The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal de...The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.展开更多
A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfigur...A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an in...Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy efficiency.Two classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely underground.For each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity levels.The proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and cooling.In addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy consumption.Results demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air quality.These findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as classrooms.Furthermore,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor spaces.This study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.展开更多
Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems.The current study introduces an audio-visual recognition framework designed ...Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems.The current study introduces an audio-visual recognition framework designed to address performance degradation caused by environmental interference,such as background noise,overlapping speech,and visual obstructions.The proposed framework employs a structured fusion approach,combining early-stage feature-level integration with decision-level coordination guided by temporal attention mechanisms.Audio data are transformed into mel-spectrogram representations,and visual data are represented as raw frame sequences.Spatial and temporal features are extracted through convolutional and transformer-based encoders,allowing the framework to capture complementary and hierarchical information fromboth sources.Across-modal attentionmodule enables selective emphasis on relevant signals while suppressing modality-specific noise.Performance is validated on a modified version of the AFEW dataset,in which controlled noise is introduced to emulate realistic conditions.The framework achieves higher classification accuracy than comparative baselines,confirming increased robustness under conditions of cross-modal disruption.This result demonstrates the suitability of the proposed method for deployment in practical emotion-aware technologies operating outside controlled environments.The study also contributes a systematic approach to fusion design and supports further exploration in the direction of resilientmultimodal emotion analysis frameworks.The source code is publicly available at https://github.com/asmoon002/AVER(accessed on 18 August 2025).展开更多
This study presents a detailed comparative analysis of three electron transport layer(ETL)materials for perovskite solar cells(PSCs),namely titanium dioxide(TiO_(2)),barium titanate(BaTiO_(3)or BTO),and strontium-dope...This study presents a detailed comparative analysis of three electron transport layer(ETL)materials for perovskite solar cells(PSCs),namely titanium dioxide(TiO_(2)),barium titanate(BaTiO_(3)or BTO),and strontium-doped barium titan-ate(Ba_(1−x)Sr_(x)TiO_(3)or BST),and their impact on the quantum efficiency(QE)and power conversion efficiency(PCE)of CH_(3)NH_(3)PbI_(3)(MAPbI_(3))PSCs.The optimized structure demonstrates that devices utilizing BST as an ETL achieved the highest PCE of 29.85%,exhibiting superior thermal stability with the lowest temperature coefficient of−0.43%/K.This temperature-induced degradation is comparable to that of commercially available silicon cells.Furthermore,BST-based ETLs show 29.50%and 26.48%higher PCE than those of TiO_(2)-based and BTO-based ETLs.The enhanced internal QE and favorable current density–voltage(J–V)characteristics of BST compared with those of TiO_(2)and BTO are attributed to its improved charge carrier separation,reduced recombination rates,and robust electrical characteristics under varied environmental conditions.Furthermore,the electric field and generation rate of the BST-based ETLs show a more favorable distribution than those of the TiO_(2)-based and BTO-based ETLs.These findings provide significant insights into the role of different ETLs in enhancing QE,indicating that BST is a superior ETL that enhances both the efficiency and stability of PSCs.This study contributes to the understanding of how perovskite-structured ETLs can be used to design and optimize highly efficient and stable photovoltaic devices.展开更多
Rapidly growing population,escalating urbanization,and industrialization are causing the depletion of non-renewable resources and air pollution,a silent pandemic responsible for billions of global mortalities.Sensors ...Rapidly growing population,escalating urbanization,and industrialization are causing the depletion of non-renewable resources and air pollution,a silent pandemic responsible for billions of global mortalities.Sensors are crucial vectors for monitoring the emission of various gases/volatile organic compoundsbased pollutants from various anthropogenic sources.Borophene-based nanomaterials(BNMs)are the latest two-dimensional flatlands to this emergent next-generation sensors family with exceptional and tunable physicochemical attributes characterized by high anisotropy,thermal/mechanical resilience,tunable bandgaps,light-weight,high charge carrier mobility,and excellent adsorption efficacies.However,the practical implementation and scalability of BNMs grapple with challenges,including instability,substrateto-device transfer complications,and optimization intricacies.This comprehensive review delves into state-of-the-art BNM sensor fabrication techniques,intertwining theoretical insights derived from density functional theory and molecular dynamics with practical evaluations and on-site applications.Besides,the fundamental challenges associated with engineering BNM sensors and their alternate solutions by employing various strategies,including surface termination,functionalization,hydrogenation,hybridization,architecting composites,and green chemistry,are detailed.This review offers a roadmap from lab to market,bridging theoretical insights with practical implementation and expediting the advanced BNM sensors with wearable,remotely accessible,point-of-care,scavenging,self-powered,biocompatible,and intelligent modules for pollution management.展开更多
