Advanced Smart Research Articles

When complementarity meets consistency: weighted collaboration fusion constrained by consistency between views for multi-view remote sensing scene classification

ABSTRACTRemote sensing scene classification is the basis of advanced smart urban planning tasks such as urban functional zone division and land use type identification. In recent years, a wide range of emerging data sources is receiving progressive attention for urban features extraction, such as satellites, unmanned aerial vehicles (UAVs), and ground sensors. How to effectively utilize these multi-view data jointly to improve scene classification performance has become a hot topic of remote sensing challenge. Existing feature fusion methods tend to map data from different views into a common feature space, which is often difficult to find when the data between views differ greatly. Furthermore, because these methods require data from all views as input, they are not flexible enough to handle situations where there is only one view input when inferring. To address the aforementioned issues, a novel Coupled Parallel Architecture (CPA) using Weighted Collaboration Fusion Constrained by Consistency Between Views (CBV-WCF) is proposed in this paper. In the training phase, the CBV module reduces the impact of the heterogeneous gap across views by capturing the consistency information between views. Well the WCF module is used to fully mine and effectively fuse the complementary information between views to improve the performance of downstream tasks. In the inference phase, the proposed architecture can effectively improve the classification performance in both cases of multi-view and single-view input. Our method is evaluated on air-ground dual-view scene classification, which is a typical multi-view task with large image differences between views. Experimental results on two publicly available air-ground dual-view datasets demonstrate that the proposed framework significantly improves classification performance while bringing some inspiration and new solutions to multi-view tasks. The code of this paper will be published at: https://github.com/Forest-repo/CBV-WCF.

Smart Crosswalk: Machine Learning and Image Processing based Pedestrian and Vehicle Monitoring System

The conventional pedestrian crossing system's shortcomings require urgent reform to enhance the safety of pedestrians and improve urban mobility. Issues such as insufficient time for pedestrians to cross, prolong waiting times, neglection of emergency vehicles, and the absence of effective 24/7 response mechanisms at traditional crosswalks present significant safety concerns in urban areas. Our primary intention is to develop a cutting-edge pedestrian crossing system that relies on deep learning and image processing technologies as its foundation. This research addresses to innovate an advanced smart crosswalk consisting of four essential components: a real-time Pedestrian Detection and Priority System customized for individuals with special needs, a responsive system for detecting road conditions, vehicle availability and speed near crosswalks, a real-time Emergency Vehicle Detection and Priority System strengthened by rigorous verification procedures, and a robust framework for identifying pedestrian accidents and violations of crosswalk rules. The entire system has been meticulously designed not only to enhance pedestrian safety by identifying potential dangers but also to optimize traffic flow. In essence, it aims to provide an improved pedestrian crossing experience characterized by increased safety and efficiency.

Field trial of a management architecture for advanced smart pluggable transceivers in diverse hosts

Field trial of a management architecture for advanced smart pluggable transceivers in diverse hosts

Coupled bandgap properties and wave attenuation in the piezoelectric metamaterial beam on periodic elastic foundation

In recent years, the innovative design of metamaterials and the study of bandgap characteristics have become the focus of research. The flexible setting of small added mass in locally resonant metamaterials can effectively regulate bandgap characteristics and wave transmission. In this paper, a novel tunable piezoelectric metamaterial beam on periodic elastic foundation is proposed for realizing the wider bandgap region and the stronger attenuation capability. Based on Timoshenko beam theory, wave equation of the metamaterial beam considering the piezoelectric and the inertial amplification effects is obtained from the transfer matrix method, and the calculational results are compared to that of numerical simulation. The results show that periodic elastic foundation can improve the overall stiffness of the structure, and the bending wave attenuation frequency domain will broaden with the increase of the elastic foundation stiffness. The metamaterial beam exhibits the strong wave attenuation in the bandgap induced by external negative capacitance shunt circuit, and the wave transmission loss is improved about 20 dB than the short circuit case. The coupling effects between the local resonance and the inertial amplification mechanism can cause the coupled bandgaps, which expand the original bandgap range by at least 25%. In addition, the coupled bandgaps boundary can be extended to lower frequencies by adjusting the angle of rigid rod and the additional mass separately, resulting in broadband wave attenuation. This work will provide valuable ideas for the design of advanced smart materials and structures in the field of vibration and noise reduction.

