Adaptive Input Research Articles

Time-efficient model predictive control for autonomous tugs with adaptive input constraints

The marine autonomous surface ship (MASS) has gained significant attention because of its wide application in waterborne transportation in recent years. The trajectory control of the MASS is crucial in ensuring safety, particularly in narrow navigation areas and urgent encounter situations. Model predictive control (MPC) is well known as a sufficient method to solve the tracking problem considering the actuator saturation with model uncertainties and environmental disturbances. However, due to the constraints on computing resources and hardware limits, the controller may fail to find a feasible solution for MPC within a reasonable amount of time, especially when the system models are coupled and complex. To improve the solution efficiency, this paper introduces adaptive constraints to reduce search space for optimization, i.e., the current tracking point’s speed is transformed into input constraints in the MPC formulation to decrease the computation burden, as well as reduce mechanical wear on the thrusters with smaller amplitudes of control actions. Simulations are carried out to evaluate the effectiveness of the proposed method with different MPC forms.

Performance Analysis and Optimization of Switch Device for VLF Communication Synchronous Tuning System Based on Coupled Inductors

The very low frequency (VLF) communication system is characterized by a limited transmit bandwidth. Due to the low operating frequency, the dimensions of VLF antennas are significantly smaller than the corresponding wavelength. Therefore, VLF antennas are considered electrically small antennas (ESAs) with high Q values and narrow bandwidth. To achieve broadband VLF communication, synchronous tuning technology is commonly employed. In this study, we focus on analyzing the performance of the switching device in the synchronous tuning system using coupled inductors and IGBT as core components. By considering the equivalent circuit of the controlled source associated with coupled inductors, we propose an adaptive input impedance optimization algorithm based on the variable capacitor (hereinafter referred to as VC-ADIO) to address issues arising from coupling coefficient variations and internal parameters within IGBTs that affect primary loop input impedance. The radiated power of the VLF antenna is improved effectively.

Research on the artistic form and image expression effect of public sculpture based on intelligent and parametric design

With the modernization of cities, public sculptures are constantly being designed and constructed. The artistic form and image expression effect of sculpture based on intelligent and parametric design needs to be designed and developed to guide and assist the construction of sculpture. This paper applies the NAS architecture search method to explore the field of image expression effect models. Through the end-to-end search of the experiment designed in this paper, the separable convolution lightweight design is used, and the new model AestheticNet is used to predict the image form effect score distribution. Secondly, this paper proposes optimization strategies combining image expression effect theory and convolutional neural network, including improvement of Loss function self-weighted Loss, two-dimensional Attention mechanism – introduction of CBAM, and adaptive pooling layer. Optimization of several aspects, such as adaptive input. Finally, the validation set is compared with other existing image-morphological effect model algorithms, which proves the effectiveness of the customized search scheme. It demonstrates the efficacy of the AestheticNet model compared to other algorithms by validating its prediction of public sculpture image form effect ratings. The artistic form using intelligent and parametric design methodologies may improve. Image expression of sculptures may be enhanced by applying the image form effect model, which should be pervasive. We can use it to intelligently and parametrically guide the design and manufacture of sculptures.

Query-Based Object Visual Tracking with Parallel Sequence Generation.

Query decoders have been shown to achieve good performance in object detection. However, they suffer from insufficient object tracking performance. Sequence-to-sequence learning in this context has recently been explored, with the idea of describing a target as a sequence of discrete tokens. In this study, we experimentally determine that, with appropriate representation, a parallel approach for predicting a target coordinate sequence with a query decoder can achieve good performance and speed. We propose a concise query-based tracking framework for predicting a target coordinate sequence in a parallel manner, named QPSTrack. A set of queries are designed to be responsible for different coordinates of the tracked target. All the queries jointly represent a target rather than a traditional one-to-one matching pattern between the query and target. Moreover, we adopt an adaptive decoding scheme including a one-layer adaptive decoder and learnable adaptive inputs for the decoder. This decoding scheme assists the queries in decoding the template-guided search features better. Furthermore, we explore the use of the plain ViT-Base, ViT-Large, and lightweight hierarchical LeViT architectures as the encoder backbone, providing a family of three variants in total. All the trackers are found to obtain a good trade-off between speed and performance; for instance, our tracker QPSTrack-B256 with the ViT-Base encoder achieves a 69.1% AUC on the LaSOT benchmark at 104.8 FPS.

