Intelligent decision support platform of new energy vehicles

New energy vehicles(NEVs) are gaining wider acceptance as the transportation sector is developing more environmentally friendly and sustainable technology. To solve problems of complex application scenarios and multi-sources heterogenous data for new energy vehicles and weak platform scalability,the framework of an intelligent decision support platform is proposed in this paper. The principle of software and hardware system is introduced. Hadoop is adopted as the software system architecture of the platform. Master-standby redundancy and dual-line redundancy ensure the reliability of the hardware system. In addition, the applications on the intelligent decision support platform in usage patterns recognition, energy consumption, battery state of health and battery safety analysis are also described.

Intelligent decision making optimization of rolling mills through data prediction

Steel rolling mills have complex processes,specifications,and varieties,along with certain process quality fluctuations and complex production events,making production management decisions difficult.With the development of industrial big data technology,several industrial event solutions based on data have been proposed.These solutions are supported by predictive data and remarkably improve the production level.Taking a heavy plate production line as the research object,through scientific calculations based on historical big data,this paper establishes an optimization logic for plan arrangement,forecasts the quality through the stable relationship between data and quality,intelligently optimizes the subsequent process flow,improves the production line capacity,and reduces the process bottlenecks.

Research on virtual entity decision model for LVC tactical confrontation of army units

According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and generalization for the enemy,the confrontation process is modeled as a zero-sum stochastic game(ZSG).By introducing the theory of dynamic relative power potential field,the problem of reward sparsity in the model can be solved.By reward shaping,the problem of credit assignment between agents can be solved.Based on the idea of meta-learning,an extensible multi-agent deep reinforcement learning(EMADRL)framework and solving method is proposed to improve the effectiveness and efficiency of model solving.Experiments show that the model meets the requirements well and the algorithm learning efficiency is high.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

Modeling heterogeneous behaviors with different strategies in a terrorist attack

Existing simulations of terrorist attacks do not consider individual variations.To overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist attacks.We constructed an emotional model that integrated personality and visual perception for pedestrians.The emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by terrorists.Experiments demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

Cognitive Intelligence Based 6G Distributed Network Architecture

5G is envisioned to guarantee high transmission rate,ultra-low latency,high reliability and massive connections.To satisfy the above requirements,the 5G architecture is designed with the properties of using service-based architecture,cloud-native oriented,adopting IT-based API interfaces and introduction of the Network Repository Function.However,with the wide commercialization of 5G network and the exploration towards 6G,the 5G architecture exposes the disadvantages of high architecture complexity,difficult inter-interface communication,low cognitive capability,bad instantaneity,and deficient intelligence.To overcome these limitations,this paper investigates 6G network architecture,and proposes a cognitive intelligence based distributed 6G network architecture.This architecture consists of a physical network layer and an intelligent decision layer.The two layers coordinate through flexible service interfaces,supporting function decoupling and joint evolution of intelligence services and network services.With the above design,the proposed 6G architecture can be updated autonomously to deal with the future unpredicted complex services.

Intelligent reasoning and management decision making with grey rough influence diagrams

Purpose-Influence diagrams(IDs)have been widely applied as a form of knowledge expression and a decision analysis tool in the management and engineering fields.Relationship measurements and expectation values are computed depending on probability distributions in traditional IDs,however,most information systems in the real world are nondeterministic,and data in information tables can be interval valued,multiple valued and even incomplete.Consequently,conventional numeric models of IDs are not suitable for information processing with respect to imprecise data whose boundaries are uncertain.The paper aims to discuss these issues.Design/methodology/approach-The grey system theory and rough sets have proved to be effective tools in the data processing of uncertain information systems,approximate knowledge acquisition and representation are also the objectives in intelligent reasoning and decision analysis.Hence,this study proposes a new mathematical model by combining grey rough sets with IDs,and approximate measurements are used instead of probability distribution,an implicational relationship is utilized instead of an indiscernible relationship,and all of the features of the proposed approach contribute to deal with uncertain problems.Findings-The focus of this paper is to provide a more comprehensive framework for approximate knowledge representation and intelligent decision analysis in uncertain information systems and an example of decision support in product management systems with the new approach is illustrated.Originality/value-Collaboration of IDs and grey rough sets is first proposed,which provides a new mathematical and graphical tool for approximate reasoning and intelligent decision analysis within interval-valued information systems.

