Number Of Complexity Research Articles

DEVELOPING A MATHEMATICAL MODEL FOR DESCRIBING THE MOTION OF A VIBRATORY TYPE SEPARATOR WITH SEQUENTIAL SCREENS

The developed mathematical model of a vibratory separator with sequential screen placement and spring suspension is nonlinear, unified, and parametric. It was derived based on the utilization of the second-order Lagrange equation and asymptotic methods of nonlinear mechanics. The model is intended for investigating the dynamics of the separator during its design and subsequent operation, particularly for selecting its optimal design parameters and determining the conditions of steady-state and transient operating modes. It is presented as a set of analytical dependencies representing the motion laws of arbitrary points of the separator's working body in terms of its geometric, mass-inertial, and kinematic parameters with the ability to cover a wide range of variations. The model can be integrated into systems for automated design and calculation of various vibratory separators, differing in the number of screens, arrangement, and complexity of the drive, suspension, working body, etc.

Number of individuals, but not habitat complexity, influences the antipredator behavior of an Amazonian floodplain fish

Abstract Trophic relationships shape ecosystem structure by regulating energy flow and nutrient cycling, impacting prey consumption and prompting behavioral adaptations in prey species to mitigate risks. While much is known about fish antipredator behavior in marine environments, less is understood about these dynamics in freshwater environments. We investigated the antipredator behavior of an Amazonian floodplain fish, Crenuchus spilurus, across different habitat structural complexities. Through experiments, we examined how the number of individuals (1 and 4 individuals) and habitat structural complexity (low, medium, high, and very high) influence the fish response to predator presence. Small groups showed longer flight initiation distance (FID) than solitary individuals, suggesting a possible collective defense strategy against approaching predators. The structural complexity of the habitat did not have a direct effect on FID. Additionally, we noted variations in refuge usage by the fish across different habitat structural complexities. These findings underscore the interplay between behavioral and environmental factors in prey adaptation to predation risk in aquatic ecosystems.

Оценка характеристик фрагмента контроля и устранения дефектов (ошибок), допущенных при реализации технологического процесса

This article considers a fragment of the technological process related to multi-stage monitoring of the production results (check, diagnostics, and elimination of the identified product defects). A general control algorithm is presented. This algorithm includes preliminary control at the workplace and diagnostic controls at thesite and workshop level, with subsequent elimination of the defects identified. An atypical elementary operation with three outputs was identified. All possible paths were analyzed followed by obtaining of analytical dependencies for probabilistic and time estimates, as well as for estimates of the number of operations, stereotypicality, and logical complexity. A method for reducing the algorithm to an enlarged work operation, whose characteristics adequately reflect the operational peculiarities of the algorithm (technological process – control and elimination of defects), is proposed. The obtained estimates can be used to solve a number of specific problems related to improving the algorithm (reducing the likelihood of defects in the resulting product, changing the structure (structural optimization) of the algorithm, etc.). The implementation of an approach based on identifying paths in the algorithm, assessing their characteristics, and calculating the characteristics of the entire algorithm confirmed the validity of the proposed approach. The results obtained can be used as a methodological and mathematical basis for creating various decision support tools in the design and development of human-machine systems for various purposes.

A Hybrid Deep Learning Approach for Modelling and Optimising Data Centre Energy Efficiency

