Intelligent Management System Research Articles

The implementation of an optimized neural network in a hybrid system for energy management

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

Unveiling Crash Casualties: A Neural Network Model for Crash Type Classification Using Traffic Datasets

Abstract: Traffic accidents, a significant public health burden with substantial economic costs, necessitate proactive measures for sustainable transportation. This study investigates the efficacy of neural networks in predicting crash types using traffic data. We propose a deep learning model that leverages the power of recurrent neural networks (RNNs) for sequential traffic pattern analysis and convolutional neural networks (CNNs) for extracting spatial features from traffic data. The model, trained on a labeled traffic dataset, learns to identify patterns and relationships between traffic characteristics (e.g., speed, volume, weather) and different crash types (e.g., rear-end collision, single-vehicle). The evaluation focuses on the model's accuracy in predicting crash types using metrics like precision, recall, and F1-score. This research contributes to the development of intelligent traffic management systems (ITMS) that can proactively identify and mitigate potential accidents, ultimately enhancing road safety and promoting a more sustainable transportation future.The implementation results indicate that the proposed method yields an accuracy of 82.93% approximately.

Intelligent crop management system for improving yield in maize production: evidence from India

PurposeManaging weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and healthDesign/methodology/approachWe propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presentedFindingsWeed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actionsPractical implicationsThe proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize cropSocial implicationsThe proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.Originality/valueThis study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature

Analysis of Intelligent Financial Management Systems in Enterprises

With the advent of the era of artificial intelligence, traditional corporate financial management cannot adapt to the rapid development of modern economic society and scientific and technological progress. Therefore, they need to pay more attention to digital transformation and artificial intelligence. Artificial intelligence is a new technology that has changed the business and operation models of various industries and has put forward new requirements for corporate financial management. This paper explores the concept of intelligent finance, delves into the principles underlying intelligent financial management, discusses the significance of adopting intelligent financial management practices, and proposes a roadmap for constructing a intelligent financial management system.

IOT Based Wastewater Management in Smart Cities

The process of extracting and refining contaminants from drainage or wastewater so they can be recycled back into the water supply with the least amount of negative environmental impact is known as wastewater management. The current declining environmental condition necessitates the development of new strategies and tactics to guarantee safe and intelligent wastewater management systems in smart cities. The Internet of Things (IoT) and wireless sensor networks are potential technologies for treating wastewater. The comprehensive literature review develops a conceptual framework utilizing blockchain technology for an Internet of Things (IoT)- based wastewater management system in smart cities (IoT- WMS). Information is now being stored using blockchain technology to create an incentive scheme that promotes the reuse of waste water. In smart cities, tokens related to the quantity and quality of recovered wastewater are distributed to homes and businesses. However, this frequently promotes manipulating the data used to award these tokens in order to incorporate particular benefits. To determine whether there is evidence of altered IoT sensor data, anomaly detector techniques are employed. In order to facilitate the most efficient distribution of water depending on user usage at the plot holding level, this study suggests a water distribution and integrated management system based on the Internet of Things (IoT) and data analytics (DA). In addition to preventing water waste, the suggested approach would assist in gathering consumption statistics for macro-level research and town planning

Standby Power Reduction of Home Appliance by the i-HEMS System Using Supervised Learning Techniques

Electricity consumption in homes is on the rise due to the increasing prevalence of home appliances and longer hours spent indoors. Home energy management systems (HEMSs) are emerging as a solution to reduce electricity consumption and efficiently manage power usage at home. In the past, numerous studies have been conducted on the management of electricity production and consumption through solar power. However, there are limited human-centered studies focusing on the user’s lifestyle. In this study, we propose an Intelligent Home Energy Management System (i-HEMS) and evaluate its energy-saving effectiveness through a demonstration in a standard house in Korea. The system utilizes an IoT environment, PID sensing, and behavioral pattern algorithms. We developed algorithms based on power usage monitoring data of home appliances and human body detection. These algorithms are used as the primary scheduling algorithm and a secondary algorithm for backup purposes. We explored the deep connection between power usage, environmental sensor data, and input schedule data based on Long Short-Term Memory network (LSTM) and developed an occupancy prediction algorithm. We analyzed the use of common home appliances (TV, computer, water purifier, microwave, washing machine, etc.) in a standard house and the power consumption reduction by the i-HEMS system. Through a total of six days of empirical experiments, before implementing i-HEMS, home appliances consumed 13,062 Wh. With i-HEMS, the total consumption was reduced to 10,434 Wh (a 20% reduction), with 9060 Wh attributed to home appliances and 1374 Wh to i-HEMS operation.

