Efficient System Research Articles

Investigating and Improving the Efficiency of Space-time Codes in Visible light Communication Systems based on a Multi-input-multi-output Channel Model

Background: Visible light communication (VLC) is a new communication method for transmitting information through semiconductor lighting devices. One of the applicable methods to improve the spectral efficiency of the system is the multi-input-multiple-output (MIMO) structure. Also, carrierless amplitude-phase (CAP) modulation is a high-dimensional modulation technique that can be used to improve the data transmission rate in modern communication systems. Methods: In this paper, carrierless amplitude-phase method is introduced in indoor optical communication systems to improve the performance of multi-input multi-output (MIMO) structures. The CAP modulation is considered a suitable method due to its low complexity and is introduced and studied in two structures with one carrier or CAP and multiple carriers or m-CAP. Results: In this study, the target system is simulated in m-CAP structures and the improvement is measured in the MIMO structure using this modulation. The results show that by using this method, the PAPR of the system was reduced compared to other research with the MIMO-OFDM structure. In addition, by not using FFT blocks, the computational and processing complexity was also reduced. Conclusion: It has been shown that the use of m-CAP increases the data transfer rate and improves the spectral efficiency of the system. In addition to CAP, spatial modulation schemes can also help improve the spectral efficiency and power efficiency of the system. Moreover, the space-time code is combined with m-CAP modulation and it is shown that this idea improves the efficiency of the MIMO system.

Systematic literature review on evaluation models and methods in enterprise architecture research

Several enterprises implemented enterprise architecture (EA) projects to align business and information technology (IT) strategies. The evaluation process is needed to ensure the implementation of EA projects provides effectiveness, efficiency, and feasibility of EA information systems (IS) and assesses previous project experience to avoid future EA project risks. The study aims to present a systematic literature review (SLR) of the models and evaluation methods used or developed, especially in the field of EA research. Based on the inclusion and exclusion criteria, 21 articles were selected for review. The results of the study present an overview of the models and methods used as well as new approaches developed for EA evaluation as well as information based on approaches related to models and methods identified as organizing information and data analysis to broaden future research insights. The literature review also provides additional simple theories related to the implications and techniques of the identified models and methods. The study contributes to company stakeholders to encourage the implementation of EA, identify improvements and enhancements to EA projects as well as further references and insights for practitioners and researchers regarding EA evaluation as an effort to assess the success of achieving enterprise goals.

A new highly efficient MAC protocol for WBAN: exceptional performance in the face of selfish behaviors

Over the last two decades, wireless body area networks (WBANs) have gained significant traction in healthcare applications. These networks facilitate connections among various sensors, which can be integrated into clothing, placed directly on the body, or implanted beneath the skin. While these sensors typically serve a single application, they generate traffic with diverse requirements. Managing this diversity necessitates tailored treatment to meet specific traffic needs while satisfying application requirements such as reliability and timeliness. In this paper, we propose a novel, flexible, and power-efficient medium access control (MAC) protocol designed to seamlessly complement existing solutions. Our protocol, available in two versions as an enhancement to the beacon-enabled mode of IEEE 802.15.4, aims to optimize quality of service (QoS) for periodic traffic applications within WBANs, irrespective of traffic and density conditions, without compromising energy efficiency. Our results demonstrate significant improvements compared to the standardized IEEE 802.15.4-MAC protocol across all test scenarios, even in the presence of selfish behaviors. These findings underscore the protocol’s efficacy in enhancing reliability and efficiency in wireless healthcare systems.

A full operating conditions ejector model for refrigeration systems driven by low-grade heat sources

