Search results

This paper aims to present an iterative path-following method with joint limits to solve the problem of large computation cost, movement exceeding joint limits and poor path-following accuracy for the path planning of hyper-redundant snake-like manipulator.

When a desired path is given, new configuration of the snake-like manipulator is obtained through a geometrical approach, then the joints are repositioned through iterations until all the rotation angles satisfy the imposed joint limits. Finally, a new arrangement is obtained through the analytic solution of the inverse kinematics of hyper-redundant manipulator. Finally, simulations and experiments are carried out to analyze the performance of the proposed path-following method.

Simulation results show that the average computation time is 0.1 ms per step for a hyper-redundant manipulator with 12 degrees of freedom, and the deviation in tip position can be kept below 0.02 mm. Experiments show that all the rotation angles are within joint limits.

Currently , the manipulator is working in open-loop, the elasticity of the driving cable will cause positioning error. In future, close-loop control based on real-time attitude detection will be used in in combination with the path-following method to achieve high-precision trajectory tracking.

Through a series of iterative processes, the proposed method can make the manipulator approach the desired path as much as possible within the joint constraints with high precision and less computation time.

The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

The study’s objective was to ascertain the connection between secondary school students' test anxiety, academic self-concept, motivation and academic performance in mathematics. The difference between the academic performances of male and female secondary school students who exhibit high and low test anxiety, academic self-concept and motivation levels in mathematics.

Four hypotheses and four research questions were adopted. The design is a correlation. 42,299 mathematics students in senior school year two (SS2) made up the research population. A sample of 1,650 students was selected through a multi-stage sampling procedure. The main instruments used were the Mathematics Test Anxiety Questionnaire (MTAQ), Academic Self-Concept Questionnaire (ASQ) and Academic Motivation Questionnaire (AMQ) and students’ math scores. These instruments were validated by three experts and the reliability coefficients of 0.69, 0.68 and 0.68 were obtained for MTAQ, ASQ and AMQ, respectively, using Cronbach alpha. Pearson product moment correlation was used to analyze the data.

The study’s results showed a correlation between secondary school students' academic performance in mathematics and test anxiety, academic self-concept and motivation. There was a significant difference between secondary school male and female students' test anxiety; there was a significant difference between secondary school male and female students' self-concept and academic performance in mathematics, and there was a significant difference between secondary school male and female students' motivation and academic performance in mathematics.

The major contribution of this study is to investigate the connection between test anxiety, academic self-concept motivation and students’ mathematics performance. There is a difference between psychological variables, gender and mathematics performance.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

This paper aims to investigate the evolution law of surface characteristic of corroded cold-formed thin-walled steel in industrial environments.

Five test specimens sourced from cold-formed thin-walled C-shaped steel that have been in service for three years in actual industrial environments were subjected to surface characteristic test. The surface characteristic of corroded hot-rolled steel and cold-formed steel were compared and analyzed. The relationship between the surface morphology parameters and the average corrosion depth was established.

The evolution law of the surface morphology of corroded cold-formed thin-walled steel and corroded hot-rolled steel was similar. The frequency histogram of corrosion depth was mainly single peak with high values on the middle and low values on both sides. The corrosion depth conformed to the normal distribution. The roughness average height and the root mean square of surface height gradually increased linearly with increasing the average corrosion depth.

The reduction in the standard deviation of corrosion depth, the maximum corrosion depth, the roughness average height and the root mean square of surface height of the cold-formed thin-walled steel was smaller than those of the hot-rolled steel.

The aim of the paper is to propose a global, automated and continuous curvature calibration strategy for bending sensors, which is used for the angle feedback control of soft fingers.

In this work, the proposed curvature calibration strategy for bending sensors is based on the constant curvature bending properties of soft fingers. The strategy is to install the bending sensor on the soft finger and use the laser distance sensor to assist calibration, then calculate the relationship between the curvature and the voltage of the bending sensor through geometric conversion. In addition, this work also develops a full set of standard calibration systems and collection procedures for the bending sensor curvature calibration and uses machine learning algorithms to fit the collected data.

