Processing Energy Consumption Research Articles

Opto-intelligence spectrometer using diffractive neural networks

Abstract Spectral reconstruction, critical for understanding sample composition, is extensively applied in fields like remote sensing, geology, and medical imaging. However, existing spectral reconstruction methods require bulky equipment or complex electronic reconstruction algorithms, which limit the system’s performance and applications. This paper presents a novel flexible all-optical opto-intelligence spectrometer, termed OIS, using a diffractive neural network for high-precision spectral reconstruction, featuring low energy consumption and light-speed processing. Simulation experiments indicate that the OIS is able to achieve high-precision spectral reconstruction under spatially coherent and incoherent light sources without relying on any complex electronic algorithms, and integration with a simplified electrical calibration module can further improve the performance of OIS. To demonstrate the robustness of OIS, spectral reconstruction was also successfully conducted on real-world datasets. Our work provides a valuable reference for using diffractive neural networks in spectral interaction and perception, contributing to ongoing developments in photonic computing and machine learning.

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoT-enabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and location-centric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency.

HBPB, applying reuse distance to improve cache efficiency proactively

Cache memories play a significant role in the performance, area, and energy consumption of modern processors, and this impact is expected to grow as on-die memories become larger. While caches are highly effective for cache-friendly access patterns, they introduce unnecessary delays and energy wastage when they fail to serve the required data. Hence, cache bypassing techniques have been proposed to optimize the latency of cache-unfriendly memory accesses. In this scenario, we discuss HBPB, a history-based preemptive bypassing technique that accelerates cache-unfriendly access through the reduced latency of bypassing the caches. By extensively evaluating different real-world applications and hardware cache configurations, we show that HBPB yields energy reductions of up to 75% and performance improvements of up to 50% compared to a version that does not apply cache bypassing. More importantly, we demonstrate that HBPB does not affect the performance of applications with cache-friendly access patterns.

On-Chip Annealing Using Embedded Micro-Heater for Highly Sensitive and Selective Gas Detection.

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time-consuming data transmission to an external processor, and high energy consumption in multi-stage data processing. In this study, a gas sensing system using on-chip annealing for fast and power-efficient gas detection is proposed. By utilizing a micro-heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET-type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on-chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability.

Energy Consumption and Tool Condition in Friction Stir Processing of Aluminum Alloys

AbstractFriction Stir Welding (FSW) and Friction Stir Processing (FSP) are solid-state joining and material processing techniques that have garnered considerable attention for their versatility and industrial applicability. In the present work, FSP was performed on AA 6056 T4, dealing with the issue of monitoring tool wear and assessing its impact on the process. The impact of tool wear on power requirements was analyzed, and it was expanded the understanding of tool behavior and its implications for the overall process performance. Specifically, variations in energy consumption, temperatures, and vibrations are observed with changing tool conditions. Further insights are provided by analyzing the microhardness and the pin volume ratio, which show distinct trends as the tool wears. Two tool maintenance ways are proposed, that are cleaning the tool with a sodium hydroxide solution and increasing the tool’s rotational speed. Both the strategies exhibit the potential to partially restore the tool’s initial characteristics. This study highlights the critical importance of assessing tool condition, energy consumption, and process sustainability, particularly in industrial settings where material processing requires efficiency and quality assurance.

Conjugate transient flow and thermal analysis of non-isothermal rubber curing process in autoclave system

Autoclave manufacturing under high pressure and elevated temperature was required to vulcanize the rubber compound applied in the aerospace industry. Nevertheless, few works have comprehensively explored the transient effect of flowing factors on curing efficiency, thermal uniformity, and exergy destruction in autoclave processing. In this study, the three-dimensional model and non-isothermal vulcanization of an autoclave with a built-in combustion chamber were solved by combining the finite volume method and the proposed mixed kinetic model, and exergy destruction was determined to estimate the energy consumption using a direct method. Variations in the Reynolds number (Re), gas pressure (P), temperature, rate of temperature change, and Prandtl number (Pr) were analyzed to improve the unstable thermo-vulcanization and reduce energy consumption in autoclave processing. The results demonstrate that increasing the inlet Re can significantly increase the curing rate while decreasing the temperature difference in the rubber area, and a reduction of 41.89 %−46.62 % for the complete vulcanization time is achieved by changing Re from 105 to 2 × 106; however, adjusting P and temperature has only a minor effect on the heat transfer under constant higher Re. Furthermore, changing the rate of temperature change and Pr is also advantageous for optimizing the curing process and reducing exergy destruction. A minimal non-dimensional exergy destruction rate arises for a given P, rate of temperature change, or Pr when the Re is raised. The knowledge gathered from this research could provide a theoretical foundation for reducing energy consumption and boosting productivity during autoclave curing.

