Joint Multi-User Detection with Weighting Factors for Unsourced Multiple Access

Multi-user detection techniques are currently being studied as highly promising technologies for improving the performance of unsourced multiple access systems. In this paper, we propose joint multi-user detection schemes with weighting factors for unsourced multiple access. First, we introduce bidirectional weighting factors in the extrinsic information passing process between the multi-user detector based on belief propagation (BP) and the LDPC decoder. Second, we incorporate bidirectional weighting factors in the message passing process between the MAC nodes and the user variable nodes in BP- based multi-user detector. The proposed schemes select the optimal weighting factors through simulations. The simulation results demonstrate that the proposed schemes exhibit significant performance improvements in terms of block error rate (BLER) compared to traditional schemes. .

Collaboration strategy for software dynamic evolution of multi-agent system

As the ability of a single agent is limited while information and resources in multi-agent systems are distributed, cooperation is necessary for agents to accomplish a complex task. In the open and changeable environment on the Internet, it is of great significance to research a system flexible and capable in dynamic evolution that can find a collaboration method for agents which can be used in dynamic evolution process. With such a method, agents accomplish tasks for an overall target and at the same time, the collaborative relationship of agents can be adjusted with the change of environment. A method of task decomposition and collaboration of agents by improved contract net protocol is introduced. Finally, analysis on the result of the experiments is performed to verify the improved contract net protocol can greatly increase the efficiency of communication and collaboration in multi-agent system.

Multi-telerobot collaboration based on coordinated controller

A coordinated controller used for multi-telerobots collaboration was presented based on the strategy of shared control. First, it can overcome the effect of time delay. And, it combines the intelligence of the master side and the slave side, which cannot only increase the efficiency and the safety of the system but also relieve the burden and requirements of the operator. The controller can also cope with the collision between two telerobots. A simulation experiment was carried out to verify the validity of the controller for two slave robots.

A Cognitive Model for Multi-Agent Collaboration

In multi-agent system, agents work together for solving complex tasks and reaching common goals. In this paper, we propose a cognitive model for multi-agent collaboration. Based on the cognitive model, an agent architecture will also be presented. This agent has BDI, awareness and policy driven mechanism concurrently. These approaches are integrated in one agent that will make multi-agent collaboration more practical in the real world.

MULTI-AGENT COLLABORATIVE DESIGN SYSTEM MODEL BASED ON THE INTERNET AND EXPERIMENTS

Development of complicated products is a project of system engineering It involves extensive and complicated knowledge,design methods and auxiliary technology Various factors affect each other So,modern product development is a typical group problem with distributed and dynamic features It is apparent superiority to solve this problem with a multi agent system representing various knowledge domains Distributed artificial intelligence knowledge being used,the multi agent collaborative design system concept and model based on Internet environment are put forward The realizing method of product developing agents,interactive process among multi agents,and organization and implementing of the design project of the multi agent collaborative design system are discussed in detail Application examples are also presented.

Current Situation and Mode of Talent Training in Marine Cultural Industry Based on Collaborative Innovation of Governments,Enterprises,Colleges,Scientific Institutions and Users

Based on the present situation of talent training in marine cultural industry,the collaborative innovation mechanism of governments,enterprises,colleges,scientific institutions and users was used to construct the mode of talent training in marine cultural industry. It is needed to give full play to the active roles of governments,enterprises,colleges,scientific institutions and users in the training of marine cultural talents,develop training mode of innovative scientific and technological talents,speed up the construction of collaborative innovation ability of marine cultural talents,and improve the efficiency of talent training.

Improved Collaborative Filtering Recommendation Based on Classification and User Trust

When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.

