Feature Recognition Technology Research Articles

A novel multi-energy load forecasting method based on building flexibility feature recognition technology and multi-task learning model integrating LSTM

Accurate multi-energy load forecasting is prerequisite for achieving balance between supply and demand in building energy system. The continuous development of building flexibility control techniques has led to increased complexity and variability in characteristics of multi-energy flexibility loads under application of various building flexibility control strategies. Existing load forecasting methods lack the ability to recognize the flexibility features and face challenges in considering complex coupling relationships and variable characteristics of multi-energy flexibility loads. To address this gap, we propose a multi-energy flexibility load forecasting method, denoted as (BFFR-MTL-LSTM), which incorporates building flexibility feature recognition (BFFR) and multi-task learning (MTL) model utilizing Long Short-Term Memory (LSTM) neural networks as the shared layer. A novel ToU-K-means method combining ToU with K-means algorithm is proposed firstly as BFFR technique to accurately recognize the flexibility features under various energy consumption patterns. The forecasting accuracy of proposed method is validated using actual operational data under various energy consumption patterns with average R2 of 0.973. Additionally, through comparisons with numerous existing models, the proposed method demonstrates a forecasting accuracy improvement ranging from 33 % to 47 %. This validation supports that the proposed method adeptly recognizes energy consumption patterns, resulting in more accurate forecasts of multi-energy flexibility loads.

Principal Component Analysis–Based Feature Recognition Technology for Marine Plankton Images in IoT Systems

Abstract To improve the accuracy of traditional plankton image feature recognition techniques, a principal component analysis (PCA)–based method for plankton image feature recognition in Internet of Things (IoT) systems was designed and proposed. According to the characteristics of a microscopic image of marine living plankton with much noise, a noise suppression method based on wavelet is established to meet the needs of edge information retention and stability in image processing. At the same time, the adaptive threshold gray segmentation, mathematical morphology operation, and canny edge detection operator are combined to complete the marine plankton image segmentation. Based on the global feature and texture information of contours, the feature set is composed. At the same time, PCA is used to reduce the dimensionality of the feature space and complete the feature recognition of plankton images in the IoT system. The test results show that this method has higher accuracy, shorter recognition time, lower cost, and certain applicability.

Application of depth feature recognition technology in foreign object recognition in distribution network monitoring video

Foreign objects identification in the distribution network is an important link in the security of electric power, and is of great significance to the normal transportation of electric power. At present, a lot of equipment in the distribution network is in the open air environment, facing a large number of foreign interference. These foreign objects not only bring potential safety hazards to the distribution network, but also easily lead to short circuit, causing power supply difficulties within the region. Therefore, the research first constructs an optimized triplet feature learning model. On this basis, the HOG-SVM depth feature recognition model is proposed. In HOG-SVM, AM is introduced to improve recognition accuracy. In addition, the research enhances the night vision ability of the model by standardizing the features in the image region block. The results show that the AP of the model is stable at more than 90.54%, the average FPR is 2.21%, and the average FNR is 3.17%. The performance of HOG-SVM is significantly better than that of traditional SVM. It verifies the contribution of this research in the field of foreign object recognition and application value in ensuring the security of distribution network.

Research on the Assessment Technology of Land Available for PV

Based on the high-resolution satellite image data, the information mining technology of the available surface elements of PV is studied, and the investigation of the available surface elements of PV in 98 counties and cities of South Hebei grid is realized. Based on the large-scale and high-resolution remote sensing data obtained by multi-source remote sensing data fusion technology, the depth-learning-based surface feature recognition technology for photovoltaic development is studied. Based on the method of automatic identification and artificial combination of depth-learning, it can identify the available ground elements (roof, water surface, road surface, dry beach, etc.), the available surface elements of PV in 98 counties and cities of Hebei South Grid were obtained. From the overall point of view, the photovoltaic land, the building occupies the main position, in the four cities are relatively high, are in the 6% ~ 15%

Nondestructive characterization and artificial intelligence recognition of acoustic identifiers of ancient ceramics

Cultural heritage identity management is the most basic and important work in the process of cultural heritage protection. It is of great significance to provide a unique and identifiable digital identity for ancient ceramics. At present, the identification information of ancient ceramics is mainly composed of external visual characteristics, and there is no report on feature identification method that can reflect the properties of ancient ceramics. Audible sound signals not only have advantages in non-destructive testing, but also can be used as voiceprint information to identify, monitor and analyze ancient ceramics. In this paper, seven ancient ceramics and 12 similar modern ceramic cups are taken as research objects, and an acoustic identifier (AID) is constructed. We put forward a reliable acoustic identification method for ancient ceramics, and established a digital code of acoustic characteristics of ancient ceramics. The results show that audible sound waves can reflect the attribute information of ancient ceramics. Sufficient acoustic data combined with deep learning can not only accurately match the identity of ancient ceramics, but also detect the real-time identity information of ancient ceramics, and make a comparative analysis of its cracks and whether it has caused damage. This method can provide a variety of practical applications for audible signal feature recognition technology in the exhibition, protection, trading, recognition and safety management of ancient ceramics and other cultural relics.

