Knowledge Base Research Articles

TRIPLE: A blockchain-based digital twin framework for cyber–physical systems security

Cyber–physical systems (CPSs) are being increasingly adopted for industrial applications, yet they involve a dynamic threat landscape that requires CPSs to adapt to emerging threats during their operation. Recently, digital twin (DT) technology (which refers to a virtual representation of a product, process, or environment) has emerged as a suitable candidate to address the security challenges faced by dynamic CPSs. DT has the capability of strengthening the security of CPSs by continuously mapping the physical to twin counterparts to detect inconsistencies. The existing DT-based security solutions are constrained by untrustworthy data dissemination as well as limited data sharing among the involved stakeholders, which, in turn, limit the ability of DTs to run accurate simulations or make valid decisions. To address these challenges, this paper proposes a modular framework called TRusted and Intelligent cyber-PhysicaL systEm (TRIPLE), that leverages blockchain, DTs, and threat intelligence (TI) to secure CPSs. The blockchain-based DT components in the framework provide data integrity, traceability, and availability for trusted DTs. Furthermore, to accurately and comprehensively model system states, the framework envisions fusing process knowledge for modeling DTs from system specification-based and learning-based information and other sources, including infrastructure-as-code (IaC) and knowledge base (KB). The framework also integrates TI for future-proofing against emerging threats, such that threats can be detected either reactively by mapping the behavior of physical and virtual spaces or proactively by TI and threat hunting. We demonstrate the viability of the framework through a proof of concept. Finally, we formally verify the TRIPLE framework to demonstrate its correctness and effectiveness in enhancing CPS security.

Research on the Practice of Ideological and Political Education in Mathematics Courses in Vocational Universities Based on Classroom-Training-Competition Mode

The purpose of this study is to explore how to effectively integrate ideological and political education into the mathematics curriculum by proposing a "combination of classroom teaching, practical training projects, and competition activities". By organically combining classroom teaching, practical training projects, and competition activities, it promotes students' learning of mathematical knowledge while enhancing their sense of social responsibility and professional ethics. Research results show that this model has significantly improved students' mathematics performance, especially in terms of basic knowledge and modelling ability. At the same time, by participating in mathematical modelling competitions and practical training, students have demonstrated strong teamwork spirit and innovation ability, and their professional ethics have been significantly improved. Through questionnaires and interviews, the feedback results show that students attach great importance to the practical value of the curriculum and ideological and political education. Finally, the research suggests that the integration of ideological and political elements into more mathematics courses should be explored in the future, in order to improve the quality of education and cultivate high-quality talents with a sense of social responsibility and professional ethics.

Development of a Knowledge Base for Construction Risk Assessments Using BERT and Graph Models

As a significant percentage of disasters and fatal accidents still occur in the construction sector, it is legally obligatory to conduct workplace risk assessments to avoid accidents and enhance safety. Identifying harmful and hazardous elements is crucial to discern the distinctive characteristics of potential accidents. However, conventional risk-assessment approaches, which rely on the skills and experience of safety managers, may overlook important factors, leading to inconsistencies in the procedures employed across different sites. Such unstructured safety knowledge reduces accessibility and utility, increases reliance on individual skills, and renders information management inefficient. Recently, the focus has shifted from efficient data storage to obtaining valuable knowledge tailored to specific use-cases. Knowledge-graph-based systems integrate and manage the relationships between knowledge entities, thereby enhancing the development of knowledge bases. Research on automatically extracting and managing predefined knowledge from various forms of data through natural language processing (NLP) is ongoing. This study proposes a novel method that uses NLP and graph models to automatically extract predefined knowledge from unstructured construction data and build an entity-relationship-based risk-assessment knowledge base. We developed an entity-name recognition and keyword-extraction engine that defines the core knowledge related to construction safety and risk assessments. This engine can automatically extract predefined knowledge from unstructured data by learning from NLP data. The extracted risk-assessment knowledsge was used to create a knowledge base, and its efficiency and effectiveness were validated through comparisons with existing methods. The results of this study are significant because they lay the foundation for an automatic knowledge-management system for construction safety and risk assessment, offering both practical and academic contributions to the field of construction safety.

