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نتیجه جستجو - Energy consumption

تعداد مقالات یافته شده: 257
ردیف عنوان نوع
1 Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application
سیستم سخت‌افزار-نرم‌افزار روی تراشه مبتنی بر شبکه‌های عصبی عمیق برای کاربرد بینایی ماشین-2022
Embedded vision systems are the best solutions for high-performance and lightning-fast inspection tasks. As everyday life evolves, it becomes almost imperative to harness artificial intelligence (AI) in vision applications that make these systems intelligent and able to make decisions close to or similar to humans. In this context, the AI’s integration on embedded systems poses many challenges, given that its performance depends on data volume and quality they assimilate to learn and improve. This returns to the energy consumption and cost constraints of the FPGA-SoC that have limited processing, memory, and communication capacity. Despite this, the AI algorithm implementation on embedded systems can drastically reduce energy consumption and processing times, while reducing the costs and risks associated with data transmission. Therefore, its efficiency and reliability always depend on the designed prototypes. Within this range, this work proposes two different designs for the Traffic Sign Recognition (TSR) application based on the convolutional neural network (CNN) model, followed by three implantations on PYNQ-Z1. Firstly, we propose to implement the CNN-based TSR application on the PYNQ-Z1 processor. Considering its runtime result of around 3.55 s, there is room for improvement using programmable logic (PL) and processing system (PS) in a hybrid architecture. Therefore, we propose a streaming architecture, in which the CNN layers will be accelerated to provide a hardware accelerator for each layer where direct memory access (DMA) interface is used. Thus, we noticed efficient power consumption, decreased hardware cost, and execution time optimization of 2.13 s, but, there was still room for design optimizations. Finally, we propose a second co-design, in which the CNN will be accelerated to be a single computation engine where BRAM interface is used. The implementation results prove that our proposed embedded TSR design achieves the best performances compared to the first proposed architectures, in terms of execution time of about 0.03 s, computation roof of about 36.6 GFLOPS, and bandwidth roof of about 3.2 GByte/s.
keywords: CNN | FPGA | Acceleration | Co-design | PYNQ-Z1
مقاله انگلیسی
2 Digital Twin-driven approach to improving energy efficiency of indoor lighting based on computer vision and dynamic BIM
رویکرد دیجیتال دوقلو برای بهبود بهره وری انرژی در روشنایی داخلی بر اساس بینایی کامپیوتر و BIM پویا-2022
Intelligent lighting systems and surveillance systems have become an important part of intelligent buildings. However, the current intelligent lighting system generally adopts independent sensor control and does not perform multi-source heterogeneous data fusion with other digital systems. This paper fully considers the linkage between the lighting system and the surveillance system and proposes a digital twin lighting (DTL) system that mainly consists of three parts. Firstly, a visualized operation and maintenance (VO&M) platform for a DTL system was established based on dynamic BIM. Secondly, the environment perception, key-frame similarity judgment, and multi-channel key-frame cut and merge mechanism were utilized to preprocess the video stream of the surveillance system in real-time. Lastly, pedestrians detected using YOLOv4 and the ambient brightness perceived by the environment perception mechanism were transmitted to the cloud database and were continuously read by the VO&M platform. The intent here was to aid timely adaptive adjustment of the digital twin and realistic lighting through the internet. The effectiveness of the proposed method was verified by experimenting with a surveillance video stream for 14 days. The key results of the experiments are as follows: (1) the accuracy rate of intelligent decision control reached 95.15%; (2) energy consumption and electricity costs were reduced by approximately 79%; and (3) the hardware cost and energy consumption of detection equipment and the time and cost of operation and maintenance (O&M) were greatly reduced.