A greedy randomized adaptive search procedure for scheduling IoT tasks in virtualized fog–cloud computing

AbstractVirtualized fog–cloud computing (VFCC) has emerged as an optimal platform for processing the increasing number of emerging Internet of Things (IoT) applications. VFCC resources are provisioned to IoT applications in the form of virtual machines (VMs). Effectively utilizing VMs for diverse IoT tasks with varying requirements poses a significant challenge due to their heterogeneity in processing power, communication delay, and energy consumption. In addressing this challenge, in this article, we propose a system model for scheduling IoT tasks in VFCCs, considering not only individual task deadlines but also the system's overall energy consumption. Subsequently, we employ a greedy randomized adaptive search procedure (GRASP) to determine the optimal assignment of IoT tasks among VMs. GRASP, a metaheuristic‐based technique, offers appealing characteristics, including simplicity, ease of implementation, a limited number of tuning parameters, and the potential for parallel implementation. Our comprehensive experiments evaluate the effectiveness of the proposed method, comparing its performance with the most advanced algorithms. The results demonstrate that the proposed approach outperforms the existing methods in terms of deadline satisfaction ratio, average response time, energy consumption, and makespan.

SIMULATION OF THE STRESSED-DEFORMED STATE OF THE ELASTIC DISCTOR RACK WITH A STIFFNESS REGULATOR

The paper involves the physical-mathematical modeling of the stress-strain state of an elastic stand of a developed disc harrow equipped with a stiffness regulator. The use of disc tools with individual attachment to elastic stands contributes to improving the quality and reducing the energy consumption of soil processing. This is due to the formation of oscillatory motion of the discs caused by the uneven resistance of the soil in the direction of the unit's movement. The goal of theoretical research is three-dimensional modeling of the stress-strain state of the elastic stand of the disc harrow with a stiffness regulator and substantiation of the range of its rational design parameters. To evaluate the interaction process between elastic working elements and the soil, a harmonic analysis was performed to determine the peak response of the system in a steady state to harmonic loads. In each step of the solution, all applied loads and base excitations have the same frequency, and the values are determined by the corresponding frequency curves. Using the SOLIDWORKS Simulation software package, the relevant physical-mathematical apparatus was compiled. On the basis of this approach, already at the stage of designing disk working bodies on elastic risers, it is possible to conclude about the possibility of them performing specific technological tasks, when by changing the parameters of the risers and conducting repeated studies, it is possible to obtain the corresponding dependencies in the form of regression equations. The results of numerical modeling provide visualization of the change in stress distribution over time in the stand. The dynamics of the change in maximum stress, located on the bend of the stand (R2) and the stiffness regulator (R1), were determined as the stress changes according to the law of damped oscillation with a specified natural frequency. In order to ensure the best working conditions of the elastic riser, it is necessary to ensure the greatest deformation of the riser at the place of attachment of the disc ΔL1 and, at the same time, the smallest value of the deformation of the tool frame ΔL2, by solving the trade-off problem in the Wolfram Cloud software package by maximizing the multiplicative function, the rational geometric dimensions of the diskator riser and constructive parameters of its placement in space.

A cloud-edge collaborative computing framework using potential games for space-air-ground integrated IoT

As a critical component of space-air-ground integrated IoT, the aerial network provides highly reliable, low-latency and ubiquitous information services to ground users by virtue of their high mobility, easy deployment and low cost. However, the current computation and resource management model of air-ground integrated networks are insufficient to meet the latency demanding of emerging intelligent services such as autonomous systems, extended reality and haptic feedback. To tackle these challenges, we propose a computation offloading and optimization method based on potential game. First, we construct an cloud-edge collaborative computing model. Secondly, we construct Offloading Decision Objective Functions (ODOF) with the objective of minimum task processing latency and energy consumption. ODOF is proved to be a Mixed Inferior Nonlinear Programming (MINLP) problem, which is hard to solve. ODOF is converted to be a full potential game, and the Nash equilibrium solution exists. Then, a computational resource allocation algorithm based on Karush–Kuhn–Tucker (KKT) conditions is proposed to solve resource allocation problem. On this basis, a distributed game-based computational offloading algorithm is proposed to minimize the offloading cost. Extensive simulation results demonstrate that the convergence performance of the proposed algorithm is reduced by 50%, the convergence time is reduced by 13.3% and the average task processing delay is reduced by 10%.

