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EV Charge Infrastructure Demand Forecast: A data-led approach 5

EV Charge Infrastructure Demand Forecast: A data-led approac...

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5th conference on Code of Practice for Electric Vehicle Charging Equipment Installation: Charging Ahead, EVI 2023

作者： Joubert, Tristan Stokes, Richard Arup Leeds United Kingdom

ISBN: (纸本)9781839539848

Electric Vehicles (EV) are quickly becoming a popular solution for car drivers looking at making the switch from Internal Combustion Engine (ICE) vehicles to a greener alternative. A significant amount of the energy demand in transportation is moving from fossil fuels to the electricity network. Arup have developed a forecasting model and methodology to determine the charging infrastructure demand associated with transitioning ICE vehicles to EV. the forecast model enables analysis of EV charge infrastructure demand and provides insight to Local Authorities and other Stakeholders under several scenarios, applying assumptions, including EV uptake, charging schemes/incentives and user behaviour. When deploying charge points, having a strategy in place maximises charge point investment benefits, Arup believes undertaking current and future demand modelling will better inform wider strategy. this paper describes the methodology used to develop several EV strategies based on a data-led approach, for local and wider Government in the British Isles. © the Institution of engineering & technology 2023.

关键词： Forecasting

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Research on foreground subject segmentation algorithm with high similarity of foreground background 5

Research on foreground subject segmentation algorithm with h...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Shen, Yating Zeng, Lujie Zong, Ping Zi Jin College Nanjing University of Science and Technology Jiangsu Nanjing210023 China

ISBN: (纸本)9781510668621

One of the advanced algorithms for image foreground segmentation is DIM (Deep Image Matting), which applies convolutional neural networks. However, for complex foreground shapes, fixed convolutional kernels are not the optimal approach. therefore, Deformable Convolution Networks (DCN) were introduced as a replacement, which partially address the issue of losing edge details in deep layers. However, DCN suffers from the extraction of undesired features, significantly reducing the model’s flexibility. To address this, the bias is reshaped using a balanced refining bias with an adaptive weight matrix, instead of a fixed 9-grid bias. To evaluate the effectiveness of the bias, a simple model called DIM_RDC is designed and trained, which utilizes the balanced refining bias. the model is tested on the Adobe dataset, and the results show that DIM_RDC improves the evaluation metrics of SAD by 1.62% and MSE by 11.05% compared to DIM. therefore, DIM_RDC demonstrates a certain level of competitiveness over DIM. © 2023 SPIE.

关键词： Convolution

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Camera-Lidar Fusion Algorithm Formed by Fast Sparse Encoder and Transformer Decoder 5

Camera-Lidar Fusion Algorithm Formed by Fast Sparse Encoder ...

引用

5th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Chen, Wenxing Chen, Songyu Chen, LinXuan Liu, Jianhe Department of functional algorithm MAXIEYE Shanghai200135 China Future Technology Institute Dalian University of Technology Dalian116024 China Department of Data MAXIEYE Shanghai200135 China

ISBN: (纸本)9781839539848

Multi-modal fusion is an important part of the automatic driving perception system. In order to raise the fusion accuracy of the target motion attributes and adapt to different traffic scenarios, the best sensor combination scheme has always been explored. Large models and high-performance chips are constantly iterated and updated in the field of automatic driving. However, the fusion technology also faces the problems of low accuracy, long model training period, the underutilization of original data information, and the error in the spatial and temporal synchronization of the sensor. In order to improve the accuracy of target fusion, this paper proposes an end-to-end efficient multi-modal Camera-Lidar fusion algorithm. Firstly, it is necessary to encode the 3D coordinates of the point cloud into multi-modal tokens to generate location-aware features, and projected into the image and lidar space for coordinate encode. then, the introduction of a variable transform method can effectively shorten the feature query time and improve the overall detection efficiency. Finally, we recommend adding a fusion post-processing at the end of the model, which belongs to the method of combining pre-fusion and post-fusion, the purpose of this is to be able to solve more corner cases. By comparing with the existing fusion algorithms, the conclusion is that the mAP and NDS of the new method proposed in this paper are relatively high, implicitly encoding technology is applied to the multi-modal features, avoiding the bias existing in the explicit cross-view feature alignment. the transform method can enhance the adaptability and robustness of the model and promote the development of high-precision and fast fusion algorithms. © the Institution of engineering & technology 2023.

关键词： Optical radar

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Newly-Designed DC-Arc Fault Detection Strategy Via EMD Signal Analysis and GRU Neural Network for Solar PV Power Generation System 5

Newly-Designed DC-Arc Fault Detection Strategy Via EMD Signa...

