Strong Application Research Articles

Optimization of Digital Media Product Interface Design Based on Multidimensional Heterogeneous Emotion Analysis of Users

Due to the characteristics of strong interaction, pertinence, and digital communication of digital media technology, this study combines it with interactive technology. It not only increases customers’ feelings in the process of experience but also promotes the progress of new media technology. However, people’s demand for digital media interface has gradually shifted from functional demand to emotional demand. In order to make a good interaction design, interaction designers must first deeply understand and study users’ cognitive behavior and psychological activities. In this study, through multidimensional heterogeneous emotion analysis of users and optimizing the interface design of digital media products, users can easily and efficiently complete tasks, increase the sense of intimacy, comfort, and success, and improve user satisfaction and brand reputation. Digital media product interface carries the function of visual invisible logic language. As the most common form of visual communication, it affects everyone’s life. Digital media carries all kinds of information. How to get more user experience in the same type of media is a problem to be considered in the design of digital media interface. Applying emotional design to digital media interface design, users can not only meet the needs of actual functions but also get a more emotional experience. Starting with the emotional design theory of digital media product interface, this paper studies the emotional instinct layer, behavior layer, and reflection layer in the creativity of user interface design, analyzes the emotional relationship between users and products with the three layers of emotion of design, and discusses the characteristics of the emotional design of digital media product interface from the perspective of design.

A Multi-Stage Visible and Infrared Image Fusion Network Based on Attention Mechanism.

Pixel-level image fusion is an effective way to fully exploit the rich texture information of visible images and the salient target characteristics of infrared images. With the development of deep learning technology in recent years, the image fusion algorithm based on this method has also achieved great success. However, owing to the lack of sufficient and reliable paired data and a nonexistent ideal fusion result as supervision, it is difficult to design a precise network training mode. Moreover, the manual fusion strategy has difficulty ensuring the full use of information, which easily causes redundancy and omittance. To solve the above problems, this paper proposes a multi-stage visible and infrared image fusion network based on an attention mechanism (MSFAM). Our method stabilizes the training process through multi-stage training and enhances features by the learning attention fusion block. To improve the network effect, we further design a Semantic Constraint module and Push–Pull loss function for the fusion task. Compared with several recently used methods, the qualitative comparison intuitively shows more beautiful and natural fusion results by our model with a stronger applicability. For quantitative experiments, MSFAM achieves the best results in three of the six frequently used metrics in fusion tasks, while other methods only obtain good scores on a single metric or a few metrics. Besides, a commonly used high-level semantic task, i.e., object detection, is used to prove its greater benefits for downstream tasks compared with single-light images and fusion results by existing methods. All these experiments prove the superiority and effectiveness of our algorithm.

Observation reconstruction and disturbance compensation-based position control for autonomous underwater vehicle

To address the position control problem of a six degrees of freedom autonomous underwater vehicle (AUV), the disturbance is observed by an observer and compensated by a controller in this paper. To ensure the vehicle reaches the specified position quickly, the sine cosine algorithm with individual memory is used to find suitable parameters for different controllers. The simulation results show that the observer can extract the system's internal and external uncertainty information in real-time, reduce the phase lag and improve the stability of AUV position control. For a system with complex nonlinearity such as AUV, the observer proposed in this paper has stronger applicability and better robustness.

Maximum power point tracking for triboelectric nanogenerator based wave energy converters

The main challenge of wave energy utilization is to improve the low energy conversion efficiency. We report a novel triboelectric nanogenerator (TENG) based wave energy converter (WEC) control system that can significantly increase the energy harvesting efficiency via pressure control. We have identified that WECs have optimal damping corresponding to the maximum energy harvesting in different wave conditions. Hence, the optimal damping values can be applied via adjusting the pressure applied on the surface of TENG. When the external load is 1 MΩ, the instantaneous power output of the proposed TENG based WEC is able to reach 3.2 mW and the instantaneous maximum power output density arrives at 502 mW/m2 under the 8 cm wave height and 1.5 s time period condition, which shows strong power output capability and practical application potential of the TENG based WEC.

A variable parameter control method for charging pile load aggregator to participate in the fast frequency market

Abstract With the access of large-scale new energy sources, fast frequency regulation resources of the power system are scarce. A variable parameter control method for charging pile load aggregator to participate in the fast frequency market is proposed in this paper. It is that charging pile load aggregator actively adjusts the active power according to the frequency of the local power system, and the response speed is within 0-2 seconds. In addition, the variable parameter control method proposed in this paper specifically controls the power of charging pile according to the speed and amplitude of frequency drop, which has strong pertinence and simple control scheme, and further improves the frequency stability of the power grid.

