Space station short-term mission planning using ontology modelling and time iteration

This paper studies the problem of the space station short-term mission planning, which aims to allocate the executing time of missions effectively, schedule the corresponding resources reasonably and arrange the time of the astronauts properly. A domain model is developed by using the ontology theory to describe the concepts, constraints and relations of the planning domain formally, abstractly and normatively. A method based on time iteration is adopted to solve the short-term planning problem. Meanwhile, the resolving strategies are proposed to resolve different kinds of conflicts induced by the constraints of power, heat, resource, astronaut and relationship. The proposed approach is evaluated in a test case with fifteen missions, thirteen resources and three astronauts. The results show that the developed domain ontology model is reasonable, and the time iteration method using the proposed resolving strategies can successfully obtain the plan satisfying all considered constraints.

Approach for analyzing,extracting and modeling e-government ontology

Taking the knowledge-intensive characteristics of governmental processes into account, an approach to analyzing, extracting and modeling e-government ontology by using both the IDEF5 ontology capture method and the web ontology language （OWL）, is presented. First, both knowledge-intensive activities and knowledge items can be identified by the analysis of governmental processes. Secondly, the IDEF5 ontology capture method is utilized to extract concepts, terms and statements from these knowledge items, which act as a starting point for ontology refinement and validation. To describe precisely the semantics of the ontologies, the OWL language is employed in our project to formally model these e-government ontologies with the help of Prot6ge-OWL tools. Finally, a case study about applying for social security cards （SSCs） in Shanghai local government is illustrated to demonstrate the effectiveness of the presented approach.

An ontology model to represent aquaponics 4.0 system’s knowledge

Aquaponics,one of the vertical farming methods,is a combination of aquaculture and hydroponics.To enhance the production capabilities of the aquaponics system and maxi-mize crop yield on a commercial level,integration of Industry 4.0 technologies is needed.Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics,internet of things,robotics,cloud computing,and artificial intelligence.The realization of aquaponics 4.0,however,requires an efficient flow and inte-gration of data due to the presence of complex biological processes.A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources.An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing,extracting,and sharing the domains’knowledge.In the field of agriculture,several ontologies are developed for the soil-based farming methods,but so far,no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model.Therefore,this study proposes a unified ontology model,AquaONT,to rep-resent and store the essential knowledge of an aquaponics 4.0 system.This ontology pro-vides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem.AquaONT is built from indoor vertical farming termi-nologies and is validated and implemented by considering experimental test cases related to environmental parameters,design configuration,and product quality.The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production,product quality,and facility layout of the aquaponics farm.For future work,a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.

Ontology Framework for Content-Based 3D Model Retrieval

The goal of the research on ontology framework for content-based 3D model retrieval is to develop a rich set of 3D model semantic representation so that both humans and machines can generate and understand model descriptions and processing for fast efficient retrieval from model collections. The purpose of ontology development for content-based 3D model retrieval is intended to describe model information regardless of storage, feature extraction and creation. The ontology includes the information on media features, low level visual descriptors, non media features of 3D model and their relationships. It is implemented in protege 3.1.

Knowledge Formalization about Perforated Stomach Ulcer on the Basis of Medical Ontology

The article defines knowledge about pelforated stomach ulcer that is formalized on the basis of an ontology model of medical diagnostics domain. The paper describes a base of observations for the disease and also knowledge base which determines a clinical presentation of the disease. The dependences on courses of the disease and process localization are taken into account during knowledge formalizing. The base of knowledge for the disease has the structure that is conventional for contemporary medicine. These knowledge will be used for building a medical intellectual system of for consulting and diagnostics.

A formal model for plastic human computer interfaces

The considerable and significant progress achieved in the design and development of new interaction devices between man and machine has enabled the emergence of various powerful and efficient input and/or output devices. Each of these new devices brings specific interaction modes. With the emergence of these devices, new interaction techniques and modes arise and new interaction capabilities are offered. New user interfaces need to be designed or former ones need to evolve. The design of so called plastic user interfaces contributes to handling such evolutions. The key requirement for the design of such a user interface is that the new obtained user interface shall be adapted to the application and have, at least, the same behavior as the previous （adapted） one. This paper proposes to address the problem of user interface evolution due to the introduction of new interaction devices and/or new interaction modes. More, precisely, we are interested by the study of the design process of a user interface resulting from the evolution of a former user interface due to the introduction of new devices and/or new interaction capabilities. We consider that interface behaviors are described by labelled transition systems and comparison between user interfaces is handled by an extended definition of the bi-simulation relationship to compare user interface behaviors when interaction modes are replaced by new ones.

Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system

Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features.These techniques are also being integrated into modern farming systems,such as aquaponics,to address the challenges hindering its commercialization and large-scale implementation.Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture,that promises to address food security issues.To complement the current research efforts,a methodology is proposed to automatically measure the morphological traits of crops such as width,length and area and estimate the effective plant spacing between grow channels.Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows.The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm.For common little gem romaine lettuce,the growth is estimated within 2 cm of error for both length and width.The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system.The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions.The proposed application finds its signifi-cance as a decision support system that can pave the way for intelligent system monitoring and control.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsuffcient precondition of the downstream in a new notion of Space Economy 4.0-Part 1:Problem background in Artificial General Intelligence(AGI)

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

Towards a Service-Oriented Architecture for a Mobile Assistive System with Real-time Environmental Sensing

With the growing aging population, age-related diseases have increased considerably over the years.In response to these, Ambient Assistive Living（AAL） systems are being developed and are continually evolving to enrich and support independent living. While most researchers investigate robust Activity Recognition（AR）techniques, this paper focuses on some of the architectural challenges of the AAL systems. This work proposes a system architecture that fuses varying software design patterns and integrates readily available hardware devices to create Wireless Sensor Networks（WSNs） for real-time applications. The system architecture brings together the Service-Oriented Architecture（SOA）, semantic web technologies, and other methods to address some of the shortcomings of the preceding system implementations using off-the-shelf and open source components. In order to validate the proposed architecture, a prototype is developed and tested positively to recognize basic user activities in real time. The system provides a base that can be further extended in many areas of AAL systems,including composite AR.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsufficient precondition of the downstream in a new notion of Space Economy 4.0-Part 2:Software developments

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.