Optimal operation of cold–heat–electricity multi-energy collaborative system based on price demand response

In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.

Collaborative Optimization of a Matrix Manufacturing System Based on Overall Equipment Effectiveness

When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.

High-efficiency CNT-Si solar cells based on a collaborative system enabled by oxide penetration

Carbon nanotube-silicon(CNT-Si)solar cells represent one of the alternative photovoltaic techniques with potential for low cost and high efficiency.Here,we report a method to improve solar cell performance by depositing conventional transitional metal oxides such as WO_(3)and establishing a collaborative system,in which CNTs are well-embedded within the WO_(3)layer and both of them are in close contact to Si substrate.This unique collaborative system optimizes the overall energy conversion process including the light absorption(antireflection by WO_(3)),carrier separation(forming quasi p-n junction)and charge collection(CNT conductive network throughout the oxide layer).Combining with our previous TiO_(2)-coating and HNO_(3)-doping techniques,a solar cell efficiency of>18%at an active area of 0.09 cm 2(air mass 1.5,100 mW/cm^(2))was achieved.The oxide-enhanced CNT-Si solar cells which integrate the advantages of traditional semiconductors and novel nanostructures represent a promising route toward next-generation high-performance silicon-based photovoltaics.

Collaborative optimization of maintenance and spare ordering of continuously degrading systems

A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to ob- tain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project attributes.Then,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more accurate.In the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large weight.The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.

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.

Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems

In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.

Distributed collaborative CAD system based on Web Service

This paper presents a novel Web Service based distributed collaborative CAD system employing feature as its collaborative design element and uses XML to define feature operations and communication protocol between the server and the client. To reduce network load and increase response ability of the system, the feature information is updated incrementally on the client. The system supports collaborative designing on heterogeneous platforms. Its framework and communication protocols are analyzed in detail. The experimental results from the developed prototype system showed that it can effectively support collaborative design under the distributed environment.

Multi-UAV collaborative system with a feature fast matching algorithm

We present a real-time monocular simultaneous localization and mapping(SLAM)system with a new distributed structure for multi-UAV collaboration tasks.The system is different from other general SLAM systems in two aspects:First,it does not aim to build a global map,but to estimate the latest relative position between nearby vehicles;Second,there is no centralized structure in the proposed system,and each vehicle owns an individual metric map and an ego-motion estimator to obtain the relative position between its own map and the neighboring vehicles'.To realize the above characteristics in real time,we demonstrate an innovative feature description and matching algorithm to avoid catastrophic expansion of feature point matching workload due to the increased number of UAVs.Based on the hash and principal component analysis,the matching time complexity of this algorithm can be reduced from 0(logN)to 0(1).To evaluate the performance,the algorithm is verified on the acknowledged multi-view stereo benchmark dataset,and excellent results are obtained.Finally,through the simulation and real flight experiments,this improved SLAM system with the proposed algorithm is validated.

A model for mold collaborative manufacturing execution system based on Web Service

Mold manufacturing Extended Enterprise （EE） has the following characteristics： distributed in locality, tight cooperation and frequent information exchange. It needs a collaborative, highly efficient, reliable and intelligent manufacturing management system. The background of the Collaborative Manufacturing is introduced. A mold Collaborative Manufacturing Execution System （c-MES） is proposed. The feature of Web Service platform is analyzed. The necessity and feasibility of importing the Web Service to mold c-MES are discussed. Based on Web Service, the model of mold c-MES is built. Every module＇ s function is described in detail, including the functions it supplies and the mechanism of information interaction among them. The feasibility of mold c-MES model is validated by a real mold manufacturing case.

Damping Collaborative Optimization of Five-suspensions for Driver-seat-cab Coupled System

Both the seat and cab system of truck play a vital role in ride comfort.The damping matching methods of the two systems are studied separately at present.However,the driver,seat,and cab system are one inseparable whole.In order to further improve ride comfort,the seat suspension is regarded as the fifth suspension of the cab,a new idea of ＂Five-suspensions＂ is proposed.Based on this idea,a 4 degree-of-freedom driver-seat-cab coupled system model is presented.Using the tested cab suspensions excitations as inputs and seat acceleration response as compared output,the simulation model is built.Taking optimal ride comfort as target,a new method of damping collaborative optimization for Five-suspensions is proposed.With a practical example of seat and cab system,the damping parameters are optimized and validated by simulation and bench test.The results show the seat vertical frequency-weighted RMS acceleration values tested for the un-optimized and optimized Five-suspensions are 0.50 m/s～2 and 0.39 m/s～2,respectively,with a decrease by 22.0%,which proves the model and method proposed are correct and reliable.The idea of ＂Five-suspensions＂ and the method proposed provide a reference for achieving global optimal damping matching of seat suspension and cab suspensions.

