Principle Of Fusion Research Articles

Nuclear fusion and nuclear fusion materials

This paper introduces the basic principle of nuclear fusion and the development status of tokamak device, which leads to the core of this paper-nuclear fusion materials. The reason why this paper studies nuclear fusion materials is that up to now, nuclear fusion materials are one of the key difficulties in realizing controlled nuclear fusion. In this paper, the introduction of nuclear fusion materials is mainly the classification of nuclear fusion materials, such as structural materials, first wall materials, etc., and the main materials used in various parts at present. After that, the reasons why nuclear fusion materials are difficult to realize are also introduced. For example, at the point of temperature, the temperature of plasma during nuclear fusion reaction can reach tens of millions of degrees Celsius, while the melting point of tungsten, the known material with the highest melting point, is only over 3,000 degrees Celsius. In addition, the first wall material of nuclear fusion needs high energy resistance.

GRU-AE-wiener: A generative adversarial network assisted hybrid gated recurrent unit with Wiener model for bearing remaining useful life estimation

Bearings play a pivotal role in various mechanical systems, and their health directly impacts the reliability and safety of these systems. Consequently, extensive research has been dedicated to the estimation of Bearing Remaining Useful Life (RUL) through the lens of information fusion theory. The absence of comprehensive life-cycle degradation data for bearings, a common challenge within the information fusion domain, can hinder the accuracy and reliability of RUL prediction models. A novel hybrid data and model approach named GRU-AE-Wiener has been developed to address this limitation. This approach combines the power of Gated Recurrent Unit (GRU) and Wiener process models within the information fusion framework. Firstly, a Loop Generative Adversarial Network (Loop-GAN) is introduced to synthesize pseudo data to enhance the quality of synthetic data. Next, a bidirectional GRU model is structurally integrated with the Wiener process. In this design, the GRU model is configured in an Auto-Encoder-like structure, with the Wiener process serving as the hidden layer within this Auto-Encoder. Importantly, both the GRU and Wiener processes are jointly optimized with the assistance of Loop-GAN, emphasizing the collaborative nature of information fusion in this approach. The effectiveness of the proposed GRU-AE-Wiener is validated using the PHM 2012 dataset and XJTU-SY dataset. Experimental results underscore its superior RUL predictive performance compared to other deep learning models, highlighting the practical application of information fusion principles in bearing health assessment.

A Crayfish-Inspired Sensor Fusion Platform for Super Additive Integration of Visual, Chemical, and Tactile Information.

The concept of cross-sensor modulation, wherein one sensor modality can influence another's response, is often overlooked in traditional sensor fusion architectures, leading to missed opportunities for enhancing data accuracy and robustness. In contrast, biological systems, such as aquatic animals like crayfish, demonstrate superior sensor fusion through multisensory integration. These organisms adeptly integrate visual, tactile, and chemical cues to perform tasks such as evading predators and locating prey. Drawing inspiration from this, we propose a neuromorphic platform that integrates graphene-based chemitransistors, monolayer molybdenum disulfide (MoS2) based photosensitive memtransistors, and triboelectric tactile sensors to achieve "Super-Additive" responses to weak chemical, visual, and tactile cues and demonstrate contextual response modulation, also referred to as the "Inverse Effectiveness Effect." We hold the view that integrating bio-inspired sensor fusion principles across various modalities holds promise for a wide range of applications.

Optimal Transport Approaches for Cloud Fusion in 3D Reconstruction

Cloud fusion plays a crucial role in achieving accurate 3D models. This paper introduces two optimal transport-based approaches, Gaussian Fusion and Cyclical Fusion, to showcase the effectiveness of optimal transport in establishing dense correspondence among incoming 3D point clouds. These approaches encompass several important components. Firstly, the fusion correspondences are derived from the optimal transport problem. The core principle of Gaussian Fusion and Cyclical Fusion involves manipulating points along geodesic curves to effectively leverage significant local geometric information. Secondly, this paper explores the fundamental concepts underlying Gaussian Fusion (displacement interpolation) and Cyclical Fusion (cyclical monotonicity), which greatly enhance the accuracy and completeness of the reconstruction process. And provides evidence for the uniqueness of displacement interpolation as geodesics on L2-Wasserstein space and the rationality of cyclical monotonicity in this context. Finally, the proposed approaches excel in capturing intricate surface details, particularly on small objects, when compared to the original fusion scheme which often introduces severe artifacts.

