Real-time Management Research Articles

A real-time energy management method for APTMS based on FCM-BP neural network

A real-time energy management method for APTMS based on FCM-BP neural network

A Real-Time Energy Management Strategy of Flexible Smart Traction Power Supply System Based on Deep Q-Learning

A Real-Time Energy Management Strategy of Flexible Smart Traction Power Supply System Based on Deep Q-Learning

Estimating lithium-ion battery capacity from relaxation voltage using a machine learning approach

Abstract Lithium-ion batteries are pivotal in the development of electric vehicles and energy storage systems, with their State of Health (SOH) being crucial for both academic research and industry applications. Estimating battery capacity accurately presents significant challenges due to the complex aging mechanisms involved. In this study, we introduce a novel approach using a one-dimensional convolutional neural network (1D CNN) that leverages relaxation voltage data to predict battery capacity. The model is mainly structured with two convolutional layers, one maxpool layer, and two fully connected layers, each specially optimized to meet the unique requirements of this application. A key feature of this model is the use of a unit kernel size in the initial layer, which enhances the capture of non-linearities in the data. Our results demonstrate a prediction percentage error of -0.03% ± 0.77%, outperforming many state-of-the-art models in terms of accuracy and robustness. Furthermore, the model’s compactness, with approximately 5k parameters, suggests its suitability for edge deployment in future applications, promising significant advancements in real-time battery management.

Fog computing in classrooms: boosting efficiency, responsiveness, user experience

In the context of rapidly advancing smart education systems, the effective management and optimization of modern classroom remain critical challenges. This research presents a novel methodology leveraging cloud and fog computing-based simulations, with a specific focus on the implementation of iFogSim. Empirical findings validate the efficacy of fog computing in monitoring classrooms, demonstrating significant improvements in performance metrics compared to traditional cloud computing architectures. Specifically, fog computing ensures remarkably low latency, with a mere 7 milliseconds, even with scalable integration across multiple classrooms. In contrast, cloud computing infrastructures exhibit considerably higher initial latencies, starting at 210 milliseconds, which further escalate with the increasing number of monitored classrooms. Furthermore, our analysis reveals substantially lower network overhead associated with fog computing, measuring at 5,231.8 kilobytes, in sharp contrast to the significantly higher network usage of 80,808 kilobytes observed with cloud computing solutions. These findings underscore the potential of fog computing as a promising solution for efficient and real-time management classroom in smart education environments.

Enhancing Smart City Infrastructure: An Iot-Integrated System Approach for Real-Time Urban Management

Objectives: The primary objective of this study is to evaluate the effects of new information technologies on the workability, scalability, and reliability of urban installations. This study also aims to enhance smart city infrastructure by integrating various Internet of Things (IoT) systems, particularly for real-time smart city management. Methods : A complete experimental setup was established in a controlled urban environment encompassing numerous IoT technologies, such as environmental sensors, traffic management units, and public security systems, as well as data communication units. The Tensorflow framework, along with Python, was used to perform simulations and experiments. The system design was categorised into four key levels: data reception and transmission, computation, network and data analytics, and application. The proposed systems were subsequently tested in real-world environments, including zones frequently hosting urban events, to analyse key performance indicators. Findings : The results demonstrate significant improvements in urban management through IoT initiatives, including a 25% reduction in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) levels, leading to decreased respiratory hospital admissions and enhanced air quality and health benefits. Additionally, notable advancements were observed in noise control, energy efficiency, and emergency response. Specifically, the findings recorded a 10 dB decrease in nighttime noise, a 30% reduction in energy consumption for public lighting, and a 40% faster emergency response time. These outcomes highlight the broad impact of IoT on urban sustainability and safety. Novelty : This paper introduces a novel concept focused on the design perspective of IoT-connected systems to boost smart city infrastructure, with particular emphasis on the real-time management of urban environments. Keywords: Smart Cities, IoT Technologies, Urban Infrastructure, Real ­Time Urban Management, System Architecture

Regulatory compliance and efficiency in financial technologies: Challenges and innovations

