Adaptive Technique Research Articles

Emotion Detection using EEG Signals in Image and Video Processing: A Survey

Brain serves as the body's processing of knowledge and management centre. The central nervous system directly produces ElectroEncephaloGram (EEG) physiological signals, which are strongly associated with human emotions. In the upcoming years, there will be an increase in interest for identifying emotion using brain waves by EEG (ElectroEncephaloGraphy) signals. It takes efficient and effective signal processing and feature extraction techniques for detection of emotions from human biological brain signals. Current approaches gather valuable information from a fixed number of ElectroEncephaloGraphy (EEG) channels utilizing variety of methodologies. This work analyses the different difficulties and problems associated with EEG signals for emotion identification and provides a comprehensive summary of several contemporary approaches. Pre-processing, feature extraction, and categorization are the first steps in the process of recognizing emotions from EEG signals. The main goal of this survey is to sought and enhance brain signal-based emotion detection ability by comparing all novel and adaptive channel selection technique that recognize the distinct changes in brain activities that varies between individuals and emotional states.

Development of Reliable Male-Specific Molecular Markers for Genetic Sex Identification in Sea Cucumber Apostichopus japonicus.

Apostichopus japonicus is an important marine aquaculture species in China, with high nutritional and economic value. In A. japonicus, there is no obvious sexual dimorphism in external appearance, and sex differentiation primarily relies on the observation of mature gonads after dissection, which leads to difficulties in sex identification. The confusion in sex identification greatly reduces breeding efficiency in the sea cucumber industry. Therefore, developing a reliable sex-specific marker is crucial. In this study, we identified 586 male-specific sequences through whole-genome assembly and sequence alignment, but did not identify any female-specific sequences, inferring an XY-type sex determination system in sea cucumbers. We developed a set of male-specific molecular markers to establish an accurate, stable, and widely adaptable genetic sex identification technique for A. japonicus. The male-specific molecular markers were validated with 100% accuracy in sea cucumber populations from six different geographical regions in China. In conclusion, this study provides further evidence for the XY-type sex determination system in A. japonicus and establishes an effective genetic sex identification method for multi-geographic populations, which benefits future study on reproductive biology and has significant implications in sea cucumber aquaculture industry.

An Integration of Adaptive Control Techniques with Portable Solar-Powered EV Charging with Particle Swarm Optimization (PSO)

Abstract: This paper presents a novel approach utilizing Particle Swarm Optimization (PSO) for enhancing the efficiency of solar-powered electric vehicle (EV) charging systems. With the increasing adoption of EVs and the imperative to transition towards renewable energy sources, optimizing the utilization of solar power for charging becomes crucial. The proposed system integrates photovoltaic (PV) panels to harness solar energy, providing a sustainable and renewable power source for EV charging. The key innovation lies in the application of PSO, a nature-inspired optimization algorithm, to dynamically adjust charging parameters based on real-time environmental conditions, grid electricity prices, and EV battery requirements. PSO offers a robust and efficient method for solving optimization problems by simulating the behavior of swarms in nature. In the context of solar-powered EV charging, the PSO algorithm iteratively adjusts charging rates and schedules to maximize solar energy utilization, minimize charging costs, and ensure optimal battery health. The integration of PSO optimization with solarpowered EV charging represents a promising step towards achieving sustainable and intelligent transportation solutions. Future research directions may include further optimization refinements, scalability studies, and real-world deployment to validate the practicality and effectiveness of the proposed approach in diverse environments and usage scenarios.

Exploring the Challenges of L1 Negative Transfer among Vietnamese English Language Learners: A Qualitative Study

The current study aimed to gain insight into the nuanced challenges that Vietnamese learners encountered due to the interference of their native linguistic structures and norms. This qualitative study explored the experiences of Vietnamese learners in acquiring English, focusing on the influence of negative L1 transfer. This study focused on the influence of negative transfer from their first language (L1) by drawing upon in-depth interviews and observational data from a group of Vietnamese university students. The results showed that despite exhibiting high levels of motivation and enthusiasm for learning English, Vietnamese learners struggled with the shadows of L1 interference in areas such as pronunciation, syntax, and idiomatic expressions. The tonal nature of the Vietnamese language led to intonation patterns in English that deviated from native norms. Direct translations also contributed to unidiomatic English, indicating a deep influence of L1 thought patterns. Additionally, cultural contexts embedded within the Vietnamese language presented challenges, as some concepts and expressions lacked direct English equivalents. Although students faced various obstacles, they utilized a variety of adaptive techniques to lessen the impact of negative transfer. These techniques included code-switching, peer correction, and self-reflection. The study emphasized the significance of recognizing and comprehending the significant role of L1 when creating English language educational programs and teaching techniques for Vietnamese students. By illuminating the specific challenges and adaptive strategies of these learners, educators could personalize their approaches, leading to a more comprehensive and efficacious language-learning atmosphere.