Affective Surveillance Management in Virtual Emotion Based Smart Complex Infrastructure

Affective computing has evolved substantially thanks to recent great advances for numerous applications including emotion sensing, human interactions, emotion-enabled communications, eHealth, metaverse. Also, it is highly anticipated that such an evolved affective computing and its technology can be applied to secure surveillance based on virtual emotion detection in advanced smart cities. In this paper, we design an energy-aware affective surveillance management system for virtual emotion based smart complex architecture. With an affective surveillance perspective in smart complex environment with multi levels of ground and underground, the proposed system basically provides low resource affective surveillance which is formed by various system components such as IoT devices, mobile robots and smart UAVs. Then, we present a formal representation of the problem that is to minimize the number of non-working affective components. Three different approaches are developed to resolve the problem as well as their performances are evaluated through extensive experiments. Besides, the open research issues are presented for new research branch of affective surveillance.

Constructing interfacial barrier from tribo-positive shell microcapsules to suppress space charge in thermochromic phase change composites for smart electronics

Thermochromic phase change composites (TPC) exhibit versatile functions under electrothermal stimuli, which are extensively applied as packaging, insulation and circuit components in considerable advanced smart electronics. However, space charge accumulation under DC voltages is still a critical and universal issue for the serious deterioration of functionalities and life-span of TPC and electronics. This study presents a novel strategy of suppressing space charge in thermochromic phase change epoxy composites (TPCE) by constructing interfacial barrier from tribo-positive shell thermochromic phase change microcapsules. The strong tribo-positive melamine-formaldehyde (MF) shell of microcapsules can establish negative charge barriers in the interfaces between microcapsules and epoxy matrix through triboelectrification, which is directly verified by Kelvin probe force microscopy. Pulsed electroacoustic measurement results exhibit the space charge accumulation in TPCE under an extreme electric field strength of 50 kV/mm and an elevated temperature of 70 °C can be significantly diminished by 39.3% compared to that of neat epoxy resin after incorporating merely 1 phr MF shell microcapsules, affirming the effect of interfacial barrier on space charge inhibition. The interfacial barrier could improve the charge carrier dissipation and mitigating the space charge accumulation as confirmed by hopping conduction analysis. Moreover, DC breakdown strength of TPCE can also be enhanced by 12.3% and 6.1% with 1phr microcapsules at 30 °C and 70 °C compared to that of neat epoxy resin owing to the suppressed space charge. The results provide a potential approach of designing high-performance thermochromic phase change composites for smart electrical and electronic devices.

Towards Nearly Zero-Energy Buildings: Smart Energy Management of Vehicle-to-Building (V2B) Strategy and Renewable Energy Sources

As countries worldwide strive for net-zero emissions, electric vehicles (EVs) have gained significant momentum. EVs, particularly through Vehicle-to-Everything (V2X) technology, including Vehicle-to-Building (V2B), are recognized as key enablers for achieving nearly-zero energy buildings (NZEB). This research presents an advanced Smart Energy Management System (SEMS) with a novel V2B strategy, offering precise one-hour resolution to reduce grid reliance and optimize peak load management. The SEMS coordinates building energy and EV charging while harnessing the potential of renewable energy resources and battery storage systems. By carefully scheduling EV charging and discharging activities, it maximizes the efficient use of available energy resources. Importantly, the algorithm integrates real-world EV parking patterns, accounting for factors such as fleet composition, parking durations, and charging priorities. Economic evaluation encompasses not only direct EV battery degradation costs but also external expenses associated with environmental impacts. Simulation outcomes underscore a remarkable reduction in building grid power demand (65%) and a substantial decrease in carbon emissions (64%). The PV+V2B Scenario emerges as the preferred choice, striking an optimal balance between initial investment costs, energy savings, and carbon footprint reductions. This study holds great academic and practical significance, contributing to the advancement of smart, sustainable, and economically viable net-zero cities.