Multi-Domain Collaborative Recommendation with Feature Selection

Collaborative f iltering, as one of the most popular techniques, plays an important role in recommendation systems. However,when the user-item rating matrix is sparse,its performance will be degenerate. Recently,domain-specific recommendation approaches have been developed to address this problem.The basic idea is to partition the users and items into overlapping domains, and then perform recommendation in each domain independently. Here, a domain means a group of users having similar preference to a group of products. However, these domain-specific methods consisting of two sequential steps ignore the mutual benefi t of domain segmentation and recommendation. Hence, a unified framework is presented to simultaneously realize recommendation and make use of the domain information underlying the rating matrix in this paper. Based on matrix factorization,the proposed model learns both user preferences of multiple domains and preference selection vectors to select relevant features for each group of products. Besides, local context information is utilized from the user-item rating matrix to enhance the new framework.Experimental results on two widely used datasets, e.g., Ciao and Epinions, demonstrate the effectiveness of our proposed model.

A Linear Interference Cancellation Multi-User Detector for Synchronous CDMA Communications

提出了一种当伪随机码相关系为常数情况下的线性干扰抵消多用户接收机．基本思想是利用参考ＰＮ码，估计出用户间干扰，然后从接收信号中减掉用户间干扰．该接收机结构与传统接收机相似，但性能却得到大大改善．

Combinative Algorithms for the Multidisciplinary Collaborative Simulation of Complex Mechatronic Products Based on Major Step and Convergent Integration Step

Multidisciplinary collaborative simulation （MCS） is an important area of research in the domain of multidisciplinary design optimization （MDO）.Although previous research for MCS has to some extent addressed some issues like using of multiple tools,integration stability,control of step size,data synchronization,etc,further work is still necessary to study how to achieve improved precision.A theoretical model is formulated to describe and analyze the integration process of MCS.A basic algorithm with equal major steps is proposed based on the model,along with two methods of implementation for the model,namely the serial method and the parallel method.A further algorithm based on convergent integration step is proposed,which has a more flexible strategy for run-time integration.The influence of interpolation techniques on simulation performance is studied as well.Simulations of the performance of various algorithms with different interpolation techniques are performed for both a simple numerical example and a complex mechatronic product.The novel algorithm based on convergent integration step,when used with a high-order interpolation technique,has better performance in terms of precision and efficiency.The innovation of this paper is mainly on the validation of high precision of the proposed convergent integration step algorithm.

Multi-target Collaborative Combat Decision-Making by Improved Particle Swarm Optimizer

A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.

Limited Feedback Bit Allocation for Cooperative Multi-cell Systems with Multi- user MIMO

For the cooperative multi-cell systems with multi-user MIMO, a new limited feedback bit allocation scheme is proposed to minimize the rate loss caused by quantization error. In the proposed scheme, the Channel State Information (CSI) feedback of cell-edge user for the local service cell and the adjacent interference cell are separately quantized. Based on the upper bound of the rate loss of cell-edge user due to the limited feedback, the number of feedback bits for quantized CSI of the local service cell and the adjacent cell are optimized with the fixed total bits of the limited feedback. The simulation shows that our proposed scheme of feedback bits allocation efficiently decreases the interference and increases the rate of systems compared with that of equal bits allocation and those of other allocations.

Complex field network-coded cooperation based on multi-user detection in wireless networks

Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.

Multi-User Detection for Spatial Modulation toward 5G Wireless Communications

Spatial modulation(SM) is a class of novel multiple-input multiple-output(MIMO) techniques toward future wireless communications,which activates only one transmit antenna in each time slot,so as to reduce the number of RF chains for saving the implement cost.Meanwhile,considering its application in 5G systems with multiple users,the detection of multi-user spatial modulation has drawn greater attention.In this paper,a pair of efficient detectors are proposed for multi-user spatial modulation.Specially,a threshold-aided approximate message passing(T-AMP) detector is proposed with the purpose of reducing the computational complexity of traditional structured approximate message passing(Str-AMP) detector.In addition,a novel probability sorting aided approximate message passing detector,called probability-ranking-aided AMP detector(P-AMP),is also proposed with the purpose of improving the performance.Simulation results show that the proposed T-AMP detector is able to achieve similar performance as traditional StrAMP with lower complexity,while the proposed P-AMP detector exhibits a better symbol error rate(SER) performance with similar complexity.