26 Culturally informed developmental paediatric care: A qualitative study

Abstract Background Paediatricians provide developmental care through developmental surveillance, identification and management of specific developmental disorders, and health advocacy. Despite being one of the most common reasons to visit a paediatrician, the provision of care in this field is challenging as it intersects with medical, mental health, and educational systems. Additionally, the rise of international migration, inequities in health systems, and unique culturally based concepts of child development require paediatricians to have unique skills for culturally informed care provision in this area. Objectives A qualitative study was conducted to describe the perceptions of paediatricians on providing culturally informed developmental paediatric care to diverse populations. Design/Methods Semi-structured interviews were completed with paediatricians who primarily provide developmental care to understand the challenges, opportunities, and potential educational enhancements to improve culturally informed care for developmental paediatrics. Thematic analysis was used to analyze the transcribed interviews using inductive and deductive approaches. Results Three major themes emerged from 18 individual semi-structured interviews. The first theme, “cultural translation,” described language differences, interpretation, and trust-building between discordant physician/family dyads. The second theme, “inequity,” described systemic barriers that diverse populations experience, power distance in healthcare relationships, and pandemic-related amplifications of inequity. The third theme, “educational opportunities,” highlighted reflective practice opportunities, immersive experiences in diverse settings, and building a community of practice for paediatricians to build their knowledge base. Paediatricians practicing developmental paediatric care highlighted unique elements to the practice which may benefit from practice changes and curricular reform. Integration of meaningful training in the use of interpretation services, multicultural health brokers, and care coordination teams is a recommended intervention which is transferable to many disciplines. Peer-mentorship from colleagues of differing cultural backgrounds may help reduce race-based stereotypes and enhance perspective taking. Conclusion Paediatricians had a variety of perspectives on the provision of developmental paediatric care to diverse populations within Canada. There were a range of perceived opportunities for improvements in care noted on individual and systems levels. The development of infrastructure to build capacity in culturally informed developmental paediatric care to reduce culturally based health gaps is a needed future direction for developmental paediatrics.

Concept of a Cyber–Physical System for Control of a Self-Cleaning Aquaponic Unit

The article aims to present a cyber–physical system (CPS) to support the cultivation of aquaculture in a closed aquaponic system using the deep-water culture (DWC) method. The CPS uses precision sensors as TriOxmatic 700 IQ (for dissolved oxygen and water temperature), AmmoLyt Plus 700 IQ (for ammonium), NiCaVis 701 IQ NI (for nitrites and nitrates), SensoLyt® 700 IQ (for pH), and SL-M5 (for water level). It is built with a Raspberry Pi 4, 8 GB as a server, OpenHAB 3.0 software, and other specialized software for measuring water parameters. Some of the parameters are maintained completely autonomously, while others are indirectly controlled. Basic knowledge of hydroponics and aquaculture is required to set up the system, but day-to-day maintenance can be carried out by employees who receive instructions from the CPS. A method for the physical modification of the fish tank surface by using laser processing is proposed. This results in a change in surface topography (creating diverse microstructure patterns) and its roughness, which is of crucial importance for the bacterial adhesion mechanism.

Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms

Accurate burn severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where the vegetation experiences above-ground combustion but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush burn severity and woody vegetation post-burn coppicing/regeneration events in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.71% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high-severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine-scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas.