The ejector refrigeration technology driven by low-grade heat sources constitutes as one of the crucial means to solve the energy shortage in present-day society and to reduce the global environmental pollution. Thermodynamics ejector models are the basis of performance prediction, system control and structural design in ejector refrigeration systems driven by low-grade heat sources. However, the majority of present ejector models are unable to simultaneously meet the general feasibility of full operating conditions and the level of prediction accuracy for improving the energy efficiency of the refrigeration system. In this study, a novel theoretical model of ejectors is successfully derived based on the foundation of compound-choking theory and gas dynamic relations, which easily predicts the entrainment performance under the on-design and off-design conditions in ejector refrigeration systems. Indeed, a direct solution algorithm is presented to quickly and accurately solve this model and a linear correlation is employed between the mixing loss coefficient and the back pressure at the subcritical mode. The developed model is verified with experimental results in R245fa, R134a and R600a ejector refrigeration system. The results show that the model provides a precise estimation of the entrainment performance with the mean relative errors of 2.45%, 5.49% and 3.67% for R245fa, R134a and R600a refrigerants at full working conditions, while the prediction of critical condensing temperatures is considerably approximated by the experimental measurements. In the comparative analysis with the previous model, this model demonstrates a superior prediction performance, especially in the subcritical mode using the linear function mixing loss coefficient. This study will be a valuable aid in the improvement of energy efficiency in ejector refrigeration systems.

Supply Chain System Optimization Model for Logistics Enterprises in the Internet of Things Era Based on Improved ECC Algorithm

In this article, a new method to optimize the supply chain system (SCS) of logistics companies in the era of Internet of Things (IoT) using an improved elliptic curve cryptography (ECC) algorithm was put forward.The focus of this research was to address the challenges faced by logistics organizations in adapting to the complexity of modern supply chains and meeting the growing demands of customers. The research aimed to improve the efficiency, security and reliability of supply chain management systems by utilizing advances in ECC algorithms.By conducting comprehensive performance tests of the proposed system, including interface testing, joggle testing, and system response time analysis, the study demonstrated the superiority of the system over traditional systems. The research findings indicated that the system based on the improved ECC algorithm outperformed traditional systems in terms of interface effectiveness, usability, and system response time. The improved ECC algorithm enhanced the security of the encryption process, making it more resistant to unauthorized access, and improved the system’s overall data processing load capacity. Additionally, the system demonstrated superior performance in managing logistics data and information, facilitating better decision-making and efficiency in logistics operations. Overall, this research contributes to the advancement of supply chain management in the IoT era by providing a practical and efficient solution for logistics enterprises to optimize their supply chain systems. The innovative application of the improved ECC algorithm offers a secure, reliable, and efficient approach to managing logistics operations, addressing the challenges posed by the complexities of modern supply chains.

Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system

This study explores two scenarios for optimizing the predictive control of flux in water desalination systems: experimental design of direct contact membrane distillation (DCMD) and artificial intelligence (AI) models. Deep learning networks, specifically Long Short-Term Memory (LSTM), and standalone AI models, including hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), were evaluated. Evaluation criteria, along with 2D graphical visualization, were used to assess model accuracy. ANFIS-C4 emerged as the standout model, achieving perfect predictive skills with 100 % accuracy and low RMSE of 0.0522 in the training phase, and maintaining high performance in testing with 99.73 % accuracy and RMSE of 0.7121. Similarly, ANN-C4 demonstrated near-perfect accuracy with 100 % accuracy and RMSE of 0.0643 in the training phase and also maintained excellent performance in the testing phase. Despite requiring multiple inputs, ANFIS and ANN show remarkably high capability in predictive accuracy. Among LSTM models, LSTM-C3 achieved the highest training DC of 91.35 %, indicating robust performance in capturing training data variability. LSTM-C2 showed superior generalization with a testing DC of 0.8539, despite using only two input parameters. The narrow distribution of predicted values around the median in violin plots for ANFIS-C4 and ANN-C4, along with their low RMSE, validates their superior performance. The exceptional results of ANFIS-C4 and ANN-C4 highlight their potential in water desalination applications, supporting Sustainable Development Goal 6 and EPA objectives for sustainable water management. This research underscores the importance of advanced computational models in enhancing the efficiency and reliability of water desalination systems, contributing to global efforts in sustainable water resource management.

