Knowledge Based Systems and Metacognition in Radar

An airborne ground looking radar sensor's performance may be enhanced by selecting algorithms adaptively as the environment changes. A short description of an airborne intelligent radar system (AIRS) is presented with an in-depth description of the knowledge based filter and detection portions. A second level of artificial intelligence (AI) processing is presented that monitors, tests, and learns how to improve and control the first level. This approach is based upon metacognition.

This paper explains the ethical implications of artificial intelligence (AI) development towards achieving the level of artificial general intelligence (AGI) and analyzes the need for its social control. With the acceleration and intensification of AI growth and development, especially with the ongoing AI race, the transition from narrow AI to AGI becomes certain. The achievement of generative AI, which climaxes with chatbots (such as ChatGPT and others), transforms AI into a machine capable of creation. Although this AI application still appears relatively limited by algorithms, its learning ability is remarkable, and continuous advancements and the launch of increasingly sophisticated versions bring it ever closer to the AGI model. Each day brings us closer to that moment, which will signify AI’s transition from narrow AI to AGI. Unlike narrow AI, AGI deeply delves into the realm of ethics, and interpersonal and social relationships. Regulating AI-related policy and legally controlling AI represents one of the most serious and complex issues. In recent years, the community, led by corporate executives developing AI, prominent experts, researchers, scientists, writers, and other stakeholders, has made significant steps towards raising public awareness of the risks posed by advanced AI and making decisions, initiatives, and measures for monitoring, analyzing, and socially controlling the use of AI.

The real-time detection of objects and persons in their approach remains vital for applications that need safety measures security protocols and efficiency in autonomous systems and surveillance operations and industrial automation. The proposed research creates a durable real-time detection system through sensor combination using ultrasonic along with infrared and LiDAR and radar sensors and computer vision-based sensors to enhance detection accuracy and response times in changing environmental scenarios. The method starts by processing data gathered from arrays of multiple sensors through optimized filtering techniques and extraction methods before machines learn to classify objects while reducing false detection instances. The assessment of performance takes place in different environmental conditions to track both detection speed along with accuracy levels and system reliability. Sensor fusion enables real-time detection systems to perform better because it reduces the impact of environmental changes and sensor distortions. System capabilities increase in different operational areas when researchers demonstrate pre-processing optimization works alongside intelligent data processing systems. The research findings combine autonomous navigation systems with smart surveillance technology together with safety protocols for industrial security which enable real-time scalability and reduced risk elements. Research demonstrates a specific achievement through its analysis that machines learn better when combined with multi-sensor integration over single-sensor models which improves system reliability when used in real-world applications. Multi-sensor arrays and advanced processing techniques generate enhanced system responsiveness alongside increased adaptability and reliability thus improving detection systems within industrial security measures and automation operations.

Currently the NOAA Center for Operational Oceanographic Products and Services (CO-OPS) is transitioning the primary water level sensor at most NWLON stations from an acoustic ranging system to microwave radars. With no stilling well and higher resolution of the open sea surface, microwave radars have the potential to provide real-time wave measurements at NWLON sites. Radar sensors at tide stations may offer a low cost, convenient way to increase nearshore wave observational coverage throughout the U.S. to support navigational safety and ocean research applications. Here we present the results of a field study comparing wave height measurements from four radar water level sensors with two different signal types (pulse and continuous wave swept frequency modulation-CWFM). A nearby bottom acoustic wave and current sensor is used as a reference. An overview of field setup and sensors will be presented, along with an analysis of performance capabilities of each radar sensor. The study includes results from two successive field tests. In the first, we examine the performance from a pulse microwave radar (WaterLOG H-3611) and two CWFM (Miros SM-94 and Miros SM-140). While both types of radars tracked significant wave height well over the test period, the pulse radar had less success resolving high frequency wind wave energy and showed a high level of noise towards the low frequency end of the spectrum. The pulse WaterLOG radar limitations were most apparent during times of high winds and locally developing seas. The CWFM radars demonstrated greater capability to resolve those higher frequency energies while avoiding low frequency noise. The initial field test results motivated a second field test, focused on the comparison of wave height measurements from two pulse radar water level sensors, the WaterLOG H3611 and the Endress and Hauser Micropilot FMR240. Significant wave height measurements from both radar water level sensors compared well to reference AWAC measurements over the test period, but once again the WaterLOG radar did not adequately resolve wind wave energy in high frequency bands and showed noise towards the low frequency end of the spectrum. The E+H radar demonstrated greater capability to measure waves.

