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Photovoltaic power prediction algorithm based on BP neural network

Photovoltaic power prediction algorithm based on BP neural n...

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IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Jiachen Bao Xiankun Zheng Guowan Ran Wenzhou University Wenzhou China Hebei University of Engineering Handan China Chengdu University of Technology Chengdu China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

With the rapid development of the global economy, non-renewable resources have been developed in large quantities, and there is great potential for the utilization of renewable resources such as solar energy. the prediction of solar power is the key to ensure power supply and dispatch. In order to ensure the stability and accuracy of prediction and reduce the adverse effects of instability factors on power system operation, BP neural network is introduced to forecast photovoltaic power generation. In this research, the Pearson correlation coefficient method is employed to identify the primary factors influencing PV power generation, namely temperature, humidity, and light radiation intensity. A BP neural network model is then constructed to predict PV power output. Data normalization techniques are applied to expedite the training process and optimize the network’s performance. Subsequent data analysis and model validation demonstrate that this approach can provide accurate and efficient predictions of PV power generation.

关键词： Photovoltaic systems Training Analytical models Accuracy Humidity predictive models Power system stability Prediction algorithms Data models Biological neural networks

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Computer Vision-Based Personnel Management in Complex Scenarios of Industrial Enterprises

Computer Vision-Based Personnel Management in Complex Scenar...

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

作者： Yongqi Chen Boying Li Yanqi Liu Weining Shi Haoyu Yang Yang Jiang Yongxuan Lai Information Technology Department Xiamen Tobacco Industry Co. Ltd. Xiamen China School of Informatics Xiamen University Xiamen China Shenzhen Research Institute / School of Informatics Xiamen University Xiamen China School of Mathematics and Information Engineering Longyan University Longyan China

Some companies have extremely strict requirements for the safety management of personnel and equipment in the production plant, including real-time counting of the number of personnel. However, in practice, it may not be convenient to use traditional access control for real-time counting due to production efficiency. this study aims to design a video streaming-based visual counting method for personnel at critical entrances and exits. To achieve this goal, we extensively evaluated and compared the performance of traditional target detection algorithms to find the one that is suitable for our task scenario. Subsequently, we delved into the RCNN [7] family of algorithms and the YOLO [8], [9]family of algorithms for both two-phase target detection algorithms and single-phase target detection algorithms. We used manually annotated datasets to train the relevant models and tested these algorithms for performance evaluation in task scenarios. In the end, we chose the YOLOv7 and StrongSORT [9], [13] based algorithms to successfully implement the personnel counting task and achieved a small error through model optimization to make it suitable for complex scenarios.

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NUMERICAL ANALYSIS OF PYROLYSIS OF PLASTIC USING OXY-HYDROGEN GAS PRODUCED BY ELECTROLYSIS OF SOLAR ENERGY

NUMERICAL ANALYSIS OF PYROLYSIS OF PLASTIC USING OXY-HYDROGE...

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international conference on Energy Conservation and Efficiency (ICECE)

作者： Muhammad Hamza Ahmad Ali Saqlain Abbas Faiza BiBi Komal Afzal Zulkanain Abbas Hamid Ashraf Department of Mechanical Engineering University of Engineering and Technology Lahore (Narowal campus) Narowal Pakistan Department of Mechanical Engineering NFC Institute of Engineering and Technology Multan Pakistan

Plastics are widely used in daily life, producing complications in the environment. Plastic materials are discharged into the surroundings, harmful to the health of people, animals, and plants and damaging marine life. Human health is spoiled by polystyrene, which is a type of plastic. In farming areas, plastics can restrict liquid and air association through the clays, thus lessening the efficiency of much-needed farmlands. Briefly, plastic contamination is one of the significant environmental problems. Plastic consumption is challenging because it requires more time and high cost. So, in the current research study, pyrolysis was used to reduce plastic waste. Firstly, a renewable source is used to electrolyze the water and HHO gas is produced. then it was determined that the required HHO gas is used for pyrolysis for plastic consumption, and the required HHO gasis produced perunit of time. SOLIDWORKS software is used in this approach to design a model and perform CFD analysis. Oxy-hydrogen gas is used for the pyrolysis of plastic. COCO Simulation software is used to simulate the pyrolysis of plastic. Asa resultof pyrolysis,most of theproduct is in liquid oilform.

关键词： Analytical models Liquids Spirals Costs Computational fluid dynamics Computational modeling software

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Research on Abnormal Detection for Wireless Communication Network based on Machine Learning

Research on Abnormal Detection for Wireless Communication Ne...

