Research on system-of-systems combat simulation model formal specification and representation

To makesystem-of-systems combat simulation models easy to be developed and reused, simulation model formal specification and representation are researched. According to the view of system-of-systems combat simulation, and based on DEVS, the simulation model＇s fundamental formalisms are explored. It includes entity model, system-of-systems model and experiment model. It also presents rigorous formal specification. XML data exchange standard is combined to design the XML based language, SCSL, to support simulation model representation. The corresponding relationship between SCSL and simulation model formalism is discussed and the syntax and semantics of elements in SCSL are detailed. Based on simulation model formal specification, the abstract simulation algorithm is given and SCSL virtual machine, which is capable of automatically interpreting and executing simulation model represented by SCSL, is designed. Finally an application case is presented, which can show the validation of the theory and verification of SCSL.

Chaos game representation walk model for the protein sequences

A new chaos game representation of protein sequences based on the detailed hydrophobic-hydrophilic （HP） model has been proposed by Yu et al （Physica A 337（2004） 171）. A CGR-walk model is proposed based on the new CGR coordinates for the protein sequences from complete genomes in the present paper. The new CCR coordinates based on the detailed HP model are converted into a time series, and a long-memory ARFIMA（p, d, q） model is introduced into the protein sequence analysis. This model is applied to simulating real CCR-walk sequence data of twelve protein sequences. Remarkably long-range correlations are uncovered in the data and the results obtained from these models are reasonably consistent with those available from the ARFIMA（p, d, q） model.

A General Representation of Geometrical Solution Model for Predicting Ternary Thermodynamic Properties

The existing geometrical solution models for predicting ternary thermodynamic properties from relevant binary ones have been analysed,and a general representation was proposed in an integral form on the bases of these models.

Slicing and support structure generation for 3D printing directly on B-rep models

Traditional 3D printing is based on stereolithography or standard tessellation language models,which contain many redundant data and have low precision.This paper proposes a slicing and support structure generation algorithm for 3D printing directly on boundary representation(B-rep)models.First,surface slicing is performed by efficiently computing the intersection curves between the faces of the B-rep models and each slicing plane.Then,the normals of the B-rep models are used to detect where the support structures should be located and the support structures are generated.Experimental results show the efficiency and stability of our algorithm.

PRODUCT MODELING AND PROCESS PLANNING WITHIN CONCURRENT ENGINEERING ENVIRONMENT

A prodouct modeling and a process planning that are two essential basses of realizing concurrent engineering are investigated , a logical modeling technique , grammar representation scheme of technology knowledge and architecture of expert system for process planning within con- current engineering environment are proposed. They have been utilized in a real reaserch project.

ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.

An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services.As a result,it has gained significant attention and become a frontier in geoscience.Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and dimensions.Therefore,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG.However,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships.To address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge.On this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships.Under the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation.This model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval.It can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

Byte-Level Function-Associated Method for Malware Detection

The byte stream is widely used in malware detection due to its independence of reverse engineering.However,existing methods based on the byte stream implement an indiscriminate feature extraction strategy,which ignores the byte function difference in different segments and fails to achieve targeted feature extraction for various byte semantic representation modes,resulting in byte semantic confusion.To address this issue,an enhanced adversarial byte function associated method for malware backdoor attack is proposed in this paper by categorizing various function bytes into three functions involving structure,code,and data.The Minhash algorithm,grayscale mapping,and state transition probability statistics are then used to capture byte semantics from the perspectives of text signature,spatial structure,and statistical aspects,respectively,to increase the accuracy of byte semantic representation.Finally,the three-channel malware feature image is constructed based on different function byte semantics,and a convolutional neural network is applied for detection.Experiments on multiple data sets from 2018 to 2021 show that the method can effectively combine byte functions to achieve targeted feature extraction,avoid byte semantic confusion,and improve the accuracy of malware detection.

Evaluating Emergency Response Capacity of Chemical Industrial Park Using an Hybrid Fuzzy AHP Method

This paper presents an expert-based fuzzy analytic hierarchy process( AHP) model for evaluating emergency response capacity of Chemical Industrial Park( ERCCIP) by jointly using an improved fuzzy preference programming( FPP) and 2-tuple fuzzy linguistic approach. An evaluation index system for ERCCIP is proposed. The weight of sub-criteria and criteria of the evaluation index system for ERCCIP are determined using the improved FPP. And the ratings of sub-criteria are assessed in linguistic values according to the experts' subjective opinions. Finally,the aggregated ratings of criteria and the overall ERCCIP are calculated.