A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm...A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.展开更多
In this study, reduction and desorption of oxides of nitrogen （NOx） were conducted using an electrical discharge plasma technique. The study was carried out using a simulated gas mixture to explore the possibility o...In this study, reduction and desorption of oxides of nitrogen （NOx） were conducted using an electrical discharge plasma technique. The study was carried out using a simulated gas mixture to explore the possibility of re-generation of used adsorbents by a nonthermal plasma desorption technique. Three different types of corona electrodes, namely, pipe, helical wire, and straight wire, were used for analyzing their effectiveness in NOx reduction/desorption. The pipe- type corona electrode exhibited a nitric oxide （NO） conversion of 50%, which is 1.5 times that of the straight-wire-type electrode at an energy density of 175 J/L. The helical-wire-type corona electrode exhibited a NOx desorption efficiency almost 4 times that of the pipe-type electrode, indicating the possibility that corona-generated species play a crucial role in desorption.展开更多
A comparison of the effectiveness of installing reactive power compensators,such as shunt capacitors,static var compensators(SVCs),and static synchronous compensators(STATCOMs),was presented in large-scale power netwo...A comparison of the effectiveness of installing reactive power compensators,such as shunt capacitors,static var compensators(SVCs),and static synchronous compensators(STATCOMs),was presented in large-scale power networks.A suitable bus was first identified using modal analysis method.The single shunt capacitor,single SVC,and single STATCOM were installed separately on the most critical bus.The effects of the installation of different devices on power loss reduction,voltage profile improvement,and voltage stability margin enhancement were examined and compared for 57-and 118-bus transmission systems.The comparative study results show that SVC,and STATCOM are expensive compared to shunt capacitor,yet the effect of installing STATCOM is better than SVC and the effect of installing SVC is better than that of shunt capacitor in achieving power loss reduction,voltage profile improvement and voltage stability margin enhancement.展开更多
This paper proposes an H-infinity combustion control method for diesel engines. The plant model is the discrete dynamics model developed by Yasuda et al., which is implementable on a real engine control unit. We intro...This paper proposes an H-infinity combustion control method for diesel engines. The plant model is the discrete dynamics model developed by Yasuda et al., which is implementable on a real engine control unit. We introduce a two-degree-of-freedom control scheme with a feedback controller and a feedforward controller. This scheme achieves both good feedback properties, such as disturbance suppression and robust stability, and a good transient response. The feedforward controller is designed by taking the inverse of the static plant model, and the feedback controller is designed by the H-infinity control method, which reduces the effect of the trubocharger lag. The effectiveness of the proposed method is evaluated in simulations using the nonlinear discrete dynamics model.展开更多
The high redundancy actuator(HRA)concept is a novel approach to fault tolerant actuation that uses a high number of small actuation elements,assembled in series and parallel in order to form a single actuator which ha...The high redundancy actuator(HRA)concept is a novel approach to fault tolerant actuation that uses a high number of small actuation elements,assembled in series and parallel in order to form a single actuator which has intrinsic fault tolerance.Whilst this structure afords resilience under passive control methods alone,active control approaches are likely to provide higher levels of performance.A multiple-model control scheme for an HRA applied through the framework of multi-agent control is presented here.The application of this approach to a 10×10 HRA is discussed and consideration of reconfguration delays and fault detection errors are made.The example shows that multi-agent control can provide tangible performance improvements and increase fault tolerance in comparison to a passive fault tolerant approach.Reconfguration delays are shown to be tolerable,and a strategy for handling false fault detections is detailed.展开更多
A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyr...A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyristor controlled series capacitor （TCSC） as a damping controller in the multi-machine power system. The coordinated design problem of PSS and TCSC controllers over a wide range of loading conditions is formulated as a multi-objective optimization problem which is the aggregation of two objectives related to damping ratio and damping factor. By minimizing the objective function with oscillation, the characteristics between areas are contained and hence the interactions among the PSS and TCSC controller under transient conditions are modified. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on a weakly connected power system subjected to different disturbances, loading conditions and system parameter variations. The cigenvalues analysis and nonlinear simulation results demonstrate the high performance of proposed controllers which is able to provide efficient damping of low frequency oscillations.展开更多
Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise info...Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.展开更多
To move the performance of lithium-ion batteries into the next stage,the modification of the structure of cells is the only choice except for the development of materials exhibiting higher performance.In this review p...To move the performance of lithium-ion batteries into the next stage,the modification of the structure of cells is the only choice except for the development of materials exhibiting higher performance.In this review paper,the employment of through-holing structures of anodes and cathodes prepared with a picosecond pulsed laser has been proposed.The laser system and the structure for improving the battery performance were introduced.The performance of laminated cells constructed with through-holed anodes and cathodes was reviewed from the viewpoints of the improvement of high-rate performance and energy density,removal of unbalanced capacities on both sides of the current collector,even greater high-rate performance by hybridizing cathode materials and removal of irreversible capacity.In conclusion,the points that should be examined and the problem for the through-holed structure to be in practical use are summarized.展开更多
The new techniques were presented for preventing undesirable distance relay maloperation during voltage collapse and power swings in transmission grids. Initially, the work focused on the development of a fast detecti...The new techniques were presented for preventing undesirable distance relay maloperation during voltage collapse and power swings in transmission grids. Initially, the work focused on the development of a fast detection of voltage collapse and a three-phase fault at transmission lines by using under impedance fault detector (UIFD) and support vector machine (SVM). Likewise, an intelligent approach was developed to discriminate a fault, stable swing and unstable swing, for correct distance relay operation by using the S-transform and the probabilistic neural network (PNN). To illustrate the effectiveness of the proposed techniques, simulations were carried out on the IEEE 39-bus test system using the PSS/E and MATLAB software.展开更多
Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone...Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.展开更多
文摘The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.
基金Project(DIP-2012-30)supported by the Universiti Kebangsaan,Malaysia
文摘A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
基金Funding Statement:This research was conducted as part of the Tech4You Project“Technologies for climate change adaptation and quality of life improvement”,n.ECS0000009,CUP H23C22000370006,Italian PNRR,Mission 4,Component 2,Investment 1.5 funded by the European Union-NextGenerationEU.
文摘Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy efficiency.Two classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely underground.For each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity levels.The proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and cooling.In addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy consumption.Results demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air quality.These findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as classrooms.Furthermore,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor spaces.This study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.
基金funded by the Institute of Information&CommunicationsTechnology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT),grant number 2021-0-01341.
文摘Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems.The current study introduces an audio-visual recognition framework designed to address performance degradation caused by environmental interference,such as background noise,overlapping speech,and visual obstructions.The proposed framework employs a structured fusion approach,combining early-stage feature-level integration with decision-level coordination guided by temporal attention mechanisms.Audio data are transformed into mel-spectrogram representations,and visual data are represented as raw frame sequences.Spatial and temporal features are extracted through convolutional and transformer-based encoders,allowing the framework to capture complementary and hierarchical information fromboth sources.Across-modal attentionmodule enables selective emphasis on relevant signals while suppressing modality-specific noise.Performance is validated on a modified version of the AFEW dataset,in which controlled noise is introduced to emulate realistic conditions.The framework achieves higher classification accuracy than comparative baselines,confirming increased robustness under conditions of cross-modal disruption.This result demonstrates the suitability of the proposed method for deployment in practical emotion-aware technologies operating outside controlled environments.The study also contributes a systematic approach to fusion design and supports further exploration in the direction of resilientmultimodal emotion analysis frameworks.The source code is publicly available at https://github.com/asmoon002/AVER(accessed on 18 August 2025).
