Problem Of High Complexity Research Articles

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.

W-band photonic-aided reconfigurable multichannel vector mm-wave transmission enabled by the digital subcarrier multiplexing technique.

As multimedia service demands rise, radio-over-fiber (RoF) systems necessitate a flexible wireless resource allocation in multi-user scenarios. Optical wavelength division multiplexing (WDM) can meet multi-user application scenarios but has the problems of high hardware cost and complexity. In this Letter, we propose a photonic-aided reconfigurable multichannel vector millimeter-wave (mm-wave) transmission scheme in the RoF system enabled by the digital subcarrier multiplexing (DSCM) technique. The scheme can flexibly adjust the number and frequency spacing of subcarriers as well as the bandwidth and modulation format allocated to each subcarrier, and it can be applied to multi-user scenarios without increasing the cost and complexity of hardware implementation. The off-line experiments verify the system transmission performance with different numbers, bandwidth, and frequency spacing of subcarriers, fully verifying the feasibility of the scheme. Furthermore, the Letter delves into the effects of the subcarrier number, bandwidth, and frequency spacing on overall system performance, highlighting the potential of this scheme for flexible and cost-effective wireless resource allocation in RoF systems.

A lightweight intrusion detection algorithm for IoT based on data purification and a separable convolution improved CNN

With the rapid development of the IoT (Internet of Things), the network data present the characteristics of large volume and high dimension. Convolutional neural networks (CNNs) have become one of the most important intrusion detection methods due to their advantages in processing high-dimensional data. The conventional intrusion detection model based on CNN lacks an effective data purification means in the process of converting unstructured data into image data, and too many parameters are generated due to the complex structure of the model in the training process, leading to the problems of high time complexity and low detection rate of the model, which limits the application of the CNN in intrusion detection of the IoT. First, based on the principle of liquid molecular distillation, a data purification algorithm (DPA) for unstructured data is proposed in this paper, which reduces the "redundant" data generated in the process of converting unstructured data to image data. Second, based on the rigid-motion convolution principle of a separable wavelet, separable convolution is used to improve the CNN structure, and then a lightweight detection algorithm LSCNN (lightweight CNN based on separable convolution) is developed to reduce the number of parameters in the network structure and improve the time efficiency and accuracy of the algorithm. The experimental results on real intrusion detection datasets show that the LSCNN trained on DPA purified data has higher time efficiency and detection accuracy than the conventional CNN, and compared with the conventional machine learning algorithm, it has higher accuracy.

Informer model with season-aware block for efficient long-term power time series forecasting

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, greatly avoiding power waste. As a result, time series forecasting in the field of power can bring great benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous researches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model's ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement, which can help power generation companies reduce costs and further reduce prices to benefit consumers; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments through visualization techniques.

Parameters estimation of complex solar photovoltaic models using bi-parameter coordinated updating L-SHADE with parameter decomposition method

Accurate estimation of unknown parameters of complex photovoltaic models is crucial to whether photovoltaic generators can efficiently convert energy. When a photovoltaic model has multiple diode branches, its complexity increases geometrically. To address the problems of high complexity and difficulty in estimation, this paper proposes an effective improved algorithm based on success-history adaptation differential evolution with linear population size reduction (L-SHADE)—Bi-parameter coordinated updating L-SHADE with parameter decomposition method (CSpL-SHADED). First in CSpL-SHADED, dynamic crossover rate ranking technology is developed to bridge the relationship between individuals and crossover rates, thereby improving effective mutation capabilities. In addition, a dynamic sub-population mechanism is also proposed to divide the entire population into multiple sub-populations so that they are evenly distributed in the search space, so the search ability of individuals in local areas is improved. Secondly, the unknown parameters of solar photovoltaic models of different complexity are effectively decomposed into linear and nonlinear parameters by the decomposition method. The nonlinear parameters are accurately estimated by CSpL-SHADED, and the linear parameters are calculated based on the constructed matrix equation. Through experiments on four solar photovoltaic models of different complexity, CSpL-SHADED showed strong competitiveness to varying degrees compared to the comparative algorithms.

