High Complexity Research Articles

CYBERSECURITY RISK MITIGATION IN INDUSTRIAL CONTROL SYSTEMS ANALYZING PHYSICAL HYBRID AND VIRTUAL TEST BED APPLICATIONS

Industrial Control Systems (ICS) play a vital role in industries such as oil, utilities, and manufacturing, forming the backbone of critical infrastructure. With the increasing integration of network capabilities in ICS, their exposure to cyber-attacks has grown significantly. However, due to the sensitivity of these systems, access to detailed technical information is limited, making cybersecurity research challenging. To address this, researchers have employed various physical, hybrid, and virtual testbeds to simulate and analyze cyber threats. This systematic review, conducted following PRISMA guidelines, aims to evaluate the effectiveness of these testbeds in mitigating cybersecurity risks in ICS, particularly within the context of a clean water supply system. The findings reveal that physical testbeds offer a comprehensive understanding of the behavior and dynamics of ICS components, such as sensors and actuators, under real-world conditions affected by external factors like pressure, temperature, and mechanical wear. However, physical testbeds' high cost and complexity limit their widespread use. While more cost-effective, hybrid testbeds fail to capture crucial physical dynamics, which may lead to incomplete assessments of cybersecurity vulnerabilities. Virtual testbeds provide the most affordable option, offering scalability and ease of implementation. However, they deliver a limited view of ICS operations that can impair the development of accurate detection and prevention mechanisms. The results underscore the trade-offs associated with each testbed type, suggesting that an integrated approach, blending physical and virtual elements, may offer the most effective framework for cybersecurity research in ICS while balancing cost and realism.

Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data

The acceleration of urbanization has resulted in a heightened awareness of the impacts of urban environments on residents’ emotional states. This present study focuses on the Lixia District of Jinan City. By using urban street view big data and deep learning methods, we undertook a detailed analysis of the impacts of urban color features on residents’ emotional perceptions. In particular, a substantial corpus of street scene image data was extracted and processed. This was performed using a deep convolutional neural network (DCNN) and semantic segmentation technology (PSPNet), which enabled the simulation and prediction of the subjective perception of the urban environment by humans. Furthermore, the color complexity and coordination in the street scene were quantified and combined with residents’ emotional feedback to carry out a multi-dimensional analysis. The findings revealed that color complexity and coordination were significant elements influencing residents’ emotional perceptions. A high color complexity is visually appealing, but can lead to fatigue, discomfort, and boredom; a moderate complexity stimulates vitality and pleasure; high levels of regional harmony and aesthetics can increase perceptions of beauty and security; and low levels of coordination can increase feelings of depression. The environmental characteristics of different areas and differences in the daily activities of residents resulted in regional differences regarding the impacts of color features on emotional perception. This study corroborates the assertion that environmental color coordination has the capacity to enhance residents’ emotions, thereby providing an important reference point for urban planning. Planning should be based on the functional characteristics of the region, and color complexity and coordination should be reasonably regulated to optimize the emotional experiences of residents. Differentiated color management enhances urban aesthetics, livability, and residents’ happiness and promotes sustainable development. In the future, the influences of color and environmental factors on emotions can be explored in depth, with a view to assist in the formulation of fine urban design.

CosGCTFormer: An end-to-end driver state recognition framework

Driver state is critical for safety in mixed traffic environments with automated and human-driven vehicles. Recognizing and monitoring the driver’s state is essential to provide early warnings of abnormal driving performance. This paper proposes a driver state recognition framework called cosGCTFormer for end-to-end recognition of the driver’s emotional state and mental load. First, we employ an inverted residual convolution module to extract local features from physiological signal data. Second, the cosGCT module is introduced to extract global features. This module uses cos attention to address the high computational complexity of the Transformer in calculating attention weights. Additionally, it employs Gated Channel Transformation (GCT) to establish relationships between different channel dimensions, something the Transformer alone cannot do effectively. Finally, a simulated driving experiment was designed to collect data for training and testing the proposed method. Results demonstrate that our framework achieves higher accuracy and a more lightweight design compared to prevalent Convolution neural network-based (CNN-based) and Transformer-based methods. Additionally, the effectiveness of both modules was verified.