A waterproof, environment‐friendly, multifunctional, and stretchable thermoelectric fabric for continuous self‐powered personal health signal collection at high humidity

AbstractThermoelectric sensors have attracted increasing attention in smart wearables due to the recognition of multiple signals in self‐powered mode. However, present thermoelectric devices show disadvantages of low durability, weak wearability, and complex preparation processes and are susceptible to moisture in the microenvironment of the human body, which hinders their further application in wearable electronics. Herein, we prepared a new thermoelectric fabric with thermoplastic polyurethane/carbon nanotubes (TPU/CNTs) by combining vacuum filtration and electrospraying techniques. Electrospraying TPU microsphere coating with good biocompatibility and environmental friendliness made the fabric worn directly and exhibits preferred water resistance, mechanical durability, and stability even after being bent 4000 times, stretched 1000 times, and washed 1000 times. Moreover, this fabric showed a Seebeck coefficient of 49 μV K−1 and strain range of 250% and could collect signals well and avoided interference from moisture. Based on the biocompatibility and safety of the fabric, it can be fabricated into devices and mounted on the human face and elbow for long‐term and continuous collection of data on the body's motion and breathing simultaneously to provide collaborative support information. This thermoelectric fabric‐based sensor will show great potential in advanced smart wearables for health monitoring, motion detection, and human–computer interaction.

Applying Super High Resolution Fluorescence Microscope and Advanced Smart Cloud Algorithm in Real-Time Quantitative Processing and Analysis of Electroconvection

In an electrochemical cell at high current densities, electroconvection plays a critical role in determining the morphology of electrodeposited metals. The resultant dendrite formation leads to risky battery performance failure. Over the past decades, though intensive theoretical analysis and experimental effort have been done, the detailed structure and dynamics of electroconvection remain unknown, because experimental observations lacked the resolution to expose the fundamental, hierarchical spatial structure of these flows, while the data processing algorithms were not fully established for large sample size. To address this shortcoming, we built an advanced optical electrochemical cell that fits in situ imaging with a super-resolution fluorescence microscope. By observing and recording real-time motions of electrolytes, electroconvection flows have been interrogated at unprecedented, high temporal and spatial resolution. As a complement to the visualization studies, a cloud-computing analysis algorithm was developed by combining a higher resolution Particle Image Velocimetry algorithm and a machine learning model to enable velocity distribution data over the entire optical field of view at nanoscale or microscale spatial resolution. Consequently, detailed velocity maps are obtained across the optical electrochemical cell, allowing us to quantitatively measure and analyze the initiation and evolution of the hierarchical microstructures inside the electroconvection in a unidirectional electric field. With these powerful tools, we further studied the effect of polymer viscoelasticity on electroconvection and electrodeposition. The addition of an ultrahigh molar mass polyethylene oxide to the electrolytes transforms the materials to a viscoelastic fluid state that changes the electroconvection dependence on time and voltage and induces a smooth electrodeposition on the metal anode by unique polymer rheological properties. Especially, the relaxation of long polymer chains in electrolyte provides a promising manner to prevent dendrite growth. We further analyzed these observations using direct numerical simulations for Newtonian and viscoelastic fluid models, and the quantitative analysis of experimental results indicates good consistency with simulation predictions.

Spider‐Silk‐Inspired Heterogeneous Supramolecular Network with Strain‐Stiffening, High Damping Capacity, and Supercontraction

AbstractNatural materials are adaptive with intriguing mechanical properties such as strain‐stiffening, high damping, and stimuli‐responsive actuation under ambient conditions, which are evolutionarily derived to adapt to variable environments. Such adaptabilities are highly desirable for advanced smart materials in soft robots and bionic applications yet rarely achieved all in one synthetic material. Inspired by the molecular chemistry and structure of spider silk, a structurally heterogeneous supramolecular network with all‐round adaptabilities is developed by evaporation‐induced self‐assembly of hydrogen‐bonded macromolecules. The supramolecular network consists of both hard (crystallites) and soft (amorphous) phases with dynamic hydrogen bonds, exhibiting strong strain, time, and hydration dependency with adaptive mechanical and structural responses to varying strain, deformation rate as well as moisture. Through multifunctional crosslinking, the network exhibits silk‐like attributes with an extensibility of >130%, intense strain‐stiffening (sixfold modulus enhancement), high damping capacity (>80%) over a wide range of strain rate, and moisture‐triggered large supercontraction (contraction ratio of >50%). Artificial materials with such combined adaptiveness under bio‐benign conditions are promising for applications in biomimetic and biomedical fields.