Key Technology of Intelligentized Welding Manufacturing and Systems Based on the Internet of Things and Multi-Agent

With the development of the Internet of Things (IoT), Big Data, Artificial Intelligence technology, and the emergence of modern information technologies such as intelligent manufacturing, welding systems are changing, and intelligentized welding manufacturing and systems (IWMS) utilizing these technologies are attracting attention from both academia and industry. This paper investigates sensing technology, multi-information sensor fusion technology, feature recognition technology, the quality prediction method, control method, and intelligent welding production line application in the IWMS. Combining IoT technology and multi-agent systems, a hierarchical structure model welding manufacturing system (IoT-MAS) in the form of “leader-following” was constructed. The multi-agent welding manufacturing system has the advantages of distribution, intelligence, internal coordination and so on. The IoT-MAS consists of several sub-agents, which are divided into five categories according to their functions and internal processing logic. Combined with the functions of the intelligent welding manufacturing system, the agent structure of the whole welding process was proposed, and the matching communication technology and algorithm were designed. The intelligent welding manufacturing system based on IoT-MAS proposed in this paper can effectively solve the integrated design problem of large welding manufacturing systems.

Online English Teaching System under the Background of Epidemic Situation Based on Intelligent Feature Recognition Technology.

In order to improve the effect of online English teaching in the context of the epidemic, this paper combines intelligent feature recognition technology to carry out an online English teaching system in the context of the epidemic and greatly reduces the number of variants by selecting mutation operators with excellent performance. Moreover, in this paper, the mutation adequacy and the number of mutation operators are regarded as two objective functions, and the selection problem of mutation operators is generated into a two-stage optimization problem, and the above problems are solved by a genetic algorithm. In addition, this paper sorts the mutation branches and preferentially covers the mutation branches of the mutants corresponding to the mutation operators with higher mutation scores. The experimental study shows that the online English teaching system based on intelligent feature recognition technology proposed in this paper meets the actual needs of online English teaching in the context of the epidemic.

Evaluation of Multimedia Popular Music Teaching Effect Based on Audio Frame Feature Recognition Technology

Music education should pay attention to popular music that exists in students’ real life and deeply affects them. Moreover, it needs to be combined with “popular classical music” to make them happily learn popular music, appreciate popular music artistically, and feel popular music aesthetically. This study combines the audio frame feature recognition technology to evaluate the effect of multimedia popular music teaching and improve the quality of multimedia popular music teaching. Moreover, this study adaptively revises the speech spectrum technology to construct a multimedia pop music system based on audio frame feature recognition technology. Finally, this study verifies the performance of this system through experimental research. According to the results of experimental research, it can be seen that the effect of the system proposed in this study is very good.

Correction of Chinese Dance Training Movements Based on Digital Feature Recognition Technology

In order to improve the effect of Chinese dance training, this paper combines digital feature recognition technology to correct and analyze Chinese dance training movements and constructs an intelligent auxiliary training system. In order to solve the travel time problem at the computationally complex interface and ensure its computational accuracy, a local adaptive triangulation technique is used in the fast-advance algorithm. Moreover, this paper designs the function of the system according to the user's needs, transforms the design concept into a figurative visual representation through the interactive prototype according to the function, carries out the visual design of the interface according to the interactive prototype, and uses the interactive technology to realize the development of the system. From the test analysis results, it can be seen that the Chinese dance training action correction system based on the digital feature recognition technology proposed in this paper has a good effect and can effectively promote the improvement of the Chinese dance training effect.

A Digital Feature Recognition Technology Used in Ballet Training Action Correction.

In order to improve the effect of ballet training, this paper combines digital technology to improve the motion recognition process, analyzes the imaging process, analyzes the system process combined with the human node model, and combines digital feature recognition technology to construct a ballet training motion correction system. Moreover, this paper inputs the ballet training action recognition results into the system and compares the standard actions to judge the rationality of the actions and builds a ballet training action correction system based on digital feature recognition. In addition, this paper designs experiments to conduct ballet training movements in the system effect evaluation. The experimental research verifies that the digital feature recognition technology proposed in this paper can play an important role in ballet movement recognition and has a good action correction effect.

