Performance Of Proposed Method Research Articles

Image Fusion Algorithm using Grey Wolf optimization with Shuffled Frog Leaping Algorithm

Data fusion is a “formal framework in which are expressed the means and tools for the alliance of data originating from different sources.” It aims at obtaining information of greater quality; the exact definition of 'greater quality will depend upon the application. It is a famous technique in digital image processing and is very important in medical image representation for clinical diagnosis. Previously many researchers used many meta-heuristic optimization techniques in image fusion, but the problem of local optimization restricted their searching flow to find optimum search results. In this paper, the Grey Wolf Optimization (GWO) algorithm with the help of the Shuffled Frog Leaping Algorithm (SFLA) has been proposed. That helps to find the object and allows doctors to take some action. The optimization algorithm is examined with a demonstrated example in order to simplify its steps. The result of the proposed algorithm is compared with other optimization algorithms. The proposed method's performance was always the best among them.

Lightweight forest smoke and fire detection algorithm based on improved YOLOv5.

Smoke and fire detection technology is a key technology for automatically realizing forest monitoring and forest fire warning. One of the most popular algorithms for object detection tasks is YOLOv5. However, it suffers from some challenges, such as high computational load and limited detection performance. This paper proposes a high-performance lightweight network model for detecting forest smoke and fire based on YOLOv5 to overcome these problems. C3Ghost and Ghost modules are introduced into the Backbone and Neck network to achieve the purpose of reducing network parameters and improving the feature's expressing performance. Coordinate Attention (CA) module is introduced into the Backbone network to highlight the object's important information about smoke and fire and to suppress irrelevant background information. In Neck network part, in order to distinguish the importance of different features in feature fusing process, the weight parameter of feature fusion is added which is based on PAN (path aggregation network) structure, which is named PAN-weight. Multiple sets of controlled experiments were conducted to confirm the proposed method's performance. Compared with YOLOv5s, the proposed method reduced the model size and FLOPs by 44.75% and 47.46% respectively, while increased precision and mAP(mean average precision)@0.5 by 2.53% and 1.16% respectively. The experimental results demonstrated the usefulness and superiority of the proposed method. The core code and dataset required for the experiment are saved in this article at https://github.com/vinchole/zzzccc.git.

Active disturbance rejection coordinated control for integrated solar combined cycle system considering system inertia difference

The integrated solar combined cycle system (ISCC) primarily consists of solar thermal power generation equipment and gas turbines. This system effectively mitigates the inherent randomness associated with power generation due to weather conditions while simultaneously providing electric and heat loads. However, due to the difference in response speed of various power generation equipment, it is difficult to respond quickly as the strength of illumination and electricity-heat load demand change, seriously affecting the system's stability operation. This paper proposes an active disturbance rejection control (ADRC) method for the ISCC system considering the different response speeds of generation equipment. Firstly, the mathematical model of the ISCC system is established, in which the system's response speed is improved through the inertial power compensation method. Then, the ADRC method is used to reduce the disturbance caused by uncertain factors, such as solar radiation intensity and load demand change, to ensure the balance between electric and thermal power supply and demand. Finally, based on the measured weather data from National Renewable Energy Laboratory (NREL), the proposed method's performance is evaluated, compared with the hybrid H2/H∞ control method and the auto-coupling proportional integral differential (ACPID) control method. Comparison results show that the ADRC method has better control effectiveness and dynamic response performance, powerfully shortens the system stability time, and reduces overshoot.

Optimal sizing and feasibility analysis of second-life battery energy storage systems for community microgrids considering carbon reduction