基金funded by the Geran Universiti Penyelidikan(GUP),under the grant number GUP-2022-011 funded by the Universiti Kebangsaan Malaysia。
文摘This study presents a detailed comparative analysis of three electron transport layer(ETL)materials for perovskite solar cells(PSCs),namely titanium dioxide(TiO_(2)),barium titanate(BaTiO_(3)or BTO),and strontium-doped barium titan-ate(Ba_(1−x)Sr_(x)TiO_(3)or BST),and their impact on the quantum efficiency(QE)and power conversion efficiency(PCE)of CH_(3)NH_(3)PbI_(3)(MAPbI_(3))PSCs.The optimized structure demonstrates that devices utilizing BST as an ETL achieved the highest PCE of 29.85%,exhibiting superior thermal stability with the lowest temperature coefficient of−0.43%/K.This temperature-induced degradation is comparable to that of commercially available silicon cells.Furthermore,BST-based ETLs show 29.50%and 26.48%higher PCE than those of TiO_(2)-based and BTO-based ETLs.The enhanced internal QE and favorable current density–voltage(J–V)characteristics of BST compared with those of TiO_(2)and BTO are attributed to its improved charge carrier separation,reduced recombination rates,and robust electrical characteristics under varied environmental conditions.Furthermore,the electric field and generation rate of the BST-based ETLs show a more favorable distribution than those of the TiO_(2)-based and BTO-based ETLs.These findings provide significant insights into the role of different ETLs in enhancing QE,indicating that BST is a superior ETL that enhances both the efficiency and stability of PSCs.This study contributes to the understanding of how perovskite-structured ETLs can be used to design and optimize highly efficient and stable photovoltaic devices.
文摘Rapidly growing population,escalating urbanization,and industrialization are causing the depletion of non-renewable resources and air pollution,a silent pandemic responsible for billions of global mortalities.Sensors are crucial vectors for monitoring the emission of various gases/volatile organic compoundsbased pollutants from various anthropogenic sources.Borophene-based nanomaterials(BNMs)are the latest two-dimensional flatlands to this emergent next-generation sensors family with exceptional and tunable physicochemical attributes characterized by high anisotropy,thermal/mechanical resilience,tunable bandgaps,light-weight,high charge carrier mobility,and excellent adsorption efficacies.However,the practical implementation and scalability of BNMs grapple with challenges,including instability,substrateto-device transfer complications,and optimization intricacies.This comprehensive review delves into state-of-the-art BNM sensor fabrication techniques,intertwining theoretical insights derived from density functional theory and molecular dynamics with practical evaluations and on-site applications.Besides,the fundamental challenges associated with engineering BNM sensors and their alternate solutions by employing various strategies,including surface termination,functionalization,hydrogenation,hybridization,architecting composites,and green chemistry,are detailed.This review offers a roadmap from lab to market,bridging theoretical insights with practical implementation and expediting the advanced BNM sensors with wearable,remotely accessible,point-of-care,scavenging,self-powered,biocompatible,and intelligent modules for pollution management.
文摘A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.
文摘In this study, reduction and desorption of oxides of nitrogen （NOx） were conducted using an electrical discharge plasma technique. The study was carried out using a simulated gas mixture to explore the possibility of re-generation of used adsorbents by a nonthermal plasma desorption technique. Three different types of corona electrodes, namely, pipe, helical wire, and straight wire, were used for analyzing their effectiveness in NOx reduction/desorption. The pipe- type corona electrode exhibited a nitric oxide （NO） conversion of 50%, which is 1.5 times that of the straight-wire-type electrode at an energy density of 175 J/L. The helical-wire-type corona electrode exhibited a NOx desorption efficiency almost 4 times that of the pipe-type electrode, indicating the possibility that corona-generated species play a crucial role in desorption.