Toward Sustainable Architecture: Structural Analysis and Energy Efficiency of Vertical Sun Control Systems

Toward Sustainable Architecture: Structural Analysis and Energy Efficiency of Vertical Sun Control Systems

Comprehensive Performance Analysis of Hybrid Solar Water Heating Systems: Synergizing Photovoltaic and Thermal Technologies

Abstract: Solar water heaters are an efficient and sustainable technology for meeting the growing demand for hot water in residential and commercial applications. This abstract presents a comprehensive overview of the performance evaluation of solar water heater systems through an extensive experimental study. The study aims to assess the effectiveness and efficiency of different solar water heater configurations under varying operating conditions. The experimental investigation involved the measurement and analysis of key performance parameters such as solar collector efficiency, heat transfer efficiency, thermal storage capacity, and overall system efficiency. A range of factors including solar radiation intensity, ambient temperature, water flow rate, and collector tilt angle were systematically varied to examine their impact on system performance. The results of the experimental study demonstrated the significant potential of solar water heaters in providing cost-effective and environmentally friendly hot water solutions. The solar collector efficiency was found to be highly influenced by the solar radiation intensity, with higher levels of solar irradiation leading to increased thermal energy generation. The heat transfer efficiency was optimized by adjusting the water flow rate and collector tilt angle, ensuring efficient transfer of heat from the collector to the water. Furthermore, the thermal storage capacity of the system played a crucial role in maintaining a consistent supply of hot water during periods of low solar radiation or high demand. Overall system efficiency was found to be strongly dependent on the integration of efficient heat exchangers and insulation materials. Based on the experimental findings, recommendations for system design and optimization were formulated, highlighting the importance of proper sizing, selection of suitable materials, and appropriate control strategies. The results also identified areas for future research, such as the integration of advanced heat transfer enhancement techniques and the utilization of hybrid solar water heater systems

Comprehensive Performance Analysis of Hybrid Solar Water Heating Systems: Synergizing Photovoltaic and Thermal Technologies

Abstract: Solar water heaters are an efficient and sustainable technology for meeting the growing demand for hot water in residential and commercial applications. This abstract presents a comprehensive overview of the performance evaluation of solar water heater systems through an extensive experimental study. The study aims to assess the effectiveness and efficiency of different solar water heater configurations under varying operating conditions. The experimental investigation involved the measurement and analysis of key performance parameters such as solar collector efficiency, heat transfer efficiency, thermal storage capacity, and overall system efficiency. A range of factors including solar radiation intensity, ambient temperature, water flow rate, and collector tilt angle were systematically varied to examine their impact on system performance. The results of the experimental study demonstrated the significant potential of solar water heaters in providing cost-effective and environmentally friendly hot water solutions. The solar collector efficiency was found to be highly influenced by the solar radiation intensity, with higher levels of solar irradiation leading to increased thermal energy generation. The heat transfer efficiency was optimized by adjusting the water flow rate and collector tilt angle, ensuring efficient transfer of heat from the collector to the water. Furthermore, the thermal storage capacity of the system played a crucial role in maintaining a consistent supply of hot water during periods of low solar radiation or high demand. Overall system efficiency was found to be strongly dependent on the integration of efficient heat exchangers and insulation materials. Based on the experimental findings, recommendations for system design and optimization were formulated, highlighting the importance of proper sizing, selection of suitable materials, and appropriate control strategies. The results also identified areas for future research, such as the integration of advanced heat transfer enhancement techniques and the utilization of hybrid solar water heater systems

Modeling and performance analysis of a new integrated solid oxide fuel cell and photovoltaic‐thermal energy supply system by heat current method

AbstractEfficient and reliable utilization of renewable energy at the user's end is the key to achieving a low‐carbon life. This paper proposed a new distributed energy system around the comprehensive utilization of solar energy by integrating solid oxide fuel cell (SOFC), energy storage equipment, photovoltaic thermal (PVT) collector, and heat pump. By integrating the use of SOFC and PVT, we can further minimize reliance on fossil fuels, while employing the coupling of PVT and heat pump effectively mitigates the inherent challenges of solar energy's variability and intermittency, all while enhancing overall system efficiency. On this basis, we apply the heat current method to construct a cross‐scale heat current model of the components and the system by considering the energy transfer, conversion, and storage characteristics of the system. By employing this model, we simulate the system's operation throughout an entire typical day, assess the COP enhancement of the PVT‐coupled heat pump system, analyze the influence of diverse operating conditions on daily system performance, and evaluate the economy of the energy storage devices in the system.

