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simulation modeling of the Choice of Metal Cutting Tool Coating 1

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6th international conference on Industrial Engineering, ICIE 2020

作者： Mokritskii, B. Morozova, A. Sitamov, E.J. Komsomolsk-Na-Amure State University 27 Lenin Av. Komsomolsk-on-Amur681013 Russia Bryansk State Technical University 7 50 Years of October Boul. Bryansk241035 Russia

ISBN: (数字)9783030548179

ISBN: (纸本)9783030548162

The aim of the study is to reduce material and time costs when choosing a rational tool material as well as when designing a new tool material for the given operating conditions of the tool. In relation to this goal, the article shows the solution to the problem of choosing a rational tool coating for turning specialized 09Kh17N7Y grade stainless steel using simulation modeling in the Deform software environment. To solve the problem, a computer numerical experiment based on simulation was used. The results of it are compared with the results of a full-scale experiment during cutting. The adequacy of the results is proved. The novelty of the solution lies in the fact that the simulation was performed on the example of turning specialized 09X17H7YU grade stainless steel, which has no analogs abroad, and previously available domestic recommendations are outdated due to the fact that there is no longer the metal-cutting equipment the recommendations were worked out for. The main result of this article is the proof of the possibility of using the Deform software environment for the goal and the problem being solved, as well as the creation of a certain bank of recommendations to be used in modern production conditions. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： simulation modeling Tool coatings Tool wear

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A Systematic Literature Review for Implementing Robust Quality Assurance Strategies Across the Software Development Life Cycle

A Systematic Literature Review for Implementing Robust Quali...

引用

Artificial Intelligence and Emerging Technology (Global AI Summit), international conference on

作者： Surendra Kumar Shukla Vishan Kumar Gupta Radha Krishna Rambola Jaishree Meena Anupriya Monika Bharti Department of Computer Engineering SVKM'S NMIMS MPSTME India Amity School of Engineering and Technology Amity University Mohali Punjab India Amity School of Biological Sciences Amity University Mohali Punjab India School of Computing Graphic Era Hill University Dehradun UK India ASET Amity University Mohali Punjab India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This systematic literature review (SLR) conducts a detailed analysis of Software Quality Assurance (SQA) regarding the Software Development Life Cycle (SDLC). Therefore, the purpose of this article is to enhance the quality of software products that are being delivered by presenting numerous research and cases that focus on SQA approaches, challenges, and strategies. It is worth focusing more on the measurement of SQA impacts, the literature review of the current practices in the industry, and the costs of implementing quality in the software development process. Thus, the paper evaluates the role of testing and quality assurance in various SDLC models and also examines the rationale of simulation models that try to measure SQA efforts. Partisan evidence mainly collected from the several contexts of software Industry is introduced, including a comprehensive Research on SQA procedures in the software business in India. It also investigates the financial implications of SQA and more focus on the issue of managing development costs.

关键词： Industries Costs Quality assurance Biological system modeling Current measurement Software quality Artificial intelligence Systematic literature review Software development management Testing

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Machine Learning Aboard the ADAPT Gamma-Ray Telescope

Machine Learning Aboard the ADAPT Gamma-Ray Telescope

引用

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

作者： Ye Htet Marion Sudvarg Andrew Butzel Jeremy D. Buhler Roger D. Chamberlain James H. Buckley Department of Computer Science and Engineering Washington University in St. Louis St. Louis Missouri USA Department of Physics Washington University in St. Louis St. Louis Missouri USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

The Advanced Particle-astrophysics Telescope (APT) is an orbital mission concept designed to contribute to multi-messenger observations of transient phenomena in deep space. APT will be uniquely able to detect and accurately localize short-duration gamma-ray bursts (GRBs) in the sky in real time. Current detection and analysis systems require resource-intensive ground-based computations; in contrast, APT will perform on-board analysis of GRBs, demanding analytical tools that deliver accurate results under severe size, weight, and power *** this work, we describe a neural network approach in our computation pipeline for GRB localization, demonstrating the capabilities of two neural networks: one to discard signals from background radiation, and one to estimate the uncertainty of GRB source direction constraints associated with individual gamma-ray photons. We validate the accuracy and computational efficiency of our networks using a physical simulation of GRB detection in the Antarctic Demonstrator for APT (ADAPT), a high-altitude balloon-borne prototype for APT.

关键词： Location awareness Adaptation models Accuracy Quantization (signal) Uncertainty Computational modeling Neural networks Pipelines Real-time systems Transient analysis

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Optimal Energy Mix modeling simulation Using Reinforcement Learning on Philippine Monthly Energy Utilization for 2020–2022

Optimal Energy Mix Modeling Simulation Using Reinforcement L...