Multimedia Pop Music Teaching Model Integrating Semifinished Teaching Strategies

In order to improve the effect of popular music teaching, this paper combines the intelligent music frame feature recognition technology and the semifinished product teaching strategy to construct a multimedia popular music teaching system. Moreover, this paper reexpresses energy detection and zero-crossing detection mathematically from the perspective of aggregation and proposes a new two-level detection method for music frames. In addition, this paper uses the fusion and expansion of the zero-crossing rate detection result to judge whether a piece of speech is a valid musical speech. Finally, this paper uses a four-tier architecture to design the system framework, constructs the system functional modules, builds a multimedia pop music teaching system that incorporates semifinished teaching strategies, and then validates the performance of the system. From the experimental research, it can be seen that the multimedia pop music teaching system integrated with the semifinished product teaching strategy proposed in this paper can play an important role in music teaching and effectively improve the effect of music teaching.

Research and application of spoken English evaluation system based on biological voiceprint feature recognition

The design of oral English evaluation system based on biological voiceprint feature recognition technology can deal with the shortcomings of speech teaching in traditional computer-aided technology, and can improve the learning interest and learning effect of oral English learners. This time, MFCC method is selected to construct the evaluation model of oral English, which can achieve good speech recognition effect. Through hierarchical recognition strategy, the algorithm mechanism of oral English is further optimised. Through simulation analysis, it can be seen that the proposed system design can objectively reflect the actual oral English level of oral English evaluators, and has good application effect and practical value.

Automatic Translation of Medical Information Based on Text Feature Recognition

In order to improve the effect of the medical information automatic translation system, based on the text feature recognition technology of medical image, this paper constructs an automatic translation system that can recognize medical images. On the basis of the medical information image retrieval based on the combination of text information and visual information, this paper uses automatic image annotation technology and semantic similarity calculation method to extract the text and semantic features of medical information images. Then, this paper uses the inherent multi-information fusion capability of the Bayesian inference network to fuse the text features of medical information images and the semantic features of image content together to realize medical information image retrieval. Finally, this paper designs experiments to test the performance of the medical information automatic translation system. The research shows that the system constructed in this paper has certain effects.

Analysis of the Effect of Intelligent Red Blood Cell Distribution Diagnosis Model on the Diagnosis and Treatment of Gastrointestinal Bleeding.

In order to explore the role of red blood cell distribution width in the diagnosis and treatment of gastrointestinal bleeding, this paper applies map feature recognition technology to red blood cell distribution broadband and constructs an intelligent red blood cell distribution width diagnosis model. To extract the content-level features of the image safely and effectively, this paper introduces the mechanism of jitter quantization to extract the content-level features at the lowest frequency of the image. In addition, this article employs an experimental approach to investigate the function of red blood cell distribution width in the diagnosis and management of gastrointestinal bleeding in the elderly. Finally, this article establishes an experimental group and a control group and then performs a research study using real-life hospital case studies. According to the statistical findings, the red blood cell distribution width index may play a significant role in the diagnosis and management of gastrointestinal bleeding, particularly in the case of severe bleeding.

Music Signal Recognition Based on the Mathematical and Physical Equation Inversion Method

Digitization and analysis processing technology of music signals is the core of digital music technology. The paper studies the music signal feature recognition technology based on the mathematical equation inversion method, which is aimed at designing a method that can help music learners in music learning and music composition. The paper firstly studies the modeling of music signal and its analysis and processing algorithm, combining the four elements of music sound, analyzing and extracting the characteristic parameters of notes, and establishing the mathematical model of single note signal and music score signal. The single note recognition algorithm is studied to extract the Mel frequency cepstrum coefficient of the signal and improve the DTW algorithm to achieve single note recognition. Based on the implementation of the single note algorithm, we combine the note temporal segmentation method based on the energy-entropy ratio to segment the music score into single note sequences to realize the music score recognition. The paper then goes on to study the music synthesis algorithm and perform simulations. The benchmark model demonstrates the positive correlation of pitch features on recognition through comparative experiments and explores the number of harmonics that should be attended to when recognizing different instruments. The attention network-based classification model draws on the properties of human auditory attention to improve the recognition scores of the main playing instruments and the overall recognition accuracy of all instruments. The two-stage classification model is divided into a first-stage classification model and a second-stage classification model, and the second-stage classification model consists of three residual networks, which are trained separately to specifically identify strings, winds, and percussions. This method has the highest recognition score and overall accuracy.

