海战场栅格节点的接入与迁移管理技术研究*

针对海战场作战平台中设备繁杂、异构的特点，栅格网络架构将各类作战平台设备以节点形式进行管理，但现有研究中尚缺乏具体的节点接入与迁移实现的流程方法与技术。论文针对上述问题，提出了海战场栅格节点接入与迁移的一种标准化流程与技术的实现方法，并利用仿真软件进行了实例的仿真验证，验证了节点接入与迁移标准化流程的可行性，并证明了其在技术上的可实现性。     

A synergistic strategy for combining thesaurus-based and corpus-based approaches in building ontology for multilingual search engines

In this article we illustrate a methodology for building cross-language search engine. A synergistic approach between thesaurus-based approach and corpus-based approach is proposed. First, a bilingual ontology thesaurus is designed with respect to two languages: English and Spanish, where a simple bilingual listing of terms, phrases, concepts, and subconcepts is built. Second, term vector translation is used – a statistical multilingual text retrieval techniques that maps statistical information about term use between languages (Ontology co-learning). These techniques map sets of t f id f term weights from one language to another. We also applied a query translation method to retrieve multilingual documents with an expansion technique for phrasal translation. Finally, we present our findings.     

Robust Mapping for Mobile Robot Based on Immobile Area Grid Map Considering Potential Moving Objects

Mobile robots need Simultaneous Localization and Mapping (SLAM) for autonomous movement in human living environments. The occupancy grid map used in SLAM is a conventional method which makes a map by an occupancy probability in each grid. This method renews a map based on whether an object is observed or not. In order to remove moving objects from a map, an additional method is required. However, conventional methods deal only with actually moving objects, and potential moving objects (e.g., standing humans) are mapped as static objects. Furthermore, only binary states, used or not used, are given to each object in map updating. This paper proposes the immobility area grid map to represent a map by an immobility probability in each grid. The proposed method renews a map based on the identification of observed objects by a robot's sensors, in addition to whether an object is observed or not. We introduce the map update parameter, which is set adaptively from the certainty of identification result of the object. Observed objects can take continuous states, truly static—unknown—truly moving, according to the parameter value. Potential moving objects are not mapped if the parameter takes values corresponding to moving objects. The experimental results show robust mapping in dynamic environments including potential moving objects.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

A hybrid optimization technique for proficient energy management in smart grid environment

Electrical energy is one of the key components for the development and sustainability of any nation. India is a developing country and blessed with a huge amount of renewable energy resources still there are various remote areas where the grid supply is rarely available. As electrical energy is the basic requirement, therefore it must be taken up on priority to exploit the available renewable energy resources integrated with storage devices like fuel cells and batteries for power generation and help the planners in providing the energy-efficient and alternative solution. This solution will not only meet electricity demand but also helps reduce greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. In this paper, a modified grey wolf optimizer approach is utilized to develop a hybrid microgrid based on available renewable energy resources considering modern power grid interactions. The proposed approach would be able to provide a robust and efficient microgrid that utilizes solar photovoltaic technology and wind energy conversion system. This approach integrates renewable resources with the meta-heuristic optimization algorithm for optimal dispatch of energy in grid-connected hybrid microgrid system. The proposed approach is mainly aimed to provide the optimal sizing of renewable energy-based microgrids based on the load profile according to time of use. To validate the proposed approach, a comparative study is also conducted through a case study and shows a significant savings of 30.88% and 49.99% of the rolling cost in comparison with fuzzy logic and mixed integer linear programming-based energy management system respectively.     

Prioritizing the alternatives of the natural gas grid conversion to hydrogen using a hybrid interval rough based Dombi MARCOS model

The aim of this study is to disclose an integrated multi-criteria decision making model as an instrument able to be used for hydrogen gas grid development via natural gas network conversion by considering four main criteria and sixteen sub-criteria. The proposed model consists of two stages using interval rough based Dombi MARCOS (Measurement Alternatives and Ranking according to the COmpromise Solution) method. In the first stage, the interval rough Bonferroni logarithmic methodology of additive weights is applied for determining the weight coefficients. In the second stage, the improved interval rough MARCOS method is used to rank the alternatives. A case study for hydrogen gas grid development in Romania set out the applicability and effectiveness of the proposed model. The proposed model is analysed from various points of view so that it can be used effectively in reality not only theoretically. This study analyzes four technical alternatives for hydrogen gas grid development. The results show that the conversion for mixtures natural gas with hydrogen a transporting a mix of gases is the best alternative among the four alternatives, for the next period and in the specific context described by this study. The current technical-economic criteria are rather a constraint when discussing gas infrastructures. For choosing a sustainable solution, a major role comes for decision makers, along with the desire to pay fairly from the consumer.     

Electric Vehicle Charging Simulation Framework Considering Traffic, User, and Power Grid

The traffic and user have significant impacts on the electric vehicle (EV) charging load but are not considered in the existing research. We propose a novel integrated simulation framework considering the traffic, the user, and power grid as well as the EV traveling, parking and charging based on cellular automaton (CA). The traffic is modeled by the traffic module of the proposed framework based on CA, while the power grid and user are modeled in the EV charging module. The traffic flow, user’s charging preference, user’s charging satisfaction, and the total supply capability (TSC) in the surveyed region are considered in the proposed framework. Two cases are carried out to show the interactions between the user and power grid. It is shown that the proposed framework can accurately simulate the interactions among traffic situation, user’s behavior and TSC， which are significantly lacking in the existing research. The proposed framework is scalable in considering additional interrelated elements.     

Specification uncertainty in modeling internet adoption: A developing city case analysis

Internet adoption fosters economic growth and development. Specifying policy control drivers is particularly relevant for developing countries. However, there is no consensus on the most relevant variables. We explored 33.6 million potential models to identify the most important determinants of household internet adoption using stochastic search variable selection and socioeconomic data from Medellín, Colombia. We found that monthly income, the head of household education and voting, and having a computer and cable television at home are the most relevant variables.     

Enriching Context Information for Entity Linking with Web Data

Journal of Computer Science and Technology - Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing...     

遗传算法和模矢法结合的概率积分法参数反演方法

遗传算法在反演概率积分法预计参数时从参数取值范围内的串集开始搜索,并使用弹性策略来维持群体的多样性,使得算法可以跨过局部收敛的障碍,向全局最优解方向进化;但这种概率化的寻优算法存在局部探索能力差、结果不稳定的缺陷,只能获得问题的近似最优解。模矢法是一种降梯度算法,算法局部探索能力强、收敛快;但这种算法对初值选取敏感,初值选择不当易陷入局部极值。本文提出并实现了一种模矢法与遗传算法结合的组合算法:先使用遗传算法求得参数的全局近似最优解,然后将近似最优解作为探索初值,使用模矢法获得参数的稳定、精确最优解。研究结果表明:组合算法反演概率积分法预计参数的精确度高、收敛快、稳定性好,综合性能较遗传算法和模矢法有明显优势。