Uniform design-based self-healing evaluation for active distribution network

With the increase in power demand and system complexity, accurately evaluate the self-healing performance of distribution network has become one of the key factors in ensuring the reliability and stability of power supply. To address the issues of incomplete evaluation scenarios and low evaluation efficiency in current self-healing evaluation, this paper proposes the uniform design-based method for evaluating the self-healing performance of the distribution network. It enables comprehensive and fast evaluation of self-healing under multiple fault scenarios. Firstly, three indicators are proposed to evaluate the self-healing performance: self-healing rate, self-healing speed, and self-healing operation complexity. Then, the calculation of three self-healing indicators is carried out under the self-healing strategy that coordinates both network reconstruction and island partition. By uniformly sampling the enumerated k-order system states, the computational time cost is greatly decreased, avoiding the problem of high computational complexity under high-order states and improving the efficiency of evaluation process. Finally, case study shows that the uniform design method greatly decreases the computational time (nearly 20 times faster than the state enumeration) on the IEEE 33-node distribution network.

4 × 4 differential index modulation for optical orthogonal frequency division multiplexing.

In this Letter, we propose and demonstrate a 4 × 4 differential index modulation (DIM) scheme for optical orthogonal frequency division multiplexing (OOFDM) systems. The 4 × 4 DIM scheme avoids complex channel estimation by performing differential index modulation in the time-frequency domain, with the key aspect lying in the design of the time-frequency dispersion matrix that integrates the indices and constellation symbols. Moreover, we design a deep learning-based DIMFormer detector for the high decoding complexity problem of maximum likelihood (ML) detection. Experimental results show that the 4 × 4 DIM in OOFDM systems eliminates the need for complex channel estimation, and the loss of signal-to-noise ratio (SNR) is no more than 1 dB compared to conventional index modulation. The designed DIMFormer detector reduces the computational complexity by 38.98% as well as the time complexity by 99% compared to ML.

Enhancement of Thermal Management Performance of Copper Foil Using Additive-Free Graphene Coating.

Advanced thermal interface materials with high thermal conductivity are crucial for addressing the heat dissipation issue in high-power, highly integrated electronic devices. One great potential way in this field is to take advantage of cooling copper foil (Cu) materials based on graphene (G). However, the current manufacturing of these cooling copper foil materials is accompanied by high cost, process complexity, and environmental problems, which limit their development and application. In this work, a simple, low-cost, environmentally friendly graphene-copper foil composite film (rGO/G-Cu) with high thermal conductivity was successfully prepared using graphene oxide directly as a dispersant and binder of graphene coating. The microstructure characterization, thermal conductivity and thermal management performance tests were carried out on the composite films. The results demonstrate that compared to pure copper foil (342.47 W·m-1·K-1) and 10% PVA/G-Cu (367.98 W·m-1·K-1) with polyvinyl alcohol as a binder, 10% rGO/G-Cu exhibits better thermal conductivity (414.56 W·m-1·K-1). The introduction of two-dimensional graphene oxide effectively enhances the adhesion between the coating and the copper foil while greatly improving its thermal conductivity. Furthermore, experimental results indicate that rGO/G-Cu exhibits excellent heat transfer performance and flexibility. This work is highly relevant to the development of economical and environmentally friendly materials with high thermal conductivity to meet the increasing demand for heat dissipation.

Feedback stabilization of probabilistic finite state machines based on deep Q-network.

