Demand System Research Articles

Mobile Camera Application to Monitor Residential Society Vehicle Activity

This project presents a machine learning-enabled surveillance system designed for real-time monitoring and tracking of vehicles in urban and residential environments. With rapid advancements in autonomous technologies, there is an increasing demand for intelligent systems that can enhance public safety, streamline traffic management, and provide secure access control within private and public spaces. The proposed system leverages deep learning algorithms for vehicle detection, classification, and speed monitoring, while utilizing IoT infrastructure to enable seamless data collection and remote access. Key words- Machine Learning, IoT, Surveillance System, Vehicle Tracking, Autonomous Technology, Deep Learning, Real-time Monitoring, Smart City, Traffic Management, License Plate Recognition, Predictive Analysis, Access Control, Situational Aware

Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50

In today's rapidly evolving world, where technology is progressing swiftly, there is an increasing demand for facial recognition systems. Technologies are similar to digital forensics in that they can recognize people by scanning faces. However, one key problem they confront is dealing with covered or occluded faces, which might restrict recognition of faces in real-world situations. To overcome this issue, we created a system that is capable of identifying individuals even when their faces are veiled. We used the face detector algorithm called Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection with 99.8% accuracy. Further we have conducted feature extraction and pre-processing on our self-created dataset. Our project utilizes the power of deep learning model: Residual Network (ResNet50), the form of deep neural network architectures well-suited for the job of features extraction. These features are matched by using Cosine similarity with accuracy of 92%. By leveraging the capabilities in the deep learning algorithms, this project provides a robust solution for automating the recognition of partially occluded faces.

Context-Aware Prediction with Secure and Lightweight Cognitive Decision Model in Smart Cities

Cognitive networks with the integration of smart and physical devices are rapidly utilized for the development of smart cities. They are explored by many real-time applications such as smart homes, healthcare, safety systems, and other unpredictable environments to gather data and process network requests. However, due to the external conditions and inherent uncertainty of wireless systems, most of the existing approaches cannot cope with routing disturbances and timely delivery performance. Further, due to limited resources, the demand for a secure communication system raises another potential research challenge to protect sensitive data and maintain the integrity of the urban environment. This paper presents a secured decision-making model using reinforcement learning with the combination of blockchain to enhance the degree of trust and data protection. The proposed model increases the network efficiency for resource utilization and the management of communication devices with the alliance of security. It provides a reliable and more adaptive paradigm by exploring learning techniques for dealing with the intrinsic uncertainty and imprecision of cognitive systems. Also, the incorporation of blockchain technology reduces the risk of a single point of failure, malicious vulnerabilities, and data leakage, ultimately fostering trust for urban sensor applications. It validates the incoming routing links and identifies any communication fault incurred due to malicious interference. The proposed model is rigorously tested and verified using simulations and its significance has been proven for network metrics in comparison to existing solutions.

Advanced Distributed Control of Parallel Resonant CLLC DAB Converters

The integration of hybrid alternating current (AC) and direct current (DC) networks has gained relevance due to the growing demand for more flexible, efficient, and reliable electrical systems. A key aspect of this integration is the parallelization of power converters, which presents several technical challenges, such as current sharing imbalances, circulating currents, and control complexity. This paper proposes a distributed control architecture for parallel resonant CLLC dual active bridge (DAB) converters to address these issues in hybrid AC–DC networks and microgrids. The approach includes a master voltage controller to regulate the output voltage and distributed local current controllers to ensure load balance. The approach minimizes the difference between the output and input voltages, allowing for independent control of power flow. Simulation and experimental results show significant improvements. The system stability has been demonstrated experimentally. Transient response has been improved with response time 80% lower using the feed-forward term. The system maintained stability with current sharing deviations below 3% under full and low load conditions. Finally, scalability is ensured by the proposed distributed controller because the central power controller is not affected by the number of units in parallel used in the application. This solution is suitable for advanced hybrid networks and microgrid applications.

