Intelligent Functions Research Articles

Association between gait features assessed by artificial intelligent system and cognitive function decline in patients with silent cerebrovascular disease: study protocol of a multicenter prospective cohort study (ACCURATE-2)

BackgroundGait disturbances may appear prior to cognitive dysfunction in the early stage of silent cerebrovascular disease (SCD). Subtle changes in gait characteristics may provide an early warning of later cognitive decline. Our team has proposed a vision-based artificial intelligent gait analyzer for the rapid detection of spatiotemporal parameters and walking pattern based on videos of the Timed Up and Go (TUG) test. The primary objective of this study is to investigate the relationship between gait features assessed by our artificial intelligent gait analyzer and cognitive function changes in patients with SCD.MethodsThis will be a multicenter prospective cohort study involving a total of 14 hospitals from Shanghai and Guizhou. One thousand and six hundred patients with SCD aged 60–85 years will be consecutively recruited. Eligible patients will undergo the intelligent gait assessment and neuropsychological evaluation at baseline and at 1-year follow-up. The intelligent gait analyzer will divide participant into normal gait group and abnormal gait group according to their walking performance in the TUG videos at baseline. All participants will be naturally observed during 1-year follow-up period. Primary outcome are the changes in Mini-Mental State Examination (MMSE) score. Secondary outcomes include the changes in intelligent gait spatiotemporal parameters (step length, gait speed, step frequency, step width, standing up time, and turning back time), the changes in scores on other neuropsychological tests (Montreal Cognitive Assessment, the Stroop Color Word Test, and Digit Span Test), falls events, and cerebrovascular events. We hypothesize that both groups will show a decline in MMSE score, but the decrease of MMSE score in the abnormal gait group will be more significant.ConclusionThis study will be the first to explore the relationship between gait features assessed by an artificial intelligent gait analyzer and cognitive decline in patients with SCD. It will demonstrate whether subtle gait abnormalities detected by the artificial intelligent gait analyzer can act as a cognitive-related marker for patients with SCD.Trial registrationThis trial was registered at ClinicalTrials.gov (NCT04456348; 2 July 2020).

Developing active and intelligent films through the incorporation of grape skin and seed tannin extracts into gelatin

To achieve sustainability in the wine industry, by-products from winery operations are being diverted from waste streams and turned into beneficial use. Grape seed tannin (SeedT) and skin tannin (SkinT) extracts were used to modify the properties of gelatin films, and to prepare active/intelligent films. The SeedT extract showed a higher phenolic content (~ 440 mg gallic acid (GA)/g extract) and antioxidant inhibition (~ 20 %) than the SkinT extract (14 mg GA/g extract, 2 % antioxidant inhibition), while both extracts presented colour variations with an increase of solution pH. The addition of extracts into the gelatin formulation resulted in coloured and transparent films with lower wettability (water contact angle increased up to 92°) and higher UV-light absorbance (secondary antioxidant function) properties. The films were capable of releasing tannins by up to 20 % which led to antioxidant inhibition values of up to 13 % (primary antioxidant function). The addition of SkinT tannins into the films provided the films with a pH indicator ability (intelligent function).

Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner.

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs of the population on Earth and the degradation of natural resources. Focusing on the “hot” area of natural resource preservation, the recent appearance of more efficient and cheaper microcontrollers, the advances in low-power and long-range radios, and the availability of accompanying software tools are exploited in order to monitor water consumption and to detect and report misuse events, with reduced power and network bandwidth requirements. Quite often, large quantities of water are wasted for a variety of reasons; from broken irrigation pipes to people’s negligence. To tackle this problem, the necessary design and implementation details are highlighted for an experimental water usage reporting system that exhibits Edge Artificial Intelligence (Edge AI) functionality. By combining modern technologies, such as Internet of Things (IoT), Edge Computing (EC) and Machine Learning (ML), the deployment of a compact automated detection mechanism can be easier than before, while the information that has to travel from the edges of the network to the cloud and thus the corresponding energy footprint are drastically reduced. In parallel, characteristic implementation challenges are discussed, and a first set of corresponding evaluation results is presented.

