State Recognition Method Research Articles

Door State Recognition Method for Wall Reconstruction from Scanned Scene in Point Clouds

Doors are important elements of building façades in scanned point clouds. Accurate door detection is a critical step in building reconstruction and indoor navigation. However, recent door detection methods may often obtain incomplete information and can only detect doors with a single state (open or closed). To improve this, a door state recognition method is proposed based on corner detection and straight-line fitting. Firstly, plane segmentation based on local features is introduced to obtain structural division from the raw scanned data to extract the wall. Next, the bounding box of each plane is calculated to obtain the corner points, which is then combined with the feature constraint to classify the elements of door and wall. Then, the boundary of each plane is extracted by normal vector, and the disordered and discontinuous boundary points are straight-line fitted based on projection. Finally, the state of the door is obtained through analysis of the angle between the straight-lines of the wall and the door. The effectiveness of the proposed method is tested and evaluated on the Livingroom of ICL-NUIM and House of Room detection datasets. Furthermore, comparative experimental results indicate that our method can extract corner points and recognize the different states of doors effectively and robustly in different scenes.

History information-based Hill-Valley technique for multimodal optimization problems

The key of multimodal optimization algorithms is to divide the population into multiple species, and each species searches for a global optimum. This article proposes a new differential evolution based on the Hill-Valley technique for multimodal optimization. This technique uses history information to classify individuals on the same peak as one species. Compared to other niching techniques, this technique is insensitive to parameters and does not waste additional computing resources. For each species, the evolutionary state recognition method is developed to judge the evolutionary state and the search strategy is designed according to the evolutionary state. In addition, the prediction mechanism is designed to save computing resources. The experimental results show that the performance of the proposed algorithm is better than other algorithms on the CEC2013 test set and the NESs test set.

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

Underwater bubble plumes multi-scale morphological feature extraction and state recognition method

Underwater bubble plumes multi-scale morphological feature extraction and state recognition method

A comparative analysis of adaptive energy management for a hybrid electric vehicle via five driving condition recognition methods

– Adaptive powertrain control is of great importance to a high energy efficient vehicle and varying parameter recognition is the first step of adaptive control. However, a comparative study on different driving condition recognition methods is lacking in literature. In this study, five recognition methods are introduced and compared, namely fuzzy logic, clustering, Markov Decision Process, and supervised learning and navigation-based method. Three driving conditions are considered as recognition target, which are urban, suburban and highway conditions. 29 driving cycles and 4 driving cycles are used in training and validation, respectively. The training results show that all five methods have training accuracy above 86% with supervised learning leading the accuracy at 91.37%. The validation results from a hybrid electric vehicle show that the five prediction methods improve the fuel economy by 2.47%–4.58% in the four validation driving cycles when compared with constant prediction method. Based on the analysis of complexity and fuel economy performance, the navigation-based and clustering methods are recommended to apply in vehicle concept and production phase, respectively. This study can be used as a guidance to select driving condition recognition method for adaptive vehicle energy management.

A New Method of Tractor Engine State Identification Based on Vibration Characteristics

Based on signal decomposition, a tractor engine state recognition method is proposed to explore the degree of information recognition of vibration signals at measurement points at different distances from the engine and the degree of correlation in different directions. The accuracy of engine operating state information recognition was obtained by analyzing the vibration signals of the tractor at different measurement points. The main contents are as follows: Based on variational mode decomposition (VMD), the modal component, which includes the state information, was obtained by measuring the vibration signal of the tractor at each measurement point under different driving conditions, and the exogenous excitation of the tractor under different road conditions was simulated by changing the tire pressure. Then, the state characteristics of the modal component were quantified based on permutation entropy (PE), and the correlation coefficient was used as the evaluation index to select the entropy of the optimal modal component as the feature vector. Finally, a support vector machine and random forest classification models were trained with 4800 feature vectors under 25 working conditions, and the remaining 900 feature vectors were used to verify the classification results. Compared with the results of empirical mode decomposition (EMD), the superiority of this method was proved. A comparative study with backpropagation demonstrated the superiority of the support vector machine and random forest identification method using a small sample size. The results indicate the following: (1) the accuracy of engine condition recognition, which was measured by longitudinal vibration signals, was better than that of vertical vibration signals at different measurement points; and (2) the closer the vibration transmission distance between the measurement point and the engine, the higher the recognition accuracy of the measured signals. This study provides a reference for the condition identification of agricultural machinery in complex working environments and lays a foundation for the fault diagnosis of agricultural machinery under working conditions.