The Application of Microcomputer Systems in Automatic Control Systems

With the continuous advancement and innovation of computer system technologies, the performance of microcomputers has improved significantly. The enhanced computing power of microcomputer hardware provides greater flexibility for the development of various intelligent optimization algorithms. Integrating microcomputers into automatic control systems effectively addresses the systems demands for signal stability, sensitivity, response speed, and manufacturing cost, thereby significantly advancing the practical application of automatic control systems. This paper comprehensively explores the current state and challenges of applying microcomputers in automatic control systems, while providing an in-depth analysis of development trends in this field. It systematically explains the specific improvements microcomputers bring to control systems and how to select appropriate algorithms based on different types of automatic control systems. Moreover, the review highlights the impact of hardware performance limitations of microcomputers on algorithm implementation and discusses potential solutions and directions for future technological advancements. It also examines successful applications and existing issues in representative automatic control systems employing microcomputers. Finally, the study anticipates the development trends of intelligent optimization algorithms for microcomputers in automatic control systems, as well as the emerging challenges and research directions

The impacts of structured and unstructured information sharing on supply chain performance: the roles of information system connectivity, relationship commitment, and demand uncertainty

ABSTRACT This research investigates the information sharing (IS) activities between firms and their customers and inquiries into their impacts on supply chain performance (SCP). Following the information processing theory, this study illuminates a less-explored yet crucial classification of IS activities, structured IS and unstructured IS, and investigates their impacts on SCP under different levels of demand uncertainty. We also probe into the influence of relationship commitment (a relational antecedent) and information system connectivity (an information system antecedent) on structured IS and unstructured IS. Using data of 410 Chinese manufacturers, our research finds that information system connectivity enables both structured and unstructured IS, whereas relationship commitment merely supports unstructured IS, which positively affects structured IS. Furthermore, structured IS improves SCP, while this impact can’t hold for unstructured IS. Finally, demand uncertainty negatively moderates the relationship between structured IS and SCP, while positively moderating the relationship between unstructured IS and SCP.

Influences of carbon nanotubes as electrode materials on the energy storage for lithium-ion batteries

Due to inherent theoretical capacity limitations and safety issues associated with commonly used anode materials in lithium-ion batteries, carbon nanotubes (CNTs), with their unique one-dimensional tubular structure and superior mechanical, electrical, and thermal properties, can partially address these deficiencies and enhance lithium storage capacity. Therefore, this paper concentrates on the impacts of carbon nanotubes as electrode materials on energy storage for lithium-ion batteries. It discusses three aspects in detail: the use of CNTs as direct anode materials, as composite anode materials, and as flexible electrode materials. Each application will be examined in terms of its specific advantages, existing challenges, and corresponding solutions to improve battery performance. The paper also evaluates the role of CNTs in enhancing the electrochemical stability, improving cycle life, and achieving higher power densities, which makes them highly promising for next-generation energy storage solutions, particularly for portable and flexible electronic devices. Additionally, the unique ability of CNTs to form a highly conductive network facilitates efficient charge transport and reduces internal resistance, further contributing to the overall performance of the battery. Future research directions may focus on optimizing the synthesis methods and improving the interface interactions between CNTs and other active materials, to fully leverage their properties for enhanced safety, higher efficiency, and lower costs. As such, carbon nanotubes are seen as key materials that could play an important role in meeting the growing demand for advanced energy storage systems.