As an important mathematical model, the finite state machine (FSM) has been used in many fields, such as manufacturing system, health care, and so on. This paper analyzes the current development status of FSMs. It is pointed out that the traditional methods are often inconvenient for analysis and design, or encounter high computational complexity problems when studying FSMs. The deep Q-network (DQN) technique, which is a model-free optimization method, is introduced to solve the stabilization problem of probabilistic finite state machines (PFSMs). In order to better understand the technique, some preliminaries, including Markov decision process, ϵ-greedy strategy, DQN, and so on, are recalled. First, a necessary and sufficient stabilizability condition for PFSMs is derived. Next, the feedback stabilization problem of PFSMs is transformed into an optimization problem. Finally, by using the stabilizability condition and deep Q-network, an algorithm for solving the optimization problem (equivalently, computing a state feedback stabilizer) is provided. Compared with the traditional Q learning, DQN avoids the limited capacity problem. So our method can deal with high-dimensional complex systems efficiently. The effectiveness of our method is further demonstrated through an illustrative example.

Overcoming Long Inference Time of Nearest Neighbors Analysis in Regression and Uncertainty Prediction

The intuitive approach of comparing like with like, forms the basis of the so-called nearest neighbor analysis, which is central to many machine learning algorithms. Nearest neighbor analysis is easy to interpret, analyze, and reason about. It is widely used in advanced techniques such as uncertainty estimation in regression models, as well as the renowned k-nearest neighbor-based algorithms. Nevertheless, its high inference time complexity, which is dataset size dependent even in the case of its faster approximated version, restricts its applications and can considerably inflate the application cost. In this paper, we address the problem of high inference time complexity. By using gradient-boosted regression trees as a predictor of the labels obtained from nearest neighbor analysis, we demonstrate a significant increase in inference speed, improving by several orders of magnitude. We validate the effectiveness of our approach on a real-world European Car Pricing Dataset with approximately 4.2×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4.2 \ imes 10^6$$\\end{document} rows for both residual cost and price uncertainty prediction. Moreover, we assess our method’s performance on the most commonly used tabular benchmark datasets to demonstrate its scalability. The link is to github repository where the code is available: https://github.com/koutefra/uncertainty_experiments.

Multi-scale reconstruction of rolling mill vibration signal based on fuzzy entropy clustering

The mill vibration signals collected during mill operation are often affected by complex working conditions and various disturbances, thus presenting strong non-stationary characteristics. After the vibration signal is processed by noise reduction, although the noise interference and modal aliasing phenomenon are solved, the extracted vibration signal of the rolling mill still has the problems of strong non-stationarity, high complexity, and great difficulty in analyzing, which largely affects the prediction model construction. To address the above problems, this study proposes a multi-scale signal reconstruction method based on Fuzzy Entropy and Gath-Geva fuzzy clustering algorithm, which effectively reduces the complexity of the original sequence, reduces the number of predictive modellings, and improves the signal predictability and prediction accuracy. The sequence reconstruction of such non-stationary vibration signals based on their internal characteristics has obvious advantages for the problem of mill vibration prediction and analysis.

A novel structure learning method of Bayesian networks based on the neighboring complete node ordering search

Learning the optimal structure of a Bayesian network (BN) from observational data has received considerable research attention. In most structure learning methods based on scoring and searching, the discrete space of BNs is used, and the scale of the network grows exponentially with the number of nodes n, that is, O(n!2Cn2). Any existing scoring function in the BN space has multiple peaks. Thus, when using heuristic algorithms, local optimum problems may occur while searching in this space. As the search process involves only a small part of the space, the final learning result is not always ideal. In this study, the scoring and searching task is implemented in the complete node ordering space, and a novel neighbor operation is proposed for improving learning accuracy. This technique reduces the search space to O(n!) and facilitates optimized learning of a structure. Furthermore, techniques for efficiency optimization are proposed, which address the problems of high computational complexity and repetitive calculation of family scores.