A transition to battery electric vehicles without V2G: an outcome explained by a strong electricity regime and a weak automobility regime?

BackgroundA sustainability transition in mobility is dependent on a transition away from a fossil fuel-based automobility regime. Smart charging, in the form of vehicle-to-grid (V2G) has been presented as one—or even the—key technology in facilitating a sustainability transition in the automobility regime. With the large global increase in battery electric vehicles (BEVs) combined with a rapid increase in the production of wind and solar energy, V2G may indeed become a key technology to enable the balancing of electricity grids worldwide. Thus far, however, the large-scale introduction of BEVs in Norway has been implemented without the use of commercial V2G systems; indeed, it has only recently been implemented in commercial smart charging stations, and then only in the less-radical form of grid-to-vehicle (G2V) systems. The Norwegian experience is contrary to expectations in the sustainability transitions literature and, therefore, merits further investigation. This article details how and why this outcome unfolded and considers the relative strength of the automobility and electricity regimes as a possible explanation. Specifically, it asks: can the absence of commercial V2G charging in Norway be explained by the structure of the existing regimes? And, if so, is this generalisable?ResultsTo answer the research question, the study employed an exploratory two-stage case study approach, drawing on 36 expert interviews. The first stage included 27 interviews with key actors, including stakeholder organisations. These were followed by nine in-depth interviews with key actors in smart charging. The interviews were analysed using a multi-level perspective (MLP) framework. The study finds that the relative strength of the involved regimes influences how the challenge is framed and which solutions are presented. Cases in point: regime actors use smart charging (G2V) as an add-on to their existing services, while start-ups without the same ties to the established regime present and promote solutions that conflict with the existing regime.ConclusionsThis article finds that the solutions presented by regime actors have thus far been more commercially successful, compared with solutions presented by start-ups. This finding is in line with previous research that suggests that actors with strong ties to the existing regime present less-radical solutions with lower transformational potential, while niche actors without these ties present more-radical solutions. Still, the absence of V2G and the relative low market penetration of other advanced smart charging solutions have not prevented the introduction of BEVs from reaching the acceleration phase. This means that V2G is not necessary for large-scale BEV introduction, in all cases. By extension, this suggests that V2G mainly addresses issues with the electrical grid, highlighted by BEVs. BEVs may be successfully introduced at scale, where the pre-existing grid is well-developed, with sufficient balancing capacity. If this precondition is not met, the transition to BEVs may be contingent on smart charging or costly grid extensions. This can be the case at specific locations in Norway, but it may be more prevalent in other locations.

Artificial Intelligence‐based Module Type Package‐compatible Smart Sensors in the Process Industry

AbstractImage analysis presents a set of powerful methods to receive additional information about multiphase processes. It enables the development of advanced applications for process monitoring and optimization or, so‐called, soft sensors. However, the integration of advanced smart sensor systems based on image analysis into the process control system presents a complex task. To address this challenge, a modular automation concept offers a standardized interface to integrate modules. This paper presents an integration profile as a service specification that allows a plug‐and‐measure integration of smart visual sensors into modular plants. To verify the concept, we applied it to three different use cases. At the end, we discuss open challenges in the integration of complex analysis systems with multidimensional data streams into modular plants.

Social Equity and Urban Housing Policy Failures: Evaluation for Rehabilitation and Housing Resettlement Plans and Policies in Indian Cities