37 Adverse events associated with paediatric emergency medicine virtual clinic visits

Abstract Background Emergency medical care inherently carries a potential for adverse events. With the increase in virtual medical care due to its ease of access and ability to limit the spread of infection, there is a need to evaluate the safety of paediatric emergency medicine virtual clinics. Objectives To elucidate the frequency of adverse events occurring within 7 days of accessing medical care provided by a Paediatric Emergency Medicine Virtual Clinic. We then seek to characterize the severity, nature, and preventability of the adverse events and to provide a knowledge base for future work related to patient safety in the virtual emergency care domain. Design/Methods This study was a prospective cohort study where the primary outcome was the proportion of patients who experienced an adverse event within 7 days of accessing care through a virtual emergency clinic. Data was collected through telephone interviews and chart analysis with the use of a validated adverse events trigger tool to determine which charts required independent physician review. The final data was categorized using descriptive statistics, and logistic regression analysis was conducted to examine potential predictors of adverse events. Results Of 593 eligible families, 232 (39%) consented to participate and completed the interview process. Of these families, 89 were flagged for further review, and subsequent analysis concluded that 5 (2.2%) had experienced an adverse event. Of the adverse events, 4 (80%) were considered preventable and the most common type of adverse event was sub-optimal follow up (60%). The median patient age was 3.0 years (IQR 1.0-5.0). There were no clinically significant differences between the patients who experienced an adverse event and those that did not across the parameters studied (age, sex, gender, medical complexity, length of symptoms, language of fluency, and self-identification of visible minority status). Conclusion 2.2% of patients, approximately 1 in 45, experienced adverse events related to the care they received at the virtual clinic. The majority of these adverse events were preventable. That said, the rates of events were within the published norm, suggesting the virtual clinic provides a viable option in paediatric emergency medicine.

Sentiment Analysis of Chinese Ancient Poetry Based on Multidimensional Attention Under the Background of Digital Intelligence Integration

Abstract Poetry was a unique literary genre in ancient China as an important way to express sentiments. Chinese ancient poetry not only has simple words, strict meters and rich semantic relationships, but also widely uses rhetorical techniques such as simile and personification, as well as metaphorical means such as allusion and imagery, which makes it difficult to understand their implicit sentiments quickly and accurately. Therefore, this paper attempts to make full use of the semantic features of Chinese ancient poetry text and the knowledge feature of related domains under the guidance of the research paradigm of digital intelligence integration, and based on which, proposed Sentiment Analysis Model of Chinese Ancient Poetry based on Multidimensional Attention (SAMCAP). This model extracts semantic features from Chinese ancient poetry text, meanwhile designs a multidimensional attention to extract the knowledge feature from the knowledge base of Chinese ancient poetry. The recognition of Chinese ancient poetry sentiment is performed by integrating the textual feature and knowledge feature. Comparison experiments on the open ancient poetry corpus verified the effectiveness of the proposed model, and the ablation experiment explored the importance of different knowledge to the sentiment analysis result of Chinese ancient poetry.

Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization

The Biomedical Entity Normalization (BEN) task aims to align raw, unstructured medical entities to standard entities, thus promoting data coherence and facilitating better downstream medical applications. Recently, prompt learning methods have shown promising results in the natural language processing field. However, existing research falls short in tackling the more complex Chinese BEN task, especially in the few-shot scenario with limited medical data, and the vast potential of the external medical knowledge base has not yet been fully exploited. To address these challenges, this article proposes a novel Knowledge-injected Prompt Learning (PL-Knowledge) method. Specifically, the approach consists of five stages: candidate entity matching, knowledge extraction, knowledge encoding, knowledge injection, and prediction output. By effectively encoding the knowledge items contained in medical entities and incorporating them into tailor-made knowledge-injected templates, the additional knowledge enhances the model’s ability to capture latent relationships between medical entities, thus achieving a better match with the standard entities. Comprehensive experiments are conducted on a benchmark dataset in both few-shot and full-scale settings. This method outperforms existing baselines, with an average accuracy improvement of 12.96 percentage points in few-shot and 0.94 percentage points in full-data cases, showcasing its excellence in the BEN task.