Analysis of Persuasive Design Mechanism Based on Unconscious Calculation

In order to make users’ behavior more standardized, a persuasive design mechanism analysis method based on unconscious calculation is proposed. Taking the concept of persuasion and the goal of persuading function as the theoretical basis, the user behavior data is obtained in the form of correlation calculation, and the ant colony algorithm is used to classify the behavior data. According to the results of data processing, analyze unconscious behavior and its characteristics, and obtain the persuasion mechanism through unconscious calculation, persuasion model, and persuasion model design. The experimental results show that the method in this paper has a higher acceptance of behavioral persuasion and higher satisfaction of the persuaded, indicating that the method has strong practical applicability.

Mental and Emotional Recognition of College Students Based on Brain Signal Features and Data Mining

Nowadays, people pay more and more attention to the psychological situation of college students. Using data mining technology to model and analyze the collected psychological data of college students is a research hotspot in psychology and computer science. In addition, the essence of human emotional change is the higher nervous activity in the cerebral cortex. Electroencephalography (EEG) has become an important feature signal for emotion recognition because of its high time resolution and portability and practicality. Therefore, to solve the problem that the accuracy and generalization of the existing research models are not ideal, a method of college students’ psychological emotion recognition based on EEG signal features and data mining is proposed. Firstly, a feature selection method based on sparse learning is used to find out a few features from the high-dimensional feature space that contribute greatly to the reconstruction of category information so as to quickly acquire a few key emotion-related features. Then, the entropy-weighted clustering algorithm is combined with sparse learning feature selection, and the local structure of heterogeneous data is divided. Experimental results show that, compared with traditional methods, the proposed method has stronger applicability and higher accuracy of five categories of emotions, which provides a valuable reference for the evaluation of depression and anxiety of college students based on brain signal characteristics.

Flowing characteristics of aluminum droplets impacting curved surface

In order to reveal the mechanism of reaction between aluminum droplet and curved wall, a numerical calculation model based on the volume of fluid method of aluminum droplet impacting curved wall is established. By analyzing the influence law of Weber number, Ohnesorge number and wall curvature on the process of droplet impacting the wall, the spreading characteristics and flow mechanism of droplet on curved surface are studied. The results show that the flow characteristics of aluminum droplets after impacting the wall are affected not only by inertial force, surface tension, and viscous force, but also by the structure of the wall. The behavior patterns of the droplets contain adhesion, rebound and splash under different Weber numbers. Because energy dissipation is produced in both spreading process and retracting process, the retracting speed of droplet is always less than its spreading speed. During the flow of the droplet, there are two pressure peaks and velocity peaks at the contact point, while the two peaks appear respectively at the moment when the droplet impacts the wall and when the droplet is about to rebound. In the behavioral mode of rebound, as Ohnesorge number increases, the maximum spreading diameter of the droplet gradually decreases, and the contact time is shorter. In the behavioral mode of adhesion, the spreading radius of the droplets is of oscillatory decay. Within the same period, the maximum spreading coefficient of the larger-Ohnesorge number droplets is smaller, and the decay rate is faster and the oscillation period is shorter. With the increase of wall curvature, the maximum spreading coefficient of droplet increases and that on the plane is the minimum. Based on the calculation results, the empirical formula is revised. Compared with the previous formula, it can well predict the maximum spreading coefficient on the curved surface, whose average error is within 3%. Further, according to the conservation of energy, theoretical models which predict the maximum spreading coefficients when droplets impact a curved and plate wall are also established. Compared with the scenario on the plane, the spreading coefficient of droplet on the curved surface is related to not only the motion parameters of droplet and the wettability of wall surface, but also the ratio of wall curvature to droplet curvature. More importantly, the new theoretical model takes into account the coupling effects of Weber number, Reynolds number, curvature ratio and contact angle, so it has stronger applicability and better robustness. The research results in this work will provide the theoretical basis for practical engineering application.

Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs

UAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and easy to fall into local optimization. Therefore, an intelligent route planning method for UAV based on chaotic random opposition-based learning and cauchy mutation improved Moth-flame optimization algorithm (OLTC-MFO) is proposed. First, the terrain environment is constructed by digital elevation map, and the threat model is established to realize the equivalent three-dimensional (3D) environment. Then, the opposite population is introduced to increase the diversity of solutions and improve the search speed of the algorithm. Then, the Logistic-Tent chaos map is introduced to realize random perturbation of flame position, which improves the global search capability of the algorithm. Finally, the probability operator and Cauchy mutation operator are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current sub-optimal solution, thus enhancing the global search capability of the algorithm. The simulation results show that when the number of iterations is 1000, the length of route planning based on OLTC-MFO algorithm is 45.3716km shorter than MFO algorithm, and the convergence result of this method is stable and more accurate, which achieves the purpose of assisting UAV combat decision-making.