Self-supervised image change detection method based on lightweight capsule network

In response to the significant impact of speckle noise on the accuracy of SAR image change detection, the high complexity of existing capsule network based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposes a self supervised image change detection method based on the Light Capsule Network (SCapsNet). First, the log ratio operator difference graph is generated, and the "pseudo label" of training samples with high confidence is obtained through the maximum inter class variance method and fuzzy C-means clustering method, which lays the foundation for self-supervised learning; Secondly, construct a three channel training sample based on the difference map of two temporal SAR images and logarithmic ratio operators to maximize the preservation of sample information; Then, SCapsNet is designed to extract training sample features through single scale convolution, and a single scale capsule network is used to mine spatial relationships between features; Finally, comparative experiments and ablation experiments were set up and tested on 5 real SAR Datasets. The experimental results show that the The advantage of this method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

Massive MIMO uplink and downlink joint representation based on couple dictionary learning

AbstractThe challenge of jointly representing both the uplink (UL) and downlink (DL) in massive multiple input multiple output (MIMO) systems have been tackled. Considering the angular reciprocity, a couple dictionary learning (CDL) support to enhance performance and address high complexity has been introduced. This approach minimizes the number of pilots and improves accuracy. Currently, accuracy analysis of UL/DL representation primarily relies on simulation. To bridge this gap, a proportion factor (PF) operator is proposed for CDL to assess accuracy. Specifically, a qualitative analysis formula is provided for accuracy and an optimal upper bound is established. Through theoretical proof, it is demonstrated that the accuracy of CDL for representation is mainly influenced by the cross‐correlation between the pilot matrix and the dictionary matrix. Inspired by PF operator, an optimal couple dictionary learning (OCDL) algorithm using singular value decomposition (SVD) is introduced to obtain dictionary updating, aiming at high‐precision representation. By establishing normalized mean squared error (NMSE), successful representation ratio, bit error rate (BER), and constellation performance, an OCDL algorithm that outperforms existing methods is showcased and channel representation accuracy is enhanced significantly.

CNTs@fabric/Ni@PU piezoresistive sensor with enhanced interfacial contact resistance variation for motion detection and deep-learning-assisted recognition

Abstract Flexible piezoresistive sensors based on the mechanism of interfacial contact resistance change are receiving increasing attention in the fields of human-computer interaction, health monitoring, and behavior tracking. However, the high cost and complex manufacturing process limit the wide application and development of these flexible piezoresistive sensors. Here, a novel carbon nanotubes@fabric (CNTs@fabric)/Ni@polyurethane (Ni@PU) piezoresistive sensor (CNPS) with low-cost, simple-preparation and high-sensitivity was proposed. The effective contact area is obtained by synergizing the woven micro-convex structure of the fabric, the large specific surface area of the CNTs and the porous three dimensional electrodes. Within the small pressure (0–9.52 kPa) effect, the area of connection with the electrodes to the active layer plays a dominant role, resulting in a sensitivity of up to 6.39 kPa−1 for CNPS. In the high pressure region (9.52–44.92 kPa), where internal mechanism of change in the sensitive material dominants, the CNPS has a response time of 85 ms at a constant pressure of 28.31 kPa. Considering the excellent output electrical performances, a variety of body movements could be detected by fixing the CNPS to different joints. Significantly, the designed intelligent object recognition system implemented by the combination of matrix stress detection module and residual neural network (ResNet) algorithm has a recognition accuracy of 99.26%. Enhancing the interfacial contact resistance change mechanism using a simple fabrication process offers a promising strategy for the rapid development of flexible piezoresistive sensors.