keywords: Computer vision | Digital Twin | Dynamic BIM | Energy-efficient buildings | Intelligent lighting control
مقاله انگلیسی
3 Quantum Information Science
علم اطلاعات کوانتومی-2022
Quantum computing is implicated as a next-generation solution to supplement traditional von Neumann architectures in an era of post-Moore’s law computing. As classical computational infrastructure becomes more limited, quantum platforms offer expandability in terms of scale, energy consumption, and native 3-D problem modeling. Quantum information science is a multidisciplinary field drawing from physics, mathematics, computer science, and photonics. Quantum systems are expressed with the properties of superposition and entanglement, evolved indirectly with operators (ladder operators, master equations, neural operators, and quantum walks), and transmitted (via quantum teleportation) with entanglement generation, operator size manipulation, and error correction protocols. This article discusses emerging applications in quantum cryptography, quantum machine learning, quantum finance, quantum neuroscience, quantum networks, and quantum error correction.
keywords:
مقاله انگلیسی
4 Resource Management for Edge Intelligence (EI)-Assisted IoV Using Quantum-Inspired Reinforcement Learning
مدیریت منابع برای IoV به کمک هوش لبه (EI) با استفاده از یادگیری تقویتی الهام گرفته از پردازش کوانتومی-2022
Recent developments in the Internet of Vehicles (IoV) enable interconnected vehicles to support ubiquitous services. Various emerging service applications are promising to increase the Quality of Experience (QoE) of users. On-board computation tasks generated by these applications have heavily overloaded the resource-constrained vehicles, forcing it to offload on-board tasks to other edge intelligence (EI)-assisted servers. However, excessive task offloading can lead to severe competition for communication and computation resources among vehicles, thereby increasing the processing latency, energy consumption, and system cost. To address these problems, we investigate the transmission-awareness and computing-sense uplink resource management problem and formulate it as a time-varying Markov decision process. Considering the total delay, energy consumption, and cost, quantum-inspired reinforcement learning (QRL) is proposed to develop an intelligence-oriented edge offloading strategy. Specifically, the vehicle can flexibly choose the network access mode and offloading strategy through two different radio interfaces to offload tasks to multiaccess edge computing (MEC) servers through WiFi and cloud servers through 5G. The objective of this joint optimization is to maintain a self-adaptive balance between these two aspects. Simulation results show that the proposed algorithm can significantly reduce the transmission latency and computation delay.
Index Terms: Cloud computing | edge intelligence (EI) | Internet of Vehicles (IoV) | multiaccess edge computing (MEC) | quantum-inspired reinforcement learning (QRL)
مقاله انگلیسی
5 Dfinder — An efficient differencing algorithm for incremental programming of constrained IoT devices
Dfinder - یک الگوریتم افتراق کارآمد برای برنامه‌ریزی تدریجی دستگاه‌های محدود شده اینترنت اشیاء-2022
Internet of Things (IoT) proliferation has been remarkably, interconnecting a vast number of devices for the support of complex data-driven applications in a variety of domains. The ability to remotely update these devices is of paramount importance, as it allows the integration of additional functionality into their firmware, the resolution of code errors, the fixing of security vulnerabilities, or even their complete re-purpose, without physically accessing them. Such Over-the-Air Programming (OTAP) solutions require the reduction of the required transmitted data during a network update, in order to minimize devices’ energy consumption due to the communication overhead.
In this paper, we present the design and evaluation of Dfinder, a differencing algorithm that operates at byte-level and is able to generate small patches based on delta encoding that makes feasible the transition from a current firmware version to a new one. The algorithm runs in ????(????????????????????) time and ????(????) space complexity, utilising enhanced suffix arrays and state-of-the-art construction techniques that enable the efficient detection of common segments between two firmware versions. Additionally, we propose an extension of the algorithm, which halves the storage requirements at the IoT device side (compared to other state-of-the-art approaches), so that devices with limited storage can also be efficiently re-programmed over-the-air. Moreover, we evaluate its performance, comparing it with other differencing algorithms, and by integrating it in a complete IoT OTAP system.