This study explores the integration of Regulatory Technology (RegTech) within dynamic regulatory frameworks tailored to the financial services industry, addressing the critical challenge of aligning fast-paced technological innovations with more traditionally static regulatory environments. The core purpose of this research is to develop adaptable regulatory frameworks that support the implementation of advanced technological tools, ensuring compliance while enhancing the efficiency of regulatory supervision. Utilizing a blend of qualitative and quantitative research methods, this paper analyzes the effectiveness of proposed frameworks through real-world application within financial institutions. Results indicate that integrating RegTech solutions streamlines compliance processes, significantly reduces operational costs, and improves real-time risk management. Interpretations of these findings suggest that adaptive regulatory frameworks facilitate a more proactive regulatory approach capable of supporting continuous technological advancements without compromising the integrity or stability of financial systems. The conclusion drawn from this study advocates for a systematic reevaluation of current regulatory practices, emphasizing the need for regulations that are as dynamic and innovative as the technologies they aim to govern. This approach promises to safeguard against emerging risks and foster an environment conducive to technological advancement within the financial sector.

E-WASTE MANAGEMENT THROUGH DEEP LEARNING: A SEQUENTIAL NEURAL NETWORK APPROACH

The goal of this research is to improve the management of electronic trash (e-waste) by using a Sequential Neural Network (SNN) with TensorFlow and Keras as part of an advanced deep learning technique. In order to address the growing problem of e-waste, the research collects a large amount of data from images of e-waste and then carefully preprocesses and augments those images. With precision, recall, and F1 scores of 87%, 86%, and 86%, respectively, the SNN architecture—which incorporates dropout, pooling, and convolutional layers—achieved an amazing 100% classification accuracy. These outstanding outcomes show how well the model can classify e-waste components, suggesting that it has the potential to be used in real-world scenarios. The results indicate that the SNN-based approach greatly improves the accuracy and efficiency of e-waste sorting, promoting environmental sustainability and resource conservation. By automating the sorting process, the suggested system decreases the need for manual labor, minimizes human error, and speeds up processing. The study emphasizes the model's suitability for integration into current e-waste management workflows, providing a scalable and dependable way to expedite the recycling process. Additionally, the model's real-time applicability highlights its potential to revolutionize current e-waste management practices, making a positive ecological impact. . Future research endeavors will center on broadening the dataset to include a wider range of e-waste image categories, investigating more advanced deep learning architectures, and incorporating the system with Internet of Things (IoT) devices to improve real-time monitoring and management.

A novel dynamic predictive control model for variable air volume air conditioning systems using deep learning

Model-based predictive control has proven effective for managing indoor temperature and humidity in variable air volume (VAV) air conditioning systems. However, delays in temperature and humidity adjustments in response to changes in internal and external environmental conditions can impede high-performance operation. This study introduces a dynamic predictive control model for the multi-zone indoor temperature and humidity of VAV systems, designed to predict and control these parameters in real-time. Utilizing the RC network two-node wall structure method and backward finite difference scheme, a multi-zone building model is developed. Coupled with a deep fuzzy cognitive map (DFCM) for temperature and humidity prediction, and controlled by a PID controller, the model dynamically regulates the opening degrees of terminal air dampers and chilled water valves. Implemented on a networked control platform in a large commercial building, the model consistently maintained indoor temperature and humidity within ±0.5 °C and ±0.1 g/(kg dry air) of the target values under varying conditions, demonstrating substantial improvements in stability and operational efficiency. The research enhances the predictability and control of HVAC systems through advanced algorithmic integration, improving real-time indoor climate management in complex building environments. This method offers a practical improvement in HVAC control technologies, which could be beneficial for applications in commercial building management systems, providing a useful tool for supporting energy efficiency and sustainability in modern infrastructures.