Enrichment of marine microbes to remove nitrogen of urea wastewater under salinity stress

Salinity (NaCl) and urea concentration significantly affect the diversity, structural and physiological function of microbial communities in the biological treatment of wastewater. However, the responses of microbial in high salt and urea wastewater remain elusive. Here, we investigated microbial community function and assembly of four regions using gradient domestication experiment combined with 16S rRNA gene sequencing and statistical methods. The results showed that with the increase of salinity and urea concentration, the consortium Xiamen could still remove most urea, while the other three consortia could not. The alpha diversity of microbial community initially decreased and then increased, showing a recovery trend. After domestication, the consortium Xiamen exhibited high physiological activity and complex network structure, and the community assembly process changed from stochastic to deterministic during the domestication. Furthermore, the keystones with low abundance were associated with urea removal and important for maintain the complexity of the networks, while Arenibacter and Oceanimonas were found to be keystones in maintaining efficient urea removal in harsh environments. To sum up, environmental effects dominated by salinity and urea concentration stress drove the community assembly and species coexistence that underpinned the microbial differentiation pattern at a geographic scale. These results provided new sights for elucidate how microbial response to salinity and urea during wastewater treatment.

Urban green spaces with high connectivity and complex vegetation promote occupancy and richness of birds in a tropical megacity

Urban growth often leads to land-use changes that result in biodiversity loss and reduced human benefits. In urban zones, green areas facilitate physicochemical processes (such as carbon capture, reducing environmental temperature and noise pollution), offer multiple benefits to human beings (e.g., water filtration and purification), and support numerous vertebrate populations, including birds. In the tropics, the capacity of green spaces to maintain bird populations is regulated by characteristics of these areas (e.g., vegetation structure) and environmental seasonality. In order to generate ecological knowledge to help conserve bird diversity in large urban settlements, this study aimed to (1) identify the most influential variables on the distribution of bird species in green areas of a tropical megacity, and (2) assess how bird richness varies between the dry and rainy seasons. Across two dry and rainy seasons between 2021 and 2022, detection records of 108 bird species were obtained from 101 green areas. Air temperature and sampling time were the primary factors influencing bird detection. Bird occupancy and richness were higher in parks near other green areas in first dry and increased with tree richness during the rainy seasons. Floral abundance explained the occupancy and richness in the second dry season. In 2021, the highest richness was observed during the dry season, while in 2022, the highest richness was estimated during the rainy season. These findings highlight the importance of resource availability and spatial arrangement in urban green areas for bird diversity, offering insights for conservation and maintaining ecosystem benefits in urban environments.

Short-time adaptive compact kernel distribution for fault diagnosis under variable working condition bearing

Time-frequency distribution (TFD) methods are extensively employed in the diagnosis of bearings operating under variable working conditions. However, these methods often face high computational complexity, limited time-frequency resolution, and cross-terms. To address these issues, this paper presents a new approach called Short-Time Adaptive Compact Kernel Distribution (STACKD). Based on the characteristics of the vibration signals, we designed Chirp-modulated Gaussian Window and used them to segment the overall signal into small segments. Then, we used a two-step Bayesian optimization method to obtain adaptive compact kernel distributions (ACKD) for these small segments of the signal. Finally, we recombined the ACKD of these small segments into a global STACKD. Results from simulations and experiments demonstrate that STACKD offers improved robustness against interference signals, lower computational complexity, and higher time-frequency resolution without cross-term effects. This method is tailored for diagnosing rolling bearings under variable working conditions, providing enhanced visualization of diagnostic processes with superior time-frequency resolution.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies.

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

Expanding the genetic spectrum of hereditary motor sensory neuropathies in Pakistan