Advanced computer architecture optimization for machine learning/deep learning

Abstract The recent progress in Machine Learning (Géron, 2022) and particularly Deep Learning (Goodfellow, 2016) models exposed the limitations of traditional computer architectures. Modern algorithms demonstrate highly increased computational demands and data requirements that most existing architectures cannot handle efficiently. These demands result in training speed, inference latency, and power consumption bottlenecks, which is why advanced methods of computer architecture optimization are required to enable the development of ML/DL-dedicated efficient hardware platforms (Engineers, 2019). The optimization of computer architecture for applications of ML/DL becomes critical, due to the tremendous demand for efficient execution of complex computations by Neural Networks (Goodfellow, 2016). This paper reviewed the numerous approaches and methods utilized to optimize computer architecture for ML/DL workloads. The following sections contain substantial discussion concerning the hardware-level optimizations, enhancements of traditional software frameworks and their unique versions, and innovative explorations of architectures. In particular, we discussed hardware including specialized accelerators, which can improve the performance and efficiency of a computation system using various techniques, specifically describing accelerators like CPUs (multicore) (Hennessy, 2017), GPUs (Hwu, 2015) and TPUs (Contributors, 2017), parallelism in multicore architectures, data movement in hardware systems, especially techniques such as caching and sparsity, compression, and quantization, other special techniques and configurations, such as using specialized data formats, and measurement sparsity. Moreover, this paper provided a comprehensive analysis of current trends in software frameworks, Data Movement optimization strategies (A.Bienz, 2021), sparsity, quantization and compression methods, using ML for architecture exploration, and, DVFS (Hennessy, 2017),, which provides strategies for maximizing hardware utilization and power consumption during training, machine learning, dynamic voltage, and frequency scaling, runtime systems. Finally, the paper discussed research opportunity directions and the possibilities of computer architecture optimization influence in various industrial and academic areas of ML/DL technologies. The objective of implementing these optimization techniques is to largely minimize the current gap between the computational needs of ML/DL algorithms and the current hardware’s capability. This will lead to significant improvements in training times, enable real-time inference for various applications, and ultimately unlock the full potential of cutting-edge machine learning algorithms.

Design of Radial Flux PM Synchronous Motor for EV Applications

Abstract: High-performance electric propulsion systems are becoming increasingly important as the automotive industry rapidly shifts to electric cars (EVs). The electric motor is an essential part of these systems as it determines the power, efficiency, and overall performance of the vehicle.This project uses Altair Flux, a full-featured finite element analysis (FEA) software suite, to build and optimize a radial flux motor (RFM) with permanent magnet synchronous motor (pmsm) . The main goal is to increase the power density and efficiency of the electric propulsion system for electric cars by utilizing Altair Flux's simulation and analytical capabilities.

A Cooperative Operation Strategy for Multi-Energy Systems Based on the Power Dispatch Meta-Universe Platform

To meet the challenges of renewable energy consumption and improve the efficiency of energy systems, we propose an intelligent distributed energy dispatch strategy for multi-energy systems based on Nash bargaining by utilizing the power dispatch meta-universe platform. First, the operational framework of the multi-energy system, including wind park (WP), photovoltaic power plant (PVPP), and energy storage (ES), is described. Using the power dispatch meta-universe platform, the models of WP, PVPP, and ES are constructed and analyzed. Then, a Nash bargaining model of the multi-energy system is built and transformed into a coalition profit maximization problem, which is solved using the alternating direction multiplier method (ADMM). Finally, the effectiveness of the proposed strategy is verified. The results show that the strategy greatly improves the consumption of renewable energy sources and the profit of the overall system.