Personalized Recommendation Algorithm of Smart Tourism Based on Cross-Media Big Data and Neural Network.

Accurate recommendation of tourist attractions is conducive to improving users' travel efficiency and tourism experience. However, the choice of tourism feature factors and the difference of recommendation algorithm will affect the accuracy of scenic spot recommendation. Aiming at the problems of sparse data, insufficient tourism factors, and low recommendation accuracy in the existing tourism recommendation research, this paper puts forward a scenic spot recommendation method based on microblog data and machine learning by using the characteristics of personalized expression and strong current situation of microblog data and the intelligent prediction function of machine learning, so as to realize accurate and personalized scenic spot recommendation. This paper extracts rich tourism characteristic factors. Typical tourism recommendation algorithms choose tourism characteristic factors from scenic spots, tourists, and other aspects, without considering the travel time, tourism season, and other contextual information of tourists' destination, which can help understand users' tourism preferences from different angles. Aiming at the problem of sparse data and cold start of collaborative filtering recommendation algorithm, this paper introduces deep learning algorithm and combines the proposed multifeature tourism factors to build dynamic scenic spot prediction models (random forest preferred attraction prediction (RFPAP) and neural networks preferred attraction prediction (NNPAP)). The experimental results show that RFPAP and NNPAP methods can overcome the problem of data sparsity and achieve 89.61% and 89.51% accuracy, respectively. RFPAP method is better than NNPAP method and has stronger generalization ability.

Research progress on intelligent assessment system for upper limb function of stroke patients

At present, the upper limb function of stroke patients is often assessed clinically using a scale method, but this method has problems such as time-consuming, poor consistency of assessment results, and high participation of rehabilitation physicians. To overcome the shortcomings of the scale method, intelligent upper limb function assessment systems combining sensors and machine learning algorithms have become one of the hot research topics in recent years. Firstly, the commonly used clinical upper limb functional assessment methods are analyzed and summarized. Then the researches on intelligent assessment systems in recent years are reviewed, focusing on the technologies used in the data acquisition and data processing parts of intelligent assessment systems and their advantages and disadvantages. Lastly, the current challenges and future development directions of intelligent assessment systems are discussed. This review is hoped to provide valuable reference information for researchers in related fields.

Body Temperature Enhanced Adhesive, Antibacterial, and Recyclable Ionic Hydrogel for Epidermal Electrophysiological Monitoring.

Hydrogel-based epidermal electrodes attract widespread attention in health monitoring and human-machine interfaces for their good biocompatibility, skin-matched Young's modulus, and stable in situ electrophysiological recording performance. However, it is difficult to make the exact conformal attachment between skin and electrodes because of the hair, wrinkles, as well as complex, curved contours of the skin. This also results in signal distortion and large noise. Here, a body temperature enhanced skin-adhesive epidermal electrode is proposed based on non-covalent cross-linked network ionic hydrogel. The ionic hydrogel is fabricated by the polyvinyl alcohol, branched polyethyleneimine, and calcium chloride (CaCl2 ), which demonstrates impressive performances including ultra-stretchability of 1291%, great adhesion to skin and other non-biological materials, stable conductivity of 3.09 S m-1 , recyclability, and outstanding antibacterial ability, simultaneously. Specifically, the adhesion of the ionic hydrogel behaves as temperature-sensitive and could be enhanced by body temperature. Furthermore, the ionic hydrogel is utilized as epidermal electrodes, which display seductive capability to record multifarious electrophysiological signals with high signal-to-noise ratio and ultra-low detection limit, including electrocardiogram, electromyogram, and electroencephalogram. The as-proposed body temperature enhanced skin-adhesive ionic hydrogel brings intelligent functions and broadens the way for epidermal electronics, promoting the development of healthcare electronics.

Consciousness, Cognition and the Neuronal Cytoskeleton - A New Paradigm Needed in Neuroscience.

