Real-time Demand Research Articles

Intelligent fuzzy-PSO based grid connected solar EV charging station for electric buses to enhance stability and energy management

Abstract Modern power networks are relying more and more on Distributed Generation (DG) and solar energy-powered Electric Bus (EB) charging stations; nevertheless, controlling the charging process under situations of peak EB load and variable solar irradiation presents considerable hurdles. This study presents a coordinated control approach that maximizes energy distribution for solar (PV)-based EB charging stations by using an intelligent energy management system (IEMS). Through the analysis of load demand and meteorological data in real time, the IEMS optimizes PV generation and grid power consumption, thereby halving peak power demand, minimizing system losses, and easing distribution grid stress. In order to enable dynamic modifications to the energy flow between PV power, the grid, and battery storage, the method integrates an adaptive neuro-based fuzzy control system that anticipates solar energy generation and predicts EB load demands. Furthermore, to balance power distribution between battery and ultracapacitor systems and control energy storage in ultracapacitors, a fuzzy inference system optimized using Particle Swarm Optimization (PSO) is utilized. This prolongs battery life and guarantees effective energy management. The overall performance of the IEMS is improved by the fuzzy logic controller, which produces output signals that specify the best power distribution for any energy storage system. Digital simulations and a real-time hardware-in-loop experimental setup are used to validate the effectiveness of the proposed system, which shows notable gains in energy management, grid stability, and system efficiency. In order to improve intelligent energy management in grid-connected solar-powered systems, this study offers a viable method for incorporating renewable energy sources into EB charging stations.

Improving healthcare application scalability through microservices architecture in the cloud

This review paper explores the scalability of healthcare applications by adopting microservices architecture in cloud environments. As healthcare systems face increasing demands for efficient and flexible application performance, traditional monolithic architectures often struggle to accommodate fluctuating workloads and rapidly evolving technological landscapes. This paper highlights the fundamental characteristics of microservices, emphasizing their advantages over monolithic systems, particularly in enhancing agility and resource optimization. It further examines the role of cloud computing in providing scalable solutions, enabling healthcare organizations to allocate resources based on real-time demands dynamically. The paper discusses essential strategies and best practices for implementing microservices, addressing common challenges and offering solutions to facilitate a smooth transition. Ultimately, the findings underscore the transformative potential of microservices architecture in improving operational efficiency and patient care within the healthcare sector.

A Study on Dynamic Pricing in the Airline Industry Using Reinforcement Learning Analyzing the Impact of Reinforcement Learning on Airline Pricing Strategies

Dynamic pricing serves as an essential tactic in the airline sector, allowing airlines to modify ticket rates in response to changing market demand, rivalry, and various other influencing elements. This research investigates the use of Reinforcement Learning (RL) in dynamic pricing strategies, emphasizing its ability to boost revenue management and increase customer satisfaction. In contrast to conventional pricing strategies, RL allows airlines to adjust prices in real-time by continuously analyzing environmental data such as seat availability, departure time, and competitor pricing. This study explores current pricing models, the framework of RL-driven dynamic pricing, and a case analysis to showcase the real-world advantages and difficulties of RL. Core discoveries reveal that RL-driven dynamic pricing provides considerable benefits in responding to real-time demand fluctuations, thereby optimizing revenue opportunities. Nonetheless, obstacles like limited data, high computational demands, and striking a balance between exploration and exploitation still persist. The research ends with observations on how RL can further reshape airline revenue management and suggests future research avenues to improve its practical uses.

An weak surface defect inspection approach using efficient multi-scale attention and space-to-depth convolution network

In the field of precision manufacturing, machine vision technology is gradually replacing traditional manual inspection methods as a key technology to improve product quality. In precision manufacturing companies, weak defects on the product surface are unacceptable. However, existing defect detection methods rarely focus on the weak surface defect detection task. To address this challenge, we acquire and build a dataset called USB-DET, which contains weak defect samples. Then, we propose an innovative lightweight deep learning model, SDIA-net, which integrates SPD-Conv, Dysample technique, and attention mechanism-iRMA, to improve the recognition and localization of weak defects effectively. On the USB-DET dataset, SDIA-net achieves 55.1% mAP, which is 3.2% higher than the existing SOTA models. The computational efficiency is 205.1 FPS, which satisfies real-time demands. SDIA-net’s advantages make it well-suited for deployment in resource-limited precision manufacturing environments, providing an effective technical solution for product surface quality control with significant practical application value.