Data-physics-driven estimation of battery state of charge and capacity

High-power density lithium-ion batteries have been utilized in both energy storage and high rate charging and discharging applications. Accurate state estimation is fundamental to enhancing battery life and safety. Therefore, a data-physics-driven estimation of the state of charge and capacity for lithium-titanate batteries was conducted using Gaussian distribution fusion. Firstly, a fractional order model was selected as the physical analytical model for lithium-titanate batteries. Secondly, a data-driven model that combines convolutional neural networks and long short-term memory networks was employed to predict the battery state of charge. Thirdly, the physical analytical model and data-driven model were fused to estimate the state of charge by employing the principle of Gaussian distribution fusion. Finally, both the state of charge and actual capacity of the battery were jointly estimated. The results showcase the capability of the proposed method to accurately estimate the state of charge and capacity, ensuring an accuracy level within 1%. Furthermore, this approach exhibits a 20% improvement in accuracy compared to traditional methods.

A Principle Design of Registration-Fusion Consistency: Toward Interpretable Deep Unregistered Hyperspectral Image Fusion.

For hyperspectral image (HSI) and multispectral image (MSI) fusion, it is often overlooked that multisource images acquired under different imaging conditions are difficult to be perfectly registered. Although some works attempt to fuse unregistered images, two thorny challenges remain. One is that registration and fusion are usually modeled as two independent tasks, and there is no yet a unified physical model to tightly couple them. Another is that deep learning (DL)-based methods may lack sufficient interpretability and generalization. In response to the above challenges, we propose an unregistered HSI fusion framework energized by a unified model of registration and fusion. First, a novel registration-fusion consistency physical perception model (RFCM) is designed, which uniformly models the image registration and fusion problem to greatly reduce the sensitivity of fusion performance to registration accuracy. Then, an HSI fusion framework (MoE-PNP) is proposed to learn the knowledge reasoning process for solving RFCM. Each basic module of MoE-PNP one-to-one corresponds to the operation in the optimization algorithm of RFCM, which can ensure clear interpretability of the network. Moreover, MoE-PNP captures the general fusion principle for different unregistered images and therefore has good generalization. Extensive experiments demonstrate that MoE-PNP achieves state-of-the-art performance for unregistered HSI and MSI fusion. The code is available at https://github.com/Jiahuiqu/MoE-PNP.

Integrated learning at primary schools in Lithuania

The research on integrated learning in primary grades in Lithuania was based on the curriculum spider web concept proposed by Akker (2003, 2010). In discussing successful experience of integrated curriculum design, development and implementation, researchers emphasize the balance between the main components of the curriculum: 1. The rationale or vision; 2. Goals and objects; 3. Content; 4. Educational activities; 5. Teacher's role; 6. Materials and resources; 7. Grouping; 8. Place; 9. Time; 10. Assessment. When evaluating the experience of implementing integrated curriculum at schools, all ten components should be addressed in a coherent way, to understand where the challenges and problems of implementing integrated curriculum exist, as well as to identify the strengths of schools’ practices and to anticipate where changes and improvements are needed. The research aim was to reveal attitudes of primary school teachers towards integrated curriculum and barriers to the implementation of integrated education in primary education. 
 The study shows that a significant number of Lithuanian primary school teachers work in collaborative teams, and that educational practice is associated with the principle of fusion, which is developed at the school level through collaboration between practicing teachers, administrators, and researchers. Such areas as character education, citizenship, cooperation, communication, creativity, critical thinking, and skills are integrated into the curriculum.
 The lack of teaching materials for the organization of integrated curriculum (especially in the case of multidisciplinary activity sets) poses a particular challenge for primary school teachers, which requires a significant increase in the amount of time for preparing learning activities. The results of the study show that teachers express concern that, if integrated learning dominates the subject-based curriculum, students will achieve lower academic results in national standardized tests. Practice of integrated learning in other countries shows that when students receive targeted individual support, and when they participate in peer learning with a systemic teacher guidance, they develop both their social skills and critical, creative thinking through project-based or experimental activities. This makes students more motivated, eager to learn in a positive learning environment, and they perform significantly better on tests and show better individual progress.