keywords: اینترنت اشیا | الگوریتم های افتراق | دلتا اسکریپت | به روز رسانی سیستم عامل | استفاده از حافظه | زمان اجرا | Internet of Things | Differencing algorithms | Delta script | Firmware update | Memory utilization | Execution time
مقاله انگلیسی
6 Biomass supply chain equipment for renewable fuels production: A review
تجهیزات زنجیره تأمین زیست توده برای تولید سوخت های تجدیدپذیر: یک مروز-2021
The production of renewable fuels is a critical component of global strategies to reduce greenhouse gas (GHG) emissions. Moreover, the collection of raw materials for its production can provide added benefits such as reduction of wildfire risk, additional income for farmers, and decreased disposal costs. Although there is substantial literature on design and modeling of supply chains, the authors were unable to find a single reference with the information needed for the selection and cost estimation of each type of equipment involved in the supply chain. Therefore, the goal of this research is to gather information necessary for the construction and utilization of models that might drive the identification of a feasible supply chain to produce renewable fuels at a commercial scale. The primary objectives are to 1) understand the supply chain of critical feedstocks for renewable fuels production; 2) identify the equipment commercially available for collection and ad equation of feedstock; and 3) consolidate information regarding equipment cost, energy consumption, and efficiency, as well as feedstock storage and transportation systems. This paper provides a compilation for five feedstock types studied for sustainable aviation fuel production: 1) agricultural residues and grasses, 2) forest residues, 3) urban wood waste, 4) oilseeds, 5) fats, oils & greases. All the technologies involved from the field to the gate of the preprocessing or conversion unit were reviewed. The information on fats, oils & greases supply chains and equipment purposely designed for forest thinning and pruning was very limited.
Keywords: Feedstock | Collection and adequation | Biofuels | Renewable fuels | Sustainable aviation fuel | Supply chain configuration
مقاله انگلیسی
7 Heat recovery in an actual LNG supply chain: Retrofitting of designed heat exchange networks (HENs) for potential fuel saving
بازیابی گرما در یک زنجیره تامین LNG واقعی: مقاوم سازی مجدد شبکه های تبادل گرما (HENs) برای صرفه جویی احتمالی در سوخت-2021
The demand for liquefied natural gas (LNG) is steadily increasing and projected to become an important component of global energy demand. Although LNG processing requires high-energy to convert the gas into liquid, it is still the most preferable method of supply due to technical, economic, safety, and political reasons. Energy integration strategies and process optimization between units have been emphasized as ways to reduce energy demand. In this study, a rigorous simulation for proposed heat exchanger networks (HENs) between sulfur recovery units (SRU) and gas sweetening units (GSU) that exhibit heat sources and sinks was conducted. The HENs were designed using pinch analysis tools in Aspen Energy Analyzer (AEA) and were used to determine the maximum energy recovery and potential fuel savings after retrofitting within LNG supply chain. The feasibility of retrofitting the HENs into LNG plant without affecting process conditions or product quality was also determined. Although universal HEN reduces energy consumption of the existing plant by 68%, the network complexity limits its practical application. Simplified HENs between the sub-units reduced energy demand by 50% and achieved fuel saving of 34%. Retrofitting HENs improved existing LNG energy integration, enhanced process economy, reduced fossil fuel burning and protected the environment.
Keywords: Supply chain management | Risk management | Policy matrix
مقاله انگلیسی
8 Redesigning immunization supply chains: Results from three country analyses
طراحی مجدد زنجیره های تأمین ایمن سازی: نتایج حاصل از سه تحلیل کشور-2021
The demand for liquefied natural gas (LNG) is steadily increasing and projected to become an important component of global energy demand. Although LNG processing requires high-energy to convert the gas into liquid, it is still the most preferable method of supply due to technical, economic, safety, and political reasons. Energy integration strategies and process optimization between units have been emphasized as ways to reduce energy demand. In this study, a rigorous simulation for proposed heat exchanger networks (HENs) between sulfur recovery units (SRU) and gas sweetening units (GSU) that exhibit heat sources and sinks was conducted. The HENs were designed using pinch analysis tools in Aspen Energy Analyzer (AEA) and were used to determine the maximum energy recovery and potential fuel savings after retrofitting within LNG supply chain. The feasibility of retrofitting the HENs into LNG plant without affecting process conditions or product quality was also determined. Although universal HEN reduces energy consumption of the existing plant by 68%, the network complexity limits its practical application. Simplified HENs between the sub-units reduced energy demand by 50% and achieved fuel saving of 34%. Retrofitting HENs improved existing LNG energy integration, enhanced process economy, reduced fossil fuel burning and protected the environment.