Cross-Domain Adaptation Techniques for Robust Plant Disease Detection: A DANN-CORAL Hybrid Approach

Plant disease detection with deep learning models has shown promising results, but these models often struggle with generalizing across diverse agricultural environments due to domain shifts in imaging conditions. This paper presents a novel hybrid approach focusing on cross-domain adaptation techniques to address the challenge of domain shift. Our proposed method combines the Domain-Adversarial Neural Network (DANN) with Correlation Alignment (CORAL) to mitigate domain shifts between datasets. The DANN framework enforces domain-invariant feature learning through adversarial training. Using the PlantVillage Dataset, with controlled environment images, and the New Plant Village Dataset, with varied conditions, the model is first trained on PlantVillage and then adapted to New Plant Village using the CORAL loss to support the second-order statistics. In case of domain shift experiments with various datasets, DANN-CORAL achieved accuracies 91.39%, precision 93.36%, recall 88.9% and F1-scores 91.05% indicating the robustness and generalizability of our model is better than the other baseline models. This approach enhances model robustness and adaptability, providing insights into combining adversarial and statistical alignment for cross-domain adaptation in agricultural imaging.

A switching norm based least mean Square/Fourth adaptive technique for sparse channel estimation and echo cancellation

To realize rapid data transmission, the broadband transmission technique is being extensively explored and applied in existing wireless communication systems. The multi-path channel in broadband wireless communication systems is sparse and this sparsity can be used as prior knowledge to estimate the channel. To make use of sparsity, this paper recommends a switching norm-based least mean square/fourth (SN-LMS/F) adaptive approach for sparse channel estimation and echo cancellation. The suggested SN-LMS/F is implemented by adding a soft parameter adjustment function (SPF) into the conventional LMS/F adaptive method's cost function and utilizes both the l0 and l1 norm to exploit system sparsity with reduced complexity. The simulated output indicates that the suggested SN-LMS/F adaptive technique provides a more desirable performance for sparse channel estimation and echo cancellation with reduced execution time.

From computed tomography to 3D printed heterogeneous scaffolds: A novel approach to simulating bone characteristics using octree adaptive subdivision

Bone microstructures or scaffolds mimicking natural bone characteristics find widespread applications in bone tissue engineering (BTE). To accurately replicate a personalized bone structure, we developed a heterogeneous scaffold by combining medical image data with the octree adaptive subdivision technique. The transformations between Hounsfield units (HU)-strength and strength-lattice features were implemented for mapping bone information to the scaffold. Initially, HU values from computed tomography (CT) images were smoothly converted into corresponding strengths through a rational Bezier curve. Then, these strengths were further translated into the features of lattice structures, specifically the strut diameter and subdivision level. The novel concept of adaptive octree subdivision, executed on the lattices, allows for controlling bone porosity and mechanical property even at local levels. As a result, a personalized heterogeneous scaffold accurately simulating natural bone structure was designed and manufactured for bone grafting application on a 3D-printed alveolar model. The scaffold has an outer cortical bone layer capable of withstanding pressures of up to 208.2 MPa, while the inner cancellous bone exhibits a high porosity ranging from 77.6 % to 93.6 %. The research provides an elegant yet effective approach to the generation of bone microstructures, opening up practical BTE applications.

Detection of Road Risk Sources Based on Multi-Scale Lightweight Networks.