Since the birth of the subject of artificial intelligence, the long-term goal of this subject is to realize the intelligent system of human level. However, after nearly 60 years of development, no general intelligent system has been able to approach the level of human beings: it has the ability to cooperate with a variety of different cognitive abilities; it has a strong adaptive ability to complex environments; it has the ability of self-learning to new things and new environments. With the development of Brain and Neuroscience and Cognitive Science, it is possible to observe some activities of Brain Neural Networks under various cognitive tasks at different scales and obtain relevant data. Therefore, inspired by brain working mechanism, the development of Brain-like Intelligence has become a hotspot in the field of Artificial Intelligence and Computational Science in recent years. Machine Intelligence Brain-like Intelligence System (MIBS) inspired by cognitive behavior mechanism and realized by hardware and software cooperation is similar to human beings in information processing mechanism, cognitive behavior and intelligence level. The goal is to enable machines to realize various human cognitive abilities and their coordination mechanism, and ultimately reach or surpass the level of human intelligence. In this paper, we will study from the perspective of brain science, cognitive science and artificial intelligence. This paper reviews the history, current situation and research focus of brain-like intelligence research, and looks forward to its development direction, potential application fields and potential far-reaching impact.

The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems.

: If we want to build intelligent information retrieval systems, we will have to give them the capabilities of understanding Natural language, automatically organizing and reorganizing their memories, and using intelligent heuristics for searching their memories. These systems will have to analyze and understand both new text and Natural Language queries. In answering questions, they will have to direct memory search to reasonable places. This requires good organization of both the conceptual content of text and knowledge necessary for understanding those texts and accessing memory. The CYRUS and FRUMP systems (Kolodner (1978), Schank and Kolodner (1979), Dejong (1979)) comprise an information retrieval system called CyFr. Together, they have the analysis and retrieval capabilities mentioned above. FRUMP analysis news stories from the UPI wire for their conceptual content, and produces summaries of those stories. It sends summaries of stories about important people to CYRUS automatically adds those stories to its memory, and can then retrieve that information to answer question posed to it in natural language. This paper describes the problems involved in building such an intelligent system. It proposes solutions to some of those problems bases on recent research in Artificial Intelligence and Natural Language processing, and describes the CyFr system, which implements those solutions. The solutions we propose and implement are based on a model of human understanding and memory retrieval. (Author)

Human-computer interaction for virtual-real fusion

Artificial intelligence is starting to gain a strong position in various industries, and it can influence issues such as climate and environmental change. Artificial intelligence can enable a more comprehensive view on systemic issues, and can interpret and analyze data at a much faster pace than previous methods, benefitting individual companies and entire industries. With its ever-growing presence, artificial intelligence can be positioned as one aspect of the solution to sustainability, and can contribute to the improvements aimed at environmental, social, and governmental change. Environmental challenges are the primary sustainability branch that can be clearly influenced by the development of artificial intelligence. Digital Crust can be map out scenarios of different environmental factors influencing each other with machine learning, showing predictions of the possibilities of climate change. One major ongoing project is the EarthCube, which uses machine learning to construct a 3-D living and adaptable model of Earth.

This chapter aims to comprehend consumer perception towards Artificial Intelligence and Robotic Process Automation in the Indian Banking sector. This study focuses on consumers' awareness level and usability of Artificial Intelligence and RPA toward various banking services. The study will explore the scope of artificial intelligence (AI) and RPA implementation in the Indian banking sector for consumer satisfaction. Exploratory-cum-descriptive research design techniques were used to analyze the data with one sixty respondents collected from NCR through cluster sampling. The semi-structured questionnaire was framed based on the Likert scale. Descriptive, Exploratory Factor Analysis, t-test, ANOVA, correlation, and regression statistical techniques have been used to analyze the data. This study gives insights into the Indian banking industry to understand the behaviour of consumers regarding the accessibility and usage of Artificial Intelligence and RPA.