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

In satellite health management, anomalies are mostly resolved after an event and are rarely predicted in advance. Thus, trend prediction is critical for avoiding satellite faults, which may affect the accuracy and quality of satellite data and even greatly impact safety. However, it is difficult to predict satellite operation using a simple model because satellite systems are complex and telemetry data are copious, coupled, and intermittent. Therefore, this study proposes a model that combines an attention mechanism and bidirectional long short-term memory (attention-BiLSTM) with telemetry correlation to predict satellite behaviour. First, a high-dimensional K-nearest neighbour mutual information method is used to select the related telemetry variables from multiple variables of satellite telemetry data. Next, we propose a new BiLSTM model with an attention mechanism for telemetry prediction. The dataset used in this study was generated and transmitted from the FY3E meteorological satellite power system. The proposed method was compared with other methods using the same dataset used in the experiment to verify its superiority. The results confirmed that the proposed method outperformed the other methods owing to its prediction precision and superior accuracy, indicating its potential for application in intelligent satellite health management systems.

Application of improved DBN and GRU based on intelligent optimization algorithm in power load identification and prediction

Non intrusive load monitoring belongs to the key technologies of intelligent power management systems, playing a crucial role in smart grids. To achieve accurate identification and prediction of electricity load, intelligent optimization algorithms are introduced into deep learning optimization for improvement. A load recognition model combining sparrow search algorithm and deep confidence network is designed, as well as a gated recurrent network prediction model on the grounds of particle swarm optimization. The relevant results showed that the sparrow search algorithm used in the study performed well on the solution performance evaluation metrics with a minimum value of 0.209 for the inverse generation distance and a maximum value of 0.814 for the hyper-volume. The accuracy and recall values of the optimized load identification model designed in the study were relatively high. When the accuracy was 0.9, the recall rate could reach 0.94. The recognition accuracy of the model on the basis of the test set could reach up to 0.924. The lowest classification error was only 0.05. The maximum F1 value of the harmonic evaluation index of the bidirectional gated recurrent network optimized by particle swarm optimization converged to 90.06%. The loss function had been optimized by particle swarm optimization, and both the convergence value and convergence speed had been markedly enhanced. The average absolute error and root mean square error of the prediction model were both below 0.3. Compared to the bidirectional gated recurrent model before optimization, the particle swarm optimization strategy had a significant improvement effect on prediction details. In addition, the research method had superior recognition response speed and adaptability in real application environments. This study helps to understand the load demand of the power system, optimize the operation of the power grid, and strengthen the reliability, efficiency, and sustainability of the power system.

Intelligent Waste Management System using IOT

The escalating population density in urban areas has fueled a surge in waste generation, underscoring the urgent need for effective waste management solutions. In response to this challenge this study proposes an innovative approach to waste management through the integration of IOT components. The system focuses on real-time monitoring and segregation of dry and wet waste using a network of sensors. The system also leverages platform to automate data logging and notification processes ,ensuring seamless oversight of waste collection operations. Our Proposed Intelligent Waste Management System employs IOT components such as sensors ,detectors, actuators to optimize waste collection and route planning. This integrated IOT-based approach represents a promising avenue for sustainable waste management in both large and small urban environments , paving the way for enhanced city livability and environmental stewardship.

Accelerating the construction of digital and intelligentialized pathology and the prospects

With the continuous development of informatization, digitalization and artificial intelligence technology, the working mode of the pathology department has gradually changed from the traditional manual check, paper circulation and physical carrier storage to the informatization process and digital storage. The traditional pathology discipline has ushered in unprecedented opportunities and challenges. Digital pathology department also emerge as the times require. Simultaneously, with the full integration of artificial intelligence technology in pathology department, the concept of "department of digital and intelligentialized pathology" was proposed. Based on information and digital technology, the digital intelligent pathology department integrates intelligent management system, optimizes the previous cumbersome management and workflow of the pathology department, develops advanced technologies such as intelligent material extraction, unmanned organization processing, artificial intelligence quality control, artificial intelligence diagnosis, and promotes the intelligent construction of the pathology department.

Enhancing bibliographic reference parsing with contrastive learning and prompt learning

Bibliographic references, typically comprising author names, journal titles, paper titles, and publication dates, play a vital role in academic research. Accurately identifying these structured pieces of information from references is a crucial step in developing intelligent bibliographic management systems. However, existing methods often rely on extensive high-quality training data. To mitigate the reliance on extensive training data, we propose a method that integrates prompt learning and contrastive learning for extracting structured information from bibliographic references, named CONT_Prompt_ParseRef. This approach aims to utilize contrastive learning to deepen the understanding of different metadata label types and employ prompt learning to provide specific guidelines for processing and recognition. We constructed a dataset comprising 12,000 samples, available in both Chinese and English versions. The experimental results on this bilingual dataset demonstrate the model's superior performance over existing techniques. Notably, CONT_Prompt_ParseRef shows remarkable robustness in low-resource environments, particularly in scenarios with limited training data, both contrastive and prompt learning play pivotal roles in label extraction from bibliographic references. The ablation study illustrates that omitting either component leads to a decline in performance, with contrastive learning being slightly more influential.

