Behavior Evaluation Method Research Articles

E-learning Learning Behavior Evaluation and Prediction Method Based on Sentiment Analysis

E-learning Learning Behavior Evaluation and Prediction Method Based on Sentiment Analysis

Comprehensive evaluation method for structural behavior of concrete dams in cold regions

In view of operation characteristics of concrete dams in cold regions, comprehensively considering the impact of overwintering layer, cold wave and freeze-thaw and the representativeness of various evaluation indexes with in-situ monitoring data, an evaluation index system affecting the structural behavior of concrete dams in cold regions is constructed. Based on typical small probability method and analytic hierarchy process, the unequal interval division method of evaluation index grade is proposed. Based on game theory, a combined subjective and objective weighting method of evaluation indexes is established. In order to solve the problem of fuzziness and randomness of evaluation indexes, an improved interval intuitionistic fuzzy set method is adopted for a comprehensive evaluation method for structural behavior of concrete dams in cold regions. The effectiveness of the proposed method is verified by practical engineering application.

A New Method of Pedestrian Abnormal Behavior Detection Based on Attention Guidance

In public places, some behavior that violates public order and endangers public safety is defined as abnormal behavior. Moreover, it is a necessary auxiliary means to maintain public order and safety by detecting abnormal behavior in a large number of surveillance videos. However, due to the small proportion of abnormal behavior in video data, the extreme imbalance of data seriously restricts the effectiveness of detection. So, weakly supervised learning has become the most suitable and effective detection method. However, existing weakly supervised methods rarely take the locality and slightness of abnormal behavior into account and ignore the details of extracted features. Based on this, an attention-directed abnormal behavior detection model is proposed. In the two common prediction and reconstruction abnormal behavior detection methods based on weak supervision, suitable attention mechanisms are introduced, respectively, and two corresponding attention-directed networks are proposed. In addition, aiming at the problem of inaccurate thresholds for abnormal behavior division, the loss function of the model is improved and a new abnormal behavior evaluation method is proposed. Experiments were carried out on three classical datasets (the USCD Ped1, USCD Ped2, and CUHK Avenue dataset) for abnormal behavior detection. The best results for the area under the curve (AUC) indicator reached 82.7%, 94.5%, and 87.3%, respectively, which are better than many existing literature results.

Application of the Deformation Behavior Evaluation Method for the Quick Inspection of U-Shaped Steel Dampers

ABSTRACT Practical cumulative damage evaluation methods are essential for commercializing metallic dampers, such as U-shaped steel dampers. The high dependence on numerical simulations, such as time-history analysis, significantly restricts the use of the previously reported cumulative damage evaluation method in the quick inspection of U-shaped steel dampers. This makes simplifying and efficiently certifying the estimated value of cumulative damage difficult. Therefore, the precision verification approach of the previously reported damage evaluation method and quick inspection method are combined in this study based on the change in the shape of the dampers caused by dynamic cyclic loading.

Non-Destruction Evaluation Method for Long-Term Oxidation Behavior of SiC/SiC Composites with Different Preforms in Wet Oxygen.

The implementation of SiC fiber reinforced SiC/SiC composites to aero-engine hot components has attracted wide attention, due to their many excellent properties. Along these lines, in order to predict the oxidation behavior of the material in extreme environments and to explore the effect of different preforms on the oxidative behavior of the composites, four SiC/SiC composites, with different preforms, were oxidized under environmental conditions of pressure of 12 kPa H2O:8 kPa O2:80 kPa Ar, at 1400 °C temperature. Moreover, the morphology and defect distribution of the samples were characterized by carrying out scanning electron microscopy, and micro-computed X-ray tomography measurements. Furthermore, the relation between the micro- and macro-scales was established, so as to be able to predict the oxidation behavior of the composites; not only the quantitative relationship between the mass change rate and the defect volume change rate, but also the combination of micro-computed X-ray images.