The rapid development of distributed renewable energy has made energy storage essential for efficient operation. However, energy storage is challenging for prosumers due to high investment costs and long payback periods. The second use of batteries has been evaluated as an alternative energy storage after the first service in electric vehicles with the remaining 80% capacity. This study investigates the feasibility of using secondary-use batteries as shared storage for prosumers. Financial and technical break-even points regarding the gradually increasing carbon limit on the way to 100% renewable have been determined. The proposed method's performance has been verified by conducting sensitivity analyses regarding various environmental and economic characteristics. The self-consumption rates have been achieved by 65.9% in Spain (Madrid) and 49.9% in England (London), and 48.5% in Türkiye (Antalya) without incentive. Increasing the carbon tax by 1 $/t for high and low energy tariffs can reduce carbon emissions by 181.3 and 1607 kg/yr. The optimum sizes remain until the carbon tax threshold is 60 $/tCO2eq for high energy tariffs. After 60 $/tCO2eq, it is tended to enlarge the hybrid power system. However, severe carbon emission restriction for zero carbon targets prevents the optimum system design since the carbon reduction cost increases to 232.7–304.8 $/kg. This study encourages investors and prosumers to plan the shared energy storage system sensibly and guides governments to develop realistic incentive mechanisms to implement second-life batteries effectively.

Learning Moore-Penrose based residuals for robust non-blind image deconvolution

This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones.

Online few-shot learning for multi-function radars mode recognition based on backtracking contextual prototypical memory

Modern multi-function radars (MFRs) can flexibly generate multiple fine-grained working modes for different missions through programmable parameters. Precise recognition of these working modes by analyzing the parameter combinations lays foundation for situational awareness. This recognition becomes more challenging to electromagnetic reconnaissance system when prior information of radiation source is unavailable and effective labeled signal data is not sufficient in adversarial scenarios. The presence of incremental mode involved in radar data flow requires that the recognition model enables to dynamic adjust and online perceive current data. This issue incorporates online learning to few-shot learning to accomplish efficiently recognition to a novel mode with the support of small amount of data, and still retain model's ability to recognize existing modes. This paper designed a backtracking contextual prototypical memory (BCPM) network for online MFR mode recognition. The proposed method learns temporal and spatial information from data stream as a reference predicting the signal segments as existing working modes or a novel mode. The BCPM network also designs a backtracking module and an alignment regularization term to utilize the data without annotation adequately and obtain a more reliable category representation. The experimental results verified the proposed method's good performance and high robustness to non-ideal factors and distractors.

Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network.

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance.

Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter (P{I}^{lambda }DN{D}^{2}{N}^{2}) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based P{I}^{lambda }DN{D}^{2}{N}^{2} controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a P{I}^{lambda }DN{D}^{2}{N}^{2} controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

Elimination of signal amplitude disturbance in ghost imaging using an auxiliary laser channel.

Ghost imaging can be used to detect objects in a nonstationary environment or in the presence of variable ambient light, making it attractive when conventional imaging methods are ineffective. However, the conventional ghost imaging algorithm is susceptible to temporal fluctuations in the detected signal. In this work, we propose a polarization-multiplexed auxiliary laser channel propagating along the same optical path with the main one. The signal in the auxiliary channel is used as a reference and allows the elimination of signal disturbance. A quantitative analysis and comparison of the proposed method's performance to the high-pass filtering method are demonstrated. For an illumination pattern refresh rate of 10Hz, effective suppression of bucket signal fluctuations has been experimentally demonstrated. For a disturbance frequency from 1Hz to 10Hz, the auxiliary channel method demonstrated a ghost image Pearson correlation coefficient (PCC) of not less than 0.70, while the high-pass filtering method showed a PCC sharp drop from 0.65 to 0.02.

Early prediction of atherosclerosis diagnosis with medical ambient intelligence.

Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system for the early detection of atherosclerosis by leveraging clinical data from medical records. The system architecture includes clinical data extraction, transformation, normalization, feature selection, medical ambient computation, and predictive generation. However, the heterogeneity of examination items from different patients can degrade prediction performance. To enhance prediction performance, the "SEcond-order Classifier (SEC)" is proposed to undertake the medical ambient computation task. The first-order component and second-order cross-feature component are then consolidated and applied to the chosen feature matrix to learn the associations between the physical examination data, respectively. The prediction is lastly produced by aggregating the representations. Extensive experimental results reveal that the proposed method's diagnostic prediction performance is superior to other state-of-the-art methods. Specifically, the Vitamin B12 indicator exhibits the strongest correlation with the early stage of atherosclerosis, while several known relevant biomarkers also demonstrate significant correlation in experimental data. The method proposed in this paper is a standalone tool, and its source code will be released in the future.