Today’s Indian cities are confronted with a wide range of problems due to social equity and urban housing policy failures, including rising populations, shifting family structures, increasing numbers of people living in informal communities and slums, inadequate urban infrastructure, growing environmental concerns, and an increase in migration. India has a severe housing shortage. A significant chasm separates housing demand from the supply. The discrepancy has led to the proliferation of urban slums, where millions of people are subjected to the lowest sanitation and hygiene standards. Housing policies in Indian cities include increasing taxes on unused or uninhabited land to fund the construction of low-cost dwellings, altering zoning laws to mandate that builders, and promoting higher densities of houses to spread out infrastructure costs among a larger population. Many people who have been the beneficiaries of land acquisition or other forms of forcible relocation are eligible for assistance through the Rehabilitation and Housing Resettlement Program. The proposed method included the city’s slums in the Advanced Smart Urban Missions (ASUM) planning process to overcome housing policy failures. A three-dimensional social equity framework with dispersion, identification, and protocol dimension aspects is applied to evaluate formal recovery plans’ objectives, priorities, and tactics. One of the most critical aspects of providing more housing alternatives for more people is ensuring everyone can afford it. Human rights, different perspectives, development initiatives in India, and policy inclusivity were also investigated. It makes suggestions for improving intelligent city policy that considers the needs of the city’s disadvantaged populations. This study looks into the problems that slum dwellers have with relocation and evictions and is limited to establishing various Smart Urban Missions. The research will help streamline the intelligent city development process sequentially, improving conditions for the urban poor and disadvantaged.

Port congestion under the COVID-19 pandemic: The simulation-based countermeasures

As the hub connecting global production and consumption, ports play a pivotal role in the global maritime supply chain system. The outbreak of the COVID-19 pandemic and its rapid global spread have caused significant port congestion, resulting in skyrocketing freight rates and posing a serious threat to the smooth operation of international trade. Based on the impact of the COVID-19 pandemic, we have developed a novel container port congestion evaluation model by integrating the classical SEIR model, using the system dynamics (SD) theory. In this study, we simulate the impact of different port congestion countermeasures using data from Ningbo Zhoushan Port. The results demonstrate that implementing smart regulations at a high level of epidemic prevention and control is the most efficient approach to swiftly eliminate port congestion. In addition, it is worth mentioning that in the scenarios with a low level of epidemic prevention and control, increasing port investment to optimize smart ports can yield even better effects. Furthermore, we have discussed the impact of factors such as the ratio of infected people, the number of contacted people, and epidemic prevention and control capacity on port congestion index. Hence, we propose that the implementation of advanced smart regulation systems can enhance port operational efficiency and effectively alleviate port congestion. Additionally, amidst the normalization of the COVID-19 pandemic, which has yet to disappear, it is crucial to maintain a vigilant approach to prevention and control to ensure the seamless operation of the port industry.

Liquid Crystals for Advanced Smart Devices with Microwave and Millimeter-Wave Applications: Recent Progress for Next-Generation Communications.

Liquid crystals (LCs) technology have a well-established history of applications in visible light, particularly in the display industry. However, with the rapid growth in communication technology, LCs have become a topic of current interest for high-frequency microwave (MW) and millimeter-wave (mmWave) applications due to promising characteristics such as tunability, continuous tuning, low losses, and price compatibility. To improve the performance of future communication technology using LCs, it is not sufficient only with the perspective of radio-frequency (RF) technology. Therefore, it is imperative to understand not only the novel structural designs and optimization of MW engineering but also the perspective of materials engineering when implementing advanced RF devices with maximum performance for next-generation satellite and terrestrial communication. Herein, based on advanced nematic LCs, polymer-modified LCs, dual-frequency LCs, and photo-reactive LCs, this article summarizes and examines the modulation principles and key research directions for the design strategies of LCs for advanced smart RF devices with improved driving performance and novel functionality. Furthermore, the challenges in development of state-of-the-art smart RF devices that use LCs are discussed.

Multi-bioinspired Janus-shedding fabric: One-way sweat transport and rapid sweat removal for improved personal moisture management

In recent years, Janus fabrics that can effectively facilitate personal moisture management have received great attention. However, they are still limited in sweat shedding issue of their superhydrophilic outer surface. Here, inspired by the lotus leaf and Namib Desert beetles, we propose a Janus-shedding fabric with superhydrophilic/hydrophobic patterns and asymmetric wetting property. Through femtosecond-laser microfabrication, we successfully construct superhydrophilic/hydrophobic patterns on the outside surface of Janus fabrics. The method prevents the usage of toxic chemical reagent, thus is completely environmentally-friendly. The as-prepared Janus-shedding fabrics possess one-way liquid transport property. More importantly, the fabrics can achieve rapid liquid/sweat removal on their hydrophilic outer surfaces. The liquid shedding mechanism can be explained that the hydrophilic/hydrophobic patterns on the outer fabric surface can create less surface tension of water which can only fix small volume of water. Moreover, they can maintain human body temperature 3.2 ℃ lower than covered with pristine cotton fabrics. This work provides new insights for the design and fabrication of advanced smart fabrics with both sweat transport and removal property for satisfying the growing demands of personal comfort.