Drivers of short-term essential air service operations: Load factor, pricing, and subsidy policies in China's domestic air market

This study analyzes the impact of the temporary flight subsidy policy on the operational performance of China's domestic air transport market. Using panel data from 42 days between May 7 and June 17, 2022, and applying a dynamic panel model with time-invariant variables, the study explores the factors influencing average load factor and average ticket price under short-term basic air service conditions. The results reveal that, during the pandemic, load factors exhibited weak dependence on historical data, with airlines adjusting to real-time demand fluctuations. Additionally, the flight subsidy policy failed to meet its intended goals, leading to a reduction in overall load factors, particularly for China Southern, China Eastern, and Hainan Airlines, while Air China demonstrated more flexibility in adapting to the policy. The study also identifies the significant influence of route distance and airport size on both load factors and ticket prices, with longer routes and major hub airports commanding higher prices. These findings suggest that future subsidy policies should be more flexible and tailored to specific market conditions to effectively support the recovery and long-term sustainability of the aviation industry.

Slice admission control in 5G wireless communication with multi-dimensional state space and distributed action space: A sequential twin actor-critic approach

Network slicing represents a paradigm shift in the way resources are allocated for different 5G network functions through network function virtualization. This innovation aims to facilitate logical resource allocation, accommodating the anticipated surge in network resource requirements. This will harness automatic processing, scheduling, and orchestration for efficient management. To overcome the challenge of managing network resources under heavy demand, slice providers need to leverage both artificial intelligence and slice admission control strategies. While 5G network resources can be allocated to maintain a slice, the logical allocation and real-time network evaluation must be continuously examined and adjusted if network resilience is to be maintained. The complex task of leveraging slice admission control to maintain 5G network resilience has not been fully investigated. To tackle this problem, we propose a machine learning approach for slice admission control and resource allocation optimization so as to maintain network resilience. Machine learning algorithms offer a powerful tool for making robust and autonomous decisions, which are crucial for effective slice admission control. By intelligently allocating resources based on real-time demand and network conditions, these algorithms can help ensure long-term network resilience and achieve key objectives. While various machine learning algorithms hold promise for 5G resource management and admission control, reinforcement learning (RL) has emerged as a particularly exciting solution. Its ability to mimic human learning processes makes it a versatile solution, well-suited to tackle the complex challenges of network control. To fill this gap, we propose a new technique known as sequential twin actor critic (STAC). Simulations show that the STAC improves network resilience through enhanced admission probability and overall utility.

Dynamic Lock-And-Release Mechanism Enables Reduced ΔG at Low Temperatures for High-Performance Polyanionic Cathode in Sodium-Ion Batteries.

Low-temperature synthesis of polyanionic cathodes for sodium-ion batteries is highly desirable but often plagued by prolonged reaction times and suboptimal crystallinity. To address these challenges, a novel self-adaptive coordination field regulation (SACFR) strategy based on a dynamic lock-and-release (DLR) mechanism is introduced. Specifically, urea is used as a DLR carrier during synthesis, which dynamically "locks" and "releases" vanadium ions for controlled release, simultaneously "locking" H+ ions to enhance phosphate group release, thereby creating a self-adaptive coordination field that can intelligently respond to real-time demands of the reaction system. This dynamic coordination behavior contributes to both an improvement in reaction kinetics and a significant reduction in Gibbs free energy change (ΔG). As a result, the kinetic efficiency and thermodynamic spontaneity of the reaction are greatly enhanced, enabling the efficient synthesis of high-crystalline Na3V2O2(PO4)2F (NVOPF) at 90 °C within just 3 hours. The as-prepared NVOPF cathode exhibits exceptional rate performance and ultra-stable cycling stability across a broad temperature range. Furthermore, the successful kilogram-scale synthesis underscores the practical potential of the innovative strategy. This work pioneers the regulation of coordination field chemistry for polyanionic cathode synthesis, providing transformative insights into material design.

Joint Optimization of Cost and Scheduling for Urban Air Mobility Operation Based on Safety Concerns and Time-Varying Demand

As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs’ entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours.

