Search Results (146)

The objective of hyperspectral pansharpening is to fuse low-resolution hyperspectral images (LR-HSI) with corresponding panchromatic (PAN) images to generate high-resolution hyperspectral images (HR-HSI). Despite advancements in hyperspectral (HS) pansharpening using deep learning, the rich spectral details and large data volume of HS images place higher demands on models for effective spectral extraction and processing. In this paper, we present HyperGAN, a hyperspectral image fusion approach based on Generative Adversarial Networks. Unlike previous methods that deepen the network to capture spectral information, HyperGAN widens the structure with a Wide Block for multi-scale learning, effectively capturing global and local details from upsampled HSI and PAN images. While LR-HSI provides rich spectral data, PAN images offer spatial information. We introduce the Efficient Spatial and Channel Attention Module (ESCA) to integrate these features and add an energy-based discriminator to enhance model performance by learning directly from the Ground Truth (GT), improving fused image quality. We validated our method on various scenes, including the Pavia Center, Eastern Tianshan, and Chikusei. Results show that HyperGAN outperforms state-of-the-art methods in visual and quantitative evaluations. Full article

The iron and steel industry is energy-intensive due to the large volume of steel produced and its high-temperature and high-weight characteristics, sensors such as high-temperature application sensors can be utilized to collect production data and support the process control and optimization. Steelmaking-refining-continuous casting (SRCC) is a bottleneck in the iron and steel production process. SRCC scheduling problems are worldwide problems and NP-hard. The problems are not only important for iron and steel enterprises to enhance production efficiency, but also play a significant role in saving energy and reducing resource consumption. SRCC scheduling problems can be modeled as hybrid flowshop scheduling problems with batch production at the last stage. In this paper, a Discrete Brain Storm Optimization (DBSO) algorithm is proposed to handle SRCC scheduling problems. In the proposed DBSO, population initialization and cluster center replacement are specially designed to enhance the intensification abilities. Moreover, a perturbation operator is devised to enhance its diversification abilities. Furthermore, a new individual generation operator is devised to improve the intensification and diversification abilities simultaneously. Experimental results have demonstrated that the proposed DBSO is an efficient method for solving SRCC scheduling problems. Full article

In the construction sector, hospitals are the buildings with the highest energy consumption. Due to the high demand for energy, hospitals’ energy efficiency is becoming very important. This study aims to examine the trends and factors that determine energy consumption in Polish hospitals from 2010 to 2019, highlighting the impact of hospital size and medical activities on energy efficiency. The analysis was carried out using data from 3061 hospital reports obtained from the e-Health Center, a state budgetary unit established by the Minister of Health. To measure and compare the efficiency of energy usage in hospitals, we developed eight energy usage efficiency indexes based on hospital size and medical activity. The size of the hospitals was described by the number of beds, operation rooms, doctors, nurses, and fixed assets value. Hospital activity was measured by the number of person-days, patients, and operations. Statistical analysis was carried out using StatSoft Statistica software version 13.3. The results show that larger hospitals are more energy efficient across various measures of energy use than smaller hospitals. The findings revealed also several important relationships between energy usage and factors connected with size and hospital activity, such as the number of beds, patients and person-days, medical staff, operations, and fixed asset values, underscoring the necessity for customizing energy efficiency strategies. This research contributes empirical insights that can guide policymakers and hospital administrators in their endeavors to improve energy efficiency and promote sustainability within healthcare facilities. Full article

Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. Full article

The rapid advancement of artificial intelligence (AI) technology, combined with the widespread proliferation of Internet of Things (IoT) devices, has significantly expanded the scope of AI applications, from data centers to edge devices. Running AI applications on edge devices requires a careful balance between data processing performance and energy efficiency. This challenge becomes even more critical when the computational load of applications dynamically changes over time, making it difficult to maintain optimal performance and energy efficiency simultaneously. To address these challenges, we propose a novel processing-in-memory (PIM) technology that dynamically optimizes performance and power consumption in response to real-time workload variations in AI applications. Our proposed solution consists of a new PIM architecture and an operational algorithm designed to maximize its effectiveness. The PIM architecture follows a well-established structure known for effectively handling data-centric tasks in AI applications. However, unlike conventional designs, it features a heterogeneous configuration of high-performance PIM (HP-PIM) modules and low-power PIM (LP-PIM) modules. This enables the system to dynamically adjust data processing based on varying computational load, optimizing energy efficiency according to the application’s workload demands. In addition, we present a data placement optimization algorithm to fully leverage the potential of the heterogeneous PIM architecture. This algorithm predicts changes in application workloads and optimally allocates data to the HP-PIM and LP-PIM modules, improving energy efficiency. To validate and evaluate the proposed technology, we implemented the PIM architecture and developed an embedded processor that integrates this architecture. We performed FPGA prototyping of the processor, and functional verification was successfully completed. Experimental results from running applications with varying workload demands on the prototype PIM processor demonstrate that the proposed technology achieves up to 29.54% energy savings. Full article

High-performance computing (HPC) in data centers increases energy use and operational costs. Therefore, it is necessary to efficiently manage resources for the sustainability of and reduction in the carbon footprint. This research analyzes and optimizes ENEA HPC data centers, particularly the CRESCO6 cluster. The study starts by gathering and cleaning extensive datasets consisting of job schedules, environmental conditions, cooling systems, and sensors. Descriptive statistics accompanied with visualizations provide deep insight into collated data. Inferential statistics are then used to investigate relationships between various operational variables. Finally, machine learning models predict the average hot-aisle temperature based on cooling parameters, which can be used to determine optimal cooling settings. Furthermore, idle periods for computing nodes are analyzed to estimate wasted energy, as well as for evaluating the effect that idle node shutdown will have on the thermal characteristics of the data center under consideration. It closes with a discussion on how statistical and machine learning techniques can improve operations in a data center by focusing on important variables that determine consumption patterns. Full article

The increasing adoption of batteries in a variety of applications has highlighted the necessity of accurate parameter identification and effective modeling, especially for lithium-ion batteries, which are preferred due to their high power and energy densities. This paper proposes a comprehensive framework using the Levenberg–Marquardt algorithm (LMA) for validating and identifying lithium-ion battery model parameters to improve the accuracy of state of charge (SOC) estimations, using only discharging measurements in the N-order Thevenin equivalent circuit model, thereby increasing computational efficiency. The framework encompasses two key stages: model parameter identification and model verification. This framework is validated using experimental measurements on the INR 18650-20R battery, produced by Samsung SDI Co., Ltd. (Suwon, Republic of Korea), conducted by the Center for Advanced Life Cycle Engineering (CALCE) battery group at the University of Maryland. The proposed framework demonstrates robustness and accuracy. The results indicate that optimization using only the discharging data suffices for accurate parameter estimation. In addition, it demonstrates excellent agreement with the experimental measurements. The research underscores the effectiveness of the proposed framework in enhancing SOC estimation accuracy, thus contributing significantly to the reliable performance and longevity of lithium-ion batteries in practical applications. Full article

Under the background of “dual carbon” development goals, the rapid expansion of internet data centers driven by advancements in 5G technology has led to increased energy consumption and elevated heat densities within the server rooms in these facilities. In this study, the modular data center is taken as the research object for the purpose of figuring out a way to improve the thermal environment of the computer room, reduce power consumption, and ensure the safe and stable running of servers. To this end, this study established an airflow organization model for the modular data center and verified this model through experimental methods. Computational Fluid Dynamics (CFD) simulations were employed to investigate the effects of raised floor height, floor opening rate, and cold/hot air channel closure on airflow organization. Furthermore, the efficiency of airflow organization was evaluated using entransy loss metrics. The results show that optimal airflow conditions are achieved when the height of the raised floor is 600–800 mm, the opening rate is 40%, and the combined opening is 40%. Additionally, the closure of either the cold channel or both the cold and hot channels significantly improves airflow performance. Specifically, cold channel closure is recommended for new data centers with underfloor air supply systems, while combined cold and hot channel closure is suitable for data centers with high power density and extended air supply distances. Full article