Keywords: Natural gas | Retrofitting design | Heat exchange networks (HENs) | Process optimization | Heat recovery
مقاله انگلیسی
9 Integrative design of the optimal biorefinery and bioethanol supply chain under the water-energy-food-land (WEFL) nexus framework
Integrative design of the optimal biorefinery and bioethanol supply chain under the water-energy-food-land (WEFL) nexus framework-2021
This study presents a comprehensive decision model for the integrative design of a biorefinery for bioethanol production and its supply chain (BPSC) under the water-energy-food-land (WEFL) nexus framework. A new optimization model was developed using a mixed integer linear programming to simultaneously identify the optimal process configuration of a bioethanol production plant and the optimal bioethanol supply network. The objective function of the model is to minimize the total annual cost for establishing and operating the BPSC to meet society’s needs (energy, water and food) under the limited resources and land availabilities, and technology capacity. The proposed model can provide the optimal solutions for design and operation of the BPSC: i) the types, and quantities of feedstocks; ii) types, number, and location of facilities and; iii) regional flows. The capability of the proposed model was validated through the case study of Jeju Island, Korea, with two scenarios: BPSC by cost (COPT) and nexus (NOPT) optimization. As a result, it was identified that the BPSC in NOPT requires higher energy supply cost (8.55 B$) than the COPT (6.44 B$). However, the BPSC in NOPT can satisfy the society demands with relatively smaller consumption of occupied land (2%), fresh water (30%) and primary energy consumption (64%) than that of the COPT, respectively.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Nexus | Optimization | Biofuel | Bioethanol supply chain | Korea
مقاله انگلیسی
10 Evaluating the urban metabolism sustainability of municipal solid waste management system: An extended exergy accounting and indexing perspective
ارزیابی متابولیسم شهری پایداری سیستم مدیریت ضایعات جامد شهری: حسابداری اگزرژی گسترده و دیدگاه نمایه سازی-2021
In this study, Extended Exergy Accounting was adopted to develop an accounting model to evaluate the performance of a Municipal Solid Waste Management System. Furthermore, urban metabolism sustainability index for waste was also proposed to represent the unified society-economy-environment impacts of the MSWMS under the framework of a comprehensive sustainability evaluation. A detailed analysis of wood and horticultural waste treatment scenarios in Singapore was done as a case study. It was found that the gasification scenario theoretically performs significantly better than the incineration scenario, in terms of energy carrier consumption, emissions, thermodynamic efficiency and sustainability. Analysis results show that, if extrapolated to Singapore’s total wood and horticultural waste, gasification technology has potential to reduce energy consumption and increase electricity output. An uncertainty analysis was carried out and it was found that the main extended exergetic parameters of the two scenarios considered were in the range of 3–8%, thus confirming the reliability of the accounting results. A sensitivity analysis of the urban metabolism sustainability index for waste was conducted for the gasification scenario to identify key influencing factors and seek potential improvements; this was done by considering changes in four variables: transportation distance, electrical efficiency, working hour increment and gross capital cost per ton waste treated. It was found that, to ensure the feasibility and sustainability of gasification scenario, the following are required: keeping the electricity production efficiency greater than 21.33%; the transportation distance between the gasification power plant and source of wood and horticultural waste should be kept within 17.08 km; employment of per kton annual treatment capacity should be less than 0.14 workers; wood and horticultural waste source should control the waste collection frequency of no more than 3 times per day and the number of workers participating in the collection each time is less than 4 persons, totaling to 12 workers per day.
keywords: تجزیه و تحلیل Exergy را گسترش دهید | زباله جامد شهری | ارزیابی پایداری | متابولیسم شهری | اگزرژی کار | Exergy اصلاح محیط زیست | Extend exergy analysis | Municipal solid waste | Sustainability assessment | Urban metabolism | Labor exergy | Environmental remediation exergy
مقاله انگلیسی
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