An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus

Mining entities from open Chinese mechanical equipment texts have become prevailing in the intelligent manufacturing field. However, compared to named entities in other domains, it is very hard to determine entity boundaries in mining Chinese mechanical equipment texts because there is no unified standard for entity simplification, and digits and units are mixed in the text. To address the issue, this paper presents an entity boundary-define strategy and constructs a mechanical equipment-oriented corpus called MECorpus using open Chinese mechanical equipment texts by combining domain knowledge. A multi-neural network collaboration model Adaptive-BERT-BiLSTM-CRF-Rating (ABBCR) is then proposed for mechanical equipment named entity recognition. The novelty of ABBCR is characterised by its adaptive input mechanism and rating score ability for identified entities. Various experiments about ABBCR model selection, evaluation and application are conducted on MECorpus. Experimental results show that the ABBCR model provides high-quality mechanical equipment entities for constructing the mechanical equipment knowledge graph. ABBCR combined with the large language model has proved to be a promising method to manage complex mechanical equipment expertise.

Adaptive Input Delay Compensation Under Event-Based Control Signal Implementation

Adaptive Input Delay Compensation Under Event-Based Control Signal Implementation

Real-time semantic segmentation for underground mine tunnel

Semantic segmentation is the underlying technology for many intelligent applications in underground mines. Unlike the ordinary scenario, the lighting of underground mines varies drastically, and there is heavy dust and mist, which results in poor image quality and greatly hinders the application of image semantic segmentation in underground mines. Because there is currently no datasets for underground mine tunnels available, a dataset named Underground Mine Tunnel Semantic Segmentation Dataset (UMTSSD) have to be constructed to support our research. UMTSSD consists of 3461 meticulously annotated images and 17 annotated categories. A real-time semantic segmentation algorithm named Fast Adaptive Deep Dual-resolution Network (FA-DDRNet) which uses Deep Dual-resolution Network (DDRNet) as the backbone is proposed for underground mines. To enhance the semantic segmentation accuracy in underground environment, FA-DDRNet introduces two modules: Fast Adaptive Input Normalization Module (FAINM) and Scale-wise Residual Cascade Module (SRCM). FAINM can autonomously and quickly adjust normal lighting images, weak lighting images, and overexposed images to improve the robustness of semantic segmentation algorithms. SRCM is integrated into the backbone to swiftly fuse multi-scale features in a cascade fashion, resulting in enhanced detection of objects with diverse shapes in underground environment. Finally, our method achieves exceptional performance with a superior inference speed compared to other semantic segmentation algorithms in UMTSSD. The method can realize running in real-time on low computational power embedded devices, which is well adapted to underground environment.

Real-time numerical differentiation of sampled data using adaptive input and state estimation

Real-time numerical differentiation plays a crucial role in many digital control algorithms, such as PID control, which requires numerical differentiation to implement derivative action. This paper proposes an algorithm for estimating the numerical derivative of a signal from noisy sampled data measurements. The method uses adaptive input estimation with adaptive state estimation (AIE/ASE), and thus it requires only minimal prior information about the signal and noise statistics. Furthermore, since the estimates of the derivative at step k provided by AIE/ASE depend only on data available up to step k, AIE/ASE is thus implementable in real time. The accuracy of AIE/ASE is compared numerically to several conventional numerical differentiation methods. Finally, AIE/ASE is applied to simulated vehicle position data, generated in the CarSim simulator software.

A Lightweight Network With Adaptive Input and Adaptive Channel Pruning Strategy for Bearing Fault Diagnosis

A Lightweight Network With Adaptive Input and Adaptive Channel Pruning Strategy for Bearing Fault Diagnosis

A Sequential VGG16+CNN based Automated Approach with Adaptive Input for Efficient Detection of Knee Osteoarthritis Stages

A Sequential VGG16+CNN based Automated Approach with Adaptive Input for Efficient Detection of Knee Osteoarthritis Stages

A ZNN-Based Solver With Adaptive Input Range Fuzzy Logic System for Time-Varying Algebraic Riccati Equation

A ZNN-Based Solver With Adaptive Input Range Fuzzy Logic System for Time-Varying Algebraic Riccati Equation

Regularization of the RLS algorithm for adaptive digital predistortion

Problem statement. Modern digital pre-image input systems, as a rule, involve the use of adaptation algorithms to correct their parameters. Adaptation algorithms are solutions based on LMS and RLS methods. At the same time, the efficiency, in terms of the convergence rate and the accuracy of the solution, of RLS methods is significantly higher. This is due to the presence of a covariance matrix as part of the solution based on the RLS method. However, when using RLS algorithms (an adaptation algorithm using the RLS method), problems may arise related to poor conditionality of the covariance matrix resulting from signal noise, high inertia of the nonlinear properties of the power amplifier, limited accuracy of parameter calculation, etc. To solve this problem, it is advisable to use the regularization procedure of the RLS algorithm, taking into account the specifics of the digital predistortion systems. Objective. Creation and analysis of the regularization procedure of the RLS algorithm for adaptive input systems of digital presets. Results. The paper presents the implementation of the regularization procedure of the RLS algorithm for adaptive input systems of digital presets. Using the regularization procedure of the RLS algorithm avoids the state of poor conditionality of the covariance matrix, thereby ensuring stable operation of the digital pre-order input system. The state of the covariance matrix is monitored at each iteration of the RLS algorithm by evaluating the trace of the matrix. Practical importance. The proposed solution can be used to stabilize the parameters of adaptive systems for entering pre-orders based on RLS algorithms.