First, compared with the traditional calibration methods, the proposed curvature calibration strategy can achieve constant curvature measurement with the advantages of better continuity. Second, using the sensor data obtained by the proposed calibration method as the feedback signal for the soft finger bending angle control, the control effect is better than that of the traditional method.

This work proposes and verifies a global, automated and continuous curvature calibration strategy for bending sensors and is used for the angle feedback control of soft fingers. In addition, this work also develops a full set of standard calibration systems and collection procedures, which can be applied to a variety of flexible bending sensors with a good adaptability.

This paper aims to bring in augmented reality (AR) into navigation systems to rectify the issues mentioned. This paper proposes an AR enhanced navigation system for location automated teller machine (ATM) counters (AR-ATM) and branches of banks based on user’s choice. Upon selecting the ATM, the navigational path to the destination is drawn from the current location, thereby the user can reach the ATM through the optimal path.

Traditional navigation systems require users to map with the real world environment as and when required and also may lead to incorrect path due to minor difference in distance. The traditional navigation systems’ also does not take into consideration the ergonomics and safety of the user.

In this system, a camera lens is used, which is directed down the street at eye level and the application displays the location of ATMs and bank branches and also provides information about the locations like distance and time through the AR superimposed object.

The application also provides indoor navigation, especially in a multi-storeyed building. Experiments are performed on smartphones that support AR, and the results are promising with no lag in time frame of the real object and virtual object. To determine the factors that regulate the suggested AR tracking mechanism, a quantitative evaluation of the experimental data is also performed. The testing of implemented AR-ATM from the standpoint of end-users is undertaken to evaluate real-time usage comfortability, and the results have been determined to be extremely satisfactory.

The purpose of this study is to present a general review that provides an overview of the concept of sustainability and the effectiveness of mathematics curricula in courses where deeper work on economic and environmental sustainability has become central.

A qualitative methodology consisting of a review based on a pre-defined systematic method was used to exhaustively search and identify the most relevant answers to the research question: What is the role of mathematics to sustainability? To facilitate answering such a broad question, several concrete questions were formulated. Answers from published and unpublished documents were analysed. The quality of the extracted data was assessed, and the results were synthesized.

It was concluded that, on the one hand, the discipline of mathematics has much to contribute to solving the problems of sustainability; on the other hand, new mathematics is appearing stimulated by new challenges.

This work presents social implications in an innovative way. It allows for an increase in educational sustainability by bringing the academic community closer to the business world and the challenges of society and, furthermore, by having a major impact on the motivation of teachers and students to develop cooperative work within university institutions.

The originality is based on an a priori analysis for the construction and implementation of didactic tools for university teacher training in the area of mathematics within the framework of sustainable development, both economically and environmentally.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

Despite numerous kinds of research regarding the potential of lesson study (LS) in internship and microteaching in teacher education curricula, studies examining the adaptation of LS in the lesson planning (LP) course have not been established. To fill this gap, this study aims to explore the perceptions of pre-service biology teachers of an adapted LS in the LP course toward their instructional design skills and teaching competency.

This study opted for basic qualitative studies. The participants were eleven pre-service biology teachers in their third year who had already taken a LP course. Data were collected through semi-structured interviews. Collected data were then transcribed and thematically analyzed using grounded theory and focused on participants' perceptions about the development of their teaching competency in lesson planning through LS.

The findings revealed that participants appreciated the planning and consultation phases, teaching practice, peer observation as well as observer feedback from the reflection phase in supporting the development of their basic teaching skills. There was also a change in their perceptions related to the concept of teaching.

The findings of this study offer insight regarding the benefits and challenges of involving pre-service biology teacher students in the LS for biology teacher education programs with a focus on the method or LP course.