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7th International conference on Life System modeling and Simulation, LSMS 2021, and the 7th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Ji, Yong Song, Mingze Wen, Jin Zhao, Xiaoting Wang, Yufeng State Grid Liaoning Integrated Energy Service Co. Ltd. ShenYang China

ISBN: (纸本)9789811672125

In the context of today’s informatization and big data, the traditional financial management model of power grid projects has fallen behind. The financial department is unable to calculate and control key indicators in the process of engineering projects, but can only calculate the results and solidify assets. In response to such problems, in order to advance the role of financial to important nodes in the process of projects, this paper proposes a method for analyzing and predicting business financial data based on LSTM neural network, which uses multi-source heterogeneous data at the current stage of the project to affect future costs Forecast with income, and the financial department calculates indicators based on the forecast results. When the indicators are poor, the project decision of the current stage can be adjusted to realize the closed-loop monitoring of the business process. The results show that the method is accurate and effective. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Thermal analysis of Ex-Vivo Liver RF Ablation through Numerical modeling and Fiber Bragg Gratings Validation

Thermal Analysis of Ex-Vivo Liver RF Ablation through Numeri...

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URSI Atlantic Radio Science conference (URSI AT-RASC)

作者： Francesca Lodato Elena De Vita Renato Patrone Agostino Iadicicco Stefania Campopiano Francesco Izzo Rita Massa Daniele Riccio Giuseppe Ruello Department of Physics “Ettore Pancini” University of Naples Federico II Naples Italy Department of Engineering University of Naples “Parthenope” Naples Italy Department of Abdominal Oncology Division of Hepatobiliary Surgical Oncology Istituto Nazionale Tumori - Fondazione G. Pascale – IRCCS Naples Italy Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy

ISBN: (数字)9789463968102

ISBN: (纸本)9798350360257

Radio frequency ablation is a well-established technique in clinical practice for the treatment of tumors. Standardized protocols, new electrode technologies, and impedance feedback generators have already made this technique very advantageous for both patient and doctors, providing minimal invasiveness and localized treatment. Treatment planning can further enhance this technique, creating a customized solution for each patient. Numerical methods are very helpful in predicting treatment outcomes. However, their use as a reference requires a high degree of accuracy. Aim of this paper is to combine the use of numerical simulations with experimental measurements based on fiber optic sensors. To do this, tests on ex-vivo porcine liver have been carried out using a bipolar probe. The results of simulations have been compared with punctual temperature data provided by optical fibers and with experimental outcomes showing a good agreement.

关键词： Temperature measurement Temperature sensors Radio frequency Liver Numerical models Temperature control Thermal analysis

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Research on Panoramic Fault Simulation Method of Electric Power Communication Transmission Network 2

Research on Panoramic Fault Simulation Method of Electric Po...

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2nd IEEE International conference on data Science and Computer Application, ICDSCA 2022

作者： Geng, Lizhuo Hao, Xue Liu, Lu Gao, Feng Training Center State Grid Hebei Electric Power Company Limited Shijiazhuang050023 China Beijing Kedong Electric Power Control System Company Limited Beijing100192 China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

With the rapid development of electric power system, it is urgent to find out an effective and flexible transmission network fault simulation method to improve the fault analysis and handling competence of communication network operation and maintenance personnel. Aiming at the problems of current fault simulation solutions, this paper proposes a new panoramic fault simulation method of electric power communication transmission network. Firstly, the overall process of the method and simulation objects are introduced. Then the details of 4 main steps of the method are explained, such as equipment/cable simulation modeling building, fault simulation model building, simulation transmission network generation, fault loading the triggering. Finally, the application results of the method are briefly introduced. The method provides a new solution to panoramically recurrence of various types of electric power communication transmission faults, and supports the communication professionals to analyze the faults and improves their fault handling competence. © 2022 IEEE.

关键词： Electric power transmission networks

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Reliability analysis of Thermal Management Structure of Power Battery Based on Deep Learning Methods

Reliability Analysis of Thermal Management Structure of Powe...

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Artificial Intelligence and Automation control (AIAC), International conference on

作者： Jun Zhao Xiangbo Wang Zhou Yang Mechanical Engineering and Automation major at Northeastern University Shenyang Liaoning China Shenyang Railway Experimental Middle School Shenyang Liaoning China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

During the charging and discharging process of lithium batteries, heat is generated. If the heat is not effectively dissipated, it may lead to excessive battery temperature, which can adversely affect battery performance, shorten its lifespan, and even pose safety risks. This paper establishes a battery pack model and employs a cooling system primarily based on liquid cooling, with air cooling as a supplementary method, aiming to improve heat dissipation efficiency and ensure that the battery pack operates within the optimal temperature range. The study analyzes the effects of different parameter settings for the coolant and air, as well as the advantages and necessity of combined cooling. The results indicate that the combined cooling method is more effective, with the temperature and flow rate of the cooling medium having a significant impact on the battery pack's maximum temperature and temperature field distribution. Based on reliability theory, a Kriging surrogate model is used to establish the objective function, followed by reliability analysis using the improved first-order second-moment (AFOSM) and Monte Carlo simulation (MCS) methods. The deep learning network shows good fitting and training capabilities. By inputting the extracted sample array data into the network for training, the predicted values closely match the real values, with minimal error.