Design and analysis of thermoelectric energy harvester with 85.6% peak end-to-end efficiency and 38 mV self-startup using 4.13 mΩ/°C indirect temperature dependent MPPT

We present an efficient power management system (PMS) to harvest energy from thermoelectric energy generators (TEGs) via indirect temperature tracking. The PMS includes a power converter with an indirect temperature-dependent (ITD) maximum power point tracking (MPPT) controller and two complementary self-startups. The ITD-MPPT controller consists of an ITD input sampling circuit (ITD-ISC) and an ITD impedance matching circuit (ITD-IMC). The ITD-ISC detects and samples the TEG voltage (VTEG) under dynamically varying temperature gradients. The ITD-ISC removes unnecessary detachments of the converter from multi-mode reconfigurable TEGs to recover the otherwise wasted power using an adaptive input sampling (AIS) technique. The ITD-IMC adjusts the on-time of the power switch to control the input impedance (RIN) of the converter with a relatively high resolution of 4.13 mΩ/°C. This approach provides an ultrawide range source tracking from 2 Ω to 200 Ω. We also present a precision zero current switching (ZCS) controller to manage the on-time of the two power switches simultaneously. Two complementary self-startup components are realized using transformer-based (TSC) and charge-pump-based startup circuits (CSC). Integrated circuits for the PMS are fabricated using 180 nm and 28 nm CMOS processes for high efficiency and low voltage, respectively. The measurements indicate that the proposed PMS achieves an end-to-end efficiency (ηEE) above 80% over a wide source VTEG range (100 mV – 450 mV). The peak ηEE of 85.6% is achieved for VTEG = 200 mV, demonstrating the high-performance MPPT using indirect temperature tracking. TSC and CSC demonstrate successful self-startups at input voltages of 38 mV and 200 mV, respectively, thus providing reliable startup methods for various renewable energy sources.

Quantum contextuality of complementary photon polarizations explored by adaptive input state control

Quantum contextuality of complementary photon polarizations explored by adaptive input state control

A Novel Meta-Learning Network With Adaptive Input Length and Attention Mechanism for Bearing Fault Diagnosis Under Different Working Conditions

A Novel Meta-Learning Network With Adaptive Input Length and Attention Mechanism for Bearing Fault Diagnosis Under Different Working Conditions

A parallel ensemble optimization and transfer learning based intelligent fault diagnosis framework for bearings

Transfer learning (TL) is an important method to accurately identify the bearing health status in cross-domain and ensure the safe operation of machinery. With the advancement in research, it will become a trend to choose different neural networks or optimization functions to improve and re-model fault diagnosis methods. However, the variants of these fault diagnostic methods are less capable of generalizing input dimensions and do not significantly increase demand for machinery expertise. The idea of ensemble learning solves the problem of low generalization. In this research, a parallel ensemble optimization loss function and multi-source TL based model are proposed to solve the problem of unknown distribution difference between source domain and target domain, thus improving the generalization of optimization objectives. Firstly, based on the signal demodulation method, an adaptive input module is constructed to automatically select the input length from the original vibration signal. Secondly, a TL network with low-dimensional features reuse is constructed to achieve weight and bias sharing. Thirdly, a parallel ensemble optimization loss function is developed to align the data whose distribution is unknown between source and target domains. Finally, two cases with multi-source, unsupervised, and cross-domain TL are used to verify the performance of the proposed method. The average accuracy in case 1 and case 2 is 99.81 % and 99.17 % respectively. It is proved that the proposed method can not only get rid of the limitation of manual input length setting, but also overcome the limitation of optimization function, which is more effective than the existing intelligent fault diagnosis models.

