MKEAH:Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering

Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.

Is“Intelligence”More Capable of Promoting the Development of the Tertiary Industry?-Analysis of the Impact of Artificial Intelligence on Different Categories of Industries

In the new scientific and technological revolution round,artificial intelligence(AI)technology has become a key leading force for industrial change.Research shows that AI not only promoted technical transformation and industry upgrades but also played a significant role in the rapid development of emerging industries.Based on the installed number of industrial robots and the industrial data by the National Bureau of Statistics,this study establishes a theoretical framework with the econometric model and compares the impact of AI on different categories of industries through empirical analysis.Our results show that AI not only promotes economic growth but also plays a key role in promoting the tertiary industry.Hence,optimization of industrial structure and economic upgrade can be induced.

Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the world.However,there is no consensus on why and what is Industry 5.0 yet.In this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development.We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies.Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

Knowledge-Based Classification in Automated Soil Mapping

A machine-learning approach was developed for automated building of knowledgebases for soil resources mapping by using a classification tree to generate knowledge from trainingdata. With this method, building a knowledge base for automated soil mapping was easier than usingthe conventional knowledge acquisition approach. The knowledge base built by classification tree wasused by the knowledge classifier to perform the soil type classification of Longyou County,Zhejiang Province, China using Landsat TM bi-temporal images and CIS data. To evaluate theperformance of the resultant knowledge bases, the classification results were compared to existingsoil map based on a field survey. The accuracy assessment and analysis of the resultant soil mapssuggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.

Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Based on the analysis of the characteristics and operation status of the process industry,as well as the development of the global intelligent manufacturing industry,a new mode of intelligent manufacturing for the process industry,namely,deep integration of industrial artificial intelligence and the Industrial Internet with the process industry,is proposed.This paper analyzes the development status of the existing three-tier structure of the process industry,which consists of the enterprise resource planning,the manufacturing execution system,and the process control system,and examines the decision-making,control,and operation management adopted by process enterprises.Based on this analysis,it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system.Finally,this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.

Knowledge-based bridge detection from SAR images

Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.

Industrial transformation and green production to reduce environmental emissions: Taking cement industry as a case

Industrial transformation and green production(ITGP) is a new 10-year international research initiative proposed by the Chinese National Committee for Future Earth. It is also an important theme for adapting and responding to global environmental change. Aiming at a thorough examination of the implementation of ITGP in China, this paper presents its objectives, its three major areas, and their progress so far. It also identifies the key elements of its management and proposes new perspectives on managing green transformation. For instance, we introduce a case study on cement industry that shows the positive policy effects of reducing backward production capacity on PCDD/Fs emissions. Finally,to develop different transformation scenarios for a green future, we propose four strategies: 1) policy integration for promoting green industry, 2)system innovation and a multidisciplinary approach, 3) collaborative governance with all potential stakeholders, and 4) managing uncertainty,risks, and long-time horizons.

New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Structural Topology Design of Container Ship Based on Knowledge-Based Engineering and Level Set Method

Knowledge-Based Engineering （KBE） is introduced into the ship structural design in this paper. From the implementation of KBE, the design solutions for both Rules Design Method （RDM） and Interpolation Design Method （IDM） are generated. The corresponding Finite Element （FE） models are generated. Topological design of the longitudinal structures is studied where the Gaussian Process （GP） is employed to build the surrogate model for FE analysis. Multi-objective optimization methods inspired by Pareto Front are used to reduce the design tank weight and outer surface area simultaneously. Additionally, an enhanced Level Set Method （LSM） which employs implicit algorithm is applied to the topological design of typical bracket plate which is used extensively in ship structures. Two different sets of boundary conditions are considered. The proposed methods show satisfactory efficiency and accuracy.

Selection of leading industries for coal resource cities based on coupling coordination of industry's technological innovation

Taking the city of Qitaihe is as an example in order to provide practical methods for the selection of leading industries for coal resource cities, this paper establishes the specific operation scheme for selection of leading industries according to the actual situation of coal resource cities based on the theory of coupling coordination together with the coupling coordination for technological innovation. The results show that the degree for coupling coordination of the technological innovation for each alternative industry differentiates with the development of coal resource cities. For example, the average degree of food processing industry from agricultural produce is 0.9. Therefore, coal resource cities should develop some industries related to coal industry, such as coking industry, some chemical and medicines industry and non-metallic mineral products manufacturing, in the near future, however, some industries with greater market and influence potentials and low carbon emissions should be attached much importance to in the future.