Route planning model based on multidimensional eigenvector processing in vehicular fog computing

With the rapid development of information technology, the informationization level of sport tourism has been improved in an all-around way, which makes a large number of data accumulated in the management system of road traffic. However, the traditional association rule algorithm cannot deal with huge data. To make up for the deficiency of predicting suitability from a single perspective, this paper, from the perspective of road daily travel planning, constructs a multi-dimensional characteristic model. Based on eigenvector processing as a research content, by carrying out a multi-dimensional prediction on the whole data set and considering the limitation problem of the influence of time, space, and items on daily travel routes, the invention provides a daily travel suitability road daily travel planning route prediction research method based on multiple dimensions. The invention also extracts the outstanding performance of a network model in a single variable problem based on deep interest and double characteristics. The combination of SVR and GBRT algorithm makes up for the one-sidedness of single perspective prediction. It uses the weighted fusion principle to fuse the results and establishes a multi-dimensional route suitability prediction model under this mode. Experiments verify that the prediction of multivariate dimensional data achieves the desired results. With the help of the dependence between the level and element dimension data, the future route planning trend can be judged. This algorithm compared to the ant colony algorithm and the traditional genetic algorithm increased by 15.6% and 15.1%. The system response time has been increased by more than 60%, which can effectively improve the accuracy of prediction. Therefore, in the VFC environment, the actual user needs can be improved, the planning and management of traffic routes can be guided, and the development of sport tourism systems of Taihang Mountain can be further promoted.

Design and Application of a Multi-Semantic Art Education Communication Platform

The lack of corresponding learning service platform will lead to the decline of the teaching quality of art education. Therefore, based on the principle of multi semantic fusion, this paper establishes a multi semantic art education exchange platform and discusses in detail the function mechanism in the process of establishing the platform. Then, the platform is applied to the art education and learning of students of different grades in colleges and universities. By comparing the art education and learning of students of different grades, the platform is designed and improved, which greatly improves the teaching quality of art education. At the same time, in view of the problems existing in the process of art education, the art education exchange platform based on multi semantic fusion can still put forward corresponding rectification measures. The experimental results show that the multi-semantic art education exchange platform is conducive to students of different grades learning art education courses and improving the quality of art education teaching.

An efficient blockchain‐based approach to improve the accuracy of intrusion detection systems

AbstractIntrusion Detection System (IDS) is a critical cybersecurity task that involves monitoring network traffic for malicious activity and taking appropriate action to stop it. However, insufficient training data or improperly chosen thresholds often limit the accuracy of such systems, resulting in high false‐positive rates. To improve the accuracy of an IDS, blockchain technology can be used as it provides a secure, decentralized, immutable ledger that can track suspicious activity over time and also identify intrusions globally. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based IDS. The approach combines different intrusion detection algorithms using a blockchain‐integrated architecture. It is based on the fusion principle and weighted votes, which the authors used to determine their results. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rates, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks.

Prediction method of coal mine gas occurrence law based on multi-source data fusion

To improve the prediction accuracy and time-consuming of coal mine gas occurrence law (OL), a new prediction method based on multi-source data fusion is proposed in this paper. Firstly, the method obtains the data of coal mine gas OL, determines the key data required in prediction through decision matrix, and preprocesses the data to reduce the influence of regular noise data. This paper analyzes the basic principle of multi-source data fusion, constructs the prediction model of coal mine gas OL with this technology, takes the optimal value of weighting factor as the input value of the model, and completes the design of coal mine gas OL prediction method based on multi-source data fusion. The experimental results show that the accuracy of this method can reach 98%, while that of the other two traditional methods is lower than the existing methods. This method has high accuracy and efficiency in predicting the coal mine gas OL.

Multimodal CNN-Based System For Mask And Maskless Face Detection

Face masks existed for much longer before the pandemic corresponding to COVID-19, whereverstaff in several sectors, such as medical, chemical, and nuclear, needed to wear masks throughout duties.Following the pandemic caused by the COVID-19 virus, most countries requested publicly covering thenose and mouth as vital life to keep the communities safe. However, 24/7 human superintendence is almostimpossible.In this paper, an efficient and automatic multimodal face mask detection was developed. The model wasengineered based on intensive investigations, where first, the performance of two well-known deep learningmodels, particularly MobileNetV2 and VGG19, was investigated. Next, the performance was furtherimproved using the late fusion principle. Four datasets consisting of roughly 6K, 12K, 4k, and 4k images,respectively, are used to confirm the results robustness of the developed model. Overall, the results of theexperimental works showed that fusion leads to a more stable and outperforming model compared to fivebase CNN models, i.e., MobileNetV2, VGG19, and three sequent models.

Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

In order to control the grassland ecological environment, an application method of multisource data fusion technology in the construction of land ecological index is proposed. Due to the high requirements for grassland environmental monitoring, the use of traditional technologies to monitor grassland environmental conditions lacks certain effectiveness, has high investment costs, and consumes a lot of manpower and material resources. The use of sensors to dynamically monitor the grassland environment is conducive to monitoring the environment from a scientific and technological level. By understanding the fusion principle and process of three fusion methods, adaptive weighted average, BP neural network, and D‐S evidence theory, the construction of Bashang grassland ecological energy big data platform based on multisource data fusion is proposed. A two‐level data fusion model based on grassland environmental monitoring is proposed. Several environmental parameters in the experimental environment were monitored, and the validity of the two‐level fusion model was verified by two evaluation indicators, the mean absolute percentage error and the corrosion error. This suggests that a combination of BP neural network and D‐S proof theory improves system performance. It provides the possibility for more comprehensive monitoring of grassland ecological environment in the future.

An Overview of the ITER Project

ITER (initially the International Thermonuclear Experimental Reactor) is the most perspective project to achieve fusion energy. In this article, the origin of the ITER project, fundamental members and how the design of Tokamak provides the demand or suitable conditions for ITER to achieve its scientific or experimental goals will be discussed. In addition, the technological improvements in several different aspects and how it can function under different situations will be introduced. The principle of nuclear fusion and nuclear reactor and how the energy is created and conserved in the whole process will also be explained. This will lead to the significant problem that is considered the relationship between the ITER project and new energy resource form. The future prospects of this type of future energy resources and comparison with other present energy recourses will be discussed as well.

Analysis of the Influence of Multimedia Information Fusion on the Psychological Emotion of Financial Investment Customers under the Background of e-Commerce

With the gradual deepening of the research on financial investment, some data show that investors’ limited rationality and self-psychological emotions do not always seek to maximize their own interests when making investment decisions, but lead to investment failure due to the influence of their bad psychological emotions. With the rise of e-commerce and the development of multimedia, multimedia information fusion and presentation are realized, and multiple terminals interact with each other in real time and effectively. It is in this context that this paper analyzes the impact of multimedia information fusion on the psychological emotions of financial investment customers in the context of e-commerce and formulates a correct and effective investment plan in line with financial investment customers. The results of the experiment are as follows: (1) the background of the current e-commerce development, the principle of multimedia information fusion, and the problems existing in the emotional impact of financial investment customers are analyzed, the research direction of the experiment is determined, and the target problem is analyzed according to the information fusion technology, which provides technical support for the research of this paper. (2) Among the existing multimedia information fusion design algorithms, Mgr fusion algorithm, advanced MCR fusion algorithm, multimodal fusion reordering algorithm, and trust function algorithm are introduced to compress and analyze the complex multimedia data information in different fields, which not only shortens the time required for a large amount of data information fusion. And The emotional data information of financial investment customers is efficiently counted and classified.

Multidimensional Sensor Data Fusion Processing System Based on Big Data

In order to process data fusion, the author proposes a multidimensional sensor data fusion processing system based on big data. The author discusses the principle and basic steps of multidimensional sensor data fusion and analyzes the classification and common data fusion methods of data fusion. Then, the structure and training process of the DBN algorithm are emphatically expounded, experiments are carried out on the randomly collected multidimensional sensor datasets through the DBN algorithm, the validity of the algorithm is verified, and the algorithm is evaluated. The experimental results show that the number of hidden layers is 100, the number of nodes is 100, the weight matrix is a matrix of 784 × 100 , the learning rate is 2, the momentum is 0.5, the number of samples is 100, and the iteration is 1 time. The average reconstruction error obtained by the MATLAB Deep Learn Toolbox is 65.7798. Conclusion. The method proposed by the author can effectively process multidimensional sensor data fusion.

Best fit of mixture for multi-sensor poisson multi-Bernoulli mixture filtering

We propose a computationally efficient, the first so far, multi-sensor extension of the Poisson multi-Bernoulli mixture (PMBM) filter that accommodates both centralized and distributed sensor networks. In the distributed case, a distributed flooding algorithm is needed for internode communication, which iteratively shares the relevant multi-target posterior among neighbor sensors, thereby making each local sensor serve like a fusion center in the centralized case. The PMBM posterior yielded at each sensor is decomposed into two components corresponding to undetected and detected targets which are modelled by Poisson and multi-Bernoulli mixture (MBM) distributions, respectively. Both communication and fusion are performed with regard to the latter only. A “best-fit-of-mixture” fusion principle is adopted at each sensor to find an MBM that best fits the mixture of MBMs from distinct sensors, which results precisely in the arithmetic average (AA) of these MBMs. The information divergence of the AA from the true density is analyzed. We also provide a Bayesian model averaging interpretation of the MBM-AA fusion. Simulations in scenarios of different target detection probabilities demonstrate the performance of the proposed PMBM filter in terms of localization error, false-alarm/misdetection errors, and communication and computation costs, in comparison with the AA-based multisensor multi-Bernoulli filter.