Review of emerging advanced smart charging flexibility business models

Review of emerging advanced smart charging flexibility business models

Cosine Windows in Interpolated DFT-based Method for an Accurate High-Frequency Distortion Assessment in Power Systems

The transformation of electrical networks in the context of the new smart grid paradigm unavoidably involves new challenges regarding Power Quality (PQ) disturbances not only for customers but also for all the other involved stakeholders. Among PQ disturbances, waveform distortions have recently gained growing interest due to the massive presence of new technologies in distributed energy resources, in modern loads and in advanced smart metering systems. The presence of these devices determines arduous electromagnetic compatibility problems since the current and voltage waveform distortions in smart grids are characterized by spectral components above the traditional 2 kHz frequency limit, in a range extended up to 150 kHz. In this paper, an interpolated DFT-based (IDFT) method, recently proposed in the relevant literature in the field of signal processing, is properly extended for an accurate and fast assessment of power system waveform distortions in the frequency range from 2 to 150 kHz. Since DFT-based methods can suffer well-known spectral leakage problems, in this paper the IDFT is applied using cosine windows that minimize interference conditions among spectral components and maximise the estimation accuracy of the spectral component amplitude, phase angle and frequency. An optimal number of cosine window terms is also searched to improve the spectral analysis of high-frequency power system waveforms. Numerical applications on synthetic test signals and measured waveforms are carried out to quantify the accuracy and computational efforts of the proposed approach and to select the cosine window terms that better optimize the waveform distortion assessment.

Influence of La3+ co-doping on the photoluminescence properties of YAG: Dy3+ electrospun light emitting nanofibers

Synthesizing Yttrium Aluminium Garnet (YAG) into nanoscale fibers significantly enhances its optical properties. These nanofibers exhibit excellent flexibility, lightweight nature, and high strength, making them highly suitable for smart clothing applications. Additionally, by doping rare earth ions into the YAG fibers, their luminescence properties can be further optimized, opening new possibilities for advanced smart clothing designs. In this paper, we present the successful fabrication of dysprosium (Dy) doped YAG nanofibers, lanthanum (La) doped YAG nanofibers, La co-doped Dy: YAG nanofibers with different concentration and investigate their photoluminescence properties. The photoluminescence excitation spectrum of the Dy3+-YAG nanofibers showed an intense peak centred at 350 nm due to absorption by Dy3+ ions and emits strong blue colour centred at 483 nm in emission spectrum, which is attributed to the 4F9/2 → 6H15/2 transition of Dy3+ions. The La3+-YAG nanofibers excited at 350 nm also showed an emission in blue region at 483 nm. Co-doping of La3+ ions in Dy3+-YAG nanofibers exhibited enhancement in the emission intensity significantly. In comparison with all as-synthesized nanofibers, specific concentration of Dy and La in YAG nanofibers would be the potential candidate for flexible light emitting devices.

YOLO Based Advanced Smart Traffic Assistance Platform for Number Plate and Helmet Detection

Abstract: Now a days road accidents are one of the major causes that are leading to human death. However the most common reason for motorcycle deaths is because many fail to confirm to the law of wearing helmet. Here is the software using YOLO V8 to recognize the motorbike drivers , who are not obeying helmet law in an automated way. The helmet and license plate detection system using YOLO V8 is a computer vision technology-based system that utilizesthe You Only Look Once (YOLO) objectdetection algorithm to detect helmets and license plates in real-time. The system is designed to improve safety on roads and highways by detecting riders without helmets and vehicles without properlicense plates. The system consists of motorcycledetection , helmet and no helmet detection as well as bike license plate recognition. The system is capable of processing image from a variety of sources, including trafficcameras and drones, and can detect the presence or absence of helmets and license plates in the image frames. It uses a deep learning model trained on a large dataset of annotated images to identify and classify objects. The output of the system includes a bounding box around each detected object and a label indicating whether it is a helmet or a license plate. The system can also be configured to generate alerts or notifications when violations are detected. Overall, this system provides a valuable tool for law.