Through the empirical research on the teaching of undergraduate professional introduction courses,the teaching experience of similar high-quality courses is refined.Based on modern educational technology,the innovativ...Through the empirical research on the teaching of undergraduate professional introduction courses,the teaching experience of similar high-quality courses is refined.Based on modern educational technology,the innovative thinking of similar course teaching is explored.By establishing a teaching community to facilitate multi-dimensional collaboration,this approach overcomes the constraints of time and space,effectively integrates teaching resources,and enhances the scientific rigor,standardization,and effectiveness of course instruction.As a result,the educational impact of professional introduction courses is continuously optimized.展开更多
This paper analyzes a problem processing mechanism in a new collaboration system between the main manufacturer and the supplier in the"main manufacturer-supplier"mode,which has been widely applied in the col...This paper analyzes a problem processing mechanism in a new collaboration system between the main manufacturer and the supplier in the"main manufacturer-supplier"mode,which has been widely applied in the collaborative development management of the complex product.This paper adopts the collaboration theory,the evolutionary game theory and numerical simulation to analyze the decision-making mechanism where one upstream supplier and one downstream manufacturer must process an unpredicted problem without any advance contract in common.Results show that both players'decision-makings are in some correlation with the initial state,income impact coefficients,and dealing cost.It is worth noting that only the initial state influences the final decision,while income impact coefficients and dealing cost just influence the decision process.This paper shows reasonable and practical suggestions for the manufacturer and supplier in a new collaboration system for the first time and is dedicated to the managerial implications on reducing risks of processing problems.展开更多
Collaborative design is recommended to solve multiphysics problems （MPPS）. Firstly, mathematical model of MPPS is constructed and solved by a proposed partitioned method, analysis of which suggests that collaborativ...Collaborative design is recommended to solve multiphysics problems （MPPS）. Firstly, mathematical model of MPPS is constructed and solved by a proposed partitioned method, analysis of which suggests that collaborative design be feasible to solve MPPS. As the key technology of col-laborative design of MPPS, a task collaboration algorithm is then proposed. To develop the applica-tion framework of collaborative design, applied unified process（AUP） is proposed based on rational unified process（RUP）. Then AUP is used to develop the collaborative design platform, whose function framework is constructed according to the process of project management. Finally three MPPS are solved on this platform and the results suggest that the proposed model, algorithm and framework be feasible.展开更多
The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborativ...The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.展开更多
In response to the uncertainty of information of the injured in post disaster situations,considering constraints such as random chance and the quantity of rescue resource,the split deliv-ery vehicle routing problem wi...In response to the uncertainty of information of the injured in post disaster situations,considering constraints such as random chance and the quantity of rescue resource,the split deliv-ery vehicle routing problem with stochastic demands(SDVRPSD)model and the multi-depot split delivery heterogeneous vehicle routing problem with stochastic demands(MDSDHVRPSD)model are established.A two-stage hybrid variable neighborhood tabu search algorithm is designed for unmanned vehicle task planning to minimize the path cost of rescue plans.Simulation experiments show that the solution obtained by the algorithm can effectively reduce the rescue vehicle path cost and the rescue task completion time,with high optimization quality and certain portability.展开更多
The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means t...The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.展开更多
Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and ...Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem.展开更多
The paper reports on collaborative learning approach to a remedial class in Dynamics. It uses the Kolb model and 4MAT learning system to introduce a learning cycle based on collaborative problem solving exercises. The...The paper reports on collaborative learning approach to a remedial class in Dynamics. It uses the Kolb model and 4MAT learning system to introduce a learning cycle based on collaborative problem solving exercises. The teaching approach based on learning cycle is described giving examples of different teaching elements applied in particular quadrants of the learning cycle. The attitude of students and their different approaches to, learning are presented and discussed in detail. The results presented cover different aspects of the course delivery and students＇ perception. The results include students＇ statistics with respect to their registration and previous history related to the course, their motivatiion, assessment and satisfaction with the approach applied. This initial introduction of cooperative activities into the remedial Dynamics class can be assessed as a positive step in increasing student understanding and performance in the course. The students＇ positive reception of the non-traditional teaching method and their overall improved performance seem to confirm the advantages of collaborative leaming. In terms of the final grades, the results of the experiment were not as good as expected. However, the application of 4MAT learning system which exposed students to a variety of diverse learning styles improved the quality of education. The positive aspect of the experiment was the attitude of students and their acceptance of the new mode of course delivery. In conclusion collaborative learning could be extended beyond remedial groups to normal student classes.展开更多
基金The undergraduate teaching reform project of Nanning Normal University in 2023“Construction and Practice of Curriculum Teaching Community of the Course‘Introduction to Tourism Management’from the Perspective of Multiple Collaborative”(2023JGX037)。
文摘Through the empirical research on the teaching of undergraduate professional introduction courses,the teaching experience of similar high-quality courses is refined.Based on modern educational technology,the innovative thinking of similar course teaching is explored.By establishing a teaching community to facilitate multi-dimensional collaboration,this approach overcomes the constraints of time and space,effectively integrates teaching resources,and enhances the scientific rigor,standardization,and effectiveness of course instruction.As a result,the educational impact of professional introduction courses is continuously optimized.