关键词： Heating systems Training Deep learning Temperature distribution Atmospheric modeling Predictive models Reliability theory Feature extraction Coolants Batteries

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Electric Propulsion System Optimization Using Performance Maps

Electric Propulsion System Optimization Using Performance Ma...

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IEEE conference on Aerospace

作者： Hong-Su Nam Hyeon-Su Hwang Seok-Hwan Lee Hak-Tae Lee Department of Aerospace Engineering Inha University Incheon Republic of Korea

This paper presents a performance map based approach that enables the holistic analysis and design of electric propulsion systems composed of an electronic speed control, a motor, a propeller, and an airframe. As all the performance parameters, including the flight speeds and climb rates, are expressed as contours, this approach provides insights into the performance of the combined propulsion system, not only for a single cruise condition, but for a wide range of operating conditions. First, by using the rotational speed and torque as the two independent variables, the process of computing the efficiency and plotting the efficiency contours for each component is presented. Selected practical techniques, which utilize data provided by the manufacturer, and which do not require costly measurements, are introduced. The approach based on the performance map is applied to evaluate eight combinations of the electronic speed control, motor, and propeller for a two-meter wingspan unmanned aircraft. The results suggest that the maximum range can vary by more than 10% depending on the selection of the components. The methodology described in this paper can be applied to design optimization, component selection for existing airframe as well as mission design and optimization of electric powered aircraft.

关键词： Torque Propellers Atmospheric modeling Velocity control Aerospace electronics data models Aircraft manufacture

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Parametric BIM-based life cycle assessment framework for optimal sustainable design

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JOURNAL OF BUILDING ENGINEERING 2023年 75卷

作者： Mowafy, Nada El Zayat, Mohamed Marzouk, Mohamed Knowledge Hub Univ Sch Engn Civil Engn Dept Coventry Branch Egypt Cairo Egypt Amer Univ Cairo Fac Engn Sch Sci & Engn Cairo Egypt Cairo Univ Fac Engn Struct Engn Dept Giza 12613 Egypt

The construction sector is liable for a sizable amount of CO2 emissions and waste. Life Cycle Assessment (LCA) is considered one of the crucial tools that aids in evaluating the impacts of construction emissions. This research aims to quantify the life cycle impacts of the initial and recurrent embodied emissions, operating and reuse/disposal emissions, and utilizing Building Information modeling (BIM) for a more holistic life cycle approach. The research proposes a parametric BIM-based LCA framework for quantifying the environmental impacts and evaluating the effects of material selection for both embodied and operating emissions. In addition, it considers the different reuse scenarios and percentages and their effect on both the embodied and operating impacts. The proposed framework consists of five main modules: data acquisition module, operating and embodied emissions module, life cycle assessment module, optimization module, and decision-making module. This parametric assessment can be applied for both the conventional and circular approach impacts. For the decision-making process, the resulting emissions are optimized utilizing the Non-dominated Sorting Genetic Algorithm ii (NSGA-ii) and evaluated through an LCA and data Envelopment analysis (DEA) methodology for the material selection. A case study of a residential villa in Cairo is worked out to illustrate the key features of the proposed framework. This assessment analyzes the building materials' characteristics and efficiency investigating the external walls, internal walls, floors, roofs, and windows design possibilities, and uses visual programming to control the resulting emissions for a more flexible approach. By comparing the integrated LCA and DEA models three optimum design solutions, the conventional approach develops design solutions of higher embodied Global Warming Potential (GWP) than the circular approach. However, it develops lower operating GWP with better applied U-values. The circularity a

关键词： Life cycle assessment Building information modeling Circular approach Multi-objective optimization data envelopment analysis

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SiEPIC openEBL: Remote Silicon Photonic Design-Fabrication-Testing Enabled by Open-Source process Design Kits and Design Tools

SiEPIC openEBL: Remote Silicon Photonic Design-Fabrication-T...