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international conference on Electronic engineering and Informatics (EEI)

作者： Qiushi Zuo College of Electronic and Information Engineering Beibu Gulf University Qinzhou City China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Communication anomaly identification is a key research area in communication network security. In wireless communication networks, node behavior analysis based on listening technology and statistical detection technology has the shortcomings of high energy consumption, sensitivity to network topology and environmental changes, insufficient data and malicious attacks. In this work, we start by generating data sequences of network behavior under normal and abnormal conditions. We then input the data series into the recurrent neural network (RNN) model for training. During training, the RNN model learns patterns of normal network behavior in order to identify anomalous behaviors subsequently. Once the model is trained, we can use it to predict whether a new data series is normal or an anomaly. When the model's predictions for a new data series are inconsistent with the actual situation, we can flag it as an anomaly. We divide the dataset into a training set and a test set, and usually use cross-validation or retention methods to ensure the reliability of the experiment. then, we input the training set into the RNN model for training, and use the test set for model evaluation.

关键词： Wireless communication Training Recurrent neural networks Protocols Accuracy Sensitivity predictive models Data models Anomaly detection Wireless fidelity

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Daily Load Forecasting of Electric Power Based on Casual Relationship Analysis and Graph Convolutional Network

Daily Load Forecasting of Electric Power Based on Casual Rel...

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international conference on Power and Energy Technology (ICPET)

作者： Siqi Liu Zhiyuan Xie Zhengwei Hu School of Electrical and Electronic Engineering North China Electric Power University Baoding China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

the prediction of daily average load has important practical significance for early distribution or load dispatch, but currently there is a lack of attention in the field of short-term load forecasting. the existing daily power load forecasting methods only rely on time data and autoregressive processes to predict loads, lacking exploration of the impact of meteorological factors on forecasting accuracy. In order to fill the research gap mentioned above, this paper proposes a graph convolutional neural network daily load forecasting method based on causal correlation analysis. In detail, firstly we analyze the main meteorological influencing factors of daily electricity load through causal relationship analysis; then, the causal relationship and daily load data are input into the graph neural network for training. the results indicate that the proposed model outperforms other reference models with smaller differences and higher prediction accuracy.

关键词： Training Radio frequency Analytical models Accuracy Load forecasting Meteorological factors Graph convolutional networks predictive models Power systems Load modeling

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MAFF: A Novel MobileNetV3 Attention Feature Fusion Network for Automatic Vehicle Classification

MAFF: A Novel MobileNetV3 Attention Feature Fusion Network f...

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

作者： Muhammad Khalid Mumtaz Bingcai Chen Muhammad Usman Saeed Muhammad Nadeem Muhammad Altaf Computer Science and Technology Dalian University of Technology Dalian China Computer Science and Engineering Central South University Changsha Hunan China

Efficient traffic and information systems heavily rely on real-time traffic data for effective regulation and management. Surveillance cameras, deployed in recent times, play a pivotal role in monitoring and controlling traffic. In this context, video surveillance technologies and image processing techniques have been extensively studied to enhance traffic management. this research focuses on the utilization of surveillance cameras for the analysis of traffic data, providing advanced warning systems and real-time extraction of vital vehicle-related information. We introduce an innovative approach that seamlessly integrates an advanced attention feature fusion block within the MobileNetV3 architecture. this integration leads to the development of a novel deep learning framework for automatic vehicle classification. Our model's performance stands out as it surpasses existing models on four distinct datasets: TAU Vehicle, Stanford Car, Vehicle Rear, and Tiny and Low-Quality Images. It achieves remarkable accuracy with the TAU Vehicle dataset at 97.64%, the Stanford Car dataset at 97.59%, the Vehicle Rear dataset at 97.41%, and even the challenging Tiny and Low-Quality Images dataset at an impressive 96.29%. Our methodology significantly enhances accuracy across these diverse datasets, setting a new benchmark in image classification and outperforming prior state-of-the-art approaches.

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A Wearable Breath Sound Monitoring Device Using the DWT-based MFCC Optimization Algorithm

A Wearable Breath Sound Monitoring Device Using the DWT-base...