Personalized Learning Behavior Evaluation Method Based on Deep Neural Network

In recent years, the research on personalized learning under the background of “Internet +” mainly focuses on the theory, design, and application and there is less research on learning evaluation. As an important means to measure the learning process and results, learning assessment plays an important role in supporting the effectiveness of personalized learning. From the perspective of educational services, how to realize learning evaluation that meets the needs of personalized learning is an important issue to be studied in the field of personalized learning. In this paper, the big data generated by learners on the online learning platform are used as the research target, and according to the level of learners’ learning ability, a deep neural network is established to cluster and group them according to the cognitive thinking method. In order to reduce data redundancy and improve processing efficiency, a deep neural network with five hidden layers is used to extract typical features, so as to obtain more accurate evaluation results. Finally, the neural network model is used to obtain the clustering results of different groups of learning behaviors and the evaluation curves of the five-course knowledge points of learners at different levels. From the experimental results, the proposed personalized evaluation method can effectively analyze the learning differences between learners with different ability levels, and it is basically consistent with the evaluation standards of artificial experts.

Comprehensive Evaluation on Teachers' Knowledge Sharing Behavior Based on the Improved TOPSIS Method.

Knowledge sharing among teachers is one of the important ways to improve their teaching and research ability. From the perspectives of teachers' knowledge sharing willingness, knowledge sharing ability, knowledge sharing environment, knowledge sharing technology support, knowledge sharing effect, and so forth, this paper constructs a teacher knowledge sharing behavior evaluation model, develops a knowledge sharing behavior evaluation index system, and proposes a comprehensive teacher's knowledge sharing behavior evaluation method based on the improved entropy-TOPSIS method. This is a comprehensive evaluation method combining subjective and objective weights, which avoids the subjectivity of traditional expert evaluation methods and other multilevel and multi-index weight determination methods and makes the evaluation results more objective, accurate, and more realistic. Finally, by taking the example of evaluating knowledge sharing behavior of teachers in a university of Chongqing, China, this paper verifies the feasibility and practicability of the proposed comprehensive teachers' knowledge sharing behavior evaluation system and method.

Detection of unrecognized spatial disorientation: A theoretical perspective.

BACKGROUND: Spatial disorientation (SD) is a problem that pilots often encounter during a flight. One reason for this problem is that among the three types of SD, there is no validated method to detect the Type I (unrecognized) SD.OBJECTIVE: In this pursuit, initially we reviewed the problems and the evaluation methods of associated with SD. Subsequently, we discussed the advantages and disadvantages of the subjective questionnaire evaluation method and the behavior evaluation method.METHODS: On the basis of these analyses, we proposed a method to detect the unrecognized SD that improved the assessment of SD to a significant extent. We developed a new direction to study the unrecognized SD based on the subjective report and the center of pressure (CoP).RESULTS: The proposed evaluation method can assist the pilots to understand the feelings and physical changes, when exposed to unrecognized SD.CONCLUSION: We hope that this evaluation method can provide a strong support in developing a countermeasure against the unrecognized SD and fundamentally solve the severe flight accidents arising due to them.

Development of evaluation method for diffusion and filtration behavior of colloid in compacted bentonites using dendrimers

Development of evaluation method for diffusion and filtration behavior of colloid in compacted bentonites using dendrimers

Thermokinetics of mass-loss behavior on direct reduction of copper slag by waste plastic char

The effective utilization of waste plastics is the key technology for environmental protection. Waste plastic char was proposed as a reductant for recovering valuable metals from copper slag. Reaction reactivity, mass-loss behavior and thermokinetics of copper slag were studied by FTIR and thermogravimetric analyzer. The main functional groups of waste plastic char are benzene ring, C-O and –OH. Mass-loss behavior evaluation method was proposed based on calculating and detection of sample’s total mass-loss and reductant mass-loss.With temperature increasing, reaction mechanism transformed. Three-dimension diffusion and interface’s chemical reaction rate are restrictive steps for the first and the second reaction regime, respectively. For waste plastic char, activation energy of the first regime and second regime are about 289.5 ~ 276.2 kJ/mol and 584.5 ~ 709.9 kJ/mol, respectively. CaO changed the second regime’s activation energy. When CaO added, activation energy of waste plastic char and coal decreased by 119.8 ~ 121.5 kJ/mol and 100.0 ~ 113.6 kJ/mol respectively. Waste plastic char presents better reduction reactivity in the second reduction regime than coal. This research makes it possible for recycling of municipal organic wastes and iron containing slags.