Automatic Semantic Annotation of Indonesian Language Phrase Using N-Gram Language Model

Building semantic data populations in unstructured data or text is challenging. In this type of data, several problems can be raised, some of which are difficult to analyze. Some groups of words or expressions cannot be defined according to their meaning and can be a source of ambiguity. It can have a different meaning depending on the context of its use. This work aims to automatically annotate Indonesian Language text, especially phrases, with the existing knowledge base. The result is text with semantic markup. Machines can automatically process this type of text because it describes its meaning. This work applies an n-gram language model to identify meaningful phrases and defines them as a unit so that every existing word or phrase is automatically semantically tagged. This work uses the DBpedia and schema.org knowledge base. The percentage of successfully labeled data in this job was 78% with 84.95% accuracy using DBpedia and 5.9% with 97.46% accuracy using schema .org. Some factors affect the accuracy score, including the availability of the required data with the data contained in the knowledge base, the system's ability in the POS tagging process, and many new terminology and local cultures that have not yet been contained in the knowledge bases, especially schema.org that is utilized as a standard for all search engines. This work will help the machine understand the semantics of text data. All pages obtained will be semantically tagged and, therefore, will be understood by machines. This ability will support the following processes.

Installation of percutaneous endoscopic gastrostomy for children. The tactics of a nurse

The article is based on the data and experience of the Blokhin National Medical Research Center of Oncology of the Ministry of Health of the Russian Federation, obtained during endoscopic practice. Currently, the number of pediatric patients requiring installation of a percutaneous endoscopic gastrostomy is significantly increasing, due to the increase in treatment regimens and the expansion of the range of indications for its installation. PEG combines endoscopic and surgical stages. The nurse must know the difference and differences of each stage, as well as have all the basic and advanced knowledge about all indications, contraindications and any complications during and after surgery. Compliance by the nurse with all the rules of asepsis and antisepsis, knowledge of SANPIN, is the foundation of her professionalism. Interest in the process will undoubtedly influence the quality of medical care provided. The nurse must study and know all the instruments, consumables and additional materials for performing endoscopic surgery. Her clear actions and orientation in what is happening guarantees the speed and success of the manipulation. The purpose of this work will be to emphasize the importance of professional development for nurses. To achieve the goal, we use all facets of a nurse’s work: knowledge of the concept of «sterility», orientation in all stages of manipulation from preparing the room to its complete routine cleaning after the intervention, all theoretical knowledge about the reasons for installing a gastrostomy tube and free orientation in any types of complications, etc. All of the above guarantees that the operating room endoscopic nurse is interested in the process, which will undoubtedly affect the quality and convenience of the doctor’s work, and subsequently the patient’s quality of life. The main goal of the entire operating team is to improve the patient’s quality of life. Understanding the meaning of all surgical endoscopic interventions obliges the nurse to constantly improve their skills and engage in self-education.

Cost-effective non-revenue water reduction: analysis through pilot activities in Kigali City, Rwanda

ABSTRACT Non-revenue water (NRW) is a major problem for water utilities, and its reduction has become an important challenge in ensuring a sustainable water supply. However, a lack of clarity regarding the cost–benefit analysis of investment in pilot activities to reduce NRW may hinder further investment in such projects. Thus, we attempted to identify strategies for effective NRW reduction and establish a method for estimating their cost-effectiveness. To this end, a project to strengthen the NRW management capacity and build a sustainable NRW reduction system was implemented in Kigali City, Rwanda, between 2016 and 2021. The project included four objectives: enhancement of the planning capacity for NRW reduction; acquisition of basic knowledge, skills, and techniques for NRW control; enhancement of staff capacity to execute NRW reduction measures; and establishment of a system to accurately measure NRW ratios. The project helped to formulate a 5-year plan for NRW reduction and 596 employees of Kigali City were trained for the same. The NRW ratios were reduced by 10 and 12% in the two pilot areas of the city. Cost–benefit analysis revealed the effectiveness of the investment in NRW reduction activities over time.

METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

Respiratory Residency: Establishing Excellence for the Newly Licensed Respiratory Therapist.