Data Centres (DCs), the core of the ever-increasing economic and societal activities, are experiencing high energy consumption due to the rapidly growing demand for digital services, which leads to the largest DCs’ operational costs and significant environmental and power security impacts. Hence, optimising their energy efficiency is a critical and top priority to ensure economic and environmentally sustainable DC management. However, prior heuristics and engineering-based solutions are inadequate due to the increasing physical complexity and sheer number of possible configurations, non-linear system interactions, and the growing monitoring of operational management data. Therefore, this paper develops a six-layered hybrid Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM), called CNN-LSTM, a suitable data-driven Deep Learning (DL) model inspired by the human brain for effectively modelling the complex and plant performance and optimising DC efficiency. The proposed model uses CNN to capture complex parameter interactions and spatial pattern recognition and LSTM to capture temporal dependencies of the time series operational data to predict the next energy consumption value and optimise it using a cooling system fan speed controllable variable. The model was extensively trained and tested using actual operational management data obtained from the Enea High-Performance Computing (HPC) CRESCO6 cluster, Italy. 80% of the data was used for training, and the rest 20% for testing. According to the experimental results, it accurately predicts energy consumption every 15 minutes with an average Mean Absolute Error (MAE) of 0.0043. A sensitivity analysis optimisation strategy is also implemented using various cooling system fan speed set points. When the fan speed optimal set-point is automatically reduced by 50%, the energy consumption is optimised by an average of 0.0029 kWh with a 0.0039 MAE over the last 15 minutes of the testing set. Hence, this paper found that the proposed CNN-LSTM deep learning predictive model can effectively mimic actual DC operations and optimise efficiency. By simulating this model, DC operators can effectively manage and optimise their DC energy efficiency while reducing energy and environmental costs.

Informal care trajectories: developments in care demands, intensity and complexity over the course of care episodes

In response to the dominant narrative that informal care episodes become increasingly heavy over time, this study empirically identifies distinct informal care trajectories in terms of developments in care demands (care receivers’ health), intensity (number of hours) and complexity (number and type of tasks) over the course of a care episode. Latent class analysis on 5,595 informal care episodes among 2,884 Dutch carers identified a ‘stable trajectory’ (41 per cent) and ‘decrease trajectory’ (22 per cent) apart from an ‘increase trajectory’ (37 per cent). The trajectories were related to care receivers’ characteristics (age), carers’ difficulty of saying ‘no’ (relationship and obligation) and changes in the care context (living situation).

Lightweight 2D Human Pose Estimation Based on Joint Channel Coordinate Attention Mechanism

Traditional human pose estimation methods typically rely on complex models and algorithms. Lite-HRNet can achieve an excellent performance while reducing model complexity. However, its feature extraction scale is relatively single, which can lead to lower keypoints’ localization accuracy in crowded and complex scenes. To address this issue, we propose a lightweight human pose estimation model based on a joint channel coordinate attention mechanism. This model provides a powerful information interaction channel, enabling features of different resolutions to interact more effectively. This interaction can solve the problem of human pose estimation in complex scenes and improve the robustness and accuracy of the pose estimation model. The introduction of the joint channel coordinate attention mechanism enables the model to more effectively retain key information, thereby enhancing keypoints’ localization accuracy. We also redesign the lightweight basic module using the shuffle module and the joint channel coordinate attention mechanism to replace the spatial weight calculation module in the original Lite-HRNet model. By introducing this new module, we not only improve the network calculation speed and reduce the number of parameters of the entire model, but also ensure the accuracy of the model, thereby achieving a balance between performance and efficiency. We compare this model with current mainstream methods on the COCO and MPII dataset. The experimental results show that this model can effectively reduce the number of parameters and computational complexity while ensuring high model accuracy.

Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition

In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and long short-term memory (LSTM), were used along with adaptive boosting and general regression neural network to forecast multi-step-ahead pan evaporation in two arid climate stations in Iran (Ahvaz and Yazd). Lagged time series of meteorological data and pan evaporation data were input to the machine learning models. Two feature selection methods, i.e., the Boruta extra tree and XGBoost, were used to select significant inputs to reduce the number of inputs and model complexity. Different statistical metrics were used to investigate the model performance. The results demonstrated that Boruta-extra-tree-based models were more accurate than XGBoost-based models. Compared with the machine learning techniques, the combination of Boruta extra tree and BiLSTM enabled more accurate one-day-ahead forecasting of pan evaporation for both sites (Root Mean Square Error (RMSE) = 1.6857, for the Ahvaz station, and RMSE = 1.3996 for the Yazd station). The proposed model was used to forecast up to 30 days ahead of pan evaporation in both stations. The results showed that the Boruta-BiLSTM model could accurately forecast the pan evaporation for up to 30 days in both stations.