Information collection system for fall detection of stroke patients under cascade algorithm in the context of multi‐modal information fusion and e‐health

AbstractTo effectively detect stroke patients after falling under e‐health, the fall situation of patients is graded, and warnings are given to improve the survival probability of stroke patients after falling. First, the fall model of stroke patients is analysed. According to the model, multi‐modal information fusion fall detection technology is proposed, including data fusion algorithm and feature recognition technology. Also, various sensor data are adjusted. The fall detection cascade algorithm is proposed to classify data with different features in order, thereby completing target detection. Finally, combined with heart rate sensor, height sensor and microcontroller unit software and patients' e‐health information, the research and development of an information collection system for fall detection are realized. Six young volunteers are selected to test the system performance. The results show that for the six testers, the heart rate detected by the ordinary device and the device of this investigation is the same when it is resting in different states of resting, walking, as well as walking and falling. The heart rate difference between walking and falling detection is not large (within the allowable error range of the device). But the best detection effect is to measure after the patient falls, which not only reduces power consumption but also keeps the detection error to a minimum. The height sensor is in the static state, increased by 75 m in the vertical direction and decreased by 75 cm from the static position in the vertical direction. The height difference of the data information exported from these three cases has some errors compared with the actual 75 cm. The tester's three situations of resting, walking and falling, standing up after sitting still and falling are observed. The waveform when resting is stable, and the acceleration information also fluctuates significantly when walking and after standing up. The accuracy of the developed system is above 80%, which achieves the expected effect and assisted medical treatment of fall detection based on patients' e‐health information. The results show that the proposed information collection system for fall detection of stroke patients under cascade algorithm in the context of multi‐modal information fusion has good performance and high practical value.

One spatial geometric characteristic identification method of a coal mine working face based on ground movement and deformation monitoring data

ABSTRACT Aiming at the bottleneck problem that plagues illegal mining supervision and the historical investigation of closed/abandoned mine mining, that is, the lack of effective large-scale, fast, accurate, and convenient underground mining space geometric feature recognition technology, this paper proposes a spatial geometric characteristic identification method of a coal mine working face based on ground movement and deformation monitoring data. Combing with the mining subsidence prediction model (probability integral method), the identification method with the working face geometric characteristic parameter B as the parameter and the space movement vectors as the observation values is proposed. And the quantum genetic algorithm for solving the parameters of the observation equation is introduced. The high precision, strong ability of anti-random error, and anti-gross error of the method are proved by the simulation experiments. Finally, the method is used to calculate the characteristic parameters of a real goaf. The obtained results are as follows: m = 3.376 m, α = 4.069°, ϑ = 205.527°, H = 530.844 m, D 3 = 620.844 m, D 1 = 196.375 m, X 0 = 56557.125 m, Y 0 = 31516.063 m, and the fitting error is 96.165 mm. The inversion results can accurately describe the spatial geometric characteristics of the goaf.

Research on electrochemical discharge machining based on image features and SVM algorithm

The electrochemical discharge machining process is affected by many factors, so the machining process is difficult to be qualitatively analyzed. In order to further understand the characteristics of the electrochemical discharge machining process and better master the machining skills, based on the image features, this article uses the SVM algorithm to build an electrochemical discharge machining system, and uses image feature recognition technology to effectively control the electrochemical discharge machining process. Moreover, this article analyzes the laser back-wet etching process mechanism and discusses the material removal mechanism of electrochemical discharge machining according to the three processes of bubble generation, gas layer formation, and spark discharge removal material. In addition, this article analyzes the system performance according to the actual situation and displays the results through statistical methods. The research results show that the electrochemical discharge machining system based on image features and SVM algorithm constructed in this paper has a good effect, and it can be applied to the management and control of the electrochemical discharge machining process.

Application of Natural Language Processing in Environmental Protection Industry Based on Monte Carlo Tree

In this paper, a method of environmental science recognition based on natural language processing model is proposed. This method takes natural language processing model as the core, adds environmental protection professional word segmentation library and external feature recognition technology on the basis of general word segmentation library, realizes automatic extraction of accurate environmental science named entity information from EIA documents, and uses MCTS Monte Carlo tree to construct search engine. The search efficiency of named entity recognition process is improved.

A New Islanding Detection Method Based on Feature Recognition Technology

Three-phase grid-connected inverters are widely applied in the fields of new energy power generation, electric vehicles and so on. Islanding detection is necessary to ensure the stability and safety of such systems. In this paper, feature recognition technology is applied and a novel islanding detection method is proposed. It can identify the features of inverter systems. The theoretical values of these features are defined as codebooks. The difference between the actual value of a feature and the codebook is defined as the quantizing distortion. When islanding happens, the sum of the quantizing distortions exceeds the threshold value. Thus, islanding can be detected. The non-detection zone can be avoided by choosing reasonable features. To accelerate the speed of detection and to avoid miscalculation, an active islanding detection method based on feature recognition technology is given. Compared to the active frequency or phase drift methods, the proposed active method can reduce the distortion of grid-current when the inverter works normally. The principles of the islanding detection method based on the feature recognition technology and the improved active method are both analyzed in detail. An 18 kVA DSP-based three-phase inverter with the SVPWM control strategy has been established and tested. Simulation and experimental results verify the theoretical analysis.