Spatial–temporal hypergraph based on dual-stage attention network for multi-view data lightweight action recognition

For the problems of irrelevant frames and high model complexity in action recognition, we propose a Spatial–Temporal Hypergraph based on Dual-Stage Attention Network (STHG-DAN) for multi-view data lightweight action recognition. It includes two stages: Temporal Attention Mechanism based on Trainable Threshold (TAM-TT) and Hypergraph Convolution based on Dynamic Spatial–Temporal Attention Mechanism (HG-DSTAM). In the first stage, TAM-TT uses a learning threshold to extract keyframes from multi-view videos, with the multi-view data serving as a guarantee for providing more comprehensive information subsequently; In the second stage, HG-DSTAM divides the human joints into three parts: trunk, hand and leg to build spatial–temporal hypergraphs, extracts high-order features from spatial–temporal hypergraphs constructed of multi-view human body joints, inputs them into the dynamic spatial–temporal attention mechanism, and learns the intra frame correlation of multi-view data between the joint features of body parts, which can obtain the significant areas of action; We use multi-scale convolution operation and depth separable network, which can realize efficient action recognition with a few trainable parameters. We experiment on the NTU-RGB+D, NTU-RGB+D 120 and the imitating traffic police gesture dataset. The performance and accuracy of the model are better than the existing algorithms, effectively improving the machine and human body language interaction cognitive ability.

A belief rule-based classification system using fuzzy unordered rule induction algorithm

A rule-based system is a widely used artificial intelligence system that employs a set of rules to make decisions. The belief rule-based (BRB) classification system is an extension of fuzzy rule-based (FRB) system that handles uncertainty and imprecision in classification tasks by incorporating Dempster-Shafer evidence theory and fuzzy set theory. However, the BRB classification system suffers from the combinatorial explosion and high time complexity problems. To solve these issues, the fuzzy unordered rule induction algorithm (FURIA) is introduced into the BRB classification systems to design a novel BRB classification system in this paper. FURIA is computationally less complex and has superior classification performance. The proposed system outperforms BRB classification systems in terms of classification performance and significantly reduces the number of rules and conditions. Furthermore, the proposed system offers a more effective solution than FURIA. To evaluate the validity and superiority of the proposed system, we conduct two classification experiments, comparing it with five traditional classifiers and seven rule-based systems, respectively, in terms of classification performance and interpretability.

Application of neural networks to synchro-Compton blazar emission models

Context. Jets from supermassive black holes at the centers of active galaxies are the most powerful and persistent sources of electromagnetic radiation in the Universe. To infer the physical conditions in the otherwise out-of-reach regions of extragalactic jets, we usually rely on fitting their spectral energy distributions (SEDs). The calculation of radiative models for the jet’s non-thermal emission usually relies on numerical solvers of coupled partial differential equations. Aims. In this work, we use machine learning to tackle the problem of high computational complexity to significantly reduce the SED model evaluation time, which is necessary for SED fittings carried out with Bayesian inference methods. Methods. We computed the SEDs based on the synchrotron self-Compton model for blazar emission using the radiation code ATHEvA. We used them to train neural networks (NNs) to explore whether they can replace the original code, which is computationally expensive. Results. We find that a NN with gated recurrent unit neurons (GRUN) can effectively replace the ATHEvA leptonic code for this application, while it can be efficiently coupled with Markov chain Monte Carlo (MCMC) and nested sampling algorithms for fitting purposes. We demonstrate this approach through an application to simulated data sets, as well as a subsequent application to observational data. Conclusions. We present a proof-of-concept application of NNs to blazar science as the first step in a list of future applications involving hadronic processes and even larger parameter spaces. We offer this tool to the community through a public repository.

Absolute attitude measurement based on multi-sensor fusion

In the field of positioning and navigation, the measurement and transmission of absolute attitude are often used in the initial calibration process of positioning and navigation equipment. This requires higher absolute attitude measurement accuracy and lower measurement complexity. In order to solve the problems of high complexity and time-consuming of traditional absolute attitude measurement, a multi-sensor fusion absolute attitude measurement method is proposed. This method uses an optical camera to calculate the angle offset, employs a biaxial inclination sensor to measure horizontal angles, and utilizes GNSS differential positioning to introduce true azimuth angle. The experimental results of the prototype show that under the condition of 60 m baseline length, the max uncertainty of yaw, pitch, and roll angle measurement is 14.13”, 4.41”, and 3.53” respectively, and the average single measurement time consumption is 2.73 minutes.