Optimization of intelligent charge compression ignition engine in hybrid electric powertrain by adaptive equivalent consumption management strategy

Dual fuel intelligent charge compression ignition (ICCI), taking advantage of two direct injection fuel supply systems, substituted low-carbon fuels for conventional fuels, improving fuel economy and emissions. This study focused on the application of the ICCI engine on the power-split hybrid powertrain and carried out the influence of the real-time energy management strategy on ICCI engine performance. Taking the ideal solution calculated by dynamic programming (DP) as the benchmark, equivalent fuel consumption minimum strategy (ECMS) and adaptive equivalent fuel consumption minimum strategy (AECMS) were compared in aspects of ICCI engine performance and state of charge (SOC) control robustness. The results showed that AECMS had better performance on SOC control, where the maximum deviation from the ideal control by DP was lower than 0.5 % during the charge sustaining stage, and the maximum deviation during the charging and discharging stages was no more than 1.5 %. In terms of engine performance control, the accumulative fuel consumption increased by no more than 20 %, and the average E85 (85 % ethanol in ethanol-gasoline blend) substitution ratio reduced by less than 8 %. Despite consuming a high proportion of E85, the accumulative fuel mass consumption of the hybrid powertrain was still 4 % lower than the conventional diesel vehicle.

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

MURE: Multi-layer real-time livestock management architecture with unmanned aerial vehicles using deep reinforcement learning

In recent years, the combination of unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) has gained popularity in livestock management (LM) due to energy constraints and network instability. Limited energy storage of sensor nodes (SNs) and the possibility of packet loss contribute to fast energy consumption and unstable networks, respectively. UAVs serve as relay nodes and data sinks, addressing these issues by temporarily storing data to reduce SN workload and establishing mobile nodes for network stability. We propose two innovations based on previous work: 1) We introduce a multi-layer wireless network architecture, categorizing UAVs into two layers based on their functions including data collection and data processing. This enhances task parallelization, bridging performance gaps among multiple UAVs; 2) We overcome the mobility limitation of SNs, considering their real-time movement in the network. Through deep reinforcement learning, UAVs learn to cooperatively locate moving SNs. This accounts for the inevitable mobility of livestock in the industry. Additionally, we simulate the environment and compare our approach to traditional methods, evaluating metrics such as collected data per timestep (DCPS), energy consumed per timestep (ECPS), and network stability (NS). Experimental results demonstrate that our method outperforms traditional approaches, achieving a data collecting gain of 4.84% and 8.20% compared to the methods without considering SN mobility or the multi-layer characteristics of WSNs, respectively. Under energy consumption limits, our method yields energy savings of 3.00% and 1.35% respectively. Furthermore, we extensively study and validate our method against other path planning algorithms, including genetic particle swarm optimization (GPSO), modified central force optimization (MCFO), and rapidly-exploring random trees (RRT). Our approach surpasses these methods in terms of data collecting efficiency and network stability.

Rapid review methods series: Guidance on the use of supportive software

This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RRs) use modified systematic review methods to accelerate the review process...

Rancang Bangun Sistem Inventory (Studi Kasus: UD. Asia Pratama Pekanbaru)

UD Asia Pratama Pekanbaru, a trading company in the building industry, is facing problems in managing inventory which is still manual, causing inefficiencies and inaccurate data. This research aims to design and implement an efficient and effective web-based inventory management system. The system development method uses the Waterfall model, including requirements analysis, design, implementation and testing. Data was collected through interviews with employees and analysis of documents related to inventory. The research results show that the system developed can increase inventory management efficiency, with real-time stock management features, recording of incoming and outgoing goods, and accurate reporting. User testing and evaluation shows that the system is acceptable and meets UD Asia Pratama's needs in overcoming inventory problems.