The inhibitory effects of bile acids on catalytic and non-catalytic functions of acetylcholinesterase as a therapeutic target in Alzheimer’s disease

Acetylcholine is a fast-acting neurotransmitter in synapses and neuromuscular junctions that is decreased in Alzheimer's disease (AD) by hyper‑activation of acetylcholinesterase (AChE), which leads to progressive loss of memory and neurobehavioral abnormalities. Therefore, AChE inhibitors have therapeutic potential in AD that could include natural compounds such as bile acids. Bile acids, as potent molecules, could improve some types of neurodegenerative diseases via antioxidant effects and other unknown mechanisms. The aim of this study was to investigate beneficial effects of bile acids on AChE catalytic and non‑catalytic functions, amyloid plaque deposit and memory in a rat model of AD. The effects of sodium deoxycholate and cholic acid on AChE activity were assessed by in vitro assay. Then, the bile acids' potential therapeutic effects were investigated on nucleus basalis of Meynert lesioned rats using behavioral evaluation, biochemical tests and histological methods. Molecular docking simulation was also implemented to investigate the possible interaction between bile acids and AChE. According to the in vitro and in vivo results, sodium deoxycholate could efficiently inhibit the catalytic function of the enzyme by interacting with the catalytic site, while cholic acid interacted with the peripheral anionic site and inhibited chaperone activity of the enzyme that led to the reduced amyloid plaque deposition. The co‑administration of cholic acid and sodium deoxycholate showed these compounds are able to simultaneously inhibit the catalytic and non‑catalytic functions of the enzyme. This study clarifies the roles of natural bile acids in the nervous system and in AChE function through multiple experimental and simulation methods.

Development of animal behavior evaluation method by image analysis

Development of animal behavior evaluation method by image analysis

A Study of Bad Driving Behavior Based on Improved K-Means Clustering and Neural Network

With the popularization of the Internet of Vehicles technology, excavating useful information from a large amount of driving behavior data to evaluate the driver's safe driving behaviors in real time, accurately and efficiently, is of great significance to improve the safety management level and transportation efficiency of road transportation process. Based on the driver's driving behavior characteristics and driving industry management standards, this paper proposes a bad driving behavior evaluation method based on improved k-means clustering and neural network. It uses the improved k-means clustering method to select typical sample points from the characteristic parameters extracted from the vehicle GPS positioning platform. The Backpropagation neural network algorithm is designed to learn the clustering results, and the online classification evaluation of bad driving behavior is realized. It provides a new direction for the transportation vehicle management department to carry out safety management of the road transportation process.

Experimental and numerical study on impact resistance of RC bridge piers under lateral impact loading

Vehicle collisions with piers are main threat to bridge structures, thus the impact resistance of pier is significant for bridge structure safety. This paper presents the experimental and numerical work undertaken for investigating the impact process, damage and failure mode, dynamic behavior, and impact resistance evaluation method of reinforced concrete (RC) piers under lateral impact loading. By using a horizontal impact system, a series of simplified truck model collision test on the square sectional RC piers are performed, in which two main designing parameters, i.e., impact velocity and longitudinal reinforcement ratio, are especially concerned. Moreover, the detailed finite element models are established by the commercial program LS-DYNA, which are verified against the test results. It indicates that, the shape of impact force time-history does not exhibit the platform stage compared with that of the conventional drop hammer impact tests attributed to the shear failure mode occurred for the present columns; the damage level, impact force, displacement at impact position and energy dissipation rise with increasing the impact velocity; increasing the longitudinal reinforcement ratio improves the impact resistance of RC piers effectively; a plastic hinge forms with the hoop reinforcement yielding before the shear failure of column; the impact force causes a considerable change of the axial force of RC piers during the impact process. Finally, based on the energy conservation law, the residual displacement at the impact position is adopted and validated as the reasonable criterion for evaluating the impact resistance of RC piers. The present work can provide helpful references for the anti-collision design and impact resistance evaluation of the urban and highway bridge piers.