This article aims to systematically sort out the development trajectory of artificial intelligence technology from theory to application, and clarify the evolution of its core model, the current challenges and the future development trend, in order to provide a clear reference for in-depth research in this field. For this reason, this article first traces the development process and milestone events of artificial intelligence since the mid-20th century. Then, the core concepts and architectures of four representative model types (Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Transformer) and the successful application cases in key fields such as health care and finance are analyzed. In addition, through structured analysis, this article deeply explores the three core challenges faced by the current artificial intelligence system in the process of moving towards reliable and reliable deployment: model interpretability ("black box" problem), insufficient generalization ability and security vulnerabilities. In response to these problems, this article explores how key technical paths such as artificial intelligence, migration learning and federal learning can provide a blueprint for building the next generation of reliable artificial intelligence systems. Analysis shows that the development of artificial intelligence is shifting from the simple pursuit of performance breakthroughs to the construction of robust, safe and understandable intelligent systems. This transformation requires the coordinated development of algorithm innovation and ethical, legal and social frameworks.

Objective: This study aims to determine the relationship between nursing students' general attitudes toward artificial intelligence, literacy, and anxiety levels. Material and Methods: The study was conducted using a descriptive-correlation-seeking design. The sample consisted of 414 nursing students at a state university. The ''Personal Information Form'', ''General Attitude Towards Artificial Intelligence Scale (GAAIS)'', ''Artificial Intelligence Anxiety Scale (AIAS)'', and ''Artificial Intelligence Literacy Scale (AILS)'' were used as data collection tools. Results: The mean age of the students was 21.07±1.84 years; the majority were female (72.7%) and in their first year of education (30.9%). While 44.4% of the students' mothers and 44.0% of their fathers completed primary school 89.6% of the students' mothers were not working, and 78.7% of the students' fathers were working. The mean total scores of the students in the GAAIS, AIAS, and AILS were 65.87±9.42, 49.20±9.24 and 43.04±6.53, respectively. It was found that there was a statistically significant relationship between the GAAIS and the AIAS in a negative direction and a positive direction with the AILS (p=0.001). Conclusion: It was determined that students' general attitudes towards artificial intelligence and artificial intelligence literacy were high, and their artificial intelligence anxiety was at a medium level. Adding courses on artificial intelligence to all students at the university level will help them gain more knowledge about technological innovations and ensure awareness, which will help reduce anxiety levels while strengthening their literacy and general attitudes.

With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote "intelligent buildings" characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.

The relevance of research is due to the rapid development of artificial intelligence. It is an important technology that supports everyday social, technical, and economic activities. Artificial intelligence allows computers to learn from their own experience, adapt to set parameters, and perform tasks that were previously only possible for humans. In this regard, this article is aimed at identifying trends and prospects for the development of artificial intelligence. Another considerable task is to highlight the principles of building artificial intelligence systems. Developing an artificial intelligence system differs from building a conventional system as it requires a systematic approach, big data analysis, and model training. Building an artificial intelligence system − is a detailed process of reverse engineering human traits, capabilities of a machine, and using its computational power to surpass humans' skills. The leading approach to the study of this issue is literature analysis, which makes it possible to comprehensively consider artificial intelligence development. This article includes the modern foundations of artificial intelligence and various representative applications. In the context of the modern digital world, artificial intelligence is the property of machines, computer programmes and systems to perform intellectual and creative human functions, independently find ways to solve issues, be able to draw conclusions and make decisions. The research materials are of practical value for a critical analysis of current artificial intelligence capabilities, reasons why it still cannot achieve human intelligence, and the challenges it faces when achieving and surpassing the level of human intelligence

Artificial Intelligence (AI) is a branch of computer science that seeks to simulate certain aspects of human intelligence [1]. Specifically, it aims to enable computers and software to impersonate human cognitive functions such as thinking, planning, learning, communicating, perceiving the environment, and moving and manipulating objects [2-3]. Such activities are generally considered to require intelligence when performed by humans or other animals. Although AI has achieved significant success in many areas, it still has some limitations. The development of AI can be broadly categorized into three groups: Narrow AI, Artificial General Intelligence, and Artificial Superintelligence. Narrow AI can perform certain specific tasks at a narrow level of intelligence. General AI can perform any intellectual task in various domains that humans are capable of. Superintelligent AI can perform intellectual tasks surpassing human intelligence [2,4]. As the definition of intelligence is subjective and no clear consensus exists, these categorizations are based on possible distinctions rather than standards. Regardless of these limitations and classifications, current advances in AI have led to widespread usage in various sectors, including e-commerce, education, research, and service industries.