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the 1D CNN-BLSTM Neural Network

Lithium-ion batteries are currently widely employed in a variety of applications. Precise estimation of the remaining useful life (RUL) of lithium-ion batteries holds significant function in intelligent battery management systems (BMS). Therefore, in order to increase the fidelity and stabilization of predicting the RUL of lithium-ion batteries, in this paper, an innovative strategy for RUL prediction is proposed by integrating a one-dimensional convolutional neural network (1D CNN) and a bilayer long short-term memory (BLSTM) neural network. Feature extraction is carried out through the input capacity data of the model using 1D CNN, and these deep features are used as the input of the BLSTM. The memory function of the BLSTM is applied to retain key information in the database and to better understand the coupling relationship among consecutive time series data along the time axis, thereby effectively predicting the RUL trends of lithium-ion batteries. Two different types of lithium-ion battery datasets from NASA and CALCE were used to verify the effectiveness of the proposed method. The results show that the proposed method achieves higher prediction accuracy, demonstrates stronger generalization capabilities, and effectively reduces prediction errors compared to other methods.

Intelligent Traffic Management System: Detecting Over Speeding, Red-Light Violations, Helmet Violations, and Triple Riding

Abstract: According to recent reports, traffic violations have mostly resulted in an increase in fatalities and injuries on Indian roads. Because manually identifying traffic violations takes time, an automatic computer vision-based object identification model was required. The fundamental idea behind this research is to identify many transgressions using a single video frame. To perform various activities, the security camera's input video stream is processed and annotated. COCO is the dataset utilized for red-light leaping, while Google pictures are annotated to provide the dataset for over boarding. Tensorboard is used to train the model and visualize its results. The criteria employed include precision, recall, Fmeasure, and Pmeasure. Red light skipping accuracy is 93%, and the over boarding mAP value is 0.5:0.95. This system makes extensive use of the video feed to detect various forms of breaches.

Unravelling the potential of Big Data-driven decision-making in sustainable water irrigation: An AI perspective

Big Data-driven initiatives have transformed several industries by using large databases to extract vital information and enhance operations. In the field of sustainable water irrigation, Big Data enables intelligent water management, allowing for exact resource allocation, monitoring and analysis. This combination enables smart decision-making, increasing productivity in agriculture while reducing water consumption and environmental impacts. The revolutionary influence of data-driven approaches on managing water resources for sustainable and efficient irrigation practices has significant potential. The research highlights the importance of Big Data-driven approaches in water management by examining their applicability. It explores the artificial intelligence (AI) techniques for water irrigation that is sustainable and determines significant affecting factors efficient water management. The study encourages AI in intelligent water management systems and explains the application of big data in irrigation. It illustrates how smart water management which includes hydrated irrigation and predictive water infrastructure can contribute to AI-driven water management. To handle complicated water supply problems, the research presents an estimated water level model, highlighting the integration of AI and indicating the revolutionary potential of these techniques in attaining sustainable water irrigation. We highlight the novel possibilities of big data-driven methods for sustainable water irrigation, illustrating the essential part of AI techniques in effective water resource allocation.

A spatially explicit framework for assessing ecosystem service supply risk under multiple land‐use scenarios in the Xi'an Metropolitan Area of China

AbstractRapid global urbanization has perturbed ecosystem structures and functions, resulting in ecological risk and threatening sustainable human well‐being and socioeconomic development. However, scientific indicators to analyze ecosystem service (ES) risk patterns need to be explored in detail. In addition, studies on ES supply risk are stagnating on historical or status explorations, especially from the view of disturbance from land‐use changes. This study seeks to develop a framework for modeling past‐future ES supply risk pattern evaluation and probing into ES risk patterns under different future land‐use scenarios. To achieve this objective, the framework integrates the Future Land Use Simulation (FLUS) model, the Intelligent Urban Ecosystem Management System (IUEMS) model, and an established indicator system incorporating ES supply trend, hotspots and coldspots, and ES trade‐offs, and synergies. The results show that: (1) In 2050, the supply of climate regulation in the Xi'an Metropolitan Area (XMA) will increase, while that of carbon sequestration and recreation will decrease. In 2050, the supply of climate regulation is the highest under ecological protection (EP) scenario, while the supply of carbon sequestration and recreation are the highest under cropland protection (CP) scenario. (2) From 2000 to 2050, the hotspots and coldspots of climate regulation increase in both natural development (ND) scenario and CP scenario. Notably, CP scenario experiences the most significant reduction in extremely significant hotspots and coldspots of carbon sequestration. From 2000 to 2050, at the regional and pixel scales, climate regulation and carbon sequestration mainly show trade‐offs, and carbon sequestration and recreation show synergies. (3) ES supply risk in XMA is high in the center and low in the north and south. The ES supply risk from 2000 to 2050 is increasing, with expanding “extremely high risk”, “high risk”, and “extremely safe” areas. ES supply risk management should adhere to more strict land‐use policies and guidelines, management zoning for areas with different levels of ES risk, and an accurate understanding of ES trade‐offs and synergies for scientific risk management. This study could provide theoretical and technical references for ES risk assessment research and promote scientific ecological risk management.