Viewing the brain as a complex computer of simple neurons cannot account for consciousness nor essential features of cognition. Single cell organisms with no synapses perform purposeful intelligent functions using their cytoskeletal microtubules. A new paradigm is needed to view the brain as a scale-invariant hierarchy extending both upward from the level of neurons to larger and larger neuronal networks, but also downward, inward, to deeper, faster quantum and classical processes in cytoskeletal microtubules inside neurons. Evidence shows self-similar patterns of conductive resonances repeating in terahertz, gigahertz, megahertz, kilohertz and hertz frequency ranges in microtubules. These conductive resonances apparently originate in terahertz quantum dipole oscillations and optical interactions among pi electron resonance clouds of aromatic amino acid rings of tryptophan, phenylalanine and tyrosine within each tubulin, the component subunit of microtubules, and the brain’s most abundant protein. Evidence from cultured neuronal networks also now shows that gigahertz and megahertz oscillations in dendritic-somatic microtubules regulate specific firings of distal axonal branches, causally modulating membrane and synaptic activities. The brain should be viewed as a scale-invariant hierarchy, with quantum and classical processes critical to consciousness and cognition originating in microtubules inside neurons.

Novel intelligent self-responsive function fillers to enhance the durable anticorrosion performance of epoxy coating

Traditional coatings struggle to match the maritime industry's long-term anti-corrosion needs due to the severe marine environment. As a result, integrating functional fillers into coatings is a viable solution to this issue. The sericite (SC) was modified with polyethyleneimine (PEI) as a “binder” and zinc molybdate (ZM) as an “additive” in this study. Following that, a new SC/PEI@ZM filler with self-responsive performance was synthesized effectively. The experimental results demonstrated that the |Z|0.01Hz value of the SC/PEI@ZM coating remained above 8.39 × 1010 Ω·cm2 after being immersed in 3.5 wt% NaCl solution at atmospheric pressure for 60 days. In particular, the coating still maintained favorable corrosion resistance after 7 days immersion under 3 MPa pure O2. The higher corrosion resistance of SC/PEI@ZM coating is mainly attributed to the barrier effect and the complex protective film formed by Zn2+/MoO42− on the surface of the substrate.

Flexible Transparent Electrochemical Energy Conversion and Storage: From Electrode Structures to Integrated Applications

The rapid progress of flexible electronics tremendously stimulates the urgent demands for the matching power supply systems. Flexible transparent electrochemical energy conversion and storage devices (FT–EECSDs), with endurable mechanical flexibility, outstanding optical transmittance, excellent electrochemical performance, and additional intelligent functions, are considered as preferable energy supplies for future self‐powered flexible electronic systems. A comprehensive review of the reasonable design of flexible transparent electrode and recent progress on the FT–EECSDs is presented herein. The manufacturing techniques of generally classified three types of flexible transparent electrodes are systematically summarized. Emphasis is given to the recent developments in the transparent solid‐state electrolyte, flexible transparent energy conversion, and storage devices. The standard evaluation methods and reasonable evaluation parameters to evaluate the flexibility and transparency of FT–EECSDs are highlighted. Additionally, the typical integrated applications of FT–EECSDs are also described. Finally, the current challenges and a future perspective on the research and development direction are further outlined.

Low complexity multichannel spectrum prediction algorithm based on optimized neural network for spectrum allocation in cognitive radio internet of things

AbstractCognitive radio has been nominated as a key technology for the internet of things (IoT), due to its intelligent functionalities ensuring continuous connectivity for IoT objects. Spectrum prediction, as one of the core CR functions, has emerged as a leading tool to alleviate the spectrum scarcity problem. Spectrum prediction minimizes sensing and decision‐making delays, and thereby it reduces collisions with primary users and guarantees safe access for secondary users (SUs). Thus, it became an inseparable part of many new spectrum allocation and mobility methods. In this work, the proposed cognitive radio IoT model consists of local sensors (LS) that perform sensing instead of SUs, and a cognitive base station that receives sensing results from different LSs to predict the next occupancy information and allocate frequencies for SUs. The predictor is followed by a channel extraction block for efficient spectrum allocation. Then, a low complexity spectrum prediction and preallocation system based on optimized neural network architecture is presented. Two nonlinear neural network models, time delay neural network and nonlinear autoregressive with exogenous input, that are trained on a real spectral occupancy dataset, are optimized using the Bayesian optimization algorithm then compared. The best predictor forecasts the next occupancy rate of multiple channels simultaneously based on three dimensions, area, time, and frequency. Performance evaluation was conducted through accuracy, mean squared error (MSE), and regression fit. The highest prediction accuracy was93.5%, the regression coefficient was0.98, and a reduced MSE of0.0013obtained. Results show that the considered scheme is efficient in forecasting the spectrum availability of different bands within the IoT spectrum resources.

