Dispersed computing has provided an important computing paradigm for task offloading and resource allocation of intelligent connected vehicles (ICV), which can improve the processing efficiency of ICV applications. This paper investigates task offloading and resource allocation in ICV scenarios based on dispersed computing. To maximize the efficiency of the computation process of ICV applications, a two-stage optimization model based on a directed task graph is established. We have formulated the optimization problem of task offloading and resource allocation in dispersed computing networks. Then we prove that the proposed problem is NP-hard by reducing it to the soft capacity facility location problem. To reduce the complexity of solving, we decompose the proposed problem into two sub-problems: task offloading and resource optimization. We first propose a dynamic sorting (DS) algorithm to solve the task offloading problem, using the resources obtained from the previous iteration to solve the task offloading sub-problem and obtain the task placement result. Meanwhile, we consider two types of ICV applications with different processing efficiency requirements and design a low-complexity heuristic algorithm (Black-headed Gulls Optimization algorithm, BGO) to solve the resource optimization problem. Furthermore, we design the numerical simulations and scalability experiments to verify the effectiveness and scalability of the proposed scheme. Compared to baseline schemes, our algorithm can effectively reduce CPU utilization by 14.3%, memory utilization by 5.1% and processing time by 8.7%. In special scenarios, the processing efficiency is better than baseline schemes by at least 6.85% and 23.79%. The proposed scheme effectively mitigates performance bottlenecks caused by application heterogeneity and network dynamics, thereby improving resource utilization and processing efficiency for heterogeneous ICV applications.

Dense low-power small cell base station (SBS)-based heterogeneous wireless cellular networks (HetNets) have attracted much attention to achieving high-traffic density and peak rate performance. However, the serious energy consumption problem is still a challenge for HetNets. The use of renewable energy (RE) has been considered as one promising solution for the above problem. This article proposes an energy trading scheme among base stations in RE-based HetNets. All SBSs in HetNets are considered as either the energy demander (SBS-ED) or the energy supplier (SBS-ES) based on their abilities in producing RE, and the macro base station (MBS) works as the energy trading manager to control the trading price. A dynamic evolutionary game-based energy trading model between SBS-ES and SBS-ED is established to achieve cooperative energy trading, and the evolutionary stable strategy (ESS) of the proposed model is analyzed. The pricing mechanism of MBS is also investigated, which can effectually affect the EES performance of the proposed model. It is concluded that the MBS’s strategy in the trading price can affect the energy trading strategies of the SBSs. An energy transmission model is proposed, and the minimum energy loss is considered as the goal to obtain the optimal solutions. Numerical results are given to prove the validity and correctness of our proposed method.

When degradation occurs in a dispersed computing network, mitigating the persistent effects of failures and improving the disposal efficiency is an open problem. However, the occurrence of failed nodes in a dispersed computing network is a random and low-probability incident. Further, the resilient resources for emergency allocation are from devices with complex spatial locations in practice. These factors pose challenges to the resource allocation problem using resilience mechanisms. In this article, we explore and analyze a resilience mechanism for dispersed computing networks as an optimization model. We investigate a resilience-aware dynamic resource allocation model to cope with a degraded dispersed computing network and obtain better emergency response at a lower cost. The uncertainties of node failures are uniquely explored to capture failure nodes more precisely and initiate the resilience mechanism for such nodes. In addition, we propose a novel approach to deal with the dynamic and complex coupling characteristics of decision variables in the model. This approach incorporates the induced artificial fish swarm algorithm with dynamic system simulation to generate and improve the scheme for the allocation of resilient resources. Finally, numerical simulation results verify the improved performance of our model and the effectiveness of the algorithm.

This paper applies machine learning technology to the Satir theory model and intelligently classifies the communication stances of the second layer according to the language and behaviour information of the first layer. We arranged a large number of dialogical language materials from a TV interview programme and used the ICTCLAS Chinese word segmentation system to create a ‘psychological consultation database’. We construct the word training set by part of making use of speech filtering and text word vectorisation, and construct the semantic training set by annotating the original data with the Satir model. These two sets form the Satir communication posture classification training set. Experimental results show that the success rate of classification of four inconsistent coping stances reached 70.37%, 75.92%, 83.33%, and 77.78%.