文摘A comparison of the effectiveness of installing reactive power compensators,such as shunt capacitors,static var compensators(SVCs),and static synchronous compensators(STATCOMs),was presented in large-scale power networks.A suitable bus was first identified using modal analysis method.The single shunt capacitor,single SVC,and single STATCOM were installed separately on the most critical bus.The effects of the installation of different devices on power loss reduction,voltage profile improvement,and voltage stability margin enhancement were examined and compared for 57-and 118-bus transmission systems.The comparative study results show that SVC,and STATCOM are expensive compared to shunt capacitor,yet the effect of installing STATCOM is better than SVC and the effect of installing SVC is better than that of shunt capacitor in achieving power loss reduction,voltage profile improvement and voltage stability margin enhancement.
文摘This paper proposes an H-infinity combustion control method for diesel engines. The plant model is the discrete dynamics model developed by Yasuda et al., which is implementable on a real engine control unit. We introduce a two-degree-of-freedom control scheme with a feedback controller and a feedforward controller. This scheme achieves both good feedback properties, such as disturbance suppression and robust stability, and a good transient response. The feedforward controller is designed by taking the inverse of the static plant model, and the feedback controller is designed by the H-infinity control method, which reduces the effect of the trubocharger lag. The effectiveness of the proposed method is evaluated in simulations using the nonlinear discrete dynamics model.
基金supported by UK s Engineering and Physical Sciences Research Council(EPSRC)(No.EP/D078350/1)
文摘The high redundancy actuator(HRA)concept is a novel approach to fault tolerant actuation that uses a high number of small actuation elements,assembled in series and parallel in order to form a single actuator which has intrinsic fault tolerance.Whilst this structure afords resilience under passive control methods alone,active control approaches are likely to provide higher levels of performance.A multiple-model control scheme for an HRA applied through the framework of multi-agent control is presented here.The application of this approach to a 10×10 HRA is discussed and consideration of reconfguration delays and fault detection errors are made.The example shows that multi-agent control can provide tangible performance improvements and increase fault tolerance in comparison to a passive fault tolerant approach.Reconfguration delays are shown to be tolerable,and a strategy for handling false fault detections is detailed.
基金Project(UKM-DLP-2011-059) supported by the National University of Malaysia
文摘A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyristor controlled series capacitor （TCSC） as a damping controller in the multi-machine power system. The coordinated design problem of PSS and TCSC controllers over a wide range of loading conditions is formulated as a multi-objective optimization problem which is the aggregation of two objectives related to damping ratio and damping factor. By minimizing the objective function with oscillation, the characteristics between areas are contained and hence the interactions among the PSS and TCSC controller under transient conditions are modified. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on a weakly connected power system subjected to different disturbances, loading conditions and system parameter variations. The cigenvalues analysis and nonlinear simulation results demonstrate the high performance of proposed controllers which is able to provide efficient damping of low frequency oscillations.
基金funded by the State Grid Jiangsu Electric Power Company(Grant No.JS2020112)the National Natural Science Foundation of China(Grant No.62272236).
文摘Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.
文摘To move the performance of lithium-ion batteries into the next stage,the modification of the structure of cells is the only choice except for the development of materials exhibiting higher performance.In this review paper,the employment of through-holing structures of anodes and cathodes prepared with a picosecond pulsed laser has been proposed.The laser system and the structure for improving the battery performance were introduced.The performance of laminated cells constructed with through-holed anodes and cathodes was reviewed from the viewpoints of the improvement of high-rate performance and energy density,removal of unbalanced capacities on both sides of the current collector,even greater high-rate performance by hybridizing cathode materials and removal of irreversible capacity.In conclusion,the points that should be examined and the problem for the through-holed structure to be in practical use are summarized.
文摘The new techniques were presented for preventing undesirable distance relay maloperation during voltage collapse and power swings in transmission grids. Initially, the work focused on the development of a fast detection of voltage collapse and a three-phase fault at transmission lines by using under impedance fault detector (UIFD) and support vector machine (SVM). Likewise, an intelligent approach was developed to discriminate a fault, stable swing and unstable swing, for correct distance relay operation by using the S-transform and the probabilistic neural network (PNN). To illustrate the effectiveness of the proposed techniques, simulations were carried out on the IEEE 39-bus test system using the PSS/E and MATLAB software.
基金This work has supported by the Xiamen University Malaysia Research Fund(XMUMRF)(Grant No:XMUMRF/2019-C3/IECE/0007)。
文摘Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.