Single Artificial Ion Channels with Tunable Ion Transport Based on the Surface Modification of pH-Responsive Polymers.

Artificial ion channels with tunable ionic transport control and intelligent iontronic functions at the nanoscale have a wide application in logic computing and biosensing. Although some artificial ion channels with smart ion transport characteristics have been developed, most of them are constructed on porous membranes with undefined channel numbers. It is still challenging to achieve multiple ion transport features in single nanochannels and to control the ion flow more accurately with excellent repeatability. In this paper, a design strategy is presented to fabricate pH-responsive ion channels with various ion transport features based on a single polydimethylsiloxane (PDMS) nanochannel. The single-ion nanochannel developed by this approach can be further integrated into electronic systems on a chip. Three types of artificial ion channels are demonstrated and investigated systematically in this work. With symmetric or asymmetric pH stimuli, these ion channels can alternatively change their working states among an opened state, a closed state, and an ionic diode state. Four different ion transport features can be realized in a triple-gated ion channel system. With these advantages of the design, it is promising to build smart nanofluidic iontronic devices with widespread applicability in energy conversions, active ion transport control, and biological analysis.

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning.

Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.

Application of Wireless Network Multisensor Fusion Technology in Sports Training.

Fencing is an advantageous event for a country to participate in the Asian Games. The discussion and research on the means and methods of special fencing ability training are of great significance to the further development of fencing in the country. Based on the multisensor information fusion technology, this paper develops a special fencing training system with digital monitoring and intelligent decision-making. The multisensor information fusion technology scheme, which is reported in this paper, is used to perform decision-level fusion based on fuzzy inference technology whereas Kalman filter is used to optimize the original information. On the basis of the overall structure, mechanical mechanism, and performance analysis, the mechanical prototype of the fencing special ability training system was developed whereas design, processing, and debugging of the prototype were completed. Combined with the mechanical prototype design of various index parameter display structures based on multisensor information fusion technology, the digital processing of the fencing special ability training system is realized. The force, speed, position, and other characteristic quantities in the fencing special training system are collected by the pressure and rotational speed sensors respectively whereas USB communication technology is used for data transmission. On the basis of in-depth analysis of the characteristics of the special training system for fencing, an abstract system of pedaling power based on the center of gravity and trend models is designed where the Kalman filter is reserved for the special application fields. Moreover, the core role of Kalman filter is carried out when original data information is needed to be obtained. By constructing a force distribution pressure center trajectory measurement model, a training load and training parameter measurement model, and a training level evaluation model, the data-level raw information is specially processed to generate more training parameters that meet specific characteristics, making the multisensor information fusion technology more efficient. It is well integrated into the fencing special ability training system. By applying fuzzy reasoning technology, the special training parameters and the relationship between these parameters are transformed into fuzzy sets and fuzzy rules, and the perceptual knowledge of sports training experience is transformed into fuzzy rules. With this decision-level data fusion method, the fencing special training system has certain intelligent functions.

Safety challenges related to autonomous ships in mixed navigational environments

Digitalization and technological advancements have accelerated the development and emergence of autonomous and remotely controlled ships in the maritime transport sector. This type of vessels consists of highly intelligent and adaptive functionalities, equipped with a variety of external sensors and actuators to gain situation awareness, automated control and adaptive maneuvering for achieving more efficient and sustainable operations. There are, however, many safety and reliability assurance challenges in autonomous operational and navigation systems due to their complex, adaptive, and non-deterministic nature. The issue of a mixed navigational environment where conventionally manned, remotely controlled, and unmanned vessels are interacting at the same sea area can be considered as one of the major obstacles in adopting of autonomous ships. Vulnerabilities can increase due to the potential divergence of vessel state awareness between autonomous operational systems and humans in such situations. Little research to date has dealt with such safety issues that a mix of human-operated, remotely controlled, and autonomous vessels will bring. This study explores the potential safety challenges related to autonomous ship operations in a mixed navigational environment and discusses several possible ways to reduce the same issues related to the identified safety risks, while including a discussion for possible future practice and research interests in ship navigation.