Context-Oriented Multi-RAT User Association and Resource Allocation with Triple Decision in 5G Heterogeneous Networks

In the upcoming 5 G heterogeneous networks, leveraging multiple radio access technologies(RATs) shows to be a crucial issue in achieving RAT multiplexing gain to meet the explosive traffic demand. For always best connection(ABC), users tend to activate parallel transmission across all available RATs. However from a system-wide perspective, this might not be optimal given the context of network load, interference and diverse service requirements. To intelligently determine how to use these multi-RAT access resources concurrently, this paper proposes a joint multi-RAT user association and resource allocation strategy with triple decision and integrated context awareness of users and networks. A dynamic game based ant colony algorithm(GACA) is designed to simultaneously maximize the system utility and the fairness of resource allocation. Simulation results show that it's more reasonable to make multi-RAT association decision from a system-wide viewpoint than from an individual one. Compared to max-SNR based and ABC based strategies, the proposed method alleviates network congestion and optimizes resource allocation. It obtains 39%~70% performance improvement.

Multi-user performance analysis of differential frequency hopping system over Rayleigh-fading channel

A novel non-coherent detection scheme for differential frequency hopping(DFH)system is proposedin asynchronous multi-user environments over Rayleigh-fading channels.The synchronous and asyn-chronous multi-user performances of DFH with the conventional detection scheme and this novel detectionscheme are analyzed,respectively.The performance results are validated with simulation.The results ofanalyses and simulations prove two conclusions.Firstly,the performance of asynchronous multi-user DFHsystem overcomes that of synchronous multi-user DFH system over Rayleigh-fading channel.Secondly,the novel detection scheme can achieve better performance than the conventional non-coherent detectionscheme in asynchronous multi-user environments.

Reliability analysis for aeroengine turbine disc fatigue life with multiple random variables based on distributed collaborative response surface method

The fatigue life of aeroengine turbine disc presents great dispersion due to the randomness of the basic variables,such as applied load,working temperature,geometrical dimensions and material properties.In order to ameliorate reliability analysis efficiency without loss of reliability,the distributed collaborative response surface method(DCRSM) was proposed,and its basic theories were established in this work.Considering the failure dependency among the failure modes,the distributed response surface was constructed to establish the relationship between the failure mode and the relevant random variables.Then,the failure modes were considered as the random variables of system response to obtain the distributed collaborative response surface model based on structure failure criterion.Finally,the given turbine disc structure was employed to illustrate the feasibility and validity of the presented method.Through the comparison of DCRSM,Monte Carlo method(MCM) and the traditional response surface method(RSM),the results show that the computational precision for DCRSM is more consistent with MCM than RSM,while DCRSM needs far less computing time than MCM and RSM under the same simulation conditions.Thus,DCRSM is demonstrated to be a feasible and valid approach for improving the computational efficiency of reliability analysis for aeroengine turbine disc fatigue life with multiple random variables,and has great potential value for the complicated mechanical structure with multi-component and multi-failure mode.

Choice between Ways of Talent Training in Marine Cultural Industry--Based on Collaborative Innovation of Governments, Enterprises, Colleges, Scientific Institutions and Users

Based on the present situation of talent training in marine cultural industry,the collaborative innovation mode of governments,enterprises,colleges,scientific institutions and users was used to study the ways of talent training in marine cultural industry.

Study on Talent Training Mechanisms of Marine Cultural Industry Based on Collaborative Innovation of Governments,Enterprises,Colleges,Scientific Institutions and Users

Based on the review of related concepts,the current situation and problems of talent training in marine cultural industry in China were analyzed,and talent training mechanisms of marine cultural industry based on collaborative innovation of governments,enterprises,colleges,scientific institutions and users were proposed to promote the transformation,upgrading and vigorous development of marine cultural industry,accelerate the spread of marine culture with Chinese characteristics,and build China into a maritime power.

Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However,such previously used approaches do not fairly and efficiently incorporate users’preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.