BackgroundHereditary motor and sensory neuropathy (HMSN) refers to a group of inherited progressive peripheral neuropathies characterized by reduced nerve conduction velocity with chronic segmental demyelination and/or axonal degeneration. HMSN is highly clinically and genetically heterogeneous with multiple inheritance patterns and phenotypic overlap with other inherited neuropathies and neurodegenerative diseases. Due to this high complexity and genetic heterogeneity, this study aimed to elucidate the genetic causes of HMSN in Pakistani families using Whole Exome Sequencing (WES) for variant identification and Sanger sequencing for validation and segregation analysis, facilitating accurate clinical diagnosis.MethodsFamilies from Khyber Pakhtunkhwa with at least two members showing HMSN symptoms, who had not previously undergone genetic analysis, were included. Referrals for genetic investigations were based on clinical features suggestive of HMSN by local neurologists. WES was performed on affected individuals from each family, with Sanger sequencing used to validate and analyze the segregation of identified variants among family members. Clinical data including age of onset were assessed for variability among affected individuals, and the success rate of genetic diagnosis was compared with existing literature using proportional differences and Cohen’s h.ResultsWES identified homozygous pathogenic variants in GDAP1 (c.310 + 4 A > G, p.?), SETX (c.5948_5949del, p.(Asn1984Profs*30), IGHMBP2 (c.1591 C > A, p.(Pro531Thr) and NARS1 (c.1633 C > T, p.(Arg545Cys) as causative for HMSN in five out of nine families, consistent with an autosomal recessive inheritance pattern. Additionally, in families with HMSN, a SETX variant was found to cause cerebellar ataxia, while a NARS1 variant was linked to intellectual disability. Based on American College of Medical Genetics and Genomics criteria, the GDAP1 variant is classified as a variant of uncertain significance, while variants in SETX and IGHMBP2 are classified as pathogenic, and the NARS1 variant is classified as likely pathogenic. The age of onset ranged from 1 to 15 years (Mean = 5.13, SD = 3.61), and a genetic diagnosis was achieved in 55.56% of families with HMSN, with small effect sizes compared to previous studies.ConclusionsThis study expands the molecular genetic spectrum of HMSN and HMSN plus type neuropathies in Pakistan and facilitates accurate diagnosis, genetic counseling, and clinical management for affected families.

Temperature Analysis of Waveform Water Channel for High-Power Permanent Magnet Synchronous Motor

Abstract High-power permanent magnet synchronous motors (HPMSM) face extremely harsh cooling conditions due to their high power and complex structure. An efficient cooling system is pivotal to ensuring the safety and operational reliability of HPMSM. To improve the uneven axial temperature distribution in HPMSM and enhance the cooling effect, this paper presents an optimization of the water channels within the motor's cooling system. Initially, targeting the maximum temperature of the motor, the number and width of the traditional water channel (TWC) ribs are parameterized, and the optimal parameters are determined. Subsequently, based on the optimal parameters, three different waveform water channels are designed: circular channel (CC), triangular channel, and square channel. By employing the computational fluid dynamics numerical simulation, the influence of three kinds of water channels on the temperature of an HPMSM is analyzed under rated conditions. When the depth is 36 mm and the span is 40 mm for the CC, the average temperature rise of the motor winding is 9.88% lower than that of the TWC, reaching 48.77 °C. Results indicate that the cooling effect of the CC is better than others, which improves the cooling effect and operation performance of the motor.

The Role of Digital Technologies in Climate Change Management: Strategies for Minimizing the Environmental Impact of USA Companies

The article examines the role of digital technologies in climate change management and reducing the environmental impact of U.S. companies. Innovations such as artificial intelligence, the Internet of Things, and big data are analyzed for their ability to help companies minimize CO2 emissions, optimize resource use, and improve energy efficiency. Challenges companies face when implementing digital solutions: high initial costs, integration difficulties, and regulatory complexities, are discussed. Strategies for overcoming these barriers to achieve sustainable development are proposed.

Diversity and interactions of rhizobacteria determine multinutrient traits in tomato host plants under nitrogen and water disturbances

Abstract Coevolution within the plant holobiont extends the capacity of host plants for nutrient acquisition and stress resistance. However, the role of the rhizospheric microbiota in maintaining multinutrient utilization (i.e., multinutrient traits) in the host remains to be elucidated. Multinutrient cycling index (MNC), analogous to the widely used multifunctionality index, provides a straightforward and interpretable measure of the multinutrient traits in host plants. Using tomato as a model plant, we characterized MNC (based on multiple aboveground nutrient contents) in host plants under different nitrogen and water supply regimes and explored the associations between rhizospheric bacterial community assemblages and host-plant multinutrient profiles. Rhizosphere bacterial community diversity, quantitative abundance, predicted function, and key topological features of the co-occurrence network were more sensitive to water supply than to nitrogen supply. A core bacteriome comprising 61 genera, such as Candidatus Koribacter and Streptomyces, persisted across different habitats and served as a key predictor of host-plant nutrient uptake. The MNC index increased with greater diversity and higher core taxon abundance in the rhizobacterial community, while decreasing with higher average degree and graph density of rhizobacterial co-occurrence network. Multinutrient absorption by host plants was primarily regulated by community diversity and rhizobacterial network complexity under the interaction of nitrogen and water. The high biodiversity and complex species interactions of the rhizospheric bacteriome play crucial roles in host-plant performance. This study supports the development of rhizosphere microbiome engineering, facilitating effective manipulation of the microbiome for enhanced plant benefits, which supports sustainable agricultural practices and plant health.

Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators

The fusion research facility ITER is currently being assembled to demonstrate that fusion can be used for industrial energy production, while several other programmes across the world are also moving forward, such as EU-DEMO, CFETR, SPARC and STEP. The high engineering complexity of a tokamak makes it an extremely challenging device to optimise, and test-based optimisation would be too slow and too costly. Instead, digital design and optimisation must be favoured, which requires strongly-coupled suites of multi-physics, multi-scale High-Performance Computing calculations. Safety regulation, uncertainty quantification, and optimisation of fusion digital twins is undoubtedly an Exascale grand challenge. In this context, having surrogate models to provide quick estimates with uncertainty quantification is essential to explore and optimise new design options. But there lies the dilemma: accurate surrogate training first requires simulation data. Extensive work has explored solver-in-the-loop solutions to maximise the training of such surrogates. Likewise, innovative methods have been proposed to accelerate conventional HPC solvers using surrogates. Here, a novel approach is designed to do both. By bootstrapping neural operators and HPC methods together, a self-improving framework is achieved. As more simulations are being run within the framework, the surrogate improves, while the HPC simulations get accelerated. This idea is demonstrated on fusion-relevant MHD simulations, where Fourier Neural Operator based surrogates are used to create neural coarse-solver for the Parareal (time-parallelisation) method. Parareal is particularly relevant for large HPC simulations where conventional spatial parallelisation has saturated, and the temporal dimension is thus parallelised as well. This Neural-Parareal framework is a step towards exploiting the convergence of HPC and AI, where scientists and engineers can benefit from automated, self-improving, ever faster simulations. All data/codes developed here are made available to the community.

The Prospect of Improving Pancreatic Cancer Diagnostic Capabilities by Implementing Blood Biomarkers: A Study of Evaluating Properties of a Single IL-8 and in Conjunction with CA19-9, CEA, and CEACAM6.

Background/Objectives: This study aims to evaluate the possible clinical application of interleukin 8 (IL-8) as a single biomarker and its capabilities in combination with carbohydrate antigen (CA19-9), carcinoembryonic antigen (CEA), and carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) as diagnostic and prognostic tools for pancreatic ductal adenocarcinoma (PDAC). Methods: A total of 170 serum samples from patients with PDAC (n = 100), chronic pancreatitis (CP) (n = 39), and healthy individuals (n = 31) were analysed. IL-8 and CEACAM6 were measured by an enzyme-linked immunosorbent assay (ELISA). CA19-9 and CEA were determined by chemiluminescent microparticle immunoassay, and bilirubin was quantified using a diazonium salt reaction. Receiver operating characteristic curve analysis, logistic regression, and Kaplan-Meier analyses were performed to evaluate the properties of a single IL-8 and in combination with other biomarkers. Results: The concentrations of IL-8 were statistically significantly higher in the PDAC group compared to the CP and control groups. Heterogeneous levels of IL-8 correlated with PDAC stages (p = 0.007). IL-8 had good and satisfactory diagnostic efficacy in differentiating PDAC from controls (0.858; p < 0.001) and patients with CP (0.696; p < 0.001), respectively. High and low expressions of IL-8 were not significantly associated with overall survival (OS) or disease-free survival (DFS). A combination of IL-8, CEACAM6, and CA19-9 reached the highest AUC values for differentiating PDAC from the control group. The best classification score between PDAC and the control group with CP patients was obtained by merging IL-8 and CA19-9 (0.894; p < 0.001). Conclusions: These results provide compelling evidence of IL-8 as a promising diagnostic biomarker. Nonetheless, due to the high complexity of PDAC, only the conjunction of IL-8, CA19-9, and CEACAM6 integrates sufficient diagnostic capabilities.