Teleoperation strategies research for collaborative welding systems based on virtual reality

Teleoperation control strategies for collaborative welding system which is targeting at giving full play to human’s superiority is designed and the fitness for teleoperation welding task of which are studied. During the teleoperation welding process, 6-DOF controller’s signal can be converted into welding torch’s position, velocity or acceleration changing which is being controlled. For welding purposes, control strategies of four modes are designed, which are static position and posture mode, dynamic position and posture mode, velocity mode and acceleration mode. A test and analysis system for testing the tracking accuracy and reliability of control strategy based on virtual reality is developed. The tracking accuracies of the four control strategies are studied in the following tests with straight line trajectory, curve trajectory or space curve trajectory. The results show that the control strategy in dynamic position and posture mode has best stability and strong adaptability which is the most suitable for the teleoperation system.

Fuzzy-Weighted Similarity Measures for Memory-Based Collaborative Recommender Systems

Memory-based collaborative recommender system (CRS) computes the similarity between users based on their declared ratings. However, not all ratings are of the same importance to the user. The set of ratings each user weights highly differs from user to user according to his mood and taste. This is usually reflected in the user’s rating scale. Accordingly, many efforts have been done to introduce weights to the similarity measures of CRSs. This paper proposes fuzzy weightings for the most common similarity measures for memory-based CRSs. Fuzzy weighting can be considered as a learning mechanism for capturing the preferences of users for ratings. Comparing with genetic algorithm learning, fuzzy weighting is fast, effective and does not require any more space. Moreover, fuzzy weightings based on the rating deviations from the user’s mean of ratings take into account the different rating scales of different users. The experimental results show that fuzzy weightings obviously improve the CRSs performance to a good extent.

A Multi-Agent Reinforcement Learning-Based Collaborative Jamming System: Algorithm Design and Software-Defined Radio Implementation

In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.

Collaborative Clustering Parallel Reinforcement Learning for Edge-Cloud Digital Twins Manufacturing System

To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.

Multi-Agent System for Real Time Planning Using Collaborative Agents

Autonomous agents are an important area of research in the sense that they are proactive, and include: goal-directed and communication capabilities. Furthermore each goals of the agent are constantly changing in a dynamic environment. Part of the challenge is to automate the process corresponding to each agent in order that they find their own objectives. Agents do not have to work individually, but can work with others and develop a coordinated group of actions. These agents are highly appreciated, when real time problems are involved, meaning that an agent must be able to react within a specific time interval, considering external events. Our work focuses on the design of a multi-agent architecture consisting of autonomous agents capable of acting through a goal-directed with: a) constraints, b) real-time, and c) with incomplete knowledge of the environment. This paper shows a model of collaborative agents architecture that share a common knowledge source, allowing knowledge of the environment;where we analyze it and its changes, choosing the most promising way for achieving the goals of the agent, in order to keep the whole system working, even if a fault occurs.

Docurity: A New Cryptographic Primitive for Collaborative Cloud Systems

Recently,there has been a sudden shift from using traditional office applications to the collaborative cloud-based office suite such as Microsoft Office 365.Such cloud-based systems allow users to work together on the same docu-ment stored in a cloud server at once,by which users can effectively collaborate with each other.However,there are security concerns unsolved in using cloud col-laboration.One of the major concerns is the security of data stored in cloud ser-vers,which comes from the fact that data that multiple users are working together cannot be stored in encrypted form because of the dynamic characteristic of cloud collaboration.In this paper,we propose a novel mode of operation,DL-ECB,for AES by which we can modify,insert,and delete the ciphertext based on changes in plaintext.Therefore,we can use encrypted data in collaborative cloud-based platforms.To demonstrate that the DL-ECB mode can preserve the confidential-ity,integrity,and auditability of data used in collaborative cloud systems from adversaries,we implement and evaluate the prototype of the DL-ECB mode.

A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.

Recommendation algorithm of cloud computing system based on random walk algorithm and collaborative filtering model

The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.

Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.