Smart Grid Technologies and Their Role in Sustainable Energy Management

Purpose: The general objective of this study was to analyze smart grid technologies and their role in sustainable energy management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to smart grid technologies and their role in sustainable energy management. Preliminary empirical review revealed that smart grid technologies significantly transformed energy management by enhancing sustainability, reliability, and efficiency through advanced metering, real-time analytics, and improved communication networks. These technologies enabled better integration of renewable energy sources, reduced greenhouse gas emissions, and empowered consumers with detailed energy usage information, leading to cost savings and increased energy efficiency. However, challenges such as high implementation costs, infrastructure needs, regulatory hurdles, and cybersecurity risks were identified. The study highlighted the importance of continued research, supportive policies, and public engagement to optimize smart grid deployment and achieve a more sustainable energy system. Unique Contribution to Theory, Practice and Policy: The Diffusions of Innovations Theory, Socio- Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on smart grid technologies. The study recommended adopting a multidisciplinary approach to smart grid research, enhancing interoperability through standardized protocols, and investing in workforce training. It emphasized supportive regulatory frameworks, including incentives and dynamic pricing models, to promote smart grid adoption. Public awareness and engagement were deemed crucial, suggesting educational campaigns to increase consumer participation. The study highlighted the importance of international collaboration for knowledge sharing and called for continuous investment in research and development to drive technological advancements and ensure the sustainability and efficiency of smart grid systems.

Reshaping the World with Computer Technologies and Their Impact on the Development of Processes in the Field of Real Estate Trading

Purpose: This study examines artificial intelligence (AI) impact on real estate, highlighting its role in improving processes, enhancing customer experiences, and ensuring competitiveness while prioritising data protection and security. Methodology/Approach: A comprehensive literature review compared AI technologies in real estate, including drones, the Internet of Things (IoT), cloud computing, big data, 3D scanning, wearables, virtual and augmented realities, and robotics. It examines their impact on operations, focusing on AI in Heating, Ventilation and Air Conditioning (HVAC) systems for energy efficiency and comfort. Findings: AI enhances real estate development by optimising operations, transforming customer relationships, and improving energy efficiency and comfort with AI-controlled HVAC systems. Benefits include automated data processing, better decision-making, and optimised marketing strategies. Research Limitation/Implication: AI has great potential, but its implementation is in the early stages, including in real estate. Challenges include data protection, security, costs, and new hardware for HVAC systems. Broader real-case testing is needed. Originality/Value of paper: This paper broadens the perspective on AI's impact on real estate, showing how to implement AI in various processes. It improves performance and transforms customer experiences, setting new standards for property management.

A methodology to correlate river inflows in state sampling Monte Carlo simulation

This paper proposes a methodology to sample inflow states of run-of-the-river hydropower plants that are spatially correlated, preserving the statistical dependence among them, in Non-Sequential Monte Carlo Simulation (MCS). The technique used to correlate the inflows of hydro plants in the Brazilian synthetic series generation model, which is based on the correlation matrix and Cholesky Decomposition, is adapted to be used in the sampling stage. Nataf transformation is used for addressing non-normality of the inflow data. The solution method is such that no assumption about the data probability distribution is necessary. Although the methodology is applied to inflows in this paper, it can be applied to any other quantity, such as winds. A case study is proposed to evaluate the correlation of the sampled inflows and the impact on the reliability indices. Besides, it is investigated the effect when inflows states are clustered to reduce the number of states in the system for better computational efficiency. Case study shows that the reliability indices obtained with the proposed methodology are within the confidence interval of the indices calculated by the Sequential MCS with an execution time as fast as the Non-Sequential one, while the non-consideration of correlation causes very significant errors. Simulation with 50 clusters produces equally good results with a reduction of 75 % of the execution time. Sensitivity analysis showed that halving the number of clusters maintains the accuracy of the results, while 10 clusters results in overestimating system reliability. Different degrees of correlation have been simulated, showing that the higher the correlation among the hydro plants, the less reliable the system becomes.