Workforce issues have highlighted the fact that newly graduated respiratory therapists (RTs) need more support during their transition to practice; however, there are few data on best practices for RT residency programs. The aim of this project was to evaluate the feasibility and effectiveness of a residency program for licensed RTs with < 1 y of experience post graduation. The approach to improve new graduate transition to practice included a streamlined clinical-based orientation, didactic learning, evidence-based practice plan, and incorporated simulation. Data were collected in a secure electronic database at a multi-campus health care system utilizing anonymous questionnaires. The participants responded to questions about demographic information, overall confidence, clinical skills, and knowledge base topic confidence. Participant responses reported an increase in their overall factor score measures for role confidence, clinical skills, and knowledge-based topics. The collected responses showed an increase in a wide range of clinical skills and other soft skills. These results indicate that a respiratory care residency program adds value to the training, overall well-being, and retention of the newly graduated RT.

Telesurgery a Systematic Literature Review and Future Directions.

To undertake a systematic review of the medical literature on telesurgery, with a key focus on identifying the key technical and non-technical themes searched in medical articles and to analyze gaps in the current knowledge base on telesurgery. It has now been over two decades since the first successful case of telesurgery and since this time there have been significant technological and telecommunications advancements. A systematic review of the literature was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Details of the protocol for this systematic review were registered on PROSPERO (CRD42024511530). In total, 102 unique references which were allocated into five categories; technical, cybersecurity, ethical, financial, regulatory considerations to discuss key themes. The vast majority of references were related to technical considerations which have demonstrated the feasibility of telesurgery. The non-technical considerations have a paucity of literature and a lack of guidance on telesurgery which appears to still be the major barriers to telesurgery. Telesurgery presents many interdisciplinary challenges, encompassing both important technical and non-technical (such as cybersecurity, ethical, financial, and regulatory) considerations. Further research, collaboration between stakeholders, collaborative community of experts, and the development of comprehensive consensus frameworks are essential steps toward the widespread adoption of telesurgery.

Development of Morality and Ethics in Educational Institutions under the Office of the Vocational Education Commission Pathum Thani Province

Background and Aims: Developing a nation to compete with advanced countries requires prioritizing human resource development, with education serving as the foundation of this progress. Quality education establishes a sustainable knowledge base, essential for achieving economic, social, and political growth. This study aims to: (1) evaluate the level of moral and ethical development among staff in vocational institutions governed by the Vocational Education Commission in Pathum Thani Province; (2) compare staff perspectives on moral and ethical growth based on variables such as gender, position, and age; and (3) identify effective strategies to enhance moral and ethical standards within these educational institutions in Pathum Thani Province. Methodology: The sample group for this research included 348 personnel from institutions under the Office of the Vocational Education Commission in Pathum Thani Province during the 2023 academic year, selected via stratified random sampling. Research tools comprised a questionnaire using a 5-point rating scale, which had an Item-Objective Congruence (IOC) value ranging from 0.80 to 1.00, a reliability coefficient of 0.83, and a structured interview. Data analysis utilized statistical methods such as frequency, percentage, mean, and standard deviation, along with t-tests, F-tests, and pairwise comparisons using Scheffe’s method. Results: The research results indicated that: (1) The overall level of morality and ethics development in educational institutions under the Office of the Vocational Education Commission in Pathum Thani Province was rated high across all dimensions, with consistently high ratings for each specific aspect. (2) Comparative analysis revealed significant differences in morality and ethics development based on gender and position at the 0.05 significance level, but no significant differences were found based on age. (3) Guidelines for enhancing morality and ethics emphasized systematic professional oversight, recognition of outstanding personnel, and structured curricula for moral and ethical growth. The Office of the Vocational Education Commission highlighted merit-based assessments grounded in moral principles, clear standards, and well-defined workloads. Additionally, educational administrators demonstrated effective leadership by modeling rational behavior, prioritizing reason over emotion.

Creating a biomedical knowledge base by addressing GPT inaccurate responses and benchmarking context.