In the age of digitalization and big data, cooling systems in data centers are vital for maintaining equipment efficiency and environmental sustainability. Although many studies have focused on the classification and optimization of data center cooling systems, systematic reviews using bibliometric methods are relatively scarce. This review uses bibliometric analysis to explore the classifications, control optimizations, and energy metrics of data center cooling systems, aiming to address research gaps. Using CiteSpace and databases like Scopus, Web of Science, and IEEE, this study maps the field’s historical development and current trends. The findings indicate that, firstly, the classification of cooling systems, optimization strategies, and energy efficiency metrics are the current focal points. Secondly, this review assesses the applicability of air-cooled and liquid-cooled systems in different operational environments, providing practical guidance for selection. Then, for air cooling systems, the review demonstrates that optimizing the design of static pressure chamber baffles has significantly improved airflow uniformity. Finally, the article advocates for expanding the use of artificial intelligence and machine learning to automate data collection and energy efficiency analysis, it also calls for the global standardization of energy efficiency metrics. This study offers new perspectives on the design, operational optimization, and performance evaluation of data center cooling systems. Full article

The rapid development of industrial and information technology is driving the demand to improve the applicability and hydraulic performance of centrifugal pumps in various applications. Enhancing the rotational speed of pumps can simultaneously increase the head and reduce the impeller diameter, thereby reducing the pump size and weight and also improving pump efficiency. This paper reviews the current application status of high-speed pumps using low-temperature thermosensitive fluids, which have been applied in fields such as novel energy-saving cooling technologies, aerospace, chemical industries, and cryogenic engineering. Due to operational constraints and thermal effects, there are inherent challenges that still need to be addressed for high-speed pumps. Based on numerical simulation and experimental research for different working fluids, the results regarding cavitation within the inducer have been categorized and summarized. Improvements to cavitation models, the mechanism of unsteady cavity shedding, vortex generation and cavitation suppression, and the impact of cavitation on pump performance were examined. Subsequently, the thermal properties and cavitation thermal effects of low-temperature thermosensitive fluids were analyzed. In response to the application requirements of pump-driven two-phase cooling systems in data centers, a high-speed refrigerant pump employing hydrodynamic bearings has been proposed. Experimental results indicate that the prototype achieves a head of 56.5 m and an efficiency of 36.1% at design conditions (n = 7000 rpm, Q = 1.5 m³/h). The prototype features a variable frequency motor, allowing for a wider operational range, and has successfully passed both on/off and continuous operation tests. These findings provide valuable insights for improving the performance of high-speed refrigerant pumps in relevant applications. Full article

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment’s state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time–frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time–frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura–Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark. Full article

Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation. Full article

The Industrial Metaverse paradigm can be broadly described as a virtual environment that integrates various technologies such as augmented reality and mixed reality to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the Industrial Metaverse to drive sustainability and resource efficiency is compelling. Using advanced technologies such as the Industrial Metaverse is vital in an endeavor to have a competitive edge in a rapidly evolving business environment. However, the environmental impact of the technologies underpinning the Industrial Metaverse, like data centers and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that, by 2025, without sustainable artificial intelligence (AI) practices, AI will consume more energy than the human workforce, significantly offsetting zero carbon gains. As the Metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. Moreover, the specific extent to which this substitution can alleviate environmental concerns remains an open issue, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the Industrial Metaverse, as well as explore the environmental impact of the Industrial Metaverse. The integrative literature review design and methodological approach involved multiple sources from the Web of Science and databases such as the ACM library, IEEE Library, and Google Scholar, which were analyzed to provide a comprehensive understanding of the developments in the Industrial Metaverse. Firstly, by considering the Industrial Metaverse’s architecture, we elucidate the Industrial Metaverse concept and the associated enabling technologies. Secondly, we performed an exploration through a discussion of the prevalent use cases and the deployment of the emerging Industrial Metaverse. Thirdly, we explored the impact of the Industrial Metaverse on the environment. Lastly, we address novel security and privacy risks, as well as upcoming research challenges, keeping in mind that the Industrial Metaverse is based on a strong data fabric. The results point to the Industrial Metaverse as having both positive and negative environmental effects in terms of energy consumption, e-waste, and pollution. Research, however, indicates that most Industrial Metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Finally, for sustainability in the Industrial Metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, we examined the effects of products on the environment, as well as in the creation of a circular economy. Full article

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space. Full article

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers. Full article