Design of an Intelligent Energy Management System for Electric Vehicle Charging Station

In today’s world there are more outcomes in environmental change due to the overutilization of petroleum products in this manner prompting a genuine effect on the climate. So there is a need for a substitute answer for lessen the consumption of such non – sustainable assets. One such exertion made in the field of Freeways is the advancement of "Solar Freeways" which can be an elective arrangement. Sun oriented streets consolidate various arrangements in one – it can assist us with improving the creation of power utilizing sun based boards, to give a computerized stage to our future country's ventures like Smart Cities, and to work with the arising electric vehicles that supplant the petroleum driven vehicles and substantially more. Motivated by the fact that there are numerous amount of clean and sustainable energy we receive from roadways, the following study puts forward some of the event and application of an innovative charging method for the renewable energy driven electric cars, buses by using the roadway and also implementation of revolutionary nanotechnology along with the latest best in the house power electronics and power system analysis tools. A small scale prototype model was made by our team to attest the working of smart inductive charging process. Our project team was successful to improve the working of the model by improving the use of the preinstalled solar panels and also implement its use on the very concept we are trying to improve. On the vehicle, there will be the use of coils which are experimentally made for the flow of charges that are needed to provide charge to a moving electric vehicle (EV). The detailed strategy is presented in this report. Key Words: Environmental change, Overutilization of petroleum products, Solar Freeways, Renewable energy, Electric vehicles (EVs), Smart Cities, Nanotechnology

Co-Design Smart Disaster Management Systems with Indigenous Communities

Tribal governments bear an uneven burden in the face of escalating disaster risks, climate change, and environmental degradation, primarily due to their deeply entrenched ties to the environment and its resources. Regrettably, accessing vital information and evidence to secure adequate funding or support poses difficulties for enrolled tribal members and their lands. In response to these challenges, this paper collaborates with tribal nations to co-design intelligent disaster management systems using AI chatbots and drone technologies. The primary objective is to explore how tribal governments perceive and experience these emerging technologies in the realm of disaster reporting practices. This paper presents participatory design studies. First, we interviewed seasoned first-line emergency managers and hosted an in-person design workshop to introduce the Emergency Reporter chatbot. Second, we organized a follow-up design workshop on tribal land to deliberate the utilization of drones within their community. Through qualitative analysis, we unveiled key themes surrounding integrating these emergency technologies within the jurisdiction of tribal governments. The findings disclosed substantial backing from tribal governments and their tribal members for the proposed technologies. Moreover, we delved into the potential of chatbots and drones to empower tribal governments in disaster management, safeguard their sovereignty, and facilitate collaboration with other agencies.

Development Status of Intelligent Traffic Management System

The intelligent traffic management system is the most important part of the intelligent traffic system. The research and analysis of the development status of the intelligent traffic management system can provide a research basis for its future development. This paper studies the three subsystems of the intelligent traffic management system: the intelligent traffic monitoring system, the By comparing the research methods used by scholars in different time periods for intelligent information service systems and signal control systems, it is found that with the application of technologies such as the Internet of Vehicles, big data, Internet of Things, and deep learning, the level of intelligent traffic management system management is constantly rising.

Vision based intelligent traffic light management system using Faster R‐CNN

AbstractTransportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Optimizing urban energy management: A strategic examination of smart grids and policy regulations

This research addresses the critical challenges in urban energy management, focusing on load forecasting and optimizing renewable energy sources considering policy regulation. A novel Hybrid Long Short-Term Memory-Artificial Neural Network (LSTM-ANN) learning technique is proposed for accurate load and renewable energy output power forecasting. The hybrid model leverages the strengths of LSTM and ANN, providing a robust solution for capturing temporal dependencies and complex patterns in the data. Additionally, the Bee Algorithm for Optimization (BAO) is employed to handle the nonlinearity and nonconvexity inherent in energy management problems. BAO optimally schedules the operation of various generation units, considering the diverse characteristics of gas turbines, photovoltaic panels, and wind turbines. The effectiveness of the proposed hybrid model and optimization algorithm is demonstrated through comprehensive comparisons, showcasing superior accuracy and convergence speed compared to traditional methods. This research contributes to the advancement of intelligent energy management systems, offering a reliable framework for optimizing renewable energy utilization in real-world applications.