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international conference on Electronics Technology (ICET)

作者： Yanxiu Li Ridong Chang Nianqiang Li School of Engineering University of Birmingham Birmingham UK Science and Engineering University of Jinan Jinan China

Respiratory sounds are the most important feature in determining whether a person with asthma has an attack. the aim of this work is to find the most suitable recognition algorithm for identifying respiratory sounds in daily life environments and to port it to a stand-alone circuit system with a microcontroller as the controller. In this paper, the DWT-MFCC, i.e., the Mel frequency cepstral coefficients (MFCC) algorithm optimized using the discrete wavelet transform(DWT), is proposed as the feature extraction algorithm and the support vector machine (SVM) is used as the classifier. And all the feature combinations that can be obtained from the optimized algorithm, including the optimized MFCC and the optimized MFCC + statistical features, are compared with the feature combinations used in related studies, such as linear predictive cepstral coefficients (LPCC), statistical features, LPCC + statistical features, or the original MFCC. the classifier was cross-validated using MATLAB software. the combination of optimized MFCC + statistical features could achieve an accuracy of up to 96%, with an average accuracy of 92.56%. this is the highest accuracy and most consistent performance of all possible combinations. After porting the recognition algorithm to a stand-alone circuit with ESP32-WROOM-32 as the controller, the desired result was achieved.

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software Defect Prediction Using a Combination of Oversampling and Undersampling Methods

Software Defect Prediction Using a Combination of Oversampli...

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international conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Aizul Faiz Iswafaza Siti Rochimah Department of Informatics Institut Teknologi Sepuluh Nopember (ITS) Surabaya Surabaya Indonesia

software quality can be improved by doing software testing, but the more features are developed the more resources are required, therefore software defect prediction (SDP) is introduced. Various kinds of machine learning methods are used to develop SDP. However, various kinds of problems arise in SDP activities, namely data redundancy, class imbalance and feature redundancy. In this study, a combination of oversampling and under-sampling (COU) model will be proposed to solve the problem of data redundancy and class imbalance. the oversampling method used is RSMOTE and the under-sampling method used is ENN. the application of the combination model will later provide a new set of datasets that are more balanced and cleaner from ambiguous, noisy and duplication of data. From the new data generated by the model, deep learning will then be applied as a prediction model. And the evaluation will be done by applying the f-measure measurement. the results of this study indicate that the COU model used gives good results in improving the quality of SDP. When compared with the average value generated by the RSMOTE model in making predictions, the COU model provides an increase in f-measure evaluation results by 11% where the average value obtained is 0.876. So, this combination method is able to improve the performance of software defect prediction activities

关键词： software testing Electrical engineering Deep learning Redundancy software quality predictive models Data models

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Predicting the Hardness of Low Alloy Metal using Machine Learning Model

Predicting the Hardness of Low Alloy Metal using Machine Lea...

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international conference on Computational Intelligence and Networks (CINE)

作者： K. Logeswaran S. Savitha P. Suresh Swati Sah B Priyanka R Ragav R S Vishal H Mohamed Arsath Department of Artificial Intelligence Kongu Engineering College Erode TamilNadu India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore TamilNadu India Department of Computer Science and Engineering K.S.R College of Engineering Trichengode Tamilnadu India School of Engineering and Technology Sharda University Nodia India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

In industries like materials engineering and manufacturing, predicting the hardness of low alloy metals is crucial for ensuring quality and performance. Traditional methods, which often rely on formulas or manual testing, can be slow and inaccurate due to variations in material properties. this paper presents a new approach for predicting metal hardness using machine learning techniques, including Multi-linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. these algorithms are used to improve the accuracy and reliability of hardness predictions compared to older methods. the paper covers how the data was collected, which features were chosen, and how the models were trained. It also compares the performance of these algorithms, looking at how accurate and effective they are in different situations. the results show that the machine learning models offer better predictions than traditional methods, leading to more consistent and reliable outcomes. Finally, the research discusses how these models can be used in real-world applications to make processes more efficient and reduce errors.

关键词： Analytical models Accuracy Metals predictive models Prediction algorithms Data models Reliability Regression tree analysis Random forests Testing

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thÖR-Magni: Comparative Analysis of Deep Learning models for Role-conditioned Human Mtion Prediction

THÖR-Magni: Comparative Analysis of Deep Learning Models fo...

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2023 IEEE/CVF international conference on Computer Vision Workshops, ICCVW 2023

作者： De Almeida, Tiago Rodrigues Rudenko, Andrey Schreiter, Tim Zhu, Yufei Maestro, Eduardo Gutierrez Morillo-Mendez, Lucas Kucner, Tomasz P. Martinez Mozos, Oscar Magnusson, Martin Palmieri, Luigi Arras, Kai O. Lilienthal, Achim J. Örebro University Sweden Corporate Research Robert Bosch GmbH Stuttgart Germany Aalto University Mobile Robotics Group Department of Electrical Engineering and Automation Finland Finnish Center for Artificial Intelligence Fcai Finland

ISBN: (纸本)9798350307443

Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel thÖR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics. © 2023 IEEE.

关键词： deep learning human motion dataset human trajectory prediction

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