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international conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Reynaldo Ted L. Penas II Prospero C. Naval Artificial Intelligence Program University of the Philippines-Diliman Quezon City Philippines Department of Computer Science University of the Philippines-Diliman Quezon City Philippines

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

A traditional method of generating an optimal energy mix involves selecting from various power producers, each with different costs per kilowatt-hour (kWh). This energy is then transmitted and distributed among various end-user sectors. However, this study explores a novel approach by replacing the traditional involvement of transmission and distribution utilities with reinforcement learning (RL) techniques. The objective was to determine an optimal energy mix over a 36-month period in the Philippines, from 2020 to 2022. The study evaluated several RL methods, including Q-learning, Deep Q-Network (DQN), Double Q-learning, and Actor-Critic algorithms. Each algorithm was tested with different learning rates $(\alpha)$ and discount factors $(\gamma)$ to find the most effective combination. Among these, the Actor-Critic algorithm, with $\alpha=0.10$ and $\gamma=0.90$ , emerged as the most optimal. The simulations showed that using each algorithm, no energy demand in kWh was left unmet, demonstrating its effectiveness in managing and optimizing energy distribution. This approach not only automated the decision-making process but also enhanced efficiency by continuously learning and adapting to changes in energy production and consumption patterns.

关键词： Adaptation models Energy consumption Q-learning Costs Instruments Decision making Production

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ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment modeling

ActiveRIR: Active Audio-Visual Exploration for Acoustic Envi...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Arjun Somayazulu Sagnik Majumder Changan Chen Kristen Grauman The University of Texas at Austin FAIR Meta

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

An environment acoustic model represents how sound is transformed by the physical characteristics of an indoor environment, for any given source/receiver location. Traditional methods for constructing acoustic models involve expensive and time-consuming collection of large quantities of acoustic data at dense spatial locations in the space, or rely on privileged knowledge of scene geometry to intelligently select acoustic data sampling locations. We propose active acoustic sampling, a new task for efficiently building an environment acoustic model of an unmapped environment in which a mobile agent equipped with visual and acoustic sensors jointly constructs the environment acoustic model and the occupancy map on-the-fly. We introduce ActiveRIR, a reinforcement learning (RL) policy that leverages information from audio-visual sensor streams to guide agent navigation and determine optimal acoustic data sampling positions, yielding a high quality acoustic model of the environment from a minimal set of acoustic samples. We train our policy with a novel RL reward based on information gain in the environment acoustic model. Evaluating on diverse unseen indoor environments from a state-of-the-art acoustic simulation platform, ActiveRIR outperforms an array of methods—both traditional navigation agents based on spatial novelty and visual exploration as well as existing state-of-the-art methods.

关键词： Visualization Three-dimensional displays Navigation Rendering (computer graphics) Robot sensing systems Acoustics Data models Spatial databases Indoor environment Streams

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Optimizing Green Internet of Things Architecture in a Multi-loop Aquaponics System

Optimizing Green Internet of Things Architecture in a Multi-...

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Electronic Communications, Internet of Things and Big Data (ICEIB), international conference on

作者： R Mahkeswaran Brendan Toh Andrew Keong Ng Chris Toh Singapore Institute of Technology Singapore Metro Farm Pte Ltd Singapore

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

With the demand for food projected to rise between 59 and 98% to sustain a population of 9.7 billion people by the year 2050, the global challenge of ensuring food security is becoming increasingly prominent. The decline in rural farmers and the unsustainable methods of traditional farming practices have spurred the emergence of urban farming. Among urban farming techniques, aquaponics systems poise technological integration. The effectiveness of aquaponics relies on a dependable cyber-physical system with Internet of Things (IoT) technology. However, integrating IoT devices results in elevated power consumption and increased production costs for fish and vegetables due to the constant need for power and uninterrupted connectivity. To address this concern, exploring green IoT strategies and techniques to minimize energy consumption and carbon emissions is imperative. Therefore, we employed an innovative modeling and simulation approach to investigate the potential of optimizing sensor placement and sampling rates' impact on energy consumption within an IoT architecture deployed in a commercial-sized multi-loop aquaponics system at Metro Farm (Downtown East, Singapore). The IoT architectures were modeled and simulated using the VisualSim software package. The achieved energy savings were quantified in Watt-hours and extrapolated to assess the overall energy conservation and cost savings for the entire urban farm.

关键词： Energy consumption Sensor placement Costs Software packages Sociology computer architecture Internet of Things

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Strategic Sensor Deployment in Traffic Networks for Enhanced Traffic Flow and Pollution Analysis through Global Search Optimization

Strategic Sensor Deployment in Traffic Networks for Enhanced...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Marco Lupia Gianni Cario Gianfranco Gagliardi Vincenzo Gallelli Department of Computer Modeling Electronics and Systems Engineering (DIMES) University of Calabria Departement of Civil Engineering (DINCI) University of Calabria

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Understanding the relationship between traffic flow estimates and air quality assessment requires analyzing how vehicle movements influence environmental pollutants. This involves a thorough examination of vehicle dynamics and pollutant concentrations across transportation networks. Strategically positioning sensors within these networks is crucial, taking into account hardware limitations and maintenance costs. To reduce data processing and network communication demands, sensors must be placed judiciously. In large systems like traffic networks, designing an optimal sensor layout is essential for reliable monitoring. In traffic systems, achieving comprehensive visibility of traffic flows involves selecting the minimum necessary sensors from a larger pool for installation. The goal is to choose a subset of p sensors from a much larger set of n possibilities. This paper presents global search optimization methods to solve this selection problem while ensuring system observability. The selected sensors enable the creation of a Luenberger observer to improve system state estimations. The effectiveness of this approach is demonstrated through numerical simulations, and its application is further explored using a digital twin of a transportation network, focusing on traffic dynamics and resulting emission levels.