Thermo-Economic Comparison between Three Different Electrolysis Technologies Powered by a Conventional Organic Rankine Cycle for the Green Hydrogen Production Onboard Liquefied Natural Gas Carriers

The high demand for natural gas (NG) worldwide has led to an increase in the size of the LNG carrier fleet. However, the heat losses from this type of ship’s engines are not properly managed, nor is the excess boil-off gas (BOG) effectively utilised when generation exceeds the ship’s power demand, resulting in significant energy losses dissipated into the environment. This article suggests storing the lost energy into green H2 for subsequent use. This work compares three different electrolysis technologies: solid oxide (SOEC), proton exchange membrane (PEME), and alkaline (AE). The energy required by the electrolysis processes is supplied by both the LNG’s excess BOG and engine waste heat through an organic Rankine cycle (ORC). The results show that the SOEC consumes (743.53 kW) less energy while producing more gH2 (21.94 kg/h) compared to PEME (796.25 kW, 13.96 kg/h) and AE (797.69 kW, 10.74 kg/h). In addition, both the overall system and SOEC stack efficiencies are greater than those of PEME and AE, respectively. Although the investment cost required for AE (with and without H2 compression consideration) is cheaper than SOEC and PEME in both scenarios, the cost of the H2 produced by the SOEC is cheaper by more than 2 USD/kgH2 compared to both other technologies.

Process Modeling and Optimization of Supercritical Carbon Dioxide-Enhanced Geothermal Systems in Poland

This paper presents a comprehensive analysis of supercritical carbon dioxide (sCO2)-enhanced geothermal systems (EGSs) in Poland, focusing on their energetic performance through process modeling and optimization. EGSs harness the potential of geothermal energy by utilizing supercritical carbon dioxide as the working fluid, offering promising avenues for sustainable power generation. This study investigates two distinct configurations of sCO2-EGS: one dedicated to power generation via a binary system with an organic Rankine cycle and the other for combined power and heat production through a direct sCO2 cycle. Through accurate process modeling and simulation, key parameters influencing system efficiency and performance are identified and optimized. The analysis integrates thermodynamic principles with geological and operational constraints specific to the Polish context. The results highlight the potential of sCO2-EGSs to contribute to the country’s energy transition, offering insights into the optimal design and operation of such systems for maximizing both power and thermal output while ensuring economic viability and environmental sustainability.

Enhancing Vapor Compression Refrigeration Systems Efficiency via Two-Phase Length and Superheat Evaporator MIMO Control

The present investigation focuses on enhancing the efficiency of a vapor compression refrigeration system (VCRS) by proposing a Multiple Input Multiple Output (MIMO) control strategy based on the evaporator’s two-phase length and superheat temperature. A moving boundary dynamic model for the VCRS is implemented using the Thermosys Matlab Toolbox. The study analyzes the influence of actuation parameters, specifically compressor speed and expansion valve opening, on control parameters, namely two-phase length and superheat temperature. A comprehensive analysis based on the first and second laws of thermodynamics is conducted across a wide range of operating conditions. Simulation results demonstrate that two-phase length can be effectively utilized as a control parameter by selecting operating points that maximize the system efficiency. Additionally, the study reveals that extending the evaporator’s two-phase length to 80–90% of its limit increases system efficiency, enabling a reduction in compressor speed while maintaining the cooling capacity.

The Impact of the Optimization Strategy of the Public Transportation System on Sustainable Urban Development

As urbanization accelerates and awareness of environmental conservation rises, the optimization and modernization of public transportation systems, crucial to urban infrastructure, encounter novel challenges and prospects. While public transportation has evolved in the past few years, the city still grapples with issues of inefficiency and poor passenger contentment, hindering its sustainable growth. In this context, there's an immediate need to investigate the ways in which creative optimization strategies can enhance the efficiency of public transit systems and aid in building cities that are both environmentally sustainable and conserve resources. Based on the above, this paper focus on the optimization strategy of the public transportation system and its impact on sustainable urban development. Firstly, the analysis focuses on the current status and challenges of public transportation systems, examining how these relate internally to objectives of sustainable development. Secondly, the leading strategies and technical frameworks for optimizing public transportation systems are presented from theoretical and technical aspects, especially the application value of big data analysis, intelligent scheduling systems, and energy-saving and emission-reduction technology. In addition, the researchers constructed a model to analyze the positive effects of optimization strategies on urban traffic flow, system efficiency, and environmental quality. They verified the actual effectiveness of these strategies through case analysis. To sum up, the research highlights the vital importance of effective and eco-friendly public transportation in lessening city traffic jams, enhancing the living standards of inhabitants, and curtailing carbon emissions. Furthermore, it offers practical experience and strategic advice for other cities.