Processing Energy Consumption Research Articles

Neu(t)ralMC: energy-efficient open source Monte Carlo algorithm for assessing photon transport in turbid media.

Light propagation in turbid mediums such as atmosphere, fluids, and biological tissues is a challenging problem which necessitates accurate simulation techniques to account for the effects of multiple scattering. The Monte Carlo method has long established itself as a gold standard and is widely adopted for simulating light transport, however, its computationally intensive nature often requires significant processing power and energy consumption. In this paper a novel, open source Monte Carlo algorithm is introduced which is specifically designed for use with energy-efficient processors, effectively addressing those challenges, while maintaining the accuracy/compatibility and outperforming existing solutions. The proposed implementation optimizes photon transport simulations by exploiting the unique capabilities of Apple's low-power, high-performance M-family of chips. The developed method has been implemented in an open-source software package, enabling seamless adaptation of developed algorithms for specific applications. The accuracy and performance are validated using comprehensive comparison with existing solvers commonly used for biomedical imaging. The results demonstrate that the new algorithm achieves comparable accuracy levels to those of existing techniques while significantly reducing computational time and energy consumption.

Deep reinforcement learning-based edge computing offloading algorithm for software-defined IoT

Edge computing offloading can effectively solve the problem of insufficient computing resources for terminal devices and improve the performance and efficiency of the system. When network states and tasks change rapidly, data-driven intelligent algorithms have difficulty obtaining comprehensive statistics for accurate prediction, resulting in degraded performance of computational offloading and difficulty in adaptive adjustment. It is a current challenge to improve the environment-aware, intelligent optimization so that the computational offloading algorithm can adapt to the dynamic changes in network state and task demands, thus achieving global multi-objective optimization. This paper presents optimized edge computing offloading algorithm for software-defined IoT. First, to provide global state for making decisions, a software defined edge computing (SDEC) architecture is proposed. The edge layer is integrated into the control layer of software-defined IoT, and multiple controllers share the global network state information via east–west message exchange. Moreover, an edge computing offloading algorithm in software-defined IoT (ECO-SDIoT) based on deep reinforcement learning is proposed. It enables the controllers to offload the computing task to the most appropriate edge server according to the global states, task requirements, and reward. Finally, the performance metrics for edge computing offloading were evaluated in terms of unit task processing latency, load balancing of edge servers, task processing energy consumption, and task completion rate, respectively. Simulation results show that ECO-SDIoT can effectively reduce task completion time and energy consumption compared with other strategies.

Application of Edible Insect Flour as a Novel Ingredient in Fortified Snack Pellets: Processing Aspects and Physical Characteristics

The aim of the study was to develop a suitable recipe for wheat-corn snack pellets fortified with insect flour addition and to evaluate the relevant processing aspects and physical characteristics of the developed products. Varying levels of edible insect flour (10, 20, and 30%) were incorporated into a new type of fortified snack pellet as a half product for further expansion. The effects of the edible insect flour level, as well as processing variables (moisture levels 32, 34, 36% and screw speeds 60, 80, 100 rpm), were analyzed on the extrusion stability and on selected snack pellets’ physical properties processed via single-screw extrusion cooking. This research indicated that an increasing amount of edible insect flour significantly affected the processing output and energy consumption. The incorporation of insect flour in blends significantly increased the specific mechanical energy and efficiency of pellet extrusion, especially at a high moisture level and high screw speed during processing. Moreover, the addition of insect flour in the snack pellets significantly reduced the bulk density and pellet durability. Principal component analysis confirmed that approximately 76.5% of the data variance was explained by the first two principal components, and significant correlations were noted between the properties of the tested snack pellets. It can be concluded that up to 20% of insect flour in newly developed extruded snack pellet formulations has no negative effect on processing and physical properties.

An improved quantum particle swarm algorithm for solving multi-objective fuzzy flexible job shop scheduling problem

Due to the dynamic nature of work conditions in the manufacturing plant, it is difficult to obtain accurate information on process processing time and energy consumption, affecting the implementation of scheduling solutions. The fuzzy flexible job shop scheduling problem with uncertain production parameters has not yet been well studied. In this paper, a scheduling optimization model with the objectives of maximum completion time, production cost and delivery satisfaction loss is developed using fuzzy triangular numbers to characterize the time parameters, and an improved quantum particle swarm algorithm is proposed to solve it. The innovations of this paper lie in designing a neighborhood search strategy based on machine code variation for deep search; using cross-maintaining the diversity of elite individuals, and combining it with a simulated annealing strategy for local search. Based on giving full play to the global search capability of the quantum particle swarm algorithm, the comprehensive search capability of the algorithm is enhanced by improving the average optimal position of particles. In addition, a gray target decision model is introduced to make the optimal decision on the scheduling scheme by comprehensively considering the fuzzy production cost. Finally, simulation experiments are conducted for test and engineering cases and compared with various advanced algorithms. The experimental results show that the proposed algorithm significantly outperforms the compared ones regarding convergence speed and precision in optimal-searching. The method provides a more reliable solution to the problem and has some application value.