Schedule of Wartime Maintenance Unit Based on Dynamic Programming

In order to solve the problems of scheduling the maintenance units in the battlefield, the dynamic programming method in model construction is used; the composition of dynamic planning process is explained; and the maintenance unit allocation model is established. By the solved dynamic programming model,the best allocation strategy for maintenance unit obtained in the battlefield will provide a basis for making maintenance unit allocation decisions in the future battlefield.

Intelligent and integrated RFID (Ⅱ-RFID) system for improving traceability in manufacturing

In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low cost in a short time.Manufacturing companies need traceability,which means a real-time view into thenproduction processes and operations.Radio frequency identification(RFID) technology enables manufacturing companies to gain instant traceability and visibility because it handles manufactured goods,materials and processes transparently.RFID has become an important driver in manufacturing and supply chain activities.However,there is still a challenge in effectively deploying RFID in manufacturing.This paper describes the importance for Norwegian manufacturing companies to implement RFID technology,and shows how the intelligent and integrated RFID(n-RFID) system,which has been developed in the Knowledge Discovery Laboratory of Norwegian University of Science and Technology,provides instant traceability and visibility into manufacturing processes.It supports the Norwegian manufacturing industries survive and thrive in global competition.The future research work will focus on the field of RFID data mining to support decision-making process in manufacturing.

Transdisciplinary Convergence:Intelligent Infrastructure for Sustainable Development

The fast-developing intelligent infrastructure landscape catalyzes transformative new relationships of human,technology,and environment and requires new socio-technical configurations of information practice and knowledge work.With a focus on data as the source of intelligence,this paper aims to explore the shifting scenarios and indicative features of data science solutions for intelligent system applications and identify the evolving knowledge spaces and integrative learning practices in the“smart”landscape.It projects and discusses the democratization of data science platforms,the distribution of data intelligence on the edge,and the transition from vertical to horizontal data solutions in solving intelligent system problems.Through mapping the changing data research landscape,this work further reveals essential new roles of knowledge architects and social engineers in enabling dynamic data linking,interaction,and exploration for transdisciplinary data convergence.

AI in Human-computer Gaming:Techniques,Challenges and Opportunities

With the breakthrough of AlphaGo,human-computer gaming AI has ushered in a big explosion,attracting more and more researchers all over the world.As a recognized standard for testing artificial intelligence,various human-computer gaming AI systems(AIs)have been developed,such as Libratus,OpenAI Five,and AlphaStar,which beat professional human players.The rapid development of human-computer gaming AIs indicates a big step for decision-making intelligence,and it seems that current techniques can handle very complex human-computer games.So,one natural question arises:What are the possible challenges of current techniques in human-computer gaming and what are the future trends?To answer the above question,in this paper,we survey recent successful game AIs,covering board game AIs,card game AIs,first-person shooting game AIs,and real-time strategy game AIs.Through this survey,we 1)compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human-level AIs;2)summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer games;3)raise the challenges or drawbacks of current techniques in the successful AIs;and 4)try to point out future trends in human-computer gaming AIs.Finally,we hope that this brief review can provide an introduction for beginners and inspire insight for researchers in the field of AI in human-computer gaming.

Future urban transport management

The incorporation of disruptive innovations into the transportation industry will inevitably cause major upheavals in the transportation sector.However,existing research lacks systematic theories and methodologies to represent the underlying characteristics of future urban transport systems.Furthermore,emerging modes in urban mobility have not been sufficiently studied.The National Natural Science Foundation of China(NSFC)officially approved the Basic Science Center project titled“Future Urban Transport Management”in 2022.The project members include leading scientists and engineers from Beijing Jiaotong University,Beihang University,and Beijing Transport Institute.Based on a wide range of previous projects by the consortium on urban mobility and sustainable cities,this project will encompass transdisciplinary and interdisciplinary research to explore critical issues affecting future urban traffic management.It aims to develop fundamental theories and methods based on social and technological developments in the near future and explores innovative solutions to implement alongside these emerging developments in urban mobility.