Lean and sustainability assessment in the sawmill industry using Sus-VSM: A case study in the context of Fiji

Waste from the perspective of lean manufacturing and sustainability has a huge influence on economic, environmental, and societal aspects. The inefficiency of the current manufacturing systems poses a huge risk to small and medium size sawmills going out of business as current prices of fossil fuel and associated emissions continue to rise at an alarming rate. In this case study, a local sawmill has been investigated to improve process efficiency using sustainable value stream mapping in the context of Fiji. The produced timber with the highest demand is selected as the product and a complete assessment is done on the downstream operation to compile data and develop the current state map. The current state map incorporates sustainability pillars to address the lacking factors such as energy and water consumption, raw material usage, labor physical load, and workspace noise level along with standard lean manufacturing time-based metrics. Sustainable value stream mapping allowed the research team along with a panel of experts to identify processor 2 as the root cause of bottlenecking, unnecessary material movement/transportation, and high physical load index score. Implementing a multi-blade horizontal bandsaw in place of processor 2 reduces; energy consumption for transport, energy consumption for processing, VAT, NVAT, process water consumption, raw material usage, and carbon emission by; 34.9%, 8.72%, 6.37%, 20%, 24.71%, 3.05%, and 10.33% respectively without compromising productivity. The findings of this research suggest evaluating and assessing sustainability performance using Sus-VSM in the sawmill industry unveils potential economic, environmental, and societal benefits.

Task Offloading Strategy for Unmanned Aerial Vehicle Power Inspection Based on Deep Reinforcement Learning.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study

As living standards improve, the growing demand for energy, comfort, and health monitoring drives the increased importance of innovative solutions. Real-time recognition of human activities (HAR) in smart homes is of significant relevance, offering varied applications to improve the quality of life of fragile individuals. These applications include facilitating autonomy at home for vulnerable people, early detection of deviations or disruptions in lifestyle habits, and immediate alerting in the event of critical situations. The first objective of this work is to develop a real-time HAR algorithm in embedded equipment. The proposed approach incorporates the event dynamic windowing based on space-temporal correlation and the knowledge of activity trigger sensors to recognize activities in the case of a record of new events. The second objective is to approach the HAR task from the perspective of edge computing. In concrete terms, this involves implementing a HAR algorithm in a “home box”, a low-power, low-cost computer, while guaranteeing performance in terms of accuracy and processing time. To achieve this goal, a HAR algorithm was first developed to perform these recognition tasks in real-time. Then, the proposed algorithm is ported on three hardware architectures to be compared: (i) a NUCLEO-H753ZI microcontroller from ST-Microelectronics using two programming languages, C language and MicroPython; (ii) an ESP32 microcontroller, often used for smart-home devices; and (iii) a Raspberry-PI, optimizing it to maintain accuracy of classification of activities with a requirement of processing time, memory resources, and energy consumption. The experimental results show that the proposed algorithm can be effectively implemented on a constrained resource hardware architecture. This could allow the design of an embedded system for real-time human activity recognition.

ELA-RCP: An energy-efficient and load balanced algorithm for reliable controller placement in software-defined networks

Software-defined networking is a modern and popular network technology that utilizes controllers as network operating systems to manage the network and allow user applications to interact with network hardware. Determining the optimal number of controllers and the optimal place to install them are some of the multi-controller model challenges that are known as the Controller Placement Problem (CPP). CPP is an optimization problem that tries to minimize delay and achieve a balanced network. CPP becomes more important when we intend to increase reliability and reduce energy consumption. This paper proposes an Energy-efficient and Load-balanced optimization Algorithm for Reliable Controller Placement (ELA-RCP) in Software-Defined Networks (SDN). The proposed ELA-RCP is a three-step solution to CPPs. First, it determines the optimal number of controllers using the proposed cellular learning automata, taking into account network traffic status, scale, and budget. Second, an innovative meta-heuristic algorithm is proposed to identify proper controller locations based on factors like path reliability, controller processing capacity, propagation delay, and energy consumption. In the last step, load balancing and the reliability of the controllers are improved using queuing theory and a proposed greedy algorithm. ELA-RCP is evaluated with real-world topologies. The simulation demonstrates up to 25% improvement in energy consumption, up to 108% improvement in the network survivability, and up to 68% improvement in mean average delay rise compared to particle swarm optimization, firefly optimization algorithm, ant lion optimization, and seagull optimization algorithm.