Comparison of the Three Cooperative Types of Agricultural Technology Popularization Based on the Industrialization of Tomato Industry in Xinjiang Autonomous Region

In the process of industrialization of tomato industry in Xinjiang, the cooperative models of rural households and tomato processing enterprises are order type, mediated type and workshop type. The contents, closeness degree and stability of cooperation of them are different. Under different cooperation models, the closeness degree of pillar industry and rural households differs, as well as the speed and effect of the technology promotion. By comparing the situation of technology promotion under the three cooperative models, the results can be obtained. The workshop type can reduce the risks of adopting new technologies of farmers greatly; strengthen the internal motivation of farmers to adopt new technology, so it can attract more farmers. Therefore, the workshop type represents the developmental direction of industrialization of tomato industry in Xinjiang to a certain extent.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Knowledge-based potentials in bioinformatics: From a physicist's viewpoint

Biological raw data are growing exponentially, providing a large amount of information on what life is. It is believed that potential functions and the rules governing protein behaviors can be revealed from analysis on known native structures of proteins. Many knowledge-based potentials for proteins have been proposed. Contrary to most existing review articles which mainly describe technical details and applications of various potential models, the main foci for the discussion here are ideas and concepts involving the construction of potentials, including the relation between free energy and energy, the additivity of potentials of mean force and some key issues in potential construction. Sequence analysis is briefly viewed from an energetic viewpoint.

A Knowledge-based System for the Analysis of the Ability of Paying back Loans

This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system developed focuses on what is bank loans risks management, how to prevent risk by the analysis of the ability of paying back loans. The paper makes the structural analysis involved in the system's decision situation, the structured situation diagram or model, dependency diagram and the document needed by the KBS prototype system thus are developed. Through testing the samples from loan business, the quality for the analysis of the ability of paying back loans can be effectively evaluated by the KBS prototype system.

Integration of Decision-Support and Knowledge-Based Techniques in a Problem-Solving Strategy for Selection Problems

The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Comparative study on industry agglomeration of the five E＆I industrial parks in Shaanxi Province

As the significant regional development driving, industry agglomeration has become one of the most interested for industrial economist and regional economist. This paper chooses the main five E＆I industrial parks in Shaanxi Province as the research object, and systematically compares their industry agglomerations, such as the situations, patterns, development approaches, and so on. Finally, based on the situation of the five E＆I industrial parks, combined with the power and the attraction of sub-sectors industry, some suggestions on the formation and consolidation of agglomeration effect for each park are proposed.

Industrial Poverty Alleviation Model in Deep Poverty-stricken Villages in the Dry-hot Valley of Jinsha River: A Case Study of Poverty Alleviation in the Green Prickleyash Planting Industry in Laopingzi Village,Luquan County

Industrial poverty alleviation is the core of poverty alleviation in rural areas of China,and it is the fundamental way for the rural poor to achieve stable income and poverty alleviation. Laopingzi Village,Jiaopingdu Town,Luquan County,Kunming County,Yunnan Province,located in the dry-hot valley area of Jinsha River,has become a typical deep poverty-stricken village due to its special natural conditions.In recent years,in the battle to win the fight against poverty,the people of Laopingzi Village have achieved a virtuous cycle of the ecological environment and an access to get rid of poverty and get rich through vigorously developing green prickleyash planting industry. By the end of 2018,the incidence of poverty in Laopingzi Village Committee dropped from 45. 62% in 2014 to 1. 11%,and the green prickleyash planting industry had achieved remarkable results in poverty alleviation. This article summarizes the specific practices of developing the green prickleyash planting industry in the village,analyzes the main results and successful experiences of the mode and discusses the inspiration of the implementation of green prickleyash cultivation on industrial poverty alleviation,so as to provide an effective practical example for the development and poverty alleviation of poverty-stricken areas.

Industrial transmission effect of international metal price shocks in perspective of industry chain

Based on a new perspective of industry chain and selecting monthly data from February2006to December2015,this paper chooses eight Chinese industrial sectors to construct a SVAR model reflecting internal relationships among metal chains,analyzes the direct effects and indirect effects of international metal prices on output of various links in metal chains,then it investigates the main transmission path of international metal price shocks through decomposing the inflation pressure sources in metal chains.The results show that international metal price shocks not only affect industrial output in a direct way,but also indirectly affect the growth of output through the increased pressure on industrial inflation and then triggering a tightening of monetary policy implementation.Affected by factors such as the lack of market demand and the price transmission mechanism blocking,the direct effects of international metal price shocks mainly impact the upstream and midstream industry,while the downstream industry is mainly affected by indirect effects;in addition,the international metal price shocks have spillover effects on the industrial inflation,and transmit along the industry chain from upstream to downstream,and their strength weakens in sequence.