The Bit Query for Labels in a Binary Tree-Based Anti-Collision Recognition Algorithm

With its benefits including recognition distance, penetration ability, and multi-object recognition, radio frequency identification technology is a non-contact automatic identification method that is currently being used extensively in a variety of industries, including document anti-counterfeiting, automation, transportation, control management, and product services. An efficient anti-collision mechanism, such as anti-collision algorithms or anti-collision protocols, must be established in order to coordinate communication between the tag and the reader in the event that an RFID system experiences signal interference in the wireless channel, conflict, or collision, which will result in tag recognition or data collection failure. In response to tag conflicts in radio frequency identification (RFID) systems, a novel hybrid blocking query tree algorithm is suggested that leverages blocking prediction and child node hedging techniques to overcome the issues of lengthy time intervals and high communication complexity. The technique uses quadruple and bifurcated query trees as the foundation for its conflict avoidance strategy. The approach minimizes the conflict time and prevents the construction of idle subunits by forcing the reader to create new query prefixes via prediction, which is accomplished by employing locking commands to extract and forecast information about the conflicting bits. According to simulation data, this strategy performs better in terms of lowering the overall number of time slots and communication complexity as well as enhancing identification and labelling abilities than the current adaptive multi-tree search (RLAMS) and improved hybrid query tree (IHQT) techniques. It greatly increases the effectiveness of label identification while lowering the overall amount of time slots and communication complexity.

High‐Frequency‐Based Volatility Model with Network Structure

This paper introduces a novel multi‐variate volatility model that can accommodate appropriately defined network structures based on low‐frequency and high‐frequency data. The model offers substantial reductions in the number of unknown parameters and computational complexity. The model formulation, along with iterative multi‐step‐ahead forecasting and targeting parameterization are discussed. Quasi‐likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation studies are carried out to assess the performance of parameter estimation in finite samples. Furthermore, a real data analysis demonstrates that the proposed model outperforms the existing volatility models in prediction of future variances of daily return and realized measures.

Hierarchical neural architecture search with adaptive global–local feature learning for Magnetic Resonance Image reconstruction

Neural architecture search (NAS) has been introduced into the design of deep neural network architectures for Magnetic Resonance Imaging (MRI) reconstruction since NAS-based methods can acquire the complex network architecture automatically without professional designing experience and improve the model’s generalization ability. However, current NAS-based MRI reconstruction methods suffer from a lack of efficient operators in the search space, which leads to challenges in effectively recovering high-frequency details. This limitation is primarily due to the prevalent use of convolution operators in the current search space, which struggle to capture both global and local features of MR images simultaneously, resulting in insufficient information utilization. To address this issue, a generative adversarial network (GAN) based model is proposed to reconstruct the MR image from under-sampled K-space data. Firstly, parameterized global and local feature learning modules at multiple scales are added into the search space to improve the capability of recovering high-frequency details. Secondly, to mitigate the increased search time caused by the augmented search space, a hierarchical NAS is designed to learn the global–local feature learning modules that enable the reconstruction network to learn global and local information of MR images at different scales adaptively. Thirdly, to reduce the number of network parameters and computational complexity, the standard operations in global–local feature learning modules are replaced with lightweight operations. Finally, experiments on several publicly available brain MRI image datasets evaluate the performance of the proposed method. Compared to the state-of-the-art MRI reconstruction methods, the proposed method yields better reconstruction results in terms of peak signal-to-noise ratio and structural similarity at a lower computational cost. Additionally, our reconstruction results are validated through a brain tumor classification task, affirming the practicability of the proposed method. Our code is available at https://github.com/wwHwo/HNASMRI.

Power Quality Disturbances Classification Through Optimal Feature Selective Mechanism

In the Detection and classification of Power Quality Disturbances, feature selection is very important because the more number of features causes more complexity and less number of feature impacts the accuracy. Hence, this paper proposed a new feature selection mechanism called as Flexible Mutual Information based Feature Selection (FMIFS) to represent the Power Quality Disturbances with an effective set of features. FMIFS computes the Mutual Redundancy between PQDs and removes redundant features between them. Once the optimal features are obtained for each PQD, then they are fed to Multi-class Support Vector Machine (MC-SVM) for classification. MC-SVM classifies each PQD based on the feature trained to the system. At experimental analysis, we applied our method on totally 11 types of PQDs for classification and the performance is measured through recall, precision, F1-score, and False Alarm Rate (FAR).

Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction

Quantum reservoir computing is strongly emerging for sequential and time series data prediction in quantum machine learning. We make advancements to the quantum noise-induced reservoir, in which reservoir noise is used as a resource to generate expressive, nonlinear signals that are efficiently learned with a single linear output layer. We address the need for quantum reservoir tuning with a novel and generally applicable approach to quantum circuit parameterization, in which tunable noise models are programmed to the quantum reservoir circuit to be fully controlled for effective optimization. Our systematic approach also involves reductions in quantum reservoir circuits in the number of qubits and entanglement scheme complexity. We show that with only a single noise model and small memory capacities, excellent simulation results were obtained on nonlinear benchmarks that include the Mackey-Glass system for 100 steps ahead in the challenging chaotic regime.

Reinforcement learning-based resilient power maximization and regulation control for large-scale wind turbines under cyber actuator attacks

Large-scale wind turbine systems (LS-WTSs) are vulnerable to cyber threats due to their flat control network topology, fragile protocols, and remote locations. To solve these security problems and improve electrical efficiency, this study aims to present a reinforcement learning (RL)-based attack-resilient coordinated pitch and torque control for LS-WTS under actuator attacks. The proposed scheme utilizes the RL technique to reduce the number of tuning parameters and computational complexity. To do this, a model-free agent-environment framework is first proposed with an aperiodic denial-of-service (ADoS) attack model to enable the RL features. Then, an RL-based resilient sliding-mode pitch and torque control scheme is proposed to optimize maximum power extraction and regulation by incorporating reward updates and adaptive learning rates for optimal control input selection under ADoS attacks with varying wind conditions. At the same time, the adaptive learning-rule-based gain fluctuations term is presented to mitigate the uncertain and disruption parameters in the control system. Finally, the effectiveness and robustness of the proposed method are verified through the evaluation of a 4.8MW benchmark and a 20MW PMSG-based LS-WTS, showing distinct advantages over conventional approaches. Theoretical insights are practically validated with experimental prototypes to confirm the resiliency of the presented method against network and physical impacts caused by cyber actuator attacks in the LS-WTS in various wind cases.

LF-YOLOv4: a lightweight detection model for enhancing the fusion of image features of surface defects in lithium batteries

In response to the low accuracy of traditional methods for detecting surface defects in lithium batteries, as well as the problems of large model size and high computational complexity in current detection models, this article proposes a new lightweight LF-YOLOv4 model that enhances image feature fusion. Firstly, replace the CSPDarknet53 backbone network in YOLOv4 with a lightweight MobileNetv2 network, thereby greatly reducing the computational parameters of the network while ensuring the ability to extract features. Secondly, in order to further reduce the number of model parameters and computational complexity, and minimize potential accuracy loss as much as possible, an improved depthwise separable convolution (DSC-SE-HsId) in this article was studied, which replaced some ordinary convolutions in the Neck and Head networks. Finally, in order to compensate for the partial accuracy loss caused by lightweight operations, and also to fuse feature maps of different scales to obtain more complete feature information, a new lightweight adaptive spatial feature fusion module (LSE-ASFF) in this article was studied and embedded behind the existing path aggregation network (PANet). In order to verify the performance and widespread applicability of the improved model, we conducted tests using the self-built lithium battery surface defect dataset, and the steel surface defect dataset provided by Northeastern University. Moreover, in order to prevent the network training from over fitting due to too few defect samples in the self-built dataset, we also used image enhancement to expand the dataset. And the experiment results show that the improved model proposed in this article achieves the highest TOPSIS score in both experimental datasets. Among them, compared with YOLOv4 on the self-built dataset, our improved model not only increases mAP50 by 2.97%, reaching 97.83%, but also has model parameters of only 18.16% of the original model, floating point of operations of only 13.87% of the original model, model size of only 21.02% of the original model, and model training time shortened by 30.67% compared to the original model. At last, the effectiveness and superiority of the improved model in this article are demonstrated through example analysis and comparison.