Macroscopic Noise Amplification by Asymmetric Dyads in Non-Hermitian Optical Systems for Generative Diffusion Models.

We study noise amplification by asymmetric dyads in freely expanding non-Hermitian optical systems. We show that modifications of the pumping strengths can counteract bias from natural imperfections of the system's hardware while couplings between dyads lead to systems with nonuniform statistical distributions. Our results suggest that asymmetric non-Hermitian dyads are promising candidates for efficient sensors and ultrafast random number generators. We propose that the integrated light emission from such asymmetric dyads can be efficiently used for analog all-optical degenerative diffusion models of machine learning to overcome the digital limitations of such models in processing speed and energy consumption.

Pulsed Electric Field Technology for Recovery of Proteins from Waste Plant Resources and Deformed Mushrooms: A Review

Proteins are complex molecules, which play a vital role in our body’s function, the building of tissues, and the regulation of metabolic activity. They are crucial to children’s growth and serve as a key component in the body’s process of distributing oxygen. Proteins fuel the body by supplying the required nutrition and energy. Currently, there is an increasing demand for proteins on large scales with no detrimental effects. The adverse health effects of animal proteins have resulted in a growing preference for plant-based proteins, which offer a healthier daily dosage. Valuable proteins can be extracted from various parts of the plant, including stems, leaves, seeds, fruits, vegetables, and roots. Notably, protein extraction from waste plant and mushroom parts minimizes the product wastage and improves the overall production to support economic sustainability. There are several protein extraction techniques available, where the replacement of non-thermal methods with thermal ones is promising nowadays due to the appreciable retainment of protein quality. Pulsed Electric Field (PEF) technology is one of the most efficient non-thermal tools used to assist with extracting these proteins at the minimum processing time and energy consumption when compared with thermal techniques. It relies on the application of a high-voltage pulse between two electrodes to treat samples inside the treatment chamber. While electrode shapes and treatment chamber designs primarily govern the electric field’s application, optimizing process parameters such as electric field strength, pulse width, number of pulses, and pulse waveshape assists in obtaining a desirable enhancement in the protein yield. The primary objective of this review is to explain the PEF-assisted protein extraction process applicable to waste plant parts and deformed mushrooms. While PEF is not a novel concept, utilizing it as a pre-extraction treatment to the aforementioned waste resources would aid in improving the production of value-added protein products economically. So far, PEF has shown immense promise in assisting with protein extraction studies, but requires further research in order to establish this area for large-scale industrial applications.

Tecnologías emergentes no térmicas en la industria alimentaria: Avances y potenciales aplicaciones en el procesamiento de alimentos

Currently, the food industry prioritizes the safety and quality of products, facing the challenge of maintaining sensory and nutritional integrity. To address this challenge, emerging non-thermal technologies are being explored that offer significant advantages in improving food quality over conventional technologies. This review aims to provide a comprehensive understanding of how these innovative technologies can transform the traditional food processing landscape as we know it. Non-thermal technologies have the potential to reduce energy consumption for food production, processing and packaging, compared to conventional thermal treatments. Furthermore, these technologies are particularly suitable for efficiently preserving bioactive compounds present in foods. This review describes the fundamental principles, applications, advantages and limitations of emerging technologies in food processing. These technologies include pulsed electric field, ultrasound, cold plasma, high pressure, irradiation, light-emitting diodes, pulsed light and oscillating magnetic fields. Despite the large number of studies on the subject, more research is required to optimize and improve the efficiency of the application of these technologies, alone or in combination, in food processing. The bioaccessibility and bioactivity of the compounds, nutritional value, shelf life and sensory aspects may be variables of interest.