We created GNQA, a generative pre-trained transformer (GPT) knowledge base driven by a performant retrieval augmented generation (RAG) with a focus on aging, dementia, Alzheimer's and diabetes. We uploaded a corpus of three thousand peer reviewed publications on these topics into the RAG. To address concerns about inaccurate responses and GPT 'hallucinations', we implemented a context provenance tracking mechanism that enables researchers to validate responses against the original material and to get references to the original papers. To assess the effectiveness of contextual information we collected evaluations and feedback from both domain expert users and 'citizen scientists' on the relevance of GPT responses. A key innovation of our study is automated evaluation by way of a RAG assessment system (RAGAS). RAGAS combines human expert assessment with AI-driven evaluation to measure the effectiveness of RAG systems. When evaluating the responses to their questions, human respondents give a "thumbs-up" 76% of the time. Meanwhile, RAGAS scores 90% on answer relevance on questions posed by experts. And when GPT-generates questions, RAGAS scores 74% on answer relevance. With RAGAS we created a benchmark that can be used to continuously assess the performance of our knowledge base. Full GNQA functionality is embedded in the free GeneNetwork.org web service, an open-source system containing over 25 years of experimental data on model organisms and human. The code developed for this study is published under a free and open-source software license at https://git.genenetwork.org/gn-ai/tree/README.md .

Knowledge of Cerebral Visual Impairment (CVI) in Children among Eye Care Professionals in Selangor and Kuala Lumpur, Malaysia

Objective: Cerebral Visual Impairment (CVI), a brain-based visual disorder that affects neural networks that process incoming visual information throughout the brain, poses challenges in diagnosis and managing visual impairment in children. With the prevalence rise in CVI cases among children, there is a growing demand for ophthalmologists and optometrists to address these conditions, emphasising the importance of improving knowledge and awareness for better visual rehabilitation. This study intends to quantify the level of understanding of CVI in children among eye care professionals in Selangor and Kuala Lumpur, Malaysia.Material and Methods: Two hundred thirty registered eye care professionals, including ophthalmologists and optometrists across Selangor and Kuala Lumpur, Malaysia, participated in this cross-sectional web-based survey. A standardised questionnaire was created through Google Forms and distributed via social media platforms between April and June 2022.Results: The finding showed that the eye care professionals have significantly basic knowledge regarding CVI in children (x2=369; df=2; p-value≤0.01). Most respondents correctly identified variable loss of vision, visual field defects, and abnormal behaviour as clinical features of CVI in children. Additionally, over half of the respondents selected magnetic resonance imaging (MRI) as the appropriate investigation choice.Conclusion: These findings indicate that eye care professionals have basic knowledge and awareness of CVI in children. More research is needed to enhance knowledge and raise awareness about CVI in children among eye-care professionals.

Design and Implementation of a Geographic Scenario Computation System Based on Knowledge Base

Abstract. The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.

Evolution of the “4-D Approach” to Dynamic Vision for Vehicles

Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its advantage is that only the last image of the sequence needs to be analyzed in detail to allow the full state vectors of moving objects, including their velocity components, to be reconstructed by the feedback of prediction errors. The vehicle carrying the cameras can, thus, together with conventional measurements, directly create a visualization of the situation encountered. In 1994, at the final demonstration of the project PROMETHEUS, two sedan vehicles using this approach were the only ones worldwide capable of driving autonomously in standard heavy traffic on three-lane Autoroutes near Paris at speeds up to 130 km/h (convoy driving, lane changes, passing). Up to ten vehicles nearby could be perceived. In this paper, the three-layer architecture of the perception system is reviewed. At the end of the 1990s, the system evolved from mere recognition of objects in motion, to understanding complex dynamic scenes by developing behavioral capabilities, like fast saccadic changes in the gaze direction for flexible concentration on objects of interest. By analyzing motion of objects over time, the situation for decision making was assessed. In the third-generation system “EMS-vision” behavioral capabilities of agents were represented on an abstract level for characterizing their potential behaviors. These maneuvers form an additional knowledge base. The system has proven capable of driving in networks of minor roads, including off-road sections, with avoidance of negative obstacles (ditches). Results are shown for road vehicle guidance. Potential transitions to a robot mind and to the now-favored CNN are touched on.