A Sufficient Descent Dai-Liao Type Conjugate Gradient Update Parameter

In recent years, conjugate gradient methods have gained popularity as efficient iterative techniques for unconstrained optimization problems without the need for matrix storage. Based on the Dai-Laio conjugacy condition, this article presents a new hybrid conjugate gradient method that combines features of the Dai-Yuan and Dai-Laio methods. The proposed method addresses the numerical instability and slow convergence of the Dai-Yuan method as well as the potential poor performance of the Dai-Laio method in highly non-linear optimization problems. The hybrid method solves optimization problems with faster convergence rates and greater stability by combining the advantages of both methods. The resulting algorithm is shown to be more effective and reliable, and theoretical analysis reveals that it has sufficient descent properties. The proposed method's competitive performance is shown through a number of benchmark tests and comparisons with other approaches, indicating that it has the potential to be an effective approach for complex, unconstrained optimization.

Demand response based microgrid's economic dispatch

The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.

Machine learning and particle swarm inspired success history based adaptive multi-objective differential evolution for optimization of heaving buoy point absorber

Success-History-Based Parameter Adaptation Multi-Objective Differential Evolution (SHAMODE) has shown excellent performance recently compared to other multi-objective evolutionary algorithms (MOEAs) and therefore caught the interest of researchers lately. The SHAMODE framework employs historical memory of control parameters, which stores set values of control parameters that have performed well in the past generation and produces new set values of the parameter by directly sampling the parameter space close to one of these stored pairs. Since the algorithm's performance depends on the problem to be optimized, this paper proposes an enhanced version of SHAMODE that is compatible with optimizing the multi-objective wave energy converter problem. The improved version of SHAMODE is achieved by incorporating competing machine learning algorithms, Particle Swarm Optimization, and a modified bubble-net attacking method formula of the Whale optimization algorithm from its original form. Simulations and comparisons are used to verify the proposed method's performance based on some famous multi-objective problem (MOP) benchmark functions. The results reveal that the proposed algorithm performs better than other algorithms that have been recently developed. In addition, the suggested technique is used to optimize the multi-objective wave energy converter model in order to maximize the annual production of electric energy while minimizing the per-unit energy cost.

A method for detecting outliers in linear-circular non-parametric regression.

This study proposes a robust outlier detection method based on the circular median for non-parametric linear-circular regression in case the response variable includes outlier(s) and the residuals are Wrapped-Cauchy distributed. Nadaraya-Watson and local linear regression methods were employed to obtain non-parametric regression fits. The proposed method's performance was investigated by using a real dataset and a comprehensive simulation study with different sample sizes, contamination, and heterogeneity degrees. The method performs quite well in medium and higher contamination degrees, and its performance increases as the sample size and the homogeneity of data increase. In addition, when the response variable of linear-circular regression contains outliers, the Local Linear Estimation method fits the data set better than the Nadaraya Watson method.

Adaptive Coordination of Relays in AC Microgrid Considering Operational and Topological Changes

Conventional protection schemes are vulnerable to varying changes in a microgrid with different types of distributed generators (DGs), which alter the fault current levels, leading to miscoordination of directional over-current relays (DOCRs). Due to limited storage in numeric relays, having one setting for each network scenario in the relays is infeasible. Also, schemes with one setting considering grid-connected mode (GCM) and islanded mode (IM) of operation with multiple scenarios may be infeasible for a microgrid with inverter-interfaced DGs due to low short-circuit level in IM. Thus, this article proposes an adaptive trip characteristics-based relay coordination scheme for a microgrid with different DGs, where two trip characteristics are assigned to each DOCR, one each for GCM and IM. Also, a novel framework for selecting the suitable trip characteristic is proposed. Hardware-in-loop simulations using Numerical Relay Development Environment for a meshed CIGRE microgrid, modified IEEE 34-bus distribution system, and comparison with previous works validate the proposed method's performance for various scenarios, such as the outage of a DG or line, variations in solar irradiation and wind speed, and network reconfiguration for different fault types during GCM and IM.