Mathematical optimization strategy for online scheduling of complex manufacturing systems based on thermal energy optimization and fuzzy mathematical model

With the rapid development of the manufacturing industry and the advancement of the intelligent process, the scheduling problem of complex manufacturing systems becomes more and more complicated, especially in the aspect of energy management and optimization. This study aims to propose an online scheduling mathematical optimization strategy based on thermal energy optimization and fuzzy mathematical model, so as to improve the scheduling efficiency and resource allocation rationality of complex manufacturing systems under different production conditions. A scheduling model considering thermal energy utilization rate, production priority and real-time demand is established by using fuzzy mathematics. The model deals with the uncertain factors by fuzzy logic and solves them by genetic algorithm to realize the dynamic adjustment and optimal scheduling of production resources. Through the experimental verification of a complex manufacturing system, the improvement of scheduling efficiency not only reduces the operating cost, but also improves the flexibility and response speed of the system. The fuzzy mathematical model based on thermal energy optimization provides an effective on-line scheduling strategy for complex manufacturing systems, which can realize efficient scheduling and energy management in dynamic environment.

Hybrid intelligent algorithm aided energy consumption optimization in smart grid systems with edge computing

The rapid proliferation of smart grid systems necessitates efficient management of energy resources, particularly in the context of mobile edge computing (MEC) networks. This paper presents a novel approach to optimize the energy consumption in smart grid systems with the integration of edge computing, employing a hybrid intelligent algorithm (HIA) empowered by particle swarm optimization (PSO). The primary objective is to enhance the sustainability and operational efficiency of smart grid infrastructures by minimizing the energy consumption in the MEC networks. The proposed HIA utilizes PSO to dynamically allocate computational tasks and manage resources among edge devices based on real-time demand fluctuations. This adaptive approach aims to achieve the optimal load balancing and energy efficiency across the smart grid ecosystem. By leveraging the PSO’s ability to iteratively refine solutions and adapt to changing environmental conditions, the algorithm optimizes the energy consumption while maintaining requisite service levels and reliability. Simulation experiments and case studies validate the effectiveness of the proposed PSO-based HIA in reducing the energy consumption without compromising system other performances. The results demonstrate substantial improvements in the energy efficiency, illustrating the feasibility and benefits of employing intelligent algorithms tailored for edge computing environments within smart grid systems. This research contributes to advancing sustainable smart grid technologies by introducing a robust framework for energy optimization through hybrid intelligent algorithms.

Optimization of Microgrid fed from Hybrid Energy Sources using the Swarm Intelligence

To combat the widening gap between demand and supply, it is crucial to include renewableenergy sources as a Distributed Generation newline (DG). Despite their usefulness in reducing the gap, REsources place a heavy pressure on generation scheduling, particularly wind power. Utilities are compelledto use wind curtailment due to fluctuations in wind energy output, which reduces the incentive to switch torenewable sources of power. Demand Side Management (DSM) is one approach to this problem, since itadjusts demand in real time to match generation while the grid is operating. Conventional DemandResponse (DR) systems were created to minimise consumption during peak hours by arranging userloads in exchange for incentives. The Smart Grid (SG) concept provides a high-tech means ofcommunication for keeping tabs on and managing the power grid. This motivated the creation of asophisticated DR technique known as Demand Dispatch (DD), in which consumption is timesynchronizedwith electricity production. In order to deal with the fluctuations in wind power production,this thesis proposes a DD method. The entities involved in DD and the way in which data is sharedbetween them are laid out in an operational framework. In order to find and evaluate all of the studiesthat have been published on a certain topic, researchers often undertake systematic literature reviews.

A dynamic yard space reservation algorithm based on reward-penalty mechanism

Increasing throughput at container terminals leads to higher yard occupancy rates, reduced yard service capacity, and inefficient use of space. These issues result in longer waiting times for trucks, underutilized yard slots, and longer distances between berth positions and the yard, particularly under high occupancy conditions. Traditional methods for allocating yard space fail to adequately consider dynamic container arrival patterns, conflict between container operations, and efficient use of yard resources. This paper introduces an innovative approach to improve yard efficiency by dividing yard container retrieval into time intervals and dynamically reserving yard slots within each interval. This method aims to minimize operational conflicts, reduce distances between blocks and berths, and avoid excessive reservations. We propose a dynamic reservation algorithm based on a reward-penalty mechanism to handle the problem's complexity and real-time demands. Furthermore, a time-series reward-penalty mechanism, which considers past reservation feedback and future reservation potential, ensures the concentration and continuity of container groups in the yard. Our evaluation metrics and multiple experimental scenarios demonstrate that this method significantly enhances yard efficiency and overall port productivity compared to static and other dynamic algorithms.