Short-Term Photovoltaic System Output Power Prediction Based on Integrated Deep Learning Algorithms in the Clean Energy Sector

Photovoltaic power generation system plays an important role in renewable energy. Therefore, accurately predicting the short-term output power of photovoltaic system has become a key challenge for real-time power grid management. This study focuses on Yingli's green energy photovoltaic system, and uses the convolution neural network and long-term and short-term memory network fusion model (CNN-LSTM) to predict the short-term power. The model integrates CNN's data feature extraction and LSTM's time series prediction ability, showing high accuracy and stability. The experimental results show that CNN-LSTM model has a low mean and variance of prediction error, and the prediction is stable and reliable, and it is consistent in different scenarios. This provides theoretical support for the output power prediction of photovoltaic system based on deep learning.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

CRYOMOVE: Cold chain real-time management of vaccine delivery using PCM and deep learning

Vaccines are temperature-sensitive biological products that can become ineffective or even hazardous if exposed to temperatures outside the recommended range. Therefore, it is crucial to maintain the cold chain during the transportation and storage of vaccines in order to ensure that they arrive at their destination in optimal condition. Vaccines are typically transported and stored in containers lined with Phase Change Material (PCM) to maintain a specific temperature range (usually lower than the ambient temperature). Firstly, we have simulated the melting of PCM at three distinct external temperatures using ANSYS Fluent software in order to forecast the Melt Fraction (MF) vs. time curve for each of these temperatures. The external temperature consistently undergoes dynamic changes. Running a simulation each time the temperature shifts is impractical due to its time and data-intensive nature. Consequently, we adopted a more efficient approach by training a simple Artificial Neural Network (ANN) based on simulation data. This ANN is designed to learn and predict the MF versus time curve for any given external temperature. Real-time temperature data from the vaccine box is transmitted to the cloud. Subsequently, the machine learning model operates on the cloud to predict the remaining time for the PCM to melt. This predictive analysis is performed recursively every 10 min to account for dynamic fluctuations in external temperatures, providing continuous updates on the time remaining for PCM meltdown. This is then conveyed to the cold chain managers to take preventive measures if the external conditions are harsh and there is a chance for vaccines to get ruined because of the temperature. This same approach can be extended to other applications beyond vaccine delivery such as food storage and transportation, pharmaceuticals, and more.

Real-Time Energy Management Strategy for Fuel Cell Vehicles Based on DP and Rule Extraction

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy.

FlowOptix : A Traffic Signal Control and Management System

The innovation in the field of vehicles has led to the access of vehicles in high reach, thus increasing the number of vehicle counts. But this increase count has created a need of managing the congestion of vehicles on road in a more efficient way. The traditional system and approach of the traffic signal systems isn’t adequate enough to tackle the increasing congestion. The traditional system tends to be efficient where the count is sparse, as the density of vehicles on a particular side of road increases or if the traffic is comparatively larger on one side than other side in such case the approach fails. Hence, the introduced system redesigns the traffic signal system that is static switching to signal switching based on real-time traffic management. So, in this project the switching time of signal will be decided based on real time image detection with good accuracy in dense traffic. This practice can prove its most effectiveness in releasing the congested traffic at an efficient and faster rate.

Forecast Uncertainties Real-Time Data-Driven Compensation Scheme for Optimal Storage Control

This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.

Predictive analytics for traffic flow optimization in urban logistics: A transformer-based time series approach.

In this study, we focus on the analysis and prediction of urban logistics traffic flow, a field that is gaining increasing attention due to the acceleration of global urbanization and heightened environmental awareness. Existing forecasting methods face challenges in processing large and complex datasets, particularly when extracting and analyzing valid information from these data, often hindered by noise and outliers. In this context, time series analysis, as a key technique for predicting future trends, becomes crucial for supporting real-time traffic management and long-term traffic planning. To this end, we propose a composite network model that integrates gated recurrent unit (GRU), autoregressive integrated moving average (ARIMA), and temporal fusion transformer (TFT), namely the GRU-ARIMA-TFT network model, to enhance prediction accuracy and efficiency. Through the analysis of experimental results on different datasets, we demonstrate the significant advantages of this model in improving prediction accuracy and understanding complex traffic patterns. This research not only theoretically expands the boundaries of urban logistics traffic flow prediction but also holds substantial practical significance in real-world applications, especially in optimizing urban traffic planning and logistics distribution strategies during peak periods and under complex traffic conditions. Our study provides a robust tool for addressing real-world issues in the urban logistics domain and offers new perspectives and methodologies for future urban traffic management and logistics system planning.