A new user behavior evaluation method in online social network

Due to the limitations of existing user evaluation in online social network (OSN), a new user behavior evaluation model is proposed. Considering the high degree of uncertainty, complexity, dynamics of user behavior in OSN, cloud model theory is innovatively introduced to evaluate the user behavior, on this basis, a credible evaluation scheme concerning user behavior combined with entropy weighting is proposed in this paper. The user's original behavior is classified as the evaluation factors according to the attribute, then the behavior attribute cloud is established via Reverse Cloud Generator algorithm; At the same time, the user is divided into different trust levels according to the actual situation, and hierarchical cloud is generated through Positive Cloud Generator algorithm. The membership matrix of the behavior attribute cloud to the hierarchical cloud is calculated by employing a mathematical formula, and the weight of each attribute is adaptively calculated by the entropy method, overcoming the limitations of subjective weight assignation. This model provides a new thinking of user behavior evaluation in OSN, meanwhile, also expands the application of cloud model theory and fuzzy comprehensive evaluation method. Finally, as an example of embodiment, a case study is presented for user behavior evaluation in Facebook, both the feasibility and effectiveness are verified.

Development of Ultra-High Strength Steel Sheet and Evaluation Method for Hydrogen Embrittlement Behavior

Development of Ultra-High Strength Steel Sheet and Evaluation Method for Hydrogen Embrittlement Behavior

Fracture Behavior and Strength Evaluation Method of CFRP Structure with Edge-on Impact Damage

Fracture Behavior and Strength Evaluation Method of CFRP Structure with Edge-on Impact Damage

Evaluation Method for Behavior of Rotary Axis Around Motion Direction Changing

Several methods for evaluating the motion accuracy of the rotary axes in five-axis machining centers have been proposed till date. As it is known that particular motion errors exist around the motion direction changing points, it is important to evaluate the behavior of the rotary axes around these points. However, the influence of the motion error in the translational axes is included in the conventional evaluation results, as the translational axes reverse at the motion direction changing points about the rotary axes. In this study, an evaluation method which can assess the behavior of a rotary axis around motion direction changes by synchronous motion of translational and rotary axes is proposed. In this method, the direction of translational axes does not change when the motion direction of a rotary axis changes. A measurement test and actual cutting tests are carried out to clarify the influence of the behaviors of rotary axes on the motion trajectory and machined surface, caused by the change in the motion direction of the rotary axis. Simulations of the motion are also carried out to discuss the causes of inaccuracy.

Real-time Evaluation Mechanism Based on Double Evidence Classification of User Behavior

With the development of trusted network, the research of trusted evaluation mechanism of user behavior is a hotpot in the network security. In order to solve the problems of subjectivity, limitations and static in traditional trusted network user behavior evaluation models, we have to find a real-time and dynamic evaluation method for user behavior. In this paper, the authors construct a real-time evaluation mechanism based on double evidence classification of user behavior (DEC-UB). The evaluation mechanism includes the process classification and characteristic classification of user behavior evidence, which makes the user behavior evidence of any time can be directly involved in the trust evaluation, and the evaluation result is more comprehensive and accurate. Simulation experiments have evaluated the three kinds of user behaviors based on the DEC-UB, and compared them with the other two kinds of trust evaluation methods of user behavior, the results show that the proposed methods can evaluate the user's behavior comprehensively, accurately and dynamically in complex network environments, and the results are more realistic.

Effective Use of the Six Sound Test

Effective Use of the Six Sound Test