. these inputs are processed through a logistic function to estimate the probability of required repairs. the predictions generated by the model are stored in an SQL database and visualized through Power BI, which offers a user-friendly interface. this interface enables operators, including those with limited experience, to interpret the data effectively and make informed maintenance decisions, thus improving operational efficiency and safety.

关键词： Logistic regression Visualization Accuracy Roads predictive models Nearest neighbor methods Maintenance Safety Random forests predictive maintenance

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Recognition of Anthracnose in Large Coffee Plantation

Recognition of Anthracnose in Large Coffee Plantation

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BioInspired Processing (BIP), IEEE international conference on

作者： Armando Fallas Garro Jason Leitón Jiménez Tecnológico de Costa Rica Computer Engineering Costa Rica

ISBN: (数字)9798350353709

ISBN: (纸本)9798350353716

this study introduces an innovative technological solution aimed at the early and accurate detection of pests in coffee crops, with a specific focus on anthracnose. Utilizing machine learning and image processing techniques, we have developed a system capable of precisely identifying the presence of this pest, offering farmers an effective tool for pest management. this approach enhances the efficiency of pest detection and reduces dependence on experts, which is especially valuable in regions with limited access to specialized knowledge.

关键词： Training Accuracy Biological system modeling Crops Manuals predictive models Feature extraction Heat maps Diseases Residual neural networks

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Towards Simplifying and Formalizing UML Class Diagram Generalization/Specialization Relationship with Mathematical Set theory 22

Towards Simplifying and Formalizing UML Class Diagram Genera...

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6th international conference on Information System and Data Mining, ICISDM 2022

作者： Shah, Kruti Grant, Emanuel School of Electrical Engineering and Computer Science University of North Dakota United States

ISBN: (纸本)9781450396257

the Unified Modeling Language (UML) is considered the de facto standard for object-oriented software model development. this makes it appropriate to be used in academia courses at both the graduate and undergraduate levels of education. Some challenges to using the UML is academia are its large number of model concepts and the imprecise semantic of some of these concepts. these challenges are daunting for students who are being introduced to the UML. One approach that can be taken in teaching UML towards addressing these concerns is to limit the number of UML concepts taught and recognize that students may not be able to develop correct UML system models. this approach leads to research work that develop a limited set of UML model concepts that are fewer in number and have more precise semantics. In this paper, we present a new approach to resolve an aspect of this problem by simplifying the generalization/specialization semantics of the class diagram through the application of mathematical formality to usage of these class diagram concepts. this research work derives a core set of concepts suitable for graduate and undergraduate comprehension of UML modeling and defines more precise semantics for those modeling concepts. the applicable mathematical principles applied in this work are from the domains of set theory and predicate logic. this approach is particularly relevant for the pedagogy of software engineering and the development of software systems that require a high level of reliability. © 2022 ACM.

关键词： Semantics

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Smart Agri-Farming on Satellite Imageries using Machine Learning 6

Smart Agri-Farming on Satellite Imageries using Machine Lear...

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6th IEEE international conference on Communication and Electronics Systems, ICCES 2021

作者： Dhedia, Ritik Paliakkara, Nixon Lobo, Vivian Brian Gupta, Deepak Sharma, Vaibhav St. John College of Engineering and Management Department of Computer Engineering Palghar India

ISBN: (纸本)9781665435871

this study aims to help farmers by using open source software that employs machine learning and hyperspectral images to analyze farm characteristics, which include crops, soil, and climate. this study makes use of two datasets, i.e., 270100 images from LANDSAT 8 and classified images from MODIS dataset provided by Google Earth Engine to classify land type, which helps in detecting farms in the future. Random forest algorithm was used as a classifier for multiclass hyperspectral data. Training the model acquired an overall accuracy of 0.997 that helped to determine the type of land in a geographical area. this paper conveys the first model built by us from various other models that are planned to develop. the data from our research work is conveyed to a farmer by means of a web application, which is built using a Spring framework, Grafana, JvaScript, and several other web technologies. © 2021 IEEE.

关键词： Remote sensing

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Research on Lateral Control method of Intelligent vehicle based on PSO-GA Optimization

Research on Lateral Control method of Intelligent vehicle ba...

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Internet of things, Automation and Artificial Intelligence (IoTAAI), international conference on

作者： Kunpeng Yan Yang Wang Pingshu Ge Tao Zhang School of Mechatronics and Electrical Engineering Dalian Minzu University Dalian China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

In this paper, a lateral feedforward LQR controller with adaptive prediction time is constructed based on the vehicle's two-degree-of-freedom lateral dynamics model and the tracking error model. then to solve the problem that it is difficult to select the coefficient matrix Q and R of the traditional LQR controller, a PSO-GA algorithm (hybrid particle swarm genetic algorithm) is proposed. Compared with the joint simulation results of Carsim/Simulink, the Q and R matrix optimized by the PSO-GA algorithm can effectively improve the tracking accuracy and control effect of the LQR controller.

关键词： Adaptation models Accuracy Heuristic algorithms Simulation Roads predictive models Prediction algorithms Stability analysis Vehicle dynamics Genetic algorithms

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