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The frequent change, unbalanced non-linear load or electric vehicle charging currents are the prominent source of distorted grid currents and poor power quality in a micro-grid (MG). Thus, in this study, the modified shrinkage widely linear complex-valued least mean squares (MSWL-CLMS) algorithm for photovoltaic-wind power generation system (PV-WPGS) based grid-interfaced MG is presented. MSWL-CLMS algorithm has zero attractor term with the special feature of DC-offset rejection capability. MSWL-CLMS method offers a variable step size, which updates the weight vector and thus provides a significant enhancement in convergence speed, improves the steady-state performance and enables the accurate and rapid estimation of fundamental current components for the generation of the reference current. The control approach is multi-objective, which is proficiently working for coordinated power flow, harmonics mitigation, load balancing, and reactive power compensation. Further, the system is capable of working satisfactorily under unbalance and distorted grid voltages since it is costumed with the improved second-order generalised integrator-frequency locked loop filter, which extracts positive sequence components and generates sinusoidal synchronising signals. To validate the efficacy of MG, simulation and experimental results of the developed scale down prototype are demonstrated in detail.

The subject of this study is a frequency estimation algorithm suitable for grid-connected power converters placed at a weak coupling point of a three-phase electrical distribution system. An upgraded version of the widely used complex least mean squares (CLMS) algorithm for frequency estimation is introduced to cope with different voltage amplitude unbalance and harmonic distortion levels, both frequently present in power system at distribution level. First, it is suggested that the CLMS algorithm uses only a positive phase-sequence component of voltage vector, the component that is inherently symmetrical and by cancelling the phase unbalance preserves the circular vector trajectory in a two-phase αβ-plane. This study shows that it is even possible to use the positive voltage phase-sequence vector extracted using a constant delay block, thus avoiding potential instability issues in the case of signal frequency feedback loop. Second, possible high signal harmonics and signal measurement noise are both removed using low-pass filters prior to CLMS algorithm deployment. Computer simulations and experiments are performed under a variety of conditions to validate the effectiveness of the proposed technique. Experimental results are achieved using the dataset sampled from the actual three-phase grid voltage at distributed level and with data processing done in the LabVIEW software environment.

Nowadays, there is tremendous growth in biometric authentication and cybersecurity applications. Thus, the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors. Therefore, designing and implementing robust security algorithms for users’ biometrics is still a hot research area to be investigated. This work presents a powerful biometric security system (BSS) to protect different biometric modalities such as faces, iris, and fingerprints. The proposed BSS model is based on hybridizing auto-encoder (AE) network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy. The employed AE network is unsupervised deep learning (DL) structure used in the proposed BSS model to extract main biometric features. These obtained features are utilized to generate two random chaos matrices. The first random chaos matrix is used to permute the pixels of biometric images. In contrast, the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional (2D) chaotic logistic map (CLM) algorithm. To assess the efficiency of the proposed BSS, (1) different standardized color and grayscale images of the examined fingerprint, faces, and iris biometrics were used (2) comprehensive security and recognition evaluation metrics were measured. The assessment results have proven the authentication and robustness superiority of the proposed BSS model compared to other existing BSS models. For example, the proposed BSS succeeds in getting a high area under the receiver operating characteristic (AROC) value that reached 99.97% and low rates of 0.00137, 0.00148, and 0.00157 for equal error rate (EER), false reject rate (FRR), and a false accept rate (FAR), respectively.

This paper coupled a unified passive and active microwave observation operator—namely, an enhanced, physically-based, discrete emission-scattering model—with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

One of the routine exercises to manage and improve the performance and utility of the cloud is the migration or transfer of cloud data whether it is large or small. However, it is extremely challenging to protect both data privacy and integrity concurrently while moving cloud data, particularly when it is very vast. Collectively, prior works fail to offer a complete solution to these problem. Even though data encryption and steganography techniques are popular and efficient, they provide higher time and space complexities and introduce information loss. As a result, the goal of this research is to provide a chaos compression and encryption system based on chaos theory to guarantee both data privacy and integrity during the transit or migration of massive cloud data. During data transmission, the entire data are compressed using a chaotic substitution box followed by an adaptive Huffman encoding algorithms. Therefore, the input data are efficiently transformed into a non-readable form which replaces the original data, making it difficult for an unethical individual or group to determine its true sense. Our evaluation results show that the proposed chaotic technique has a maximum entropy value of 7.99, which supports its ability to provide more privacy when compared to previous techniques. It also delivers the best bits per code of 4.41, a throughput of 2.89 MB/s, and a minimal information loss percentage of 0.0011%, demonstrating its superior time, space efficiency, and ability to improve data integrity over existing methods.

The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net Primary Production (NPP) and the effects of meteorological factors on the NPP change on the Tibetan Plateau (TP) using version 5.0 of the Community Land Model. The results showed that the average NPP was 256 (g C·m2·yr−1) over the past 40 years, with a continuously increasing trend of 2.38 (g C·m2·yr−1). Precipitation was the main factor affecting NPP changes, temperature had no significant effect on NPP changes, while radiation showed a negative trend. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively. Based on grass coverage, we categorized alpine grasslands into three types: high, medium, and low coverage. Our findings indicate the NPP change of the high-coverage grasslands was mainly affected by precipitation, and then the temperature and radiation. Comparatively, the precipitation change is the driving factor of the increased NPP of low-coverage grasslands, but the temperature increase is the negative factor. Our studies have implications for assessing and predicting vegetation responses to future climate change.