Hyperspectral image classification based on convolutional neural network

Hyperspectral remote sensing is the acquisition of spatial and spectral data of objects by detecting the electromagnetic waves reflected from them using a hyperspectral sensor. It enables the evaluation and identification of material composition, morphology, structure, and other aspects. Currently, hyperspectral imaging is widely applied in fields such as agriculture, environmental monitoring, forestry, medicine, and geology. However, traditional hyperspectral image classification encounters the problem of high complexity and overfitting when dealing with small sample sizes, requiring the use of advanced convolutional neural network models to address this issue. This paper primarily employs literature analysis, review, and comparative analysis methods to summarize the main challenges encountered when using convolutional neural networks for hyperspectral image classification. It also selects popular models in recent years to introduce, namely 3D-CNN model, hyperspectral pyramidal ResNet model and HybridSN model. This article focuses on whether these three models can solve problems that traditional classification models cannot solve, and what room for improvement there is, which will provide a reference for future research.

Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature fusion networks for intelligent fault diagnosis of storage stacking machinery, which is aimed at improving the diagnostic precision and robustness of feature fusion network and learning the broader feature representation. Firstly, the long short-term memory layer is introduced to extract the feature information of multiple time steps to improve the self-extraction ability of multi-temporal features. Then, the maximum temporal feature fusion module is utilized to highlight the recognizability of deep fusion features. Finally, a residual layer with spanning connections is added to increase the utilization and characterization capability of deep fusion features. Experimental results demonstrate the effectiveness and superiority of the proposed method in intelligent fault diagnosis of storage stacking machinery under variable working conditions compared with some state-of-the-art deep learning-based methodologies.

Average effective subcarrier-domain sparse representation approach for target information estimation in CP-OFDM-based passive bistatic radar

Although some existing sparse representation (SR) methods are robust for target detection in passive bistatic radar (PBR), they still face the challenges of high computational complexity and poor detection performance for extremely low-signal-to-clutter ratio (SCR) target. So, an average effective subcarrier (AES)-domain sparse representation approach is investigated in this paper. Firstly, the AES-based SR model is proposed to solve the problem of high computational complexity, which is established by utilizing the sparseness of the orthogonal frequency-division multiplexing (OFDM) with cyclic prefix (CP) signals in each effective subcarrier domain. Then, considering the difficulty of detecting extremely low-SCR targets, clutter cancellation is implemented by the SR-based optimization model. Two AES-S algorithms, namely AES-S-based clutter cancellation in the time domain (AES-S-T) and AES-S-based clutter cancellation in the subcarrier domain (AES-S-C), are proposed, and the computational complexity is further reduced. Finally, extensive simulation and experimental results illustrate that the proposed algorithms have good detection performance and low computational complexity in PBR detection scene.

Construction and Efficacy Verification of Color Theory Optimization Model in AI Painting Assistant

Abstract The application of artificial intelligence in the field of painting has been greatly developed, which is a new way of thinking in the digital era, bringing innovation and infinite possibilities to the art of painting. The article first researches the art and expression of AI painting, mainly taking various network models as the main form of expression. Then, color theory and color space are introduced to provide a theoretical basis for the proposal of a color optimization model. Considering that the ACE algorithm has problems of high complexity and image enlargement, the acceleration algorithm LLLUT is used to improve data processing. The AI imitation painting works “Monet’s Garden” and “Neon” are taken as case studies. In terms of color preference, AI painting prefers to use three primary colors: black, white, and red. For example, AI’s imitation painting work Han Xizai Night Banquet has three peak points higher than the average peak, respectively 0.04, 0.09, 0.15, and 0.99. The corresponding colors used are red, black, and yellow. The main colors used in the composition are red and yellow. In the application evaluation of the color model, the comparative analysis concludes that the color mixing efficiency is higher using the research method in this paper, and in the practical application, both painting professionals and amateurs have a stable effectiveness in generating color preference schemes after using the AI painting assistant based on the ACE color model optimized by LLLUT.