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Education and Training in Optics and Photonics (ETOP), IEEE conference on

作者： Mustafa Hammood Sudip Shekhar Lukas Chrostowski The University of British Columbia Vancouver Canada

ISBN: (数字)9798331543013

ISBN: (纸本)9798331543020

We present the SiEPIC openEBL platform, an open-access, remote silicon photonic design-fabrication-testing workflow enabled by open-source process design kits (PDKs), electronic design automation (EDA) tools, a design submission and aggregation platform, and an automated electro-optic probe station. Design is supported using the open-source SiEPIC-Tools EDA, a Python-based tool that can operate as a standalone module for experienced designers or integrate with the KLayout layout viewer for ease of use. Additional support is provided for popular EDA tools such as gdsfactory and Luceda IPKISS. Designers utilize the mature SiEPIC EBeam PDK, which offers a variety of hardware-validated devices with accompanying measurement data and compact models, alongside experimental ones. Fabrication is performed using electron beam lithography, enabling rapid prototyping of photonic integrated circuits (PICs) and facilitating the transition from design to experimental validation within short turnaround times. Designs are aggregated through an open-source repository, allowing designers to submit and verify layouts. During the design and aggregation phase, designers can specify measurement sites and test routines for characterization using a PIC probe station. The probe station’s hardware configuration and control software are available in an open-source repository for replication in local facilities. The SiEPIC openEBL platform has been widely adopted, with over 3,000 students and researchers from 70 countries participating in the associated online edX course, Silicon Photonics Design, Fabrication, and data analysis. This paper presents an overview of the platform, its impact on silicon photonics education and research, and future directions to expand capabilities and improve accessibility within the silicon photonics ecosystem.

关键词： Fabrication process design Training Layout Photonic integrated circuits Silicon photonics Rapid prototyping Probes Integrated circuit modeling Testing

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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High Performance Computing, Networking, Storage and analysis, SC-W: Workshops of the International conference for

作者： Haochen Pan Ryan Chard Sicheng Zhou Alok Kamatar Rafael Vescovi Valérie Hayot-Sasson André Bauer Maxime Gonthier Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science Southern University of Science and Technology China Department of Computer Science Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus’ ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients.

关键词： Automation Scientific computing Instruments Laboratories process control Computer architecture Organizations Dynamic scheduling Fabrics Reliability

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Research on Advanced Digital Model of Regional Manufacturing Export Based on Computer Aided analysis

Research on Advanced Digital Model of Regional Manufacturing...

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Signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Feng Tianyang Business School Nankai University Tianjin China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

In this paper, a new computational intelligence method is proposed to optimize the organizational decision process in industrial automation. The approach studied in this paper utilizes advanced machine learning algorithms to analyze real-time sensor data and predict system failures before they occur, enabling proactive maintenance and reducing downtime. In this paper, a multi-agent system is designed to work with the traditional manufacturing control system to improve the decision-making ability. The architecture of the system is specifically tailored to deal with the complexity and dynamics of modern industrial environments, where fast response times and accurate forecasting are critical to maintaining operational efficiency and product quality. Major contributions of this paper’s research include the development of a hybrid machine learning model that combines deep learning with evolutionary optimization techniques to improve prediction accuracy, and the integration of this model into a multi-agent system that can adapt to changing environmental conditions and evolve over time to maintain optimal decision performance.

关键词： Adaptation models Accuracy Computational modeling Collaboration Predictive models Manufacturing Internet Information management Time factors Multi-agent systems

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Comparison of Stochastic Steepest Gradient Descent and Extended Kalman Filter as ARMA-FNN Learning Algorithms for data-Driven System Identification of Batch Distillation Column

Comparison of Stochastic Steepest Gradient Descent and Exten...

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IEEE International conference on System Engineering and Technology

作者： Muhammad Alifsyah Putra Nasution Dimitri Mahayana Pranoto Hidaya Rusmin Hasan Zidni School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

A process that is almost always present in the chemical industry is distillation. The plant used in this study is a batch-type distillation column system located in the ITB Honeywell control Systems Laboratory, capable of separating binary mixtures of ethanol and water. To apply the control scheme effectively, the plant’s dynamics model is required. However, the challenge lies in the fact that the distillation column plant is a highly nonlinear and multivariable system based on its physical laws, with a limited number of sensors available to collect all states information, where only 1 state, which is also an output, can be measured. As a result, this research performs data-driven system identification through black-box modeling based on experimental input-output data using a neural network to generate a linear model. This research employs a discrete-time ARMA-FNN linear model for online identification of the distillation column system, with a focus on comparing learning algorithms for weight updating in the FNN. Specifically, a comparison is made between the stochastic Steepest Gradient Descent (SGD) algorithm and the Extended Kalman Filter (EKF) algorithm. Both algorithms are suitable for comparison because they both operate in an instance-by-instance mode and can be employed for online system identification. Based on the results presented in TABLE ii, it can be observed that the best Mean Squared Error (MSE) is achieved by entry number 2, utilizing the EKF algorithm, with a value of 5,5418e-05. Furthermore, as shown in TABLE ii, entry number 3 demonstrates that the EKF algorithm can converge the fastest, approximately by the $6^{\mathrm{t}\mathrm{h}}$ instance. This achievement comes with a trade-off, as the resulting MSE slightly deteriorates.

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