Timely discovery and disposal of road risk sources constitute the cornerstone of road operation safety. Presently, the detection of road risk sources frequently relies on manual inspections via inspection vehicles, a process that is both inefficient and time-consuming. To tackle this challenge, this paper introduces a novel automated approach for detecting road risk sources, termed the multi-scale lightweight network (MSLN). This method primarily focuses on identifying road surfaces, potholes, and scattered objects. To mitigate the influence of real-world factors such as noise and uneven brightness on test results, pavement images were carefully collected. Initially, the collected images underwent grayscale processing. Subsequently, the median filtering algorithm was employed to filter out noise interference. Furthermore, adaptive histogram equalization techniques were utilized to enhance the visibility of cracks and the road background. Following these preprocessing steps, the MSLN model was deployed for the detection of road risk sources. Addressing the challenges associated with two-stage network models, such as prolonged training and testing times, as well as deployment difficulties, this study adopted the lightweight feature extraction network MobileNetV2. Additionally, transfer learning was incorporated to elevate the model's training efficiency. Moreover, this paper established a mapping relationship model that transitions from the world coordinate system to the pixel coordinate system. This model enables the calculation of risk source dimensions based on detection outcomes. Experimental results reveal that the MSLN model exhibits a notably faster convergence rate. This enhanced convergence not only boosts training speed but also elevates the precision of risk source detection. Furthermore, the proposed mapping relationship coordinate transformation model proves highly effective in determining the scale of risk sources.

Matlab-Simulink Model of CHMT for Internal Climate in Greenhouses

Over the past twenty years, Kenya's food security has been threatened by the sub-division of land into tiny areas and the clearance of forests to make way for settlement. These actions have an impact on soil moisture, rainfall patterns, and regional temperature changes. Clear response plans and adaption techniques have been required to address the threats that have arisen. Greenhouse farming, where warmer temperatures are attained and the impact of unfavourable weather conditions on plants is mitigated by the enclosure, is one strategy being used to combat the production of food and climate change. Nevertheless, crop production and quality are negatively impacted by traditional techniques of regulating temperature and humidity through arbitrary opening and closing of the greenhouse walls. In light of this, the goal of this research was to enhance greenhouse farming as it exists today by implementing a dynamic, adjustable system that would create ideal climate conditions for plant growth. This mostly entailed controlling the greenhouse's humidity, temperature, and vapour pressure deficit to the ideal ranges needed by various plants. The humidity and air temperature within the greenhouse were the controlled microclimate conditions. These were accomplished by simulating the convectional heat transfer and mass transfer that occur inside the greenhouse to control the temperature and humidity, and by developing mathematical model utilizing differential equations. Proportional Integral Derivative (PID) was utilized to automatically modify SIMULINK, a block-based modelling and simulation tool. Regardless of the different external conditions, the numerical values for internal temperature and humidity were calculated and graphically depicted. The model made it possible to modify the outcomes according to the needs of the plant. To increase crop productivity in greenhouse farming, it was suggested that a physical prototype model be constructed and integrated into the greenhouse construction.

Adaptive fixed-time difference synchronization for different classes of chaotic dynamical systems

This article examines adaptive fixed-time difference synchronization for various classes of chaotic dynamical systems. The adaptive fixed-time control technique has been used in this article to investigate the difference synchronization for the Sprott chaotic system, both with and without delay. The fixed settling time (T) has been estimated successfully. It is also shown that the trajectories of error states approach to the origin within a fixed time (T). The theoretical analysis is validated by simulating Sprott chaotic systems both with and without delay. On the other hand, various nonlinear chaotic systems are explored for difference synchronization in discrete chaotic systems. Several chaotic maps, including Tinkerbell, Henon, and Hitzl-Zele, have been used to achieve synchronization in these discrete systems. The numerical results are presented graphically, verifying the theoretical outcomes of difference synchronization for various classes of chaotic dynamical systems.

ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.

Integrated Control Design for Hybrid Grid-Photovoltaic Systems in Distillation Applications: A Reference Model and Fuzzy Logic Approach

This paper presents a novel hybrid structural control solution designed for distillation systems that utilize a solar source alongside an electrical grid. The power conversion architecture incorporates a reversible bridge rectifier and a quadratic boost converter. The hybrid photovoltaic grid configuration offers several benefits, including source complementarity, enhanced dependability, and energy availability aligned with power requirements. Leveraging a photovoltaic source operating at maximum power facilitates energy conservation. On the control front, an adaptive technique based on a reference model is proposed. Fuzzy logic governs the quadratic boost converter, simplifying the management of its complex nonlinear nature. The control strategy aims to maximize solar power utilization, minimize harmonic components in the grid current, synthesize an adaptive controller, and achieve a near-unit power factor on the grid. The simulation results for a steady distillation system demonstrate promising findings. Despite variations in irradiation, load power, and grid drops, the system maintains a minimal bus voltage ripple, remaining close to the intended value. Optimization of the panel-generated power leads to improved PV source utilization and enhanced system efficiency. Furthermore, the combination with an electrical grid achieves a low rate of grid current distortion and a unitary power factor.