PCB Defect Detection Algorithm Based on Improved YOLOv8

In order to solve the performance and efficiency problems in PCB defect detection tasks, a PCB defect detection algorithm based on improved YOLOv8 is proposed, which aims to improve detection accuracy, reduce model complexity, adapt to small target detection, and operate in resource-constrained environments. achieve efficient detection. First, an improved neck network structure is introduced, which reduces the number of parameters and computational complexity of the model and improves resource utilization. Subsequently, the ShuffleAttention and BiFPN structure were added to enhance the model's multi-scale feature fusion capabilities and better adapt to small target detection. Finally, the WIoU loss function is used to replace the traditional CIoU loss function, thereby improving the detection accuracy and robustness of the model. Experimental results show that the improved algorithm achieved 94.2% and 49.0% in mAP50 and mAP90-95 respectively. The number of parameters, GFLOPs and weight size of the model were reduced by 33%, 12% and 32% respectively, reaching 1.992M, 7.1 and 4.2M. Provides an efficient, accurate and lightweight PCB defect detection solution, making it ideal for resource-constrained environments such as mobile devices and embedded systems.

Automotive Electronic Control Unit Ground Line Health Monitoring Method

Electronic Control Units (ECUs) have been used in the automotive industry for decades to control one or more of the vehicle subsystems. The ECUs communicate primarily using the in-vehicle Controller Area Network (CAN) communication protocol. The recent rapid development of connected, electric, and autonomous vehicles expands the number of ECUs and complexity of the CAN network required to integrate vehicle systems and deliver the desired functionalities. This demands increased reliability of the ECUs to ensure for robust vehicle performance. One of the most common ECU failure modes is the ECU ground fault. A ground fault occurs when the ground path in the ECU circuit is corroded, which is usually developed slowly over time. Such failure usually results in various symptoms including ECU incapable of functioning and further impacts the vehicle functionalities negatively. This type of fault can be difficult to detect prior to vehicle functionality loss. It usually involves routinely testing the resistance of the ground circuit, visually inspecting the connectors and wirings, and checking the voltage drop across the ground circuit. Therefore, it is highly desirable to continuously monitor the ECU ground line health status to predict any degradation and thus prevent vehicle functionality losses.
 This paper presents a novel method to monitor the health status of ECU ground line. The method leverages measured CAN voltage data to estimate the ECU ground state of health. The CAN voltage measurements are preprocessed and fed into a real-time data buffer of predefined size. Statistical moments are calculated from the buffered data to generate health indicators, which are then combined to form a fused health indicator. The fused health indicator is used to determine the health stage of ECU ground line. The health stage is classified based on the relationship between ground line degradation level and the ECU communication loss status. The method was developed and validated using actual vehicle data.

Winning the Lottery With Neural Connectivity Constraints: Faster Learning Across Cognitive Tasks With Spatially Constrained Sparse RNNs.

Recurrent neural networks (RNNs) are often used to model circuits in the brain and can solve a variety of difficult computational problems requiring memory, error correction, or selection (Hopfield, 1982; Maass etal., 2002; Maass, 2011). However, fully connected RNNs contrast structurally with their biological counterparts, which are extremely sparse (about 0.1%). Motivated by the neocortex, where neural connectivity is constrained by physical distance along cortical sheets and other synaptic wiring costs, we introduce locality masked RNNs (LM-RNNs) that use task-agnostic predetermined graphs with sparsity as low as 4%. We study LM-RNNs in a multitask learning setting relevant to cognitive systems neuroscience with a commonly used set of tasks, 20-Cog-tasks (Yang etal., 2019). We show through reductio ad absurdum that 20-Cog-tasks can be solved by a small pool of separated autapses that we can mechanistically analyze and understand. Thus, these tasks fall short of the goal of inducing complex recurrent dynamics and modular structure in RNNs. We next contribute a new cognitive multitask battery, Mod-Cog, consisting of up to 132 tasks that expands by about seven-fold the number of tasks and task complexity of 20-Cog-tasks. Importantly, while autapses can solve the simple 20-Cog-tasks, the expanded task set requires richer neural architectures and continuous attractor dynamics. On these tasks, we show that LM-RNNs with an optimal sparsity result in faster training and better data efficiency than fully connected networks.