Design of Multimodal Neural Network Control System for Mechanically Driven Reconfigurable Robot.

According to the characteristics and division rules of the modules, this paper divides the rotary module, the swing module, and the mobile module. In order to realize the rapid identification of modules, according to the basic principles of module design, the above modules are put into the module library established by Access. According to the modular modeling method, kinematic models are established, respectively. In order to automatically establish the kinematic model of the robot, a unified expression of modules is established. According to the unified expression of the modules, the kinematics of the reconfigurable robot is analyzed. According to the characteristics of the configuration plane, the configuration plane is divided, and the expression form of the position and attitude of the configuration plane is given. Combined with the principle of neural network and multimodal information fusion, a multimodal information fusion model based on long- and short-term memory neural network is established. Aiming at the control problem of mechanically driven reconfigurable robots, a specific long- and short-term memory neural network model is designed, and the long- and short-term memory neural network algorithm is applied to the robot control problem based on multimodal information fusion. The design of the controller and driver of each joint is the basis of the distributed control system. This paper discusses the hardware design of the joint controller and driver and the realization of the position control system and discusses the method of realizing distributed control based on the Modbus protocol of RS485 communication. Through the comprehensive experiment of the configuration, the point control and the continuous path control are carried out to verify the correctness of the theoretical analysis of the system and the reliability of the system hardware and software operation.

Children's Motor Intelligence Evaluation System Based on Multi-data Fusion.

Along with the main feedback information of early childhood education activities, children's movement evaluation plays an important guiding role in early childhood education activities. Teachers can teach children in accordance with their aptitude according to the results of children's sports evaluation. Traditional physical education overemphasizes the leading role of teachers, and young children can only passively receive the education of teachers. Teachers always use the same standard to evaluate each child, simply assigning a rating of “strong,” “moderate,” and “poor” to children, ignoring the differences in children's physical intelligence. The emergence of multidata fusion technology can use the differences and complementarity of various data in evaluation functions to make up for the insufficiency of a single exercise evaluation result, purposefully choose the evaluation method flexibly according to the content of the exercise and the scene of the exercise, and gradually improve the level of children's exercise evaluation. This paper studied the children's motor intelligence evaluation system based on multidata fusion. To this end, this paper will focus on children's autonomous sports games and determine the first-level indicators in the children's sports evaluation index system as four indicators: classroom performance, physical fitness, motor skills, and extracurricular fitness. It used the principle of multidata fusion, firstly evaluates children's exercise physiology data through a fuzzy neural network algorithm, and then combines the adaptive weighted data fusion algorithm with D-S evidence theory to evaluate children's movement intelligence. Experiments showed that the multidata evaluation system can take effective measures to intelligently evaluate children's comprehensive motor ability. Compared with the traditional evaluation method, the evaluation results are increased by 5%, and the children's sports evaluation results are more average, which can enhance children's sports confidence and promote children's effective exercise.

Design of an equipment for polishing of medical vagin dilators in order to eliminate edges and burrs

In medical applications, specifically in the handling of mechanical utensils, hygiene and safety must be ensured, which implies a complete cleaning and sterilization of these, as well as the absence of burrs on support edges, in order that there are no harmful cuts of the tissues that are being examinated or manipulated. In such a way, the so-called mirrors or vaginal dilators are very useful elements in the review of women's health and should not be exempt from the minimum quality of hygiene and safety. These devices, are developed using a special polymer, which are commonly elaborated through plastic injectors using molds, due to, they are implemented using different parts, generate the creation of sharp edges and shavings that must be eliminated. The geometry of the mirrors implies a certain complexity in the elimination of the cutting elements. In the present work the design of a polishing machine is presented, which is designed under the principle of fusion of sharp edges and shavings by applying heat from butane gas flames. The proposed machine transports the mirrors to the position to the location of the heating source, with the proper orientation, in order to eliminate the edges and shavings.