Fabrication and Analysis of BaTiO3-Nb2O5 Ceramics for Advanced Energy Storage Applications

As the demand for high-performance energy storage systems surges, dielectric materials have emerged as frontrunners due to their exceptional power density. Yet, their relatively low energy density has long been a bottleneck for practical deployment. This study breaks new ground by addressing this limitation, focusing on the enhancement of Barium Titanate-based ceramics for energy storage through the strategic incorporation of Niobium Oxide (Nb2O5). By investigating the effects of Nb2O5 on 0.98BT-0.02BMC ceramics, we unlock unprecedented improvements in both dielectric and energy storage properties. X-ray diffraction (XRD) analysis reveals the stability of a single perovskite phase across all compositions, paving the way for reliable performance. Most strikingly, the x = 4 composition delivers a groundbreaking dielectric constant (~2200) alongside a remarkable energy density of 1.40 J/cm3 and a recoverable energy density of 1.10 J/cm3, achieving an efficiency of 78.8%. These extraordinary results propel the material to the forefront of next-generation energy storage technologies, making it a powerhouse for high-demand applications such as power pulse systems. With its unparalleled combination of high energy density, exceptional efficiency, and long-term stability, this material holds the promise to redefine energy storage solutions, setting new benchmarks in both performance and reliability.

Resilience assessment of water supply and demand in the Yellow River Basin based on integrated optimisation weighting–improved grey-target model

Context As a result of the combined impacts of global climate change and intensive human activities, the water-resources supply–demand imbalance has become prominent, particularly in the Yellow River Basin. Aims This study aims to accurately assess the changes in resilience of water-resource supply and demand over the past few decades. Methods This study examines the resources, economy, society, and ecology of both the supply and demand aspects in assessing the resilience of water supply and demand. The composite evaluation model based on integrated optimisation weighting and an improved grey-target model was constructed. Key results Using the Yellow River Basin as an example, from 2011 to 2020, the resilience index of the water-resource supply–demand system (RIWRSS) in the Yellow River Basin increased significantly by 69.4%. Among the 12 indicators in the Yellow River Basin, the contribution proportion of the inhibiting factors decreased by 3.7%, whereas the contribution of the driving factors increased by 70.9%. Conclusions The present study has provided insights into evaluating the RIWRSS and highlighted resilience disparities between the supply and demand sides in the Yellow River Basin. Implications This study expects to provide evidence for the improvement of water-supply conditions of water resources in the Yellow River Basin.

Continuous monitoring of partial discharge activities in power cables and their stimulation due to the temperature rise

AbstractWith the development of the power distribution systems demands, the grid reliability becomes a strategic issue. Continuous monitoring of power transmission networks is a key component in addressing this issue. Partial discharge (PD) characterisation instruments provide a reliable solution and an important indicator of the state of cable insulation. An increase in the PD level usually indicates the development of a fault that affects the integrity of the cable and can lead to various problems related to the quality and quantity of the transmitted energy. In this context, the use of Internet of Things (IoT) systems for power networks monitoring can provide a significant improvement to the localisation and detection of faults. In this paper, the authors show the advantages of continuous monitoring of power grid cables using a new IoT based framework using an advanced signal processing technique, namely the phase diagram. Also, the authors show that variations in temperature can impact the initiation and progression of PD, affecting the reliability and lifespan of electrical insulation systems. Understanding this temperature dependence is crucial for accurate PD detection and effective maintenance of power cables. Our results show that higher temperatures can accelerate the PD activity, while lower temperatures may suppress it.

Fat vs. Sugar: The Case for a Saturated Fat Tax in Italy.

When judging the distributional impact of unhealthy food taxes, what matters is not just how much low income people would pay but how much the such taxes would benefit or harm them overall. In this paper, we assess the consumer welfare impact of a fat tax net of its expected benefits computed as savings from weight loss. Using Italian data, we estimate a censored Exact Affine Stone Index (EASI) incomplete demand system for food groups, simulating changes in purchases, calorie intake, consumer welfare, and the monetary value of short-run health benefits. While the Italian government has proposed a sugar tax, we show that there is no significant excess consumption of added sugars among Italian adults. Instead, excessive fat consumption is more prevalent, making a fat tax a more compelling and effective solution to address diet-related health risks. Our results suggest costs from fat taxation are larger than benefits at all income levels. As a fraction of income, the net impact would be slightly regressively distributed.