Charge‐Mediated Copper‐Iodide‐Based Artificial Synaptic Device with Ultrahigh Neuromorphic Efficacy

In the realm of artificial intelligence, ultrahigh‐performance neuromorphic computing plays a significant role in executing multiple complex operations in parallel while adhering to a more biologically plausible model. Despite their importance, developing an artificial synaptic device to match the human brain's efficiency is an extremely complex task involving high energy consumption and poor parallel processing latency. Herein, a simple molecule, copper‐iodide‐based artificial synaptic device demonstrating core synaptic functions of human neural networks is introduced. Exceptionally high carrier mobility and dielectric constant in the developed device lead to superior efficacies in neuromorphic characteristics with ultrahigh paired‐pusle facilitation index (>195). The results demonstrate biomimetic capabilities that exert a direct influence on neural networks across multiple timescales, ranging from short‐ to long‐term memory. This flexible reconfiguration of neural excitability provided by the copper‐iodide‐based synaptic device positions it as a promising candidate for creating advanced artificial intelligence systems.

Multi-objective green scheduling of integrated flexible job shop and automated guided vehicles

The traditional flexible job shop scheduling problem (FJSP) ignores transportation issues or merely introduces a time lag for transportation tasks while assuming an infinite number of transportation resources. With the development of intelligent manufacturing, automated guided vehicles (AGVs), which are the key transportation equipment for manufacturing enterprises, have been widely used for their high flexibility and stability. In addition, the increase in energy consumption and the trend of green manufacturing make it critical to take into account energy-related objectives in the decision-making of scheduling. Therefore, the multi-objective green scheduling problem of integrated flexible job shop and AGVs (MOGSP-IFJS&AGVs) is addressed in this paper. To solve this problem effectively, the multi-objective mixed-integer programming (MMIP) model is formulated to minimize total energy consumption and makespan simultaneously. An efficient heuristic algorithm (EHA) is designed to solve the MMIP model. In the EHA, one solution encoding scheme and corresponding greedy insertion decoding method considering the selection of AGVs are presented. To acquire a high-quality initial population, the population initialization method balancing the processing time and energy consumption is designed. Further, a local search strategy is presented to enhance the quality of solutions and accelerate the convergence speed of the EHA. Experiment results of 45 test instances indicate that the EHA can obtain better solutions than that of comparison algorithms, which confirms the effectiveness of the EHA for solving the MOGSP-IFJS&AGVs.

Joint DNN partitioning and task offloading in mobile edge computing via deep reinforcement learning

As Artificial Intelligence (AI) becomes increasingly prevalent, Deep Neural Networks (DNNs) have become a crucial tool for developing and advancing AI applications. Considering limited computing and energy resources on mobile devices (MDs), it is a challenge to perform compute-intensive DNN tasks on MDs. To attack this challenge, mobile edge computing (MEC) provides a viable solution through DNN partitioning and task offloading. However, as the communication conditions between different devices change over time, DNN partitioning on different devices must also change synchronously. This is a dynamic process, which aggravates the complexity of DNN partitioning. In this paper, we delve into the issue of jointly optimizing energy and delay for DNN partitioning and task offloading in a dynamic MEC scenario where each MD and the server adopt the pre-trained DNNs for task inference. Taking advantage of the characteristics of DNN, we first propose a strategy for layered partitioning of DNN tasks to divide the task of each MD into subtasks that can be either processed on the MD or offloaded to the server for computation. Then, we formulate the trade-off between energy and delay as a joint optimization problem, which is further represented as a Markov decision process (MDP). To solve this, we design a DNN partitioning and task offloading (DPTO) algorithm utilizing deep reinforcement learning (DRL), which enables MDs to make optimal offloading decisions. Finally, experimental results demonstrate that our algorithm outperforms existing non-DRL and DRL algorithms with respect to processing delay and energy consumption, and can be applied to different DNN types.