A Novel Motion State Recognition Method for Blast Furnace Burden Surface in Ironmaking Process

A Novel Motion State Recognition Method for Blast Furnace Burden Surface in Ironmaking Process

Condition Recognition Method Based on K-AHNs for In-wheel Motor

Condition Recognition Method Based on K-AHNs for In-wheel Motor

Single-channel electroencephalogram signal used for sleep state recognition based on one-dimensional width kernel convolutional neural networks and long-short-term memory networks

Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.

Research on Parking Space Status Recognition Method Based on Computer Vision

To improve the utilization rate of parking space resources and reduce the cost of installing and maintaining sensor recognition, this paper proposed an improved computer vision-based parking space status recognition method. The overall recognition accuracy was improved by graying the video, filtering smoothing noise reduction, image enhancement pre-processing, introducing texture feature extraction method based on LBP operator, improving the background difference method, and then, we used a perceptual hash algorithm to calculate the Hamming distance between the background image and the hash string of the current frame of the video, excluding the influence of light and pedestrian on recognition accuracy. Finally, a parking space status recognition system is developed relied on the Python environment, and parking spaces are recognized in three environmental states: with direct light, without direct light, and in rain and snow. The overall average accuracy of the experimental results was 97.2%, which verifies the accuracy of the model.

Road surface state recognition using deep convolution network on the low-power-consumption embedded device

Many fatal traffic accidents have happened due to road conditions, such as wet, ice, snow and water. So accurate road condition information can help the transportation departments to take effective actions. In this study, we propose a real-time road state recognition method based on a low-power-consumption embedded device, NVIDIA Jetson AGX Xavier. First, we design a new residual unit of Ref-Block, and build a lightweight network, named as RefNet. Then, the TensorRT inference engine of NVIDIA is used for acceleration, so as to meet the real-time requirements. We classify the road surface state into five types including dry, wet, snow, ice and water, and build a dataset according to these five different road surface states. Our recognition method has achieved 88.93% AP on this dataset at 669framespersecond(FPS). The RefNet has also been compared with state-of-the-art methods in the experiments.

Research on an SAE-based Substation Noise Monitoring Method

In the area of transformer noise monitoring research, condition monitoring technology based on the combination of AI technology and modern signal processing methods gradually becomes a hot research topic. General pattern recognition methods often have higher requirements for signal acquisition and processing and are often restricted by the limited generalization ability of the models. In order to solve this problem, a sparse autoencoder-based transformer state recognition method is proposed. The sparse autoencoder (SAE) is trained by FFT spectral signals, and the intrinsic features of big data are adaptively refined into simple feature functions. Intelligent diagnosis of transformer conditions is realized by the expression of feature functions. The experimental results show that, compared with BP (backpropagation) neural network and SVM (support vector machine) classification algorithms, this method can quickly and effectively improve the accuracy of transformer state recognition, which is of great significance for industrial development.