Heat Transfer in U-Tubes: Simulating the Performance of Delta Winglet Pairs in Laminar and Turbulent Flows

As the environmental crisis intensifies, the demand for energy-efficient systems has never been greater. Vortex generators have emerged as an effective method for enhancing heat transfer within tubes. While extensive research has been conducted on their application in straight tubes, studies focusing on their performance in curved tubes remain limited. This simulation study examined three different arrangements of triangular vortex generators in a common flow-down configuration within a U-turn tube to optimize heat transfer. The analysis conducted under constant wall temperature conditions across a range of Reynolds numbers spans both laminar and turbulent flow regimes to evaluate the broader impacts of vortex generators on flow and thermal fields. The efficiency of each arrangement was evaluated based on the Nusselt number and friction factor. Results show a remarkable increase in the Nusselt number, reaching up to 115% for the configuration with the highest number of vortex generators. However, this enhancement was accompanied by a significant increase in the friction factor, rising by up to 383% at higher Reynolds numbers. Overall, vortex generators demonstrated their highest effectiveness in curved tubes during the laminar-to-turbulent flow transition. In fully turbulent flow, the friction factor increased disproportionately to the modest gains in heat transfer. Despite these limitations, the use of vortex generators in curved tubes offers promising efficiency improvements and merits further exploration.

Desain dan Prototipe Integrasi IoT dalam Pertanian Hidroponik Cerdas Berbasis Energi Terbarukan

This research aims to develop a prototype for an Internet of Things-based automation system powered by renewable energy, specifically solar panels, to enhance the efficiency and productivity of hydroponic farming. The study addresses critical challenges, including high energy costs and the intensive monitoring demands of conventional hydroponic systems. The experimental methodology involves designing hardware featuring Arduino Uno as the central control unit, environmental sensors (DHT22) for real-time monitoring, and automated actuators such as water pumps, fans, and LED lights. The system integration process encompasses linking the hardware to an IoT interface, followed by rigorous functional testing to evaluate system stability, energy efficiency, and operational performance under varying environmental conditions. The results demonstrate that the prototype successfully operates the entire hydroponic system using energy exclusively from solar panels, eliminating reliance on conventional electricity. The system achieves precise control over critical plant growth parameters such as temperature, humidity, and lighting, with high operational stability observed during trials. This research makes a notable contribution by innovatively integrating IoT, automation, and renewable energy, thereby enhancing energy efficiency while delivering a sustainable and environmentally friendly hydroponic farming solution. The findings have significant practical implications, particularly for implementation in remote areas with limited access to electricity. Furthermore, the study paves the way for future developments, including applications in other agricultural systems and the integration of artificial intelligence to enhance predictive analytics and automated control.

Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks

Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks

Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage

This manuscript focuses on optimizing a Hybrid Renewable Energy System (HRES) that integrates photovoltaic (PV) panels, wind turbines (WT), and various energy storage systems (ESS), including batteries, supercapacitors (SCs), and hydrogen storage. The system uses a multi-objective optimization strategy to balance power management, aiming to minimize costs and reduce the likelihood of loss of power supply probability (LPSP). Seven different algorithms are assessed to identify the most efficient one for achieving these objectives, with the goal of selecting the algorithm that best balances cost efficiency and system performance. The system is assessed across three operational scenarios: (1) when energy supply meets demand with help from backup systems, (2) when demand exceeds supply and energy storage systems are depleted, and (3) when energy generation surpasses demand and storage systems are full. The HBA-based optimization effectively manages energy flow and storage, ensuring grid stability and minimizing overcharging risks. This system offers a reliable and sustainable power supply for isolated microgrids, effectively managing energy production, storage, and distribution. The research sets a new benchmark for future studies in decentralized energy systems, particularly in balancing technical efficiency and economic feasibility.

Flexible Electrode Arrays Based on a Wide Bandgap Semiconductors for Chronic Implantable Multiplexed Sensing and Heart Pacemakers.