引用

5th international conference on Electrical, Control and Instrumentation engineering, ICECIE 2023

作者： Wai, Rong-Jong Cai, Xuerong National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan

ISBN: (纸本)9798350325041

In this study, an intelligent arc-fault detection algorithm for solar photovoltaic (PV) power generation systems is investigated based on the empirical mode decomposition (EMD) and the gate recurrent unit neural network (GRU-NN). the proposed algorithm uses the EMD to extract valuable information from current signals and then sequences the statistical indexes of each mode from the EMD according to modal orders. Moreover, the GRU-NN captures the features and variation trends among different modes and eventually detects the dc-arc fault. through experimental verifications, the average detection accuracy is over 98.7%, with an 8ms execution time under all examined conditions. In addition, an online updating method is also introduced in this study to ensure the adaptability of the proposed algorithm. Combined with this online-updating method, the proposed EMD-GRU-NN scheme could quickly modify the model and ensure the accuracy of the arc fault identification, even for different PV stations. the online updating performance will also be verified by experiments in Taiwan and China PV stations to improve over 25% detection accuracy to previous research. © 2023 IEEE.

关键词： Empirical mode decomposition

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Value Creation of Flue Gas for Hydrogen and Power Production Using RSOFC System 5th

Value Creation of Flue Gas for Hydrogen and Power Production...

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proceedings of the 5th international conference on Clean Energy and Electrical Systems - proceedings of CEES 2023

作者： Mungkalasiri, Woranee Mungkalasiri, Jitti Chemical Engineering Department Engineering Faculty Thammasat School of Engineering Thammasat University Pathumthani Khlong Luang Thailand Technology and Informatics Institute for Sustainability National Science and Technology Development Agency Pathumthani Khlong Luang Thailand

ISBN: (纸本)9789819938872

Flue gas is an exhaust gas generated through the combustion process of fossil fuels which consisted of CO2. For low-carbon society, flue gas treatment or utilization should be considered. In this work, value creation of flue gas producing hydrogen and electrical power using reversible solid oxide fuel cell (RSOFC) was proposed. RSOFC is the integration of SOEC and SOFC operations. the SOEC operation requires electrical power to produce hydrogen and the SOFC operation consumes hydrogen to produce electrical power. the RSOFC was studied via process simulation by ASPEN Plus program. the objective was to evaluate the optimal operating conditions of RSOFC which can produced maximum net power. the results revealed that the RSOFC system with condition of minimum SOEC power consumption should be operated as it achieved electrical power benefit. the optimal operating conditions for RSOFC were 1000°C of SOEC temperature, 1 bar of SOEC pressure, 1000°C of SOFC temperature and 3 bar of SOFC pressure. the net power production of RSOFC was 0.88 kW/m2. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Flue gases

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HD-PointNet: Point cloud processing in higher dimensions 5

HD-PointNet: Point cloud processing in higher dimensions

引用

5th international conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Tang, Wenjuan Wang, Hainan Shan, Hua Meng, Yue Wu, Mei Jiangsu Frontier Electric Power Technology Co. Ltd. Jiangsu Nanjing211100 China

ISBN: (纸本)9781510668621

PointNet++ is a simple but effective network designed for point cloud processing. However, the accuracy of PointNet++ has been surpassed by many other methods, like DGCNN and Point Cloud Transformer. these methods are way heavier compared to PointNet++, which is not favorable for the deployment of real-world products. In this paper, we propose a module called HD projection layers that was inspired by nonlinear kernels used in support vector machines. the HD projection layers project the features of the point cloud into a higher dimension, increasing the linear separability and therefore relieving the burden on the classifier. Equipped with HD projection layers, we extended PointNet++ into a new network, HD-PointNet, which also involves many other improvements and better training techniques. Experiments show that the accuracy of HD-PointNet is competitive against other modern methods while using fewer computation resources. © 2023 SPIE.

关键词： Support vector machines

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big data-Driven Active Optimization Algorithm for Grid Fault Disposal Plan Assisted Decision Logic

Big Data-Driven Active Optimization Algorithm for Grid Fault...

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international conference on Electronic engineering and Informatics (EEI)

作者： Hebin Jiang Junhong Guo Yanlin Cui Bo Zhou Zhangguo Chen Power Dispatching Control Center of Guangdong Power Grid Co. Ltd. Guangzhou China NARI Information & Communication Technology Co. Ltd. Nanjing China

In the current active optimisation of the grid fault disposal plan auxiliary decision logic, the logical relationships are more ambiguous, resulting in a low accuracy of the active optimisation results. To this end, a big data-driven active optimisation algorithm is proposed for the auxiliary decision logic of grid fault handling plans. Deep level access to grid fault information. Integration of fault type disposal plans. Develop auxiliary decision logic based on big data, extract keywords and correspond to fault states logically. Generate an active optimisation algorithm for the auxiliary decision logic. the experiments show that the average detection rate of the method is 95.14%, which is a substantial improvement and has high application value.