关键词： Roads Transportation Traffic control Search problems Numerical simulation Sensor systems Sensors Observability Vehicle dynamics Sensor arrays

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Assessing Resilience of Smart Distribution Grid with Electric Vehicle Charging Stations Under Cyber-Physical Threats

Assessing Resilience of Smart Distribution Grid with Electri...

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international conference on Control, Mechatronics and Automation (ICCMA)

作者： Feras Alasali Naser El-Naily Salah AbuGhalyon Awni Itradat Eyad Zarour Department of Electrical Engineering Faculty of Engineering The Hashemite University Zarqa Jordan College of Electrical and Electronics Technology Benghazi Libya Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan The University of Jordan Amman Jordan

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

The global demand for electric vehicles is predicted to grow rapidly. However, charging Electric Vehicles (EVs) presents a challenge for Distribution Networks (DN) utilities. In a smart grid, effective communication between the charging stations, DN operator, and customers is crucial for arranging multiple charging sessions based on client preferences while following operational grid limits and costs. The modern and smart EV charging stations with interconnectivity requirements and online applications within the smart DN introduce potential risks of cyber-attacks and cause significant technical and financial consequences. Therefore, this study investigates the resilience of a smart distribution grid, specifically integrating EV charging stations and considering different cyber-physical threats. The impact of different cyber-attacks, namely: False Data Injection (FDI) and Man in The Middle (MITM), on low voltage microgrid models including the EV charging stations, are evaluated. The simulation uses a 24-hour load and generation profiles scenario and the results highlight the impact of cyber-physical threats on energy loss, transformer loading and voltage terms.

关键词： Microgrids Distribution networks Charging stations Transformers Electric vehicle charging Smart grids Cyberattack Resilience Load modeling Testing

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Performance evaluation using execution time aware bio-inspired virtual machine placement strategy 10

Performance evaluation using execution time aware bio-inspir...

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10th IEEE international conference on Communication Systems and Network Technologies, CSNT 2021

作者： Rawat, Pradeep Singh Bhadoria, Robin Singh Department of Computer Science & Engineering Uttarakhand Technical University Dehradun India Department of Computer Science & Engineering Birla Institute of Applied Sciences Bhimtal Nainital India

ISBN: (纸本)9780738105239

In the generation of computing, technology, and communication, cloud computing plays a prominent role. The utility computing paradigm which provides resources as a service is known as cloud computing. Cloud computing provides efficient use of cloud resources using a scalable simulation and modeling process. This work focuses on the performance evaluation of task assignments on virtual machine execution using a bio-inspired strategy. The quality of service is evaluated using average waiting time and average completion time (ms) of tasks assigned to the virtual machines using heavy-duty virtualization. An integrated architecture is proposed which inculcates the features of the bio-inspired technique and Kubernetes-based orchestration of container or lightweight virtual machine. The presented results are compared against static approaches, i.e., time shared and first come first serve techniques. © 2021 IEEE.

关键词： Virtual machine

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A Comparative Study of Ensemble Learning Models for Accurate Solar Irradiance Forecasting

A Comparative Study of Ensemble Learning Models for Accurate...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Tejaram Chaudhari Aditya Joshi Devkumar Bharti Pratibha Chavan M. K. Deshmukh Pritam Bhalgat Department of Electronics & Telecommunication Engineering Trinity College of Engineering & Research Pune India Department of Electrical & Electronics Engineering Birla Institute of Technology & Science Pilani Goa India Department of Electronics & Telecommunication Engineering SCTR's Pune Institute of Computer Pune India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Accurate estimation of solar radiation is very important in energy planning and the integration of renewable energy infrastructures into power grids for most places where the data may be sparse. This paper investigates advanced machine learning technologies specifically, ensemble methods such as XGBoost, LightGBM, CatBoost, NGBoost, AdaBoost, Gradient Boosting, Random Forest, and SGD Boost—to improve the prediction of solar radiation. It shows the performance of the above-mentioned models to forecast solar irradiance from meteorological factors using data from the Hawaii Space Exploration Analog and simulation (HI-SEAS) weather station. It has been shown that ensemble learning boosts the forecast accuracy considerably, especially for ensemble learners optimized for categorical data and probabilistic predictions. This shows how machine learning can be potential and support the global shift to sustainable energy with reliable forecasts of solar radiation in data-scarce regions and offering valuable insights for optimizing renewable energy deployment.

关键词： Solar irradiance Renewable energy sources Accuracy Computational modeling Weather forecasting Predictive models Space exploration Solar radiation Ensemble learning Reliability

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