Implantable systems with chronic stability, high sensing performance, and extensive spatial-temporal resolution are a growing focus for monitoring and treating several diseases such as epilepsy, Parkinson's disease, chronic pain, and cardiac arrhythmias. These systems demand exceptional bendability, scalable size, durable electrode materials, and well-encapsulated metal interconnects. However, existing chronic implantable bioelectronic systems largely rely on materials prone to corrosion in biofluids, such as silicon nanomembranes or metals. This study introduces a multielectrode array featuring a wide bandgap (WBG) material as electrodes, demonstrating its suitability for chronic implantable applications. Our devices exhibit excellent flexibility and longevity, taking advantage of the low bending stiffness and chemical inertness in WBG nanomembranes and multimodalities for physical health monitoring, including temperature, strain, and impedance sensing. Our top-down manufacturing process enables the formation of distributed electrode arrays that can be seamlessly integrated onto the curvilinear surfaces of skins. As proof of concept for chronic cardiac pacing applications, we demonstrate the effective pacing functionality of our devices on rabbit hearts through a set of ex vivo experiments. The engineering approach proposed in this study overcomes the drawbacks of prior WBG material fabrication techniques, resulting in an implantable system with high bendability, effective pacing, and high-performance sensing.

Predictive Energy Demand and Optimization in Metro Systems Using AI and IoT Technologies

Introduction: With the rapid urbanization of modern cities, metro systems have become indispensable for efficient mobility. However, the increasing demand for public transportation has led to rising energy consumption, posing significant challenges for operational sustainability. Current energy management strategies in metro networks rely on static models and centralized systems, which often fail to adapt to real-time fluctuations in energy demand, leading to inefficiencies and wasted resources. Methods: This paper proposes an innovative approach to optimizing energy demand in metro systems by integrating Artificial Intelligence (AI) and the Internet of Things (IoT). By leveraging real-time data collected from IoT sensors deployed throughout the metro network, we apply machine learning algorithms such as Random Forests and Neural Networks to dynamically predict energy demand. These predictions enable metro operators to adjust energy consumption in real-time, thus improving overall system efficiency and reducing operational waste. Our approach was validated using data from the Parisian metro system through extensive simulations. Results: The results of simulations demonstrate significant improvements in energy efficiency. Optimized energy demand management led to a reduction in wasted energy during metro operations, particularly through the utilization of regenerative braking systems. Conclusions: Our findings suggest that integrating AI and IoT technologies into metro systems significantly improves energy efficiency by enabling dynamic energy demand prediction and real-time adjustment of energy consumption. The proposed system is scalable and adaptable, making it suitable for application in metro networks globally, thereby enhancing energy efficiency and supporting sustainable transport initiatives.

How does structural reform affect cost and demand in railway systems

How does structural reform affect cost and demand in railway systems

Advancing explainability of adversarial trained Convolutional Neural Networks for robust engineering applications

The great success of Convolutional Neural Networks has driven their widespread use in Industry 5.0, especially in safety-critical applications where model robustness is essential. While adversarial training is commonly used to make models more robust, it often changes how the model fuses features, leading to complex trade-offs and making its behavior harder to understand. This lack of transparency contradicts the demand for interpretable and trustworthy artificial intelligence systems in modern industry, where applications like autonomous driving and intelligent medical systems require model interpretability to assign post-accident responsibility and support legal and liability decisions. Our work introduces an innovative approach to enhance the interpretability of adversarially trained Convolutional Neural Networks, providing detailed and quantifiable insights through a six-dimensional semantic framework. This framework, which examines objects, parts, scenes, materials, textures, and colors, offers fine-grained explanations that closely mirror human cognitive processes. We conduct extensive experiments across multiple classical models, and the results reveal that adversarial training induces structure-dependent changes in feature preferences, offering insights into the trade-off between standard and robust accuracy in adversarially trained Convolutional Neural Networks. These findings provide rational support for the use of artificial intelligence in real-world applications and promote the development and implementation of explainable artificial intelligence in modern industries.

The marginal utility of income and homogeneous demand systems

The marginal utility of income and homogeneous demand systems