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Numerical-Study of Large-Liquid Rocket Plume-Flow-Field 5

Numerical-Study of Large-Liquid Rocket Plume-Flow-Field

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2023 5th international conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2023

作者： Ren, Fantao School of Aerospace Engineering Beijing Institute of Technology Beijing China

Aiming at the complexity of plume field structure caused by plume collision in the rising section of multi-nozzle parallel rocket, this paper establishes an analytical model of the rising section of a nine-nozzle configuration rocket. the plume field phenomenon is studied at different heights through numerical simulation. the reliability of the numerical method is verified based on the comparison of simulation and wind tunnel test data. the analytical results. Show that violent collision occurs between the jets in the ascent section of the multi-nozzles rocket, and many phenomena such as circulating vortex, gas reflux, reflux back splash and so on occur at different altitudes. When the altitude is less than 25km, gas reflux and circulation vortex appear in the rocket base. With the increasing of altitude, the jet collision impacts the base. After the altitude reaches 45km, back splash appears in some areas of the rocket base. there is an obvious flow diversion phenomenon on the side wall surface. the higher the altitude, the greater the expansion angle of the jet after the gas exits the nozzle. © Published under licence by IOP Publishing Ltd.

关键词： Numerical methods

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Research on Tea Leaf Recognition Based on YOLOv5 Algorithm 23

Research on Tea Leaf Recognition Based on YOLOv5 Algorithm

引用

7th international conference on Computing and data Analysis, ICCDA 2023

作者： Xie, Mingshan Peng, Yijun Zhang, Song Chen, Jun Liu, Linfeng Wang, Hao Liao, Renjie College of Big Data and Information Engineering Guizhou University China

ISBN: (纸本)9798400700576

Tea picking has always been mainly manual, but with the development of technology, it has been possible to use computer image recognition technology to assist robots in identifying tea leaves and picking them. However, the special characteristics of tea leaves make it difficult for the machine to recognize them with high accuracy and efficiency. In this paper, we compare the currently available algorithms and perform tea image recognition based on the YOLOv5 algorithm model. Based on the original YOLOv5 model, the CBAM attention mechanism is added to improve the recognition ability of tea. the mAP of our model can reach 89.1%, which is 13.6 percentage points higher than the original YOLOv5 model. © 2023 ACM.

关键词： Deep learning

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An Intelligent Approach to Segment the Liver Cancer using Machine Learning Method 4

An Intelligent Approach to Segment the Liver Cancer using Ma...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Anand, L. Maurya, Mahesh Seetha, J. Nagaraju, D. Ravuri, Ananda Vidhya, R.G. School of Computing Srm Institute of Science and Technology Department of Networking and Communications India University of Mumbai K. C. College of Engineering Management and Research Mith Bunder Road Near Hume Pipe Maharashtra Thane400603 India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Sri Venkatesa Perumal College of Engineering and Technology Department of CSE Andhra Pradesh Puttur India Senior Software Engineer Intel Corporation HillsboroOR97124 United States Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350300093

Cancer Liver cancer is difficult to detect and diagnose early. Machine learning has improved liver cancer diagnosis in recent years. this abstract describes a novel liver cancer detection and classification method using Autoencoder-Extreme Learning Machine (AE-ELM) and Convolutional Neural Network (CNN) technology. AE-ELM models extract features in the suggested method. the autoencoder-extreme learning machine AE-ELM technique learns high-level abstract characteristics from incoming data. AE-ELM extracts liver cancer-specific features by reducing the dimensionality of input data. A CNN model classifies the extracted features. CNNs are good at detecting spatial patterns in medical pictures like CT and MRI scans. the CNN model learns hierarchical representations of input information and classifies them into liver cancer subtypes or stages using various convolutional and pooling processes. AE-ELM and CNN are advantageous. First, AE-ELM decreases input data dimensionality to extract critical features without manual feature engineering. this simplifies feature selection and improves the model's capacity to handle big data. Second, the CNN model uses hierarchical convolutional processes to capture subtle patterns and spatial correlations in input data, enhancing liver cancer classification accuracy. the proposed liver cancer diagnostic method works in experiments. AE-ELM and CNN models outperform classic machine learning approaches and standalone CNN models. Improved liver cancer detection accuracy, sensitivity, and specificity help healthcare practitioners make quick and precise diagnosis. Finally, AE-ELM and CNN technologies can identify and classify liver cancer. this unique strategy uses both models to extract relevant characteristics and capture complicated patterns, improving liver cancer diagnosis accuracy and efficiency. To evaluate this approach's therapeutic promise and application in real-world healthcare settings, additional datasets are needed. © 2023 IEEE.

关键词： Convolutional neural networks

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