Panoramic image quality assessment based on multi-viewport adaptive fusion

Existing panoramic image quality assessment models are relatively independent when extracting local features of each viewport, resulting in high computational complexity and difficulty in characterizing the correlation between viewports using an end-to-end fusion model. To address the above problems, a quality assessment method based on feature sharing and multi-viewport adaptive fusion is proposed. Using a shared backbone network, the viewport segmentation and calculation tasks that are independent of each other in the existing method are converted to the feature domain, so that the local features of the entire image can be extracted with only one feedforward calculation. On this basis, a feature domain viewport segmentation method based on spherical uniform sampling is introduced to ensure that the pixel density of the observation space and the representation space is consistent, and semantic information is used to guide the adaptive fusion of local quality features of each viewport. The linear correlation coefficient and rank correlation coefficient on the CVIQ and OIQA datasets are both above 0.96, which is the best compared with the existing mainstream evaluation methods. Compared with the traditional evaluation method SSIM, its average linear correlation coefficient and average rank correlation coefficient on the two datasets are improved by 9.52%and respectively 8.69%; compared with the latest evaluation method MPFIQA, its average linear correlation coefficient and average rank correlation coefficient are improved by 1.71%and respectively 1.44%.

Adaptive hierarchical interval type-2 fuzzy control for trajectory tracking of an underactuated AUV with uncertain dynamics

In this study, an adaptive hierarchical interval type-2 fuzzy control (AHIT2FC) scheme is presented for three-dimensional (3D) trajectory tracking control of an underactuated autonomous underwater vehicle (AUV), which has only three independent control inputs and is subject to partially parametric uncertainty and unknown external disturbances. To decrease reliance on hydrodynamic parameters, an interval type-2 fuzzy logic system (IT2FLS) is utilized, which exhibits superior capabilities in handling high levels of uncertainties prevalent in the underwater environment as compared to a type-1 fuzzy logic system. However, the traditional fuzzy system structure encounters the “curse of dimensionality” issue, which results in high computational complexity and imposes a significant burden on the limited computing resources underwater. The proposed AHIT2FC framework, based on a hierarchical structure, can substantially enhance computational speed while ensuring excellent control performance. Subsequently, Lyapunov’s theory is adopted to guarantee that all the tracking errors in the closed-loop system are semiglobally uniformly bounded (SGUB). Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed AHIT2FC scheme.

Robot inverse kinematics solution for center selection battle royale optimization algorithm

In order to solve the problems of high time complexity and insufficient global exploration ability of the battle royale optimization algorithm, this paper proposes an improved battle royale optimization algorithm based on chaos mapping, center selection and elite adaptive strategy. Through benchmark function testing, compared to particle swarm optimization algorithm, whale optimization algorithm, and the battle royale optimization algorithm, the improved algorithm significantly reduces time complexity and notably enhances convergence precision, speed, and stability. The improved algorithm is applied to the inverse kinematics problem of robots. The accuracy and The stability of the improved algorithm is better than those of the traditional battle royale optimization algorithm, it demonstrates superior solving precision and stability, proving its practicality and potential for development in solving robot inverse kinematics problems.

Signal modulation waveform recognition method based on STF-Net

Signal modulation waveform recognition is one of the key technologies in the field of spatial spectrum cognition and an important means to realize the monitoring and control of spectrum resources of low-orbit satellites. In view of the problems of large number of parameters and high computational complexity in the current modulation waveform recognition methods based on deep learning, a lightweight signal modulation waveform recognition method based on space-time fusion network (STF-Net) is proposed. The signal is preprocessed into dual-channel data in the form of time domain and frequency domain, and the signal spatial features are extracted and feature redundancy is reduced by convolutional neural network (CNN). Then, the long short-term memory network (LSTM) is used to extract the timing information and output the recognition result. The experimental results show that when the signal-to-noise ratio is greater than 0 dB, the average recognition accuracy of the modulation waveform of the proposed method reaches 91.79%; compared with the same method, the number of parameters of the proposed method is reduced 96%and the efficiency is increased by 2.7 times.