Accelerated Bitrate Ladder Creation for Video Live Streaming Using Temporal Layer Injection

Video streaming systems aim to provide high-quality video adapted to clients’ device and network conditions. For this purpose, adaptive streaming architectures encode video content at a variety of quality levels, organized in a bitrate ladder. However, compressing a video into multiple streams is resource-intensive, which may become especially problematic in live streaming applications with real-time demands. Therefore, this paper proposes a novel solution for fast bitrate ladder creation, and provides the requirements for implementation in the H.266/VVC standard. More specifically, the proposed method creates new intermediate combined streams by injecting the lowest temporal layers of a higher-quality augmentation stream in a lower-quality base stream. Since the lowest layers are used as reference by the remaining layers, this procedure indirectly increases the quality of the frames in those untouched remaining layers as well. Although altering the reference frames may cause some drift-error artifacts, we demonstrate that, on average, the quality of the intermediate streams are higher than the base stream. Additionally, we demonstrate that injecting more layers brings both the quality and bitrate closer to that of the augmentation stream. The disadvantage of the combined streams is that their quality fluctuates more than the quality of the source streams, and that they are compressed less efficiently, comparable to going from a slower to fast or faster preset in the VVenC encoder. Most importantly, their main advantage is that they were generated at no significant additional computational complexity. In this way, the proposed method is of great benefit when generating a bitrate ladder of video streams under constrained computational resources. The code has been made open source and is available on https://github.com/IDLabMedia/NALUProcessing .

MLino bench: A comprehensive benchmarking tool for evaluating ML models on edge devices

In today’s rapidly evolving technological landscape, Machine Learning (ML) has become an integral part of our daily lives, ranging from recommendation systems to advanced medical diagnostics and autonomous vehicles. As ML continues to advance, its applications extend beyond conventional boundaries. With the continuous refinement of the models and frameworks, the possibilities for leveraging these technologies into devices that traditionally lacked any form of computational autonomy are ever-expanding. This shift towards embedding ML capabilities directly into edge devices brings new challenges due to the stringent limitations these devices have in terms of memory, power consumption, and cost. The ML models implemented on such devices must find an equilibrium between memory footprint and performance, attaining a classification time that fulfills real-time demands and maintains a similar level of accuracy as the desktop version. Without automated assistance in managing these considerations, the complexity of evaluating multiple models can lead to suboptimal decisions.In this paper, we introduce MLino Bench, an open-source benchmarking tool tailored for assessing lightweight ML models on edge devices with limited resources and capabilities. The tool accommodates various models, frameworks, and platforms, presenting a meticulous design that enables a comprehensive evaluation directly on the target device. It encompasses crucial metrics such as on-target accuracy, classification time, and model size, providing a versatile framework that assists practitioners in decision-making when deploying models to such devices.The tool employs a fully streamlined benchmark flow involving training the ML model in a high-level interpreted language, porting, compiling, flashing, and finally benchmarking on the actual target. Our experimental evaluation of the tool highlights its flexibility in assessing multiple ML models across different model hyperparameters, frameworks, datasets, and embedded platforms. Furthermore, a distinctive advantage compared to state-of-the-art ML benchmarking tools is the inclusion of classical ML models, including Random Forests, Decision Trees, Support Vector Machines, Naive Bayes, and more. This sets our tool apart from others that predominantly emphasize only neural network models. Due to this inclusive approach, our tool facilitates the evaluation of ML models across a broad spectrum of devices, ranging from resource-constrained edge devices to those with medium and advanced computational capabilities.