Bi-objective energy-efficient scheduling in a seru production system considering reconfiguration of serus

Owing to movable workstations, light equipment and multi-skilled workers, serus can be constructed, modified and dismantled rapidly. Such an advantage enables serus to be reconfigured in response to frequent changes in product types. As different configurations lead to different processing time and energy consumption, an important issue is how to arrange a best configuration of seru with the tasks assigned to it. To this end, we focus on solving the task dispatching, product sequencing and seru reconfiguration problems simultaneously considering economic and environmental performance. A bi-objective mathematical model with objectives of minimizing the total earliness and tardiness and the total carbon dioxide emission is formulated. The problem is proved to be strongly NP-hard, and a non-dominated sorting genetic algorithm-II (NSGA-II) is developed. The algorithm is tested by a medium-size numerical example and ninety sets of large-size problems. Based on the computational results, the impact of configuration flexibility and the relation between processing time and energy consumption on the value of objective functions is analyzed.

Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidics

AbstractPrecise diagnosis and immunity to viruses, such as severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and Middle East respiratory syndrome coronavirus (MERS‐CoV) is achieved by the detection of the viral antigens and/or corresponding antibodies, respectively. However, a widely used antigen detection methods, such as polymerase chain reaction (PCR), are complex, expensive, and time‐consuming Furthermore, the antibody test that detects an asymptomatic infection and immunity is usually performed separately and exhibits relatively low accuracy. To achieve a simplified, rapid, and accurate diagnosis, we have demonstrated an indium gallium zinc oxide (IGZO)‐based biosensor field‐effect transistor (bio‐FET) that can simultaneously detect spike proteins and antibodies with a limit of detection (LOD) of 1 pg mL–1 and 200 ng mL–1, respectively using a single assay in less than 20 min by integrating microfluidic channels and artificial neural networks (ANNs). The near‐sensor ANN‐aided classification provides high diagnosis accuracy (>93%) with significantly reduced processing time (0.62%) and energy consumption (5.64%) compared to the software‐based ANN. We believe that the development of rapid and accurate diagnosis system for the viral antigens and antibodies detection will play a crucial role in preventing global viral outbreaks.image

In-situ artificial retina with all-in-one reconfigurable photomemristor networks

Despite that in-sensor processing has been proposed to remove the latency and energy consumption during the inevitable data transfer between spatial-separated sensors, memories and processors in traditional computer vision, its hardware implementation for artificial neural networks (ANNs) with all-in-one device arrays remains a challenge, especially for organic-based ANNs. With the advantages of biocompatibility, low cost, easy fabrication and flexibility, here we implement a self-powered in-sensor ANN using molecular ferroelectric (MF)-based photomemristor arrays. Tunable ferroelectric depolarization was intentionally introduced into the ANN, which enables reconfigurable conductance and photoresponse. Treating photoresponsivity as synaptic weight, the MF-based in-sensor ANN can operate analog convolutional computation, and successfully conduct perception and recognition of white-light letter images in experiments, with low processing energy consumption. Handwritten Chinese digits are also recognized and regressed by a large-scale array, demonstrating its scalability and potential for low-power processing and the applications in MF-based in-situ artificial retina.

Scheduling energy consumption-constrained workflows in heterogeneous multi-processor embedded systems

Heterogeneous multi-processor architecture which achieves rich functionalities with different types of processors, is widely used to provide powerful calculating capability while keeping energy consumption under control. Although this architecture can improve system flexibility for adapting to future requirement variations, it results in a complex multi-task scheduling problem for system designers to produce a reasonable schedule that satisfies all deadline, data dependency, and energy limitation constraints. In this paper, we concentrate on the energy consumption-constrained scheduling problem of workflows in heterogeneous multi-processor embedded systems. First, we model the workflows and energy consumption of processors, and formulate the energy consumption-constrained scheduling problem as an optimization one whose objective is to shorten the schedule length of workflows as much as possible. Then, with an improved energy per-assignment strategy, we propose a novel energy difference coefficient-based scheduling algorithm to produce an approximately optimal allocation of processors, frequencies, and start times for each task while guaranteeing that the data dependency and energy limitation constraints are satisfied. Finally, experiments on both randomly-generated and real-world workflows are conducted to verify the reliability and efficiency of the proposed approach.