Economic, Functional, and Social Factors Influencing Electric Vehicles’ Adoption: An Empirical Study Based on the Diffusion of Innovation Theory

Although electric vehicles (EVs) have been heavily promoted as an effective solution to sustainable problems such as environmental pollution and resource constraints, the market penetration of EVs remains below expectations. By viewing EVs as innovative products that are different from traditional fuel vehicles, this study proposes a research model based on the diffusion of innovation theory, in which a series of factors influencing the adoption of EVs are identified. We collected 375 valid responses through an offline survey, and the structural equation modeling technique was used to empirically test the proposed model. The empirical results indicate that consumer adoption of EVs can be effectively predicted by three important innovation characteristics, namely perceived compatibility, perceived complexity, and perceived relative advantage. Furthermore, the results also suggest that factors in the economic aspect (monetary subsidy and risk of a price reduction), functional aspect (intelligent function and risk of sustainability), and social aspect (status symbol and risk of reputation), exert significant impacts on the adoption of EVs by influencing consumers’ perceptions of innovation characteristics. Theoretically, this study contributes to the literature by providing an appropriate theoretical perspective for understanding consumer adoption of EVs and identifying numerous significant antecedents of such behavior. Practically, the findings of this study can be applied to promote the market penetration of EVs.

Design of Interactive Teaching System of Physical Training Based on Artificial Intelligence

Nowadays, with the continuous change and innovation of teaching methods in Colleges and universities, the curriculum system of students is also constantly enriched and developed. Therefore, people’s requirements for teaching management and teaching system are also improving. Physical education curriculum is usually based on outdoor teaching, and some schools have not established a complete teaching system. Therefore, the interactive teaching system of physical training based on artificial intelligence is designed. First of all, through the construction of the interactive teaching system of the total control circuit, determine the corresponding circuit address decoding, improve the audio control circuit, associated video connection interactive drive three parts, the intelligent sports training interactive system hardware design. Then, through the creation of intelligent training function module, the design of training database and the realisation of effective training and teaching of intelligent sports, the software design of intelligent sports training interactive system is carried out. Finally, through the test of the system, to verify the corresponding effect, further improve the relevant system, make it more safe and accurate, improve the efficiency of sports training interactive system, enhance the integrity of the teaching process.

A smart anti-corrosion coating based on triple functional fillers

The long-term anti-corrosion performance of ordinary coatings is unsatisfactory due to their poor barrier properties. Herein, we developed a strategy to construct a triple functional filler by first modifying basalt scales (Bt) with polydopamine (PDA) to introduce active sites, followed by in situ growth of molybdate-loaded layered double hydroxide (LM) on the surface of PDA-modified basalt. This novel strategy not only avoided stacking between LDHs, but also improved the interfacial compatibility between fillers and epoxy resin, which endowed the anti-corrosion system with intelligent self-healing function and barrier anti-corrosion characteristic. Tafel curves obtained in solution phase proved that compared with carbon steel immersed in pure 3.5 wt% NaCl solution, the introduction of Bt@PDA@LM causes the Icorr to decrease from 1.18 × 10-5 A/cm2 to 2.05 × 10-6 A/cm2 and the corrosion inhibition efficiency reaches 82.63%. Combined with the significant enhancement of the low-frequency impedance of the scratched Bt@PDA@LM/EP coating during immersion in 3.5 wt% NaCl solution, the effective corrosion inhibition of the designed filler was demonstrated. Moreover, the low-frequency impedance of the Bt@PDA@LM/EP coating is about 660 times higher than that of the pure epoxy (EP) coating after immersion in 3.5 wt% NaCl solution for 120 days and is still as high as 4.96 × 1010 ohm·cm2 after 200 days of immersion. The excellent corrosion resistance can be attributed to the synergistic effect of barrier property, chloride ion trapping effect and corrosion inhibition of the obtained filler. Thence, it is believed that this research will provide insights in developing new long-term anti-corrosion coatings and break the application limitations of polymer-based coatings to some extent.