Optimasi dan Modifikasi Debian untuk Meningkatkan Kinerja Sistem Operasi Real-Time

In today's digital era, operating systems play a crucial role in various technological devices. This paper discusses the optimization and modification of Debian, one of the most stable Linux distributions, to enhance its performance as a real-time operating system (RTOS). The primary objectives of this research are to improve the responsiveness, reliability, and stability of Debian in handling real-time tasks. The study involves modifying the kernel, adjusting scheduling settings, optimizing memory management, and integrating specific real-time software. The results of the tests show that the modified Debian provides lower and more stable task execution latency compared to the standard Debian. Tests were conducted using the cyclictest software to measure system latency and Htop to monitor CPU and memory performance. Additionally, the use of the VLC application as a real-world workload demonstrated that the optimized operating system could handle task priorities more efficiently, allocate resources as needed for real-time demands, and maintain stability under heavy workloads. This research significantly contributes to the development of Debian-based RTOS, which can be applied in fields such as industrial automation, robotics, and IoT devices. The optimizations ensure that the operating system can meet the high-performance and real-time reliability demands of these critical applications.

Optimizing real-time demand response in smart homes through fuzzy-based energy management and control system

Optimizing real-time demand response in smart homes through fuzzy-based energy management and control system

An incentive strategy for the retention of impatient passengers in ride-sourcing markets

A prominent problem plagued the ride-sourcing service providers is the instantaneous real-time demand–supply imbalance. This entails a terrible user experience and causes adverse sustainable results such as customer churn for the platform, which will damage both drivers’ income and the platform revenue. To reduce passenger cancellation caused by extended wait times, we propose an incentive-based queue management strategy —a discount method— to enhance the waiting experience and encourage passengers to stay in a post-peak period . We formulate the waiting process for a ride-sourcing service as an M/M/c+M queue system with impatient passengers, and model passengers’ reneging behaviors by characterizing the impact of discounted trip fare and updated queue information on their travel utilities. Based on such a behavior model, we can analyze the effect of the discount strategy on the queueing process, which allows us to maximize the platform’s profit growth by tailoring the discount strategy. Our result shows that the discount strategy can effectively increase the platform’s profit. Under the specific condition of the discount strategy, the earlier the strategy is implemented in the post-peak period, the higher platform profit can be achieved. Moreover, we explore the impact of queueing system parameters on the performance of the discount strategy and gain some interesting insights.

Control system for automatic adjustment of departure intervals of public transport vehicles

In numerous urban areas, fine-tuning bus schedules to align with fluctuating passenger numbers poses a sizable challenge, compounded by diverse factors such as weather and unexpected events. This paper conducts a comprehensive examination of existing optimization methods aimed at enhancing the departure intervals of public transport systems, particularly focusing on the Bidirectional Coordination Optimization Algorithm (BCOA) and the genetic algorithm. Through systematic analysis, these methodologies are meticulously explored for their effectiveness in addressing the scheduling challenges faced by urban transit systems. Additionally, it delves into specific instances where the Proportional-Integral-Derivative (PID) control mechanism has been employed to adeptly adjust the timing of bus departures based on real-time demand. The PID system's proven reliability and efficacy spotlight its potential as a transformative tool for advancing public transit systems. By leveraging such technology, there's tangible optimism for achieving a more responsive, efficient, and passenger-friendly public transportation network in the years to come.

Optimizing water quality in urban distribution networks: Leveraging digital twin technology for real-time demand management

Optimizing water quality in urban distribution networks: Leveraging digital twin technology for real-time demand management

The Future of DevOps Compute: A Survey of Innovative Strategies for Efficient Resource Utilization

This paper investigates innovative strategies employed by DevOps teams to optimize compute resource utilization within their environments. High compute resource utilization is critical for efficient application development and deployment in DevOps workflows. Traditional virtual machines (VMs) often lead to resource waste due to overprovisioning and static allocation. This research analyzes the growing adoption of containers for achieving higher density and finer-grained resource control. Additionally, the impact of autoscaling and serverless functions on dynamically adjusting compute resources based on real-time demand is examined. Through a comprehensive survey, the paper identifies key trends, challenges, and best practices associated with optimizing compute utilization in DevOps. The findings aim to guide DevOps teams towards efficient and cost-effective compute resource management strategies within the ever- evolving landscape of application development. Keywords—DevOps, Compute Resource Utilization, Containers, Virtual Machines, Autoscaling, Serverless Functions, Resource Management, Application Development, Cost Optimization, Dynamic Resource Allocation