Score Level Fusion for Iris and Periocular Biometrics Recogniton Based on Deep Learning

Abstract Traditional iris recognition has high recognition accuracy and low misrecognition rate. However, in the case of mobile terminal or distance, the image resolution and image quality decrease, and the recognition rate also decreases. To solve the above problems, this article is based on deep learning technology, on the basis of single mode state recognition, from different levels of multimodal integration, the iris and the eyes in the score level fusion recognition research, put forward the adaptive dynamic weighted score fusion method, to determine the weighing values can adaptive algorithm of the modal, without artificial specified, dynamic weighting algorithm more flexible, stronger applicability. Experimental results of casIA-Iris-LAMP and CasIA-Iris-Distance Iris database in Chinese Academy of Sciences show that the proposed fusion algorithm has higher recognition accuracy and better recognition performance than the single mode recognition algorithm and the traditional fractional fusion method, which proves the effectiveness of the algorithm.

Exploration on the Reform of New Business Talents Training Mode in Application-Oriented Universities

The rapid development of big data and artificial intelligence has an increasingly profound impact on society and business, and the demand for talents in the market has changed dramatically. In the context of the development of new business forms, this paper analyzes the new demands of enterprises for business talents by conducting surveys on enterprises, re-examines the orientation of new business talents training in application-oriented universities, and puts forward that talents training needs to follow people-oriented, industry-universal, practical innovative and socially engaged principles. In the era of digital economy and intelligence, new business talents must have strong practical application ability and interdisciplinary knowledge. Application-oriented universities have to reform the talent training model and carry out all-round reform in terms of majors, courses, teaching methods, teachers and evaluation mode. The purpose of the reform is to continuously optimize and innovate the business talent training concept and training model.

Product packaging text and composition colour matching model design based on visual communication

In order to overcome the problems of low pixel extraction accuracy and long matching time in traditional models, a product packaging text and composition colour matching model based on visual communication was designed. Under the concept of visual communication, the phased feature matching method is used to extract the text and composition pixels in the product packaging in blocks. The morphological transformation method is used to construct the regional pixel threshold denoising function, and the colour representation model of text and composition is constructed. The adaptive block feature matching method is used to match the colour of text pixels and composition pixels to achieve the matching of product packaging text and composition colours. The experimental results show that the designed model has high pixel extraction accuracy, and the shortest time is only 2.5 s, which fully shows that the designed model has strong practical application performance.

Task scheduling method of production line workflow based on firefly algorithm

In order to reduce the production cost, production efficiency and energy consumption of production equipment, the task scheduling method of production line workflow based on firefly algorithm is studied. The workflow task scheduling model of production line is constructed to establish the hypothesis conditions that conform to the production environment. Considering the various production factors in the production process, a multi-objective function is constructed and constraints are set for the production process. The firefly algorithm is used to iteratively solve the model, and the optimal solution of the model is obtained, and then the optimal scheme of task scheduling is obtained. The results show that the production cost of the three parts scheduling schemes obtained by this method is less than 350 yuan, the production time is less than 30 seconds and the production energy consumption is less than 55 kWH, which is significantly lower than that of the comparison method, which proves that the scheduling method studied in this paper has strong practical application value.

Sports Parameter Acquisition Based on Internet of Things and Wavelet Analysis

In this study, a sports parameter acquisition model based on the internet of things and wavelet analysis is studied to improve the accuracy and timeliness of human sports parameter acquisition. A motion parameter acquisition model including a sensing layer, transmission layer, and application layer is designed. The acceleration sensor and temperature sensor in the information acquisition node in the sensing layer are used to collect the motion parameter data, which are uploaded to the application layer by the network in the transmission layer. The received data are denoised by the wavelet analysis method through the data processing unit in this layer and then sent to the ZigBee coordinator for coordination. The results show that the model can achieve the effective acquisition of different sports parameters of different moving objects and analyze the actual movement of moving objects according to the acquisition results. In the acquisition process, the signal burr can be effectively removed, the signal noise can be reduced, the high signal-to-noise ratio signal can be output, and the accuracy of acquisition is improved. It has high timeliness, stable performance, and strong practical application, which can provide an effective guarantee for users to monitor sports parameter data in real time.

Path Planning of Mobile Robots in Warehouse Logistics Relying on Computer Multimedia 3D Reconstruction Technology

Because traditional methods are difficult to solve the problems related to the path planning of logistics robots, this study proposes a method of using computer multimedia 3D reconstruction technology to realize the path planning of warehouse logistics robots. Without fully considering the accurate movement path between points, according to the warehouse logistics robot, it is judged whether the starting point is at the exit. The planning problem of the movement path is converted into a TSP problem and a TS-TSP problem. Finally, the analysis of experimental results shows that the method proposed in this study converges faster than traditional algorithms and can quickly obtain the global optimal solution. At the same time, the warehousing logistics robot requires less path planning time and has strong practical application.

Research Progress of Photo-/Electro-Driven Thermochromic Smart Windows.