Intelligent and compact coherent Doppler lidar with fiber-based configuration for robust wind sensing in various atmospheric and environmental conditions.

The intelligent and compact coherent Doppler lidar (CDL) for wind sensing is demonstrated. The configuration is fiber-based. Several functions for the robust wind sensing in various atmospheric and environmental conditions are shown. The main feature of this CDL is the intelligent functions of the beam focusing, spectral accumulation, and window wiping. The supplemental functions of the robust noise floor reduction and motion compensation are also introduced. The effect of the above-mentioned main feature is demonstrated for the improvement of data availability. The evaluation results of the highly accurate wind velocity measurement are additionally shown.

Blockchain-Enabled Joint Resource Allocation for Virtualized Video Service Functions

Power information is an important guarantee for energy security. As an important technical means of safety management and risk control, video monitoring is widely used in the power industry. Power video monitoring system uses efficient processing of multimodal video data and automatically identifies abnormal events and equipment status, replacing human monitoring with machine. Video monitoring data of power substations usually contain both visual information and auditory information, and the data types are diversified. The multimodal video data provides a rich underlying data source for the intelligent monitoring function, but it requires multiple service forms for efficient processing. Most intelligent edge monitoring equipment are only equipped with lightweight computing resources and limited battery supply, limited resources, and weak local processing data capabilities. Power video monitoring system has the characteristics of distribution, openness, interconnection, and intellectualization. Its intelligent edge video equipment is widely distributed, which also brings convenience and also brings security risks in terms of data security and reliability. For the outdoor multimodal power video monitoring system scenario, this paper adopts the edge-cloud distributed system architecture to solve the problem of resource shortage and adopts the first proposed service function virtualization (SFV) to solve the problem of multimodal video data processing. At the same time, the problem of security protection is solved by introducing blockchain to establish trust among intelligent video equipment and service providers. Under the security protection of virtualized service consortium blockchain (VSCB), virtualization technology is introduced into the service function chain (SFC) to realize SFV and solve the resource optimal allocation problem of multimodal video data processing. The work mainly involves the joint mapping of virtual resources, physical resources, and the joint optimization of computing and communication resources. Problems such as large state space and high dimensionality of action space have an impact on resource allocation. The stochastic optimization problem of resource allocation is established as a Markov decision process (MDP) model, and SFV technology is used to optimize cost and delay. The resource allocation optimization algorithm (RAOA-A3C) based on asynchronous advantage actor-critic algorithm (A3C) is proposed. Simulation experiments show that the RAOA-A3C proposed in this paper is more suitable for high-dynamic, multidimensional, and distributed power video monitoring system scenario and has achieved better optimization results in reducing time delay and deployment costs.

The Cultivation and Training Effect of the Subconscious Mind in Physical Education and Training by Intelligent Internet of Things Network Computing

With the deep integration of the Internet of Things and artificial intelligence, the intelligent Internet of Things is becoming a cutting-edge technology with broad application prospects. The intelligent Internet of Things can realize the interconnection, interaction, perception, and calculation of human, machine, object, environment, and other elements, thereby realizing the intelligent space function with self-organization, self-learning, self-adaptation, and continuous evolution. Based on the intelligent Internet of things, this paper analyzes the cultivation and training effect of network computing on the subconscious in physical education training. An experimental study of its influence on the learning of shooting techniques in the compulsory basketball course of the physical education class of the high school affiliated to Jiangxi Normal University in 2020 was conducted. It is concluded that the average score of students’ learning motivation in the intelligent Internet of Things network computing group is 4.06, and the average score of learning interest is 3.9. After the experiment, the pass rate of students’ shooting was 92%, and the excellent rate was 74%. The use of intelligent Internet of Things network computing technology can significantly improve students’ learning motivation, learning interest, and training effect.