Dynamic load is a critical factor affecting the stability of hybrid microgrids (MG) due to their sensitivity to voltage and frequency fluctuations. This sensitivity underscores the importance of considering load dynamics in MG stability analysis, especially during islanded operation. This paper investigates the small signal (SS) stability of hybrid MGs, utilizing a composite load model (CLM) to accurately represent load dynamics. A SS state-space model of an inverter-based complete hybrid microgrid which includes droop controllers, network, and CLM load is considered for accurate stability analysis with improved dynamic characteristics. To enhance the dynamic performance of hybrid MGs, Sequential Quadratic Programming with Gradient Sampling (SQP-GS) is proposed for the optimization of key controller parameters as compared with different other metaheuristic optimization methods including Genetic Algorithms (GA), Mematic Algorithms (MA), Particle Swarm Optimization (PSO). Simulation results verify the effectiveness of the proposed SS model of hybrid microgrid with CLM load modeling as compared to the Constant power load (CPL) model and Constant impedance load (CIL) model. Further SQP-GS is found to be effective in optimizing the key controller parameters and subsequently improving the dynamic performance of the hybrid MGs as compared with the other optimization techniques. Extensive simulations validate the viability of our proposed SS dynamic model utilizing the CLM model and the effectiveness and efficiency of the SQP-GS optimization algorithm.

Tracking the head in a video stream is a common thread seen within computer vision literature, supplying the research community with a large number of challenging and interesting problems. Head pose estimation from monocular cameras is often considered an extended application after the face tracking task has already been performed. This often involves passing the resultant 2D data through a simpler algorithm that best fits the data to a static 3D model to determine the 3D pose estimate. This work describes the 2.5D constrained local model, combining a deformable 3D shape point model with 2D texture information to provide direct estimation of the pose parameters, avoiding the need for additional optimization strategies. It achieves this through an analytical derivation of a Jacobian matrix describing how changes in the parameters of the model create changes in the shape within the image through a full-perspective camera model. In addition, the model has very low computational complexity and can run in real-time on modern mobile devices such as tablets and laptops. The point distribution model of the face is built in a unique way, so as to minimize the effect of changes in facial expressions on the estimated head pose and hence make the solution more robust. Finally, the texture information is trained via local neural fields—a deep learning approach that utilizes small discriminative patches to exploit spatial relationships between the pixels and provide strong peaks at the optimal locations.

The demand for image encryption with the advent of technology is increasing rapidly. In this paper we leverage chaos-based methods for image encryption and decryption to enhance the digital image security. The system’s strong suit is its sophisticated approach that starts with data scrambling and includes chaotic permutations, bitwise operations, and XOR substitution. The security features are further enhanced through the incorporation of additional complexity levels brought by Laplace transforms and series expansions. Applications needing strict security measures might choose the cryptosystem because of its complete approach, which guarantees the integrity and confidentiality of digital images. The proposed approach provides a flexible and efficient solution by combining advanced cryptographic algorithms, mathematical transformations, and chaos theory to create a strong foundation for image encryption in wide range of security-sensitive situations. The proposed system uniquely combines chaos theory, Laplace transforms, and series expansions to create a highly secure and advanced framework for image encryption. This innovative method significantly strengthens resistance to cryptographic attacks, marking a notable advancement in secure digital communication. The proposed technique demonstrate good performance in terms of information entropy, histogram, correlation of adjacent pixels, unified average changing intensity (UACI), number of pixel change rate (NPCR), and peak signal to noise ratio (PSNR), demonstrate the security and robustness of the suggested algorithm.Article highlightsRobust image security enhanced through efficient Chaos-based encryption methods.Integration of Laplace transforms adds a sophisticated layer of security.Comprehensive methodology ensures confidentiality, integrity, and versatility.

This paper evaluates convective precipitation as simulated by the convection-permitting climate model (CPM) Consortium for Small-Scale Modeling in climate mode (COSMO-CLM) (with 2.8 km grid-spacing) over Germany in the period 2001–2015. Characteristics of simulated convective precipitation objects like lifetime, area, mean intensity, and total precipitation are compared to characteristics observed by weather radar. For this purpose, a tracking algorithm was applied to simulated and observed precipitation with 5-min temporal resolution. The total amount of convective precipitation is well simulated, with a small overestimation of 2%. However, the simulation underestimates convective activity, represented by the number of convective objects, by 33%. This underestimation is especially pronounced in the lowlands of Northern Germany, whereas the simulation matches observations well in the mountainous areas of Southern Germany. The underestimation of activity is compensated by an overestimation of the simulated lifetime of convective objects. The observed mean intensity, maximum intensity, and area of precipitation objects increase with their lifetime showing the spectrum of convective storms ranging from short-living single-cell storms to long-living organized convection like supercells or squall lines. The CPM is capable of reproducing the lifetime dependence of these characteristics but shows a weaker increase in mean intensity with lifetime resulting in an especially pronounced underestimation (up to 25%) of mean precipitation intensity of long-living, extreme events. This limitation of the CPM is not identifiable by classical evaluation techniques using rain gauges. The simulation can reproduce the general increase of the highest percentiles of cell area, total precipitation, and mean intensity with temperature but fails to reproduce the increase of lifetime. The scaling rates of mean intensity and total precipitation resemble observed rates only in parts of the temperature range. The results suggest that the evaluation of coarse-grained (e.g., hourly) precipitation fields is insufficient for revealing challenges in convection-permitting simulations.