U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution

Bone semantic segmentation is essential for generating a bone simulation model for automatic diagnoses, and a convolution neural network model is often applied to semantic segmentation. However, ground-truth (GT) images, which are generated based on handwriting borderlines, are required to learn this model. It takes a great deal of time to generate accurate GTs from handwriting borderlines, which is the main reason why bone simulation has not been put to practical use for diagnosis. With the above in mind, we propose the U-net-based semi-automatic semantic segmentation method detailed in this paper to tackle the problem. Moreover, bone computed tomography (CT) images are often presented in digital imaging and communications in medicine format, which consists of various parameters and affects the image quality for segmentation. We also propose a novel adaptive input image generator using an adaptive differential evolution. We evaluate the proposed method compared to conventional U-net and DeepLabv3 models using open bone datasets, the spine and the femur, and our artificial bone data. Performance evaluations show that the proposed method outperforms U-net and DeepLabv3 in terms of Dice, IoU, and pairwise accuracy, and DeepLabv3 show the lowest performance, due to a lack of training data. We verify that the U-net-based model is effective for bone segmentation, where a large quantity of training data are available. Moreover, we verify that the proposed method can effectively create proper GTs and input images, resulting in increased performance and reduced computational costs. We believe that the proposed method enhances the wide use of bone simulation based on CT images for practical use.

Integrating adaptive input length selection strategy and unsupervised transfer learning for bearing fault diagnosis under noisy conditions

Transfer learning (TL) has made great progress in intelligent fault diagnosis of bearing. However, due to the harsh working conditions of bearings in engineering practice, in addition to the bearing vibration signals, sensors also inevitably collect noise signals, which leads to the performance degradation of TL. In this paper, a novel model based on TL is proposed to solve the problem. Different from previous studies, an adaptive input length model instead of a fixed input length is established to diagnose bearings with different parameters. A signal processing method combining wide kernels in the first convolutional layer and pooling layer is used to feature denoise the input. Group convolutional and instance normalization based on 1D-CNN are constructed for feature extraction, health conditions classification, and denoise. An optimization objective function based on maximum mean discrepancy is introduced to align the feature distribution discrepancy. The model does not rely on any bearing data label information and realizes unsupervised TL. In the meanwhile, seven experiments including TL model and anti-noise model are conducted to compare and analyze the performance of intelligent fault diagnosis. Experimented on the axlebox bearing dataset of High-speed train, the accuracy is 85.98%, 93.62%, and 99.82% under SNR equal -6 dB, -4 dB, and -2 dB respectively. Experimented between bearing in electromechanical drive systems and high-speed train axlebox bearing dataset, the accuracy of the proposed method is 77.56%, 79.33%, and 96.22% under SNR equal -6 dB, -4 dB, and -2 dB respectively. In the meanwhile, the accuracy of the three advanced TL models and four state-of-the-art anti-noise models are below 50 %. The results indicate that the proposed model has good bearing fault diagnosis ability for TL different datasets under noisy conditions.

Unsupervised transfer learning for intelligent health status identification of bearing in adaptive input length selection

Input length (IL) is an important element in transfer learning (TL) network for intelligent health status identification of bearing (IHSIB). However, fixed IL are used in most studies. In this paper, a TL network via adaptive IL selection module for IHSIB (AILTLN) is proposed, which includes adaptive IL module, feature extractor module, health status identification module, and domain discriminator module. Firstly, an adaptive IL selection module based on envelope spectrum analysis is proposed. The module varies with bearing structure, motor speed, and sampling frequency. Secondly, group convolution, transposed convolution, and instant normalization are constructed in feature extractor. Thirdly, softmax cross-entropy loss function and maximum mean discrepancy are used for health status identification and domain alignment. The TL results of open bearing dataset and high-speed train bearing experiment show that AILTLN is better than the other existing methods in the TL of IHSIB. The ablation study shows the reuse of low-dimensional features and the adaptive IL help to improve to the accuracy of the proposed method.

Hierarchical fault diagnosis and severity identification method of building air condition systems

Faults are inevitable of building Heating, Ventilation and Air Conditioning (HVAC) system, which causing either excessive energy consumption, bad thermal comfort, or undesirable indoor air quality. Existing fault diagnosis research generally uses algorithm optimization and data processing to improve performance, but not enough consideration is given to process design and the difference of physical properties of parameters during fault. Thus, this paper proposed a two-level hierarchical fault diagnosis and severity identification method for building Variable Refrigerant Flow (VRF) system. The Extreme Gradient Boosting (XGB) was studied to train fault diagnosis model at first level, and the association rule dug out each fault feature with different severities and XGB was used to identify fault severity at second level. Three faults with ten severities were simulated to verify this method. The results indicated that the fault diagnosis accuracy of abnormal refrigerant charge, four-way valve fault and Electronic Expansion Valve (EXV) fault could reach 99.46%, 100% and 99.65% respectively. The association rule analysis found that the discharge temperature, the sub-cooler gas outlet temperature, and sub-cooler EXV opening had the highest support to three faults respectively. Based on adaptive input features, the average fault severity identification rate of three faults could reach 98.23%, 99.94% and 96.16% respectively.