Early-warning signals for an outbreak of the influenza pandemic

Over the course of human history, influenza pandemics have been seen as major disasters, so studies on the influenza virus have become an important issue for many experts and scholars. Comprehensive research has been performed over the years on the biological properties, chemical characteristics, external environmental factors and other aspects of the virus, and some results have been achieved. Based on the chaos game representation walk model, this paper uses the time series analysis method to study the DNA sequences of the influenza virus from 1913 to 2010, and works out the early-warning signals indicator value for the outbreak of an influenza pandemic. The variances in the CCR wall〈 sequences for the pandemic years （or ＋ -1 to 2 years） are significantly higher than those for the adjacent years, while those in the non-pandemic years are usually smaller. In this way we can provide an influenza early-warning mechanism so that people can take precautions and be well prepared prior to a pandemic.

Advanced Geometric Modeler with Hybrid Representation

An advanced geometric modeler GEMS4.0 has been developed, in whichfeature representation is used at the highest level abstraction of a productmodel. Boundary representation is used at the bottom level, while CSG modelis adopted at the median level. A BRep data structure capable of modelingnon-manifold is adopted. NURBS representation is used for all curved surfaces.Quadric surfaces have dual representations consisting of their geometric datasuch as radius, center point, and center tals. Boundary representation of freeform surfaces is easily built by sweeping and skinning method with NURBSgeometry Set operations on curved solids with boundary representation areperformed by an evaluation process consisting of four steps. A file exchangefacility is provided for the conversion between product data described by STEPand product information generated by GEMS4.0

A Practical Approach to Constructing a Geological Knowledge Graph:A Case Study of Mineral Exploration Data

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new knowledge.Geological knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or processes.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.In this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling study.Based on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic relations.Finally,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph representation.The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

Geoscience knowledge graph in the big data era

Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means.It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph.Based on adopting the graph pattern of general knowledge representation,the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge,and integrates geoscience knowledge elements,such as map,text,and number,to establish an all-domain geoscience knowledge representation model.A federated,crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here,which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists.We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis,which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph.A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis,but also advance the construction of the high-precision geological time scale driven by big data,the compilation of intelligent maps driven by rules and data,and the geoscience knowledge evolution and reasoning analysis,among others.It will further expand the new directions of geoscience research driven by both data and knowledge,break new ground where geoscience,information science,and data science converge,realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.

Effects of Dynamic Vegetation on Global Climate Simulation Using the NCEP GFS and SSiB4/TRIFFID

Two global experiments were carried out to investigate the effects of dynamic vegetation processes on numerical climate simulations from 1948 to 2008.The NCEP Global Forecast System(GFS)was coupled with a biophysical model,the Simplified Simple Biosphere Model(SSi B)version 2(GFS/SSi B2),and it was also coupled with a biophysical and dynamic vegetation model,SSi B version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics(TRIFFID)(GFS/SSi B4/TRIFFID).The effects of dynamic vegetation processes on the simulation of precipitation,near-surface temperature,and the surface energy budget were identified on monthly and annual scales by assessing the GFS/SSi B4/TRIFFID and GFS/SSi B2 results against the satellite-derived leaf area index(LAI)and albedo and the observed land surface temperature and precipitation.The results show that compared with the GFS/SSiB2 model,the temporal correlation coefficients between the globally averaged monthly simulated LAI and the Global Inventory Monitoring and Modeling System(GIMMS)/Global Land Surface Satellite(GLASS)LAI in the GFS/SSi B4/TRIFFID simulation increased from 0.31/0.29(SSiB2)to 0.47/0.46(SSiB4).The correlation coefficients between the simulated and observed monthly mean near-surface air temperature increased from 0.50(Africa),0.35(Southeast Asia),and 0.39(South America)to 0.56,0.41,and 0.44,respectively.The correlation coefficients between the simulated and observed monthly mean precipitation increased from 0.19(Africa),0.22(South Asia),and 0.22(East Asia)to 0.25,0.27,and 0.28,respectively.The greatest improvement occurred over arid and semiarid areas.The spatiotemporal variability and changes in vegetation and ground surface albedo modeled by the GFS with a dynamic vegetation model were more consistent with the observations.The dynamic vegetation processes contributed to the surface energy and water balance and in turn,improved the annual variations in the simulated regional temperature and precipitation.The dynamic vegetation processes had the greatest influence on the spatiotemporal changes in the latent heat flux.This study shows that dynamic vegetation processes in earth system models significantly improve simulations of the climate mean status.