An adaptive region-segmentation based multi-focus image fusion method is presented using a Laplacian pyramid transform which decomposes the pre-registered source images into approximate and detail coefficients. In order to avoid the disadvantage of fixed-size blocks, the adaptive differential evolution scheme is designed to compute the optimal-size block. Firstly, with approximate coefficients, the optimal-size blocks are iteratively calculated by an adaptive differential evolution algorithm. The initial decision diagram is then completed by comparing the regional sum-modified Laplacian energy of two corresponding blocks after the regional sum-modified Laplacian energy is calculated. Secondly, the initial decision diagram is optimized by the guided image filter to obtain the final decision diagram in order to avoid the block effect of boundary. With the decision diagram, the approximate coefficients are fused using the weighted mean rules, while the detail coefficients are fused using the regional gradient energy method. Finally, an inverse Laplacian pyramid transform is used to reconstruct the fused approximate coefficients and fused detail coefficients, and to acquire the fused image where all objects are clear. The experimental result proves that the proposed method produces fusion images of fewer artifacts or additional noise, with higher computational efficiency. The proposed method is also superior to the other state-of-the-art methods in both subjective visual effect and objective quantitative evaluation indicators.

In this paper, the concepts and characteristics of SoftMan and SoftMan Group are introduced. And SoftMan is a virtual robot which has characteristics of distributed mobile agent, Robot and Artificial Life living in network. The characteristics of research platform for SoftMan are introduced. The model of SoftMan about fitness, physiological development, self-reproduce and sport is given, and architecture for SoftMan group based on Intelligent Autonomous Decentralized Systems is proposed. This architecture introduces inner data structures, function modules and database of SoftMan. At the same time, SoftMan communication model with multi-level hierarchical architecture on the basis of Client/Server Model and Peer-To-Peer Model is put forward. This model has the merits of the two models discussed above.

In this paper, the concept of HADS - humanized ADS is proposed on the basis of humanized systems and humanics, artificial life & its applications and SoftMan. The typical architecture and basic working mode of HADS is presented.

In the paper, we focus on exploring to construct a novel digital gas fields produce real-time scheduling system, which is based on the theory of intelligent autonomous decentralized system (IADS). The system combines the practical demand and the characteristics of the gas fields, and it aims at dealing with the real-time property, dynamic and complexity during gas fields produce scheduling. Besides embodying on-line intelligent expansion, intelligent fault tolerance and on-line intelligent maintenance of IADS particular properties, the scheme adequately attaches importance to the flexibility. The model & method based on intelligent information pull push (IIPP) and intelligent management (IM) is applied to the system, thus it is helpful to improve the performance of the scheduling system. In according with the current requirement of gas fields produce scheduling, the concrete solvent is put forward. The related system architecture and software structure is presented. An effective method of solving the real-time property was developed for use in scheduling of the gas field produce. We simulated some experiments, and the result demonstrates the validity of the system. At Last, we predict its promising research trend in the future of gas fields practice application.

On the basis of the concept of Intelligent Autonomous Decentralized Systems (IADS) and SoftMan, the organization structural model and coordination control schedule of SMC (SoftMan Community) is put forward, SM.man management and control strategy is designed and the coordination control characteristics, forms and strategy of SMC are discussed. At the same time, the interacting control mechanism of local SMC and remote SMC is discussed.

In order to realize proactive service, we design and improve relative fuzzy-neural approaches. Generally, the network can be classified into two. One is that fuzzy logic reasoning is completed by fuzzy weight in neural system. The other is that the input data must be fuzzified in the first or second level, but not weight. We discuss and study the second sort fuzzification in this paper. For proactive decision, fusion method based on fuzzy-neural can make Web-based intelligent system keep advantage of fuzzy logic system and remain adaptive optimum in proactive/attentive service. The correctness and validity of our new approach have been tested.