Microstructures and Properties of FeCoNiCr High-Entropy Alloy Coatings Prepared by Electrodeposition

High-entropy alloys (HEAs) have attracted increasing attention owing to their multicomponent characteristics with notable high-entropy effects. However, obtaining HEAs with improved properties is still challenging. The properties of HEAs can be modulated by the fabrication technique. Electrodeposition could achieve the desired performance characteristics of HEA coatings while operating at reduced processing temperatures and energy consumption levels. Herein, novel FeCoNiCr HEA coatings were electrodeposited on copper substrates under various current densities. The microstructure, coating thickness, hardness, wear resistance, and corrosion properties of the FeCoNiCr HEA coatings prepared at different current densities were all examined. X-ray diffraction revealed HEA coatings with a single disordered face-centered cubic solid solution phase. Scanning electron microscopy indicated uniform and dense surfaces of FeCoNiCr HEA coatings fabricated under a current density of 25 A/dm2, with significantly reduced coating cracking and improved structural integrity. The coatings prepared at 25 A/dm2 also exhibited maximum thickness and favorable bonding with the substrate, as well as notably enhanced wear resistance. As the preparation current density increases, the hardness of the coating increases. The hardness of the coating reaches its maximum at 30 A/dm2. FeCoNiCr HEA coatings fabricated under a current density of 25 A/dm2 in a 3.5 wt% NaCl solution simulated seawater conditions demonstrated improved electrochemical resistance to corrosion. By comparing the microstructure, elemental content, and properties of coatings prepared at various current densities, it was found that the FeCoNiCr HEA coating prepared at 25 A/dm2 showed the best performance.

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

Task offloading in mobile edge computing (MEC) improves the efficacy of mobile devices in terms of computing performance, data storage, and energy consumption by offloading computational tasks to edge servers. Efficient task offloading can leverage MEC technology to reduce task processing latency and energy consumption. By integrating the reasoning ability and machine intelligence of the cognitive computing architecture, such as SOAR and ACT-R, reinforcement learning (RL) algorithms have been applied to resolve the task offloading in MEC. To solve the problem that conventional Deep RL (DRL) algorithms cannot adapt to dynamic environments, this paper proposed a task offloading scheduling strategy which combined multi-agent reinforcement learning and meta-learning. In order to make the two actions of charging time and offloading strategy fully considered at the same time, we implemented a learning network of two agent on a mobile device. To efficiently train the policy network, we proposed a first order approximation method based on clipped surrogate objective. Finally, the experiments are designed with variety of the number of subtasks, transmission rate, and edge server performance, and the results show that the MRL-based strategy has the overwhelming overall performance and can be quickly applied in various environments with good stability and generalization.

Miniaturized spectrometer with intrinsic long-term image memory

Miniaturized spectrometers have great potential for use in portable optoelectronics and wearable sensors. However, current strategies for miniaturization rely on von Neumann architectures, which separate the spectral sensing, storage, and processing modules spatially, resulting in high energy consumption and limited processing speeds due to the storage-wall problem. Here, we present a miniaturized spectrometer that utilizes a single SnS2/ReSe2 van der Waals heterostructure, providing photodetection, spectrum reconstruction, spectral imaging, long-term image memory, and signal processing capabilities. Interface trap states are found to induce a gate-tunable and wavelength-dependent photogating effect and a non-volatile optoelectronic memory effect. Our approach achieves a footprint of 19 μm, a bandwidth from 400 to 800 nm, a spectral resolution of 5 nm, and a > 104 s long-term image memory. Our single-detector computational spectrometer represents a path beyond von Neumann architectures.

Mobility-aware task offloading in MEC with task migration and result caching

Numerous computation-intensive applications have emerged, demanding lower delay and energy consumption. Multi-access Edge Computing (MEC) provides task offloading services for mobile users (MUs) through proximate MUs, effectively minimizing service delay and energy consumption. However, the challenges of supporting MUs’ mobility persist, and ensuring uninterrupted task offloading services from MEC servers still needs to be solved. Additionally, applications like autonomous driving generate repetitive tasks, introducing unnecessary computational overhead. This paper adopts a task migration strategy to tackle these issues, specifically addressing task offloading failure due to MU mobility. Simultaneously, a computation-result cache-assisted technique is leveraged to alleviate the burden of repetitive computation. The objective is to minimize the system cost, which consists of the processing delay and energy consumption of tasks for all MUs. The problem is modeled as an integer linear programming problem, and the optimal solution is meticulously derived. In response to the high computational complexity inherent in obtaining the optimal solution, a low-complexity heuristic algorithm (CMHA) is introduced. Simulation results demonstrate that optimal and heuristic schemes significantly reduce the overall system delay and energy consumption.