Exploring the conflict risk characteristics of air weaving sections in Metroplex terminal areas with flight trajectory data and adaptive graph spatial-temporal transformer

Metroplex terminal areas is involved with numerous air weaving sections, where various types of arrival and departure traffic flows intersect or converge, leading to high-risk aircraft conflicts and inefficient Metroplex operation. The primary objective of this study is to explore the conflict risk characteristics of operational interactions between airports in Metroplex terminal areas with large-scale trajectory data, including risk evaluation, risk propagation and risk prediction. One complete month of ADS-B trajectory data is collected from a representative multi-airport system in Central and South China to illustrate the procedure. Specifically, flight trajectories are firstly clustered within Metroplex terminal areas, and a trajectory-tube model is then built to characterize the airport flows within the same cluster. Then, a clustering-based collision probability calculation method is proposed to evaluate the risk of air weaving sections. The spatial patterns of risks in air weaving sections reveal that operational interactions among Metroplex airports are quite different under various runway configurations. Furthermore, an adaptive graph spatial-temporal transformer (ASTT) network is developed to predict the future risk of each air weaving section in Metroplex terminal areas. The results indicate that the developed ASTT network can collaboratively encode the spatiotemporal characteristics in the air weaving section risk data, which achieves more accurate and robust prediction performance than other compared models during multiple look-ahead time steps. Moreover, the adaptive spatial graph technique designed in the ASTT can also reveal the hidden risk propagation effects among air weaving sections. The results of this study could provide insightful suggestions to air traffic authorities for developing effective coordination and conflict mitigation strategies, such as redesigning flight procedures, reallocating routes, and optimizing aircraft sequences to enhance the efficiency and safety of Metroplex operations.

A novel hybrid adaptive differential evolution for global optimization

Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants.

Multi-objective sand cat swarm optimization based on adaptive clustering for solving multimodal multi-objective optimization problems

Multimodal multi-objective optimization problems (MMOPs) represent a highly challenging class of complex problems, characterized by the presence of several Pareto solution sets in the decision space which map to the identical Pareto-optimal front. The goal of solving MMOPs is to find multiple distinct Pareto sets to sustain a balance between good convergence and diversification of populations. In this paper, a multi-objective sand cat swarm optimization algorithm (MOSCSO) is developed to address MMOPs. In the MOSCSO algorithm, an adaptive clustering-based specific congestion distance technique is introduced to compute the level of crowdedness. This ensures an even distribution of individuals, avoiding excessive crowding in the local area. Subsequently, enhanced search-and-attack prey updating mechanisms are designed to effectively increase not only the exploration and exploitation capabilities of the algorithm but also to enhance the diversity of the swarm in both the decision space and the objective space. To verify the effectiveness of the proposed algorithm, the MOSCSO is applied to solve the CEC2019 complex multimodal benchmark function. The experimental outcomes illustrate that the proposed approach possesses excellent performance in searching for Pareto solutions compared with other algorithms. Meanwhile, the method is also employed to address the map-based distance minimization problem, which further validates the usefulness of the MOSCSO.

Unlocking the Potential of Zero-tillage Farming: Challenges, Opportunities, and Key Influences on Adoption

Zero tillage (ZT) is a crucial agricultural strategy that prioritizes little soil disturbance. This research investigates the future possibilities of ZT, concentrating on three critical dimensions: technology developments, climate change considerations, and possible rise in adoption rates. Precision agriculture, robotics, artificial intelligence, and biotechnology have been shown to play a critical role in improving ZT efficiency and sustainability. These advancements enable more intelligent and focused methods, lowering waste and harmonizing farming operations with overall sustainability goals. Climate change is also a crucial factor in influencing ZT's future. ZT's intrinsic qualities of soil moisture conservation, decreased erosion, and carbon sequestration make it an effective technique for climate mitigation and adaptation. According to the report, the worldwide need to address climate change may serve as a stimulus for ZT expansion, linking it with crucial agricultural initiatives in the future. The possible rise in ZT adoption rates is investigated in light of these technological and environmental aspects. The findings indicate that technology's role in eliminating obstacles and increasing efficiency, together with government and organizational backing, might encourage widespread ZT adoption, particularly in developing nations. Collaborative efforts among multiple stakeholders, including researchers, policymakers, farmers, and industry, are emphasized as critical to optimizing ZT for different situations and requirements. Zero Tillage's future possibilities are broad and varied, characterized by technical innovation, compatibility with climate goals, and a clear route to widespread implementation. The combination of these variables offers a favorable scenario for ZT, establishing it as a key technique in developing sustainable agriculture in the future. This study adds to our understanding of ZT's future trajectory and provides insights that can help direct its ongoing evolution and effect in the agriculture sector.