Process parameters of microwave heating-assisted vacuum evaporation of tomato juice: quality, energy consumption, exergy performance, and kinetic processing

Increasing heating performance and preservation of product quality are critical factors in the food industry. Although microwave-vacuum heating is applied in many food processes, there are many unanswered questions about its effects on sensitive foods. In this study, the tomato juice was concentrated in microwave-vacuum heating system and results compared with conventional and conventional-vacuum heating methods. The effect of pressure and microwave power was investigated on quality parameters, energy consumption, exergy performance, and processing kinetics of tomato juice concentration. The processing time in the microwave-vacuum method was 2.3 to 6.3-fold lower than conventional and conventional-vacuum methods. The exergy performance was obtained in the range of 7.16–13.29% for microwave-vacuum heating and 2.04–2.99% for conventional methods. Microwave vacuum heating could reduce energy consumption by 30–71%, compared with conventional-vacuum methods. The minimum decline in ascorbic acid and total phenol content of tomato juice concentrate was observed for microwave-vacuum evaporation. Microwave-vacuum heating was an excellent concentration technique that provided tomato paste with improved nutritional quality, increasing exergy efficiency, reducing processing time and energy consumption compared to conventional and conventional vacuum processes.

A multi-objective optimization algorithm for flow shop group scheduling problem with sequence dependent setup time and worker learning

The optimization of production systems has become increasingly important in manufacturing industries due to the growing competition and market demands. One of the overriding concerns of managers is the efficient exploitation of workers' learning to increase output and decrease downtime. The learning effect symbolizes the improvement of workers' ability and performance through the repetition of similar jobs. On the other hand, it is a critical requirement for decision-makers to have effective management of the transportation phase to achieve an optimal production plan. This paper considers a flow shop sequence-dependent group scheduling problem (FSDGS) with a learning effectto minimize two contradictory objective functions, namely makespan and energy consumption.A mixed-integer linear programming model is proposed to find optimal jobs, group schedules, and appropriate production and transportation speeds to enhance the overall performance of the system. Due to the complexity of the planning process, we propose lower bounds and an efficient resolution method based on multi-objective simulated annealing metaheuristic (MOSA), enhanced by a local search procedure to tackle this problem. The proposed method is evaluated through several experiments based on a real case study, using different learning rates and setup time ratio levels. The obtained results demonstrate the effectiveness of the algorithm in improving the performance of production systems by reducing processing time and energy consumption. These findings have significant implications for the design and optimization of production systems in manufacturing industries.

Multi-objective optimization model of cutting parameters for a sustainable multi-pass turning process

The turning process involves the linear removal of material from the work-piece and requires a relatively high amount of energy. The high energy consumption of the machining process increases carbon emissions, which affects the environment. Moreover, production costs will rise as the cost of energy rises. Energy savings during the machining process are crucial for achieving sustainable manufacturing. In order to determine and optimize the cutting parameters, this study creates a multi-pass turning processes optimi¬zation model. It considers cutting speeds, feed rates, and depth of cut. In this study, the model uses multi-objective optimization by incorporating three objective functions: processing time, energy consumption and product¬ion costs. OptQuest completed the proposed model in Oracle Crystal Ball software, then normalized and weighted the sum. Ordering preferences, the Multi-Objective Optimization based on Ratio Analysis (MOORA) approach is utilized. It ranks items based on their higher priority values. This paper provides a numerical example to demonstrate the application of an optimi¬zation model. Based on the preference order ranking results, the optimal values for three objective functions are as follows: total processing time of 4.953 min, the total energy consumption of 5.434 MJ, and total production cost of 395.21$.

Investigation of pressure-controlled injection molding on the mechanical properties and embodied energy of recycled high-density polyethylene

Manufacturing with secondary feedstock has been identified as an effective strategy to improve plastic circularity. However, product quality inconsistencies arise due to variations in molecular weight, rheology, and mechanical properties. This work evaluates the processing of recycled high-density polyethylene using pressure-controlled injection molding with a focus on processing behavior and energy consumption. The effects of injection velocity, packing pressure, and transfer position are benchmarked against a conventional velocity-controlled process. The experimental results show that the novel process control strategy significantly affects the mechanical properties, in-mold rheology, and energy consumption. Parts fabricated using pressure-controlled injection molding showed higher tensile properties due to increased macromolecular orientation. Additionally, reduced energy was used due to lower melt pressures required to completely replicate the cavity geometry. The results demonstrate the potential of the technology to support increased utilization of secondary feedstock and reduced carbon footprint.