Working Condition Recognition of a Mineral Flotation Process Using the DSFF-DenseNet-DT

The commonly used working condition recognition method in the mineral flotation process is based on shallow features of flotation froth images. However, the shallow features of flotation froth images frequently have an excessive amount of redundant and noisy information, which has an impact on the recognition effect and prevents the flotation process from being effectively optimized. Therefore, a working condition recognition method for the mineral flotation process based on a deep and shallow feature fusion densely connected network decision tree (DSFF-DenseNet-DT) is proposed in this paper. Firstly, the color texture distribution (CTD) and size distribution (SD) of a flotation froth image obtained in advance are approximated by the nonparametric kernel density estimation method, and a set of kernel function weights is obtained to represent the color texture and size features, while the deep features of the flotation froth image are extracted through the densely connected network (DenseNet). Secondly, a two-stage feature fusion method based on a stacked autoencoder after Concat (Cat-SAE) is proposed to fuse and reduce the dimensionality of the extracted shallow features and deep features so as to maximize the comprehensive description of the features and eliminate redundant and noisy information. Finally, the feature vectors after fusion dimensionality reduction are fed into the densely connected network decision tree (DenseNet-DT) for working condition recognition. Multiple experiments employing self-built industrial datasets reveal that the suggested method’s average recognition accuracy, precision, recall and F1 score reach 92.67%, 93.9%, 94.2% and 0.94, respectively. These results demonstrate the proposed method’s usefulness.

Bearing Running State Recognition Method Based on Feature-to-Noise Energy Ratio and Improved Deep Residual Shrinkage Network

Aiming at the difficulty in evaluating the degradation performance of rolling bearing and recognizing bearing running state, a new method based on feature-to-noise energy ratio (FNER) indicator and improved deep residual shrinkage network (IDRSN) model is proposed. First, Hilbert transform and fast Fourier transform are used to obtain the envelope spectrum of bearing run-to-failure test signal and the fault feature energy of the envelope spectrum amplitude is calculated. Subsequently, the total energy of the envelope signal is obtained according to the autocorrelation function (ACF). After that the ratio of the fault feature energy and the noise energy is used as the bearing performance degradation indicator. Moreover, bearing running states are divided according to FNER indicator and tagged bearing samples realized. Then, tagged samples are used to train the IDRSN, which introduces the densely connected network (DenseNet) to obtain the bearing running state recognition model. In order to improve the anti-interference ability, the DropBlock layer is introduced into the first large convolution kernel and the Dropout technique is introduced before the global average pooling layer. Finally, a failure test data is used to verify the feasibility of the FNER indicator and two bearing run-to-failure test data are used to verify the validity of the FNER indicator and the IDRSN recognition model. Through comparison and verification, the results show that the proposed FNER indicator is better than other indicators and IDRSN can more accurately recognize bearing running state recognition.

Health Status Recognition of Rotating Machinery Based on Deep Residual Shrinkage Network Under Time-Varying Conditions

Currently, the research on the health state of rotating machinery under time-varying operating conditions mainly focuses on using a combination of several constant operating conditions or uniformly changing speed and load. This article studied the health status recognition of rotating machinery under nonlinear and continuous changes in speed and load. A health status recognition method of rotating machinery was proposed based on the gram angle field and deep residual contraction network. Considering the influence of working conditions on signal characteristics, the speed, load, and multidimensional time-domain features are fused to form feature vectors. The feature vectors were transformed into images by gram coding. The color contrast relationship mapped from the overall difference distribution of sample feature indexes to the image was not changed while the feature timing was retained, which weakened the influence of working condition information on the sample state, improved the deep residual shrinkage network (DRSN) structure, and introduced the Gaussian error linear unit (GELU) activation function. The experimental verification is completed on the reducer experimental platform and the Xi’an Jiaotong University (XJTU)-Changxing Sumyoung Technology Company Ltd. (XJTU-SY) dataset. The results show that the method can effectively identify the health state of rotating machinery under time-varying working conditions.

A milling cutter state recognition method based on multi-source heterogeneous data fusion

A milling cutter state recognition method based on multi-source heterogeneous data fusion

An online human-robot collaborative grinding state recognition approach based on contact dynamics and LSTM.