Cost-Effective Scalability in Cloud Monitoring Systems: A Comparative Stud

This research article explores strategies for cost-effective scalability in cloud monitoring systems. As the complexity and scale of IT-based infrastructure are growing, an efficient monitoring system becomes vital for maintaining performance and optimizing resources while managing costs. The current study aims to shed light on various approaches to cost-effective scalability by considering factors such as data collection methods, storage optimization, and adaptive monitoring techniques. A comparison between cloud monitoring tools, Amazon Cloud Watch and Datadog, has been made to gain a better understanding of the monitoring tools. The research indicates that a multi-faceted approach is necessary for cost-effective scalability in cloud monitoring systems, and there must be a holistic approach in the selection of cloud monitoring tools, depending on the organization's requirements. In the later sections, strategies, like distributed data collection, hierarchical aggregation, adaptive sampling, and machine learning- based predictive scaling, can significantly improve monitoring system scalability while optimizing resource utilization.

Combined Reynolds-averaged Navier-Stokes/Large-Eddy Simulations for an aircraft wake until dissipation regime

A new methodology has been devised to simulate the aerodynamic wake of an airliner during cruise flight using Reynolds-averaged Navier-Stokes (RANS) and Large Eddy Simulations (LES). The wake evolution is simulated considering the full geometry of the aircraft and spans from the jet regime to the dissipation regime. First, the jet regime is computed using RANS modeling and state-of-the-art anisotropic mesh adaptation techniques. Second, the vortex and dissipation regimes are solved using temporal LES with flow field initialization from the previous RANS calculation. RANS information on turbulent quantities is transferred to the LES domain using synthetic turbulence methods. The methodology is first tested on a NACA-0012 wing. It is then applied to the NASA Common Research Model (CRM) geometry on cruise flight conditions. Jet/vortex interaction is studied during the jet regime up to twenty wingspans behind the aircraft. The influences of atmospheric turbulence, jet turbulence, and stable atmospheric stratification are investigated for the vortex and dissipation regimes. It is observed that atmospheric turbulence intensity and stratification both accelerate the vortex decay. Moreover, the secondary wake structure is found to be much more turbulent for numerical RANS initialization than for classical analytic initialization, showcasing the importance of early aerodynamics on wake evolution.

A label-free battery state of health estimation method based on adversarial multi-domain adaptation network and relaxation voltage

The state of health (SOH) estimation of lithium-ion batteries is crucial for the operational reliability and safety of electric vehicles. However, traditional data-driven methods face problems of label shortage and domain discrepancy caused by diverse battery types and operating conditions. This paper proposes a label-free SOH estimation method based on adversarial multi-domain adaptation technique and relaxation voltage (RV). Firstly, the raw RV and integral voltage are proposed to construct the two-dimensional input sequence to ensure high adaptability across various target domains. Secondly, a two-dimensional convolutional neural network integrated with fully connected layers is developed to establish the feature extractor and SOH estimator, which paves the way for effective knowledge transfer. Furthermore, an adversarial multi-domain adaptation method with a domain discriminator is developed to optimize the extraction of domain-invariant features and enable accurate SOH estimation without SOH labels. Datasets of 50 batteries with different temperatures and materials are used for the validation. The proposed method shows outstanding performance, achieving the average RMSE and MAE of 0.0274 and 0.0240 across various working temperatures, and 0.0177 and 0.0148 across different battery types. It suggests that the proposed method can achieve robust adaptive battery SOH estimation across multiple target domains without using SOH labels.

Optimizing motion detection performance: Harnessing the power of squeeze and excitation modules.

This paper introduces an innovative segmentation model that extends the U-Net architecture with a Squeeze and Excitation (SE) mechanism, designed to enhance the detection of moving objects in video streams. By integrating this model into the ViBe motion detection algorithm, we have significantly improved detection accuracy and reduced false positive rates. Our approach leverages adaptive techniques to increase the robustness of the segmentation model in complex scenarios, without requiring extensive manual parameter tuning. Despite the notable improvements, we recognize that further training is necessary to optimize the model for specific applications. The results indicate that our method provides a promising direction for real-time motion detection systems that require high precision and adaptability to varying conditions.