Thermochromic smart windows can automatically control solar radiation according to the ambient temperature. Compared with photochromic and electrochromic smart windows, they have a stronger applicability and lower energy consumption, and have a wide range of application prospects in the field of building energy efficiency. At present, aiming at the challenge of the high transition temperature of thermochromic smart windows, a large amount of innovative research has been carried out via the principle that thermochromic materials can be driven to change their optical performance by photothermal or electrothermal effects at room temperature. Based on this, the research progress of photo- and electro-driven thermochromic smart windows is summarized from VO2-based composites, hydrogels and liquid crystals, and it is pointed out that there are two main development trends of photo-/electro-driven thermochromic smart windows. One is exploring the diversified combination methods of photothermal materials and thermochromic materials, and the other is developing low-cost large-area heating electrodes.

New energy generation forecasting and dispatching method based on big data

In order to realize the optimal and reasonable distribution of the load of the power system and improve the ability of the power system to consume new energy generation, this paper analyzes the operation big data of photovoltaic power plants, wind farms and thermal power units with the example of a region. Based on this, a predictive scheduling method of new energy generation based on big data is proposed. By constructing a model of new energy output and power load, the method is able to predict the dispatch value of wind power, PV and power load. Finally, through the simulation analysis, the results show that the proposed method can deeply tap the demand response potential of the integrated energy system, provide strong data support for the formulation of a reasonable integrated demand response scheme, and has strong practical application value.

Research on Reliability Optimization Design of Mechanical Structure Based on Computational Intelligence

In order to extend the actual service life of mechanical equipment components and realize the reasonable protection of applied structural components, the reliability optimization design of mechanical structure based on computational intelligence is studied. With the help of wavelet neural network, the established reliability and reliability index are obtained, and then the basic theory of reliability and computational intelligence technology are studied through the reliability optimization of mechanical zero structure. On this basis, the application concepts of reliability design method are defined, and the final calculation result of reliability optimization value is obtained by combining fuzzy set and level cut set, so as to realize the reliability optimization design of mechanical structure based on computational intelligence. The experimental results show that, compared with the traditional design method, the optimization design method supported by computational intelligence technology can better improve the disadvantages of mechanical structure object, and has strong practical application value in extending the reliability application time.

PIEZOELECTRIC METAMATERIALS AND WAVE CONTROL: STATUS QUO AND PROSPECTS

Elastic wave metamaterial is a kind of artificially designed periodic structure. It has received extensive attention due to its special mechanical properties and has shown valuable and unique application prospects in both military and civilian fields. Actively or passively controlling the characteristics of elastic wave metamaterials according to the needs can endow them with stronger applicability. There are lots of tuning ways, among which using piezoelectric materials is a convenient, fast, high-precision, small-sized and low-cost method. In this article, we first briefly introduced the basic aspects of elastic wave metamaterials, tunable metamaterials, piezoelectric materials and several commonly used shunt circuits. Then, according to the different application forms of piezoelectric materials in elastic wave metamaterials, they are divided into two categories: in the first category, the piezoelectric material constitutes the major structure or acts as a part of the major structure; in the second category, the piezoelectric material is used in the form of a spring or a patch attached to the surface of the major structure or embedded in the structure, acting as an actuator or/and a sensor. We elaborated on the research topics and the development history of the two categories of piezoelectric elastic wave metamaterials, related to band gap regulation, waveguide, negative refraction, super transmission, topological state, cloak as well as shunt circuits. Finally, we summarized the research deficiencies of piezoelectric elastic wave metamaterials and outlined corresponding future research prospects.

Challenges of College Students' Ideological and Political and Psychological Education in the Information Age.

The purpose of the study is to analyze the current situation of ideological and political education and psychological education of college students, and explore the challenges faced by these two kinds of education in the information age. First, different research methods, such as literature research, questionnaire survey, and interdisciplinary research, are used to investigate and study the current situation of ideological and political education and psychological education of college students. Second, the survey data are analyzed to reveal the challenges they have encountered. The results show that there are many problems in ideological and political education, which need to be improved and strengthened. The Internet has a strong attraction for college students, but the purpose of getting online of many college students is not clear. Although it plays an important role in improving teaching quality by helping college students establish correct attitudes toward their study, values, life, and society, ideological and political education lack strong pertinence and effectiveness due to the professionalism of the teachers. And the negative impact of network information on ideological and political education of college students is also obvious. About 11.4% of the respondents believe that network information affects their physical and mental health. About 8.4% of the respondents believe that harmful information on the network is easy to induce students to behave wrongly. About 41% believe that computer games distract them from learning, which indicates that the negative impact of network information on college students cannot be underestimated, and it is imperative to strengthen the ideological and political education of college students. The research provides a useful reference for the ideological and political and psychological education in the information environment and helps to solve the problem in the ideological and political education and psychological education of college students.