Collaborative state recognition is a critical issue for physical human–robot collaboration (PHRC). This paper proposes a contact dynamics-based state recognition method to identify the human–robot collaborative grinding state. The main idea of the proposed approach is to distinguish between the human–robot contact and the robot–environment contact. To achieve this, dynamic models of both these contacts are first established to identify the difference in dynamics between the human–robot contact and the robot–environment contact. Considering the reaction speed required for human–robot collaborative state recognition, feature selections based on Spearman's correlation and random forest recursive feature elimination are conducted to reduce data redundancy and computational burden. Long short-term memory (LSTM) is then used to construct a collaborative state classifier. Experimental results illustrate that the proposed method can achieve a recognition accuracy of 97% in a period of 5 ms and 99% in a period of 40 ms.

Research on pipeline state recognition method based on acoustic signal frame PCA

Accurate buried pipeline state recognition based on acoustic signal is a difficult and important issue. This paper proposes a feature extraction method based on acoustic signal frame and principal component analysis (PCA) for condition monitoring in pipes. This method makes use of the property of nonstationary and multivariate data decomposition scales of pipeline acoustic signal. Signal framing is processed on the collected acoustic signals so that the signal frame series is obtained. Then, the sound pressure level of each frame signal is extracted, and the feature vector of the total sound pressure level is established. The PCA method is applied to optimize the extracted feature vector set for detecting the feature parameters. The acoustic signals related to different operating conditions of a pipeline are identified with the support vector machine. Research on a series of experiments shows that the proposed method for acoustic signal analysis can perform effectively for robust feature extraction and pipeline state identification.

DC Motor Control Technology Based on Multisensor Information Fusion.

To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has excellent speed regulation performance and is easy to control. It is widely used in applications that require high motor startup and speed regulation characteristics. This research mainly discusses DC motor control technology. Evidence theory can combine various fault information at different levels to enhance mutual support between pieces of evidence, thereby improving the accuracy of motor fault detection. Based on the steps of signal processing, feature extraction, feature dimensionality reduction, and state recognition, the research on the state recognition method of belt conveyor drive motor based on multisource information fusion is carried out. By studying the multisource information fusion, this paper proposes a two-stage belt conveyor drive motor information fusion model based on the optimal D-S evidence theory. The correct identification rate of broken rotor bars during fault monitoring is 99.8%. This method divides the specific motor fault feature set into multiple fault subspaces and uses different diagnostic neural networks and different fault feature parameters for local diagnosis, respectively. The scheme designed in this study significantly improves the recognition accuracy of the motor in the same working condition and under variable working conditions. The drive motor state recognition and intelligent decision-making system designed by combining the results of multisource information fusion can effectively describe the fault type and has strong operability.

Recognition Method of Corn and Rice Crop Growth State Based on Computer Image Processing Technology

The agriculture field is one of the most important fields where computational techniques play an imperative role for decision-making whether it is the automation of watering of plants, controlling of humidity levels, and detection of plant diseases and growth of plants. There are problems in the conventional methods where newer computational techniques and image processing methods are not used to keep track of growth of plants. The traditional image capturing and processing models have problems of large image segmentation error, excessive feature extraction time, and poor recognition output. In order to overcome the problems in the traditional plant growth methods based on image processing automations, computer image processing with computational method has been proposed to analyze the plant growth by utilizing state recognition method for corn and rice crops. An image acquisition platform is established on the basis of CMOS image sensor for crop image acquisition. The binary processing is performed, and then the images are segmented to reduce error of segmentation results in the traditional methods. To extract image features of corn and rice crops, convolution neural network (CNN) with newer architecture is used. According to contour information of images, the block wavelet transform method is used for feature adaptive matching. The binary tree structure is used to divide the growth period of corn and rice crops. The fuzzy mathematical model is also devised to identify the characteristics of crops in different growth periods and to complete the identification of growth state. Experimental results show that the proposed method effectively improves problems of traditional methods with better image recognition effect and reduces the time of feature recognition. The time to extract features by the proposed method is 1.4 seconds, whereas comparative methods such as random forest (RF) take 3.8 s and other traditional techniques take 4.9 s. Segmentation result error of the recognition method is also reduced significantly.