基金supported by the National Natural Science Foundation of China(7117111271502073)。
文摘This paper analyzes a problem processing mechanism in a new collaboration system between the main manufacturer and the supplier in the"main manufacturer-supplier"mode,which has been widely applied in the collaborative development management of the complex product.This paper adopts the collaboration theory,the evolutionary game theory and numerical simulation to analyze the decision-making mechanism where one upstream supplier and one downstream manufacturer must process an unpredicted problem without any advance contract in common.Results show that both players'decision-makings are in some correlation with the initial state,income impact coefficients,and dealing cost.It is worth noting that only the initial state influences the final decision,while income impact coefficients and dealing cost just influence the decision process.This paper shows reasonable and practical suggestions for the manufacturer and supplier in a new collaboration system for the first time and is dedicated to the managerial implications on reducing risks of processing problems.
文摘Collaborative design is recommended to solve multiphysics problems （MPPS）. Firstly, mathematical model of MPPS is constructed and solved by a proposed partitioned method, analysis of which suggests that collaborative design be feasible to solve MPPS. As the key technology of col-laborative design of MPPS, a task collaboration algorithm is then proposed. To develop the applica-tion framework of collaborative design, applied unified process（AUP） is proposed based on rational unified process（RUP）. Then AUP is used to develop the collaborative design platform, whose function framework is constructed according to the process of project management. Finally three MPPS are solved on this platform and the results suggest that the proposed model, algorithm and framework be feasible.
基金supported by the National Key R&D Program of China(2018AAA0101700)the Program for HUST Academic Frontier Youth Team(2017QYTD04).
文摘The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.
基金supported by the National Natural Science Foundation of China(No.61903036)。
文摘In response to the uncertainty of information of the injured in post disaster situations,considering constraints such as random chance and the quantity of rescue resource,the split deliv-ery vehicle routing problem with stochastic demands(SDVRPSD)model and the multi-depot split delivery heterogeneous vehicle routing problem with stochastic demands(MDSDHVRPSD)model are established.A two-stage hybrid variable neighborhood tabu search algorithm is designed for unmanned vehicle task planning to minimize the path cost of rescue plans.Simulation experiments show that the solution obtained by the algorithm can effectively reduce the rescue vehicle path cost and the rescue task completion time,with high optimization quality and certain portability.
基金supported by the deanship of Scientific Research at Prince Sattam Bin Abdulaziz University,Alkharj,Saudi Arabia through Research Proposal No.2020/01/17215。
文摘The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.
文摘Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem.
文摘The paper reports on collaborative learning approach to a remedial class in Dynamics. It uses the Kolb model and 4MAT learning system to introduce a learning cycle based on collaborative problem solving exercises. The teaching approach based on learning cycle is described giving examples of different teaching elements applied in particular quadrants of the learning cycle. The attitude of students and their different approaches to, learning are presented and discussed in detail. The results presented cover different aspects of the course delivery and students＇ perception. The results include students＇ statistics with respect to their registration and previous history related to the course, their motivatiion, assessment and satisfaction with the approach applied. This initial introduction of cooperative activities into the remedial Dynamics class can be assessed as a positive step in increasing student understanding and performance in the course. The students＇ positive reception of the non-traditional teaching method and their overall improved performance seem to confirm the advantages of collaborative leaming. In terms of the final grades, the results of the experiment were not as good as expected. However, the application of 4MAT learning system which exposed students to a variety of diverse learning styles improved the quality of education. The positive aspect of the experiment was the attitude of students and their acceptance of the new mode of course delivery. In conclusion collaborative learning could be extended beyond remedial groups to normal student classes.