Delay-Aware Edge-Terminal Collaboration in Green Internet of Vehicles: A Multiagent Soft Actor-Critic Approach

The high performance requirement of task processing and low energy consumption requirement of network operation result in a contradiction for the Internet of Vehicles (IoV). This paper addresses the contradiction and proposes an efficient edge-terminal collaboration scheme to sustain the diverse task requirements for a green IoV network. Specifically, two types of tasks are first modeled in terms of data size and delay requirements and then an energy minimization problem was formulated under the diverse task requirements. Thereafter, a multi-agent reinforcement learning framework is constructed, based on which the time-varying environment of the IoV can be modeled as a partially observable Markov decision process. Furthermore, a multi-agent soft actor-critic (MASAC) scheme is proposed to train the edge-terminal collaboration policy in terms of task offloading, spectrum sharing as well as vehicle power control. Finally, the effectiveness of the proposed scheme is validated by comparing with other baseline schemes through extensive simulation experiments.

Efficient and Accurate Object Detection With Simultaneous Classification and Tracking Under Limited Computing Power

Interacting with the environment, such as object detection and tracking, is a crucial ability of mobile robots. Besides high accuracy, efficiency in terms of processing effort and energy consumption are also desirable. To satisfy both requirements, we propose a detection framework based on simultaneous classification and tracking in the point stream. In this framework, a tracker performs data association in sequences of the point cloud, guiding the detector to avoid redundant processing (i.e. classifying already-known objects). For objects whose classification is not sufficiently certain, a fusion model is designed to fuse selected key observations that provide different perspectives across the tracking span. Therefore, performance (accuracy and efficiency of detection) can be enhanced. This method is particularly suitable for detecting and tracking moving objects, a process which would require expensive computations if solved using conventional procedures. Experiments were conducted on the benchmark dataset, and the results showed that the proposed method outperforms original tracking-by-detection approaches in both efficiency and accuracy.

Energy‐Efficient Operation Conditions of MoS2‐Based Memristors

Sufficient energy consumption for conventional information processing makes it necessary to look for new computational methods. One of the possible solutions to this problem is neuromorphic computations using memristive devices. Memristors based on molybdenum disulfide () are a promising way to provide a sizeable amount of hysteresis at low energy costs. Herein, different configurations of memristors as well as the mechanisms involved in hysteresis formation are shown. Bottom gated configuration is beneficial in terms of hysteresis area and energy efficiency. The impact of device channel dimensions on the hysteresis area and energy consumption is discussed. Different operation conditions with triangular, rectangular, sinusoidal, and sawtooth drain‐to‐source pulses are simulated, and rectangular pulses demonstrate the highest energy efficiency. The study shows the potential to realize low‐power neuromorphic systems using memristive devices.

Investigation on the impact of Thermo-Drying towards Freeze-Thaw cycle processing for recycled coarse aggregate

The use of recycled coarse aggregate (RCA) is limited due to the presence of attached mortar (AM), which affects its performance. To overcome this limitation, traditional methods such as mechanical and chemical techniques have been used to remove AM from RCA and obtain natural aggregate (NA). Freeze-thaw cycles (FTC) processing has shown high efficiency in removing AM while causing negligible damage to the NA. This paper aims to investigate the impact of thermo-drying (TD) on the FTC processing quantitatively. The experimental data, ranging from micro- to macro-scale, was used to reveal the mechanism behind the process. To further analyze the impact of TD, a theoretical model was established to calculate the strain distribution of AM during FTC processing while taking into account the influence of TD. The findings suggest that TD has a positive impact on FTC processing. However, an integrated analysis is necessary to balance processing efficiency and energy consumption. This analysis will allow for the development of an optimized TD-FTC processing scheme.

Joint parallel-machine scheduling and maintenance planning optimisation with deterioration, unexpected breakdowns, and condition-based maintenance

The joint optimisation of production scheduling and maintenance planning can significantly decrease production interruptions (or stoppages) and, simultaneously, improve production stability and enhance the reliability and availability of equipment and machines. This paper studies the joint optimisation of production schedules and CBM plans in a parallel-machine production setting. The machines are subject to deterioration, unexpected breakdowns, and deterioration-based failures. The reliability of the machines is modelled as a multi-state system in which two deterioration thresholds are introduced to initiate maintenance and prevent deterioration-based failures. An integrated optimisation model is proposed to solve this new problem. The proposed model employs Markov chains to formulate machines’ reliability and a matrix-based approach to estimate the expected processing times, energy consumption, and maintenance costs. Then, a mixed-integer programming model is proposed that jointly optimises production schedules and maintenance plans by minimising a weighted sum objective function that includes expected lateness, maintenance, and energy consumption costs. A genetic algorithm (GA) is used to solve the new problem, and extensive computational experiments are performed to test the performance of the proposed GA. The results show the superiority of the proposed GA for all the test problems compared to two well-known metaheuristics.