Correlates of word likeness in the perception of pseudowords

New words are often introduced into languages from borrowings or neologisms. Pseudowords (possible words of a language that have not yet been assigned meaning) are used in many experiments to test aspects of speech perception. However, what we know about the perception of pseudowords is relatively limited. In this study, we investigate word likeness ratings of pseudowords on a 1–7 scale. A total of 659 listeners responded to sets of 200 pseudowords from an item list of 9599 English-sounding pseudowords varying in number of syllables and morphological complexity. We analyze the listeners' responses with the following predictors: phonotactic predictability, duration, phonological neighborhood density, phonological uniqueness point, pseudo morphological complexity, and acoustic distinctiveness. We analyze the ordinal data to identify how these predictors influence listeners’ ratings. We also take average word likeness ratings of the pseudowords and use them to predict a different set of listeners’ reaction times in a lexical decision task. In this presentation, we discuss what properties of the pseudowords influence what makes them seem more word-like or not and how word-likeness affects auditory word recognition.

Mobile-DenseNet: Detection of building concrete surface cracks using a new fusion technique based on deep learning

Crack detection is very important during the inspection of building structures to determine whether they are safe. Therefore, to ensure the reliability and longevity of buildings, it is necessary to have experts periodically carry out building inspections. Building inspection has traditionally been conducted using human-based visual inspection methods as well as artificial intelligence methods that have shown great success in computer vision in recent years. In this study, 9 different models (Xception, VGG16, ResNet101, InceptionV3, InceptionResNetV2, MobileNetV2, DenseNet169, NASNetMobile, and EfficientNetB6), which have shown significant success in the field of artificial intelligence, are discussed to detect and classify cracks in building structures. In addition, a new fusion model structure called Mobile-DenseNet has been proposed by making block cutting and adding auxiliary layers to the MobileNetV2 and DenseNet169 model structures. With this proposed model structure, cracks in concrete structures were classified. A dataset consisting of concrete surface images was used to detect and classify cracks occurring in concrete structures, and a 99.87 % success rate was achieved with the proposed Mobile-DenseNet model in classifying cracks occurring on the concrete surface. The proposed model outperformed the traditional pretrained model structures in the study in terms of the number of transactions, density, features, complexity, and success accuracy.

Time and phase features network model for automatic modulation classification

Automatic Modulation Classification (AMC) constitutes a fundamental technology for enabling automatic demodulation in Cognitive Communication Systems (CCS). Due to the size, weight, and power (SWaP) constraints of embedded computers employed in CCS, there are limited computational and memory resources. While deep neural networks possess strong feature representation and high accuracy recognition capabilities, they usually come with a high number of network parameters and high computational complexity, thereby reducing the real-time processing ability of CCS. Therefore, neural network structures intended for CCS must be lightweight and computationally efficient. In this paper, we propose a high-performance and resource-friendly network model based on an analysis of the modulation mechanism of communication signals. The network extracts phase features and short-time features sequentially using directional convolutional filters. Long short-term memory (LSTM) units are then used to extract long-term features, and only one fully connected layer is used for classification. Experiments with a standard dataset consisting of 11 communication modulation types demonstrate that our proposed model achieves an accuracy greater than 84.5%, even when the signal-to-noise ratio (SNR) is 0 dB, and the model has only 29187 parameters. On a Jetson Nano embedded platform, the model achieves a processing speed of up to 375366 in-phase and quadrature samples/s. Overall, the results suggest that our proposed approach is both lightweight and highly efficient, making it more suitable for CCS applications.