Versatile LiDAR-Inertial Odometry With SE(2) Constraints for Ground Vehicles

LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly. In this paper, we propose a hybrid LiDAR-inertial SLAM framework that leverages both the on-board perception system and prior information such as motion dynamics to improve localization performance. In particular, we consider the case for ground vehicles, which are commonly used for autonomous driving and warehouse logistics. We present a computationally efficient LiDAR-inertial odometry method that directly parameterizes ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected but incorporated into an integrated noise term of a novel SE(2)-constraints model. For odometric measurement processing, we propose a versatile, tightly coupled LiDAR-inertial odometry to achieve better pose estimation than traditional LiDAR odometry. Thorough experiments are performed to evaluate our proposed method's performance in different scenarios, including localization for both indoor and outdoor environments. The proposed method achieves superior performance in accuracy and robustness.

Online motion planning based on swept volume search with replanning using sequential quadratic programming

This paper describes a method for obtaining a mobile manipulator's motion sequence by indicating a goal-hand pose. The proposed method entails recording various robot motions as a large number of motion sequences and associated swept volumes (SVs) and then selecting the most appropriate SV for the current situation. In addition, the motion sequences can be altered while the robot is in motion, and the transition motion is generated using sequential quadratic programming, enabling the robot to avoid collisions with obstacles found after it has begun to move. The proposed method's performance is verified by simulation to confirm the appropriate amount of data and the method's superiority.

An improved method for signal de‐noising based on multi‐level local mean decomposition

AbstractThe product functions (PFs) extracted by local mean decomposition (LMD) of the noisy signal contain obvious energy‐concentrated pulses. As a result, the conventional amplitude threshold filtering used in wavelet transform (WT)‐based and empirical mode decomposition (EMD)‐based de‐noising methods is no longer applicable. To address this issue, an improved signal de‐noising method is proposed by using the multi‐level local mean decomposition (ML‐LMD), the superposition and recombination (SR) of high‐order PFs, the outlier detection, and waveform smoothing (OD‐WS) to remove noise by eliminating the pulse components. The proposed method's superior noise reduction performance is demonstrated through theoretical analysis and experimental verification. Compared to well‐known methods like WT‐based and EMD‐based de‐noising, the results show that the proposed method has significant comparative advantages in reducing noise in rolling bearing signals.

Demosaicking DoFP images using edge compensation method based on correlation.

With the development of nanotechnology, the division of focal plane (DoFP) infrared polarization imaging system with real-time imaging has matured. Meanwhile, the demand for real-time acquisition of polarization information is growing, but the super-pixel structure of the DoFP polarimeter will bring instantaneous field of view (IFoV) errors. Existing polarization demosaicking methods cannot satisfy both accuracy and speed in terms of efficiency and performance. According to the characteristics of DoFP, this paper proposes an edge compensation demosaicking method by analyzing the channel correlations of polarized images. The method performs demosaicing in the differential domain, and the proposed method's performance is verified by comparison experiments using synthetic and authentic polarized images in the near-infrared (NIR) band. The proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency. It achieves an average peak signal-to-noise ratio (PSNR) improvement of 2 db on public datasets compared to current state-of-the-art methods. A typical 768 × 1024 specification short-wave infrared (SWIR) polarized image can be processed in 0.293s on the Intel Core i7-10870 H CPU, and the technique significantly outperforms various existing demosaicking methods.

Novel Load Balancing Optimization Algorithm to Improve Quality-of-Service in Cloud Environment

Scheduling cloud resources calls for allocating cloud assets to cloud tasks. It is possible to improve scheduling outcomes by treating Quality of Service (QoS) factors as essential constraints. However, efficient scheduling calls for improved optimization of QoS parameters, and only a few resource scheduling algorithms in the available literature do so. The primary objective of this paper is to provide an effective method for deploying workloads to cloud infrastructure. To ensure that workloads are executed efficiently on available resources, a resource scheduling method based on particle swarm optimization was developed. The proposed method's performance has been measured in the cloud. The experimental results prove the efficiency of the proposed approach in reducing the aforementioned QoS parameters. Several metrics of algorithm performance are used to gauge how well the algorithm performs.