Intelligent System Research Articles

Design of a hazard prediction system with intelligent multimodal fusion based on artificial intelligence & internet of things technology: taking a crib as an example

Problem How to design an intelligent multimodal fusion hazard prediction system using AIoT (Artificial Intelligence & Internet of Things) technology to predict some potential dangers. This article will provide some ideas and methods. Introduction This article designs an intelligent multimodal fusion hazard prediction system based on AIoT technology. The system mainly consists of an IoT hardware device and an AI multimodal and multi-dimensional Hazard Prediction Algorithm. Method This article will take baby cribs as an example, using this system to empower traditional baby cribs and transform them into intelligent baby cribs. Results In this example, the system can detect the real-time status of the baby and predict upcoming dangers, including kicking the quilt, wetting the bed, fever, crying, climbing over the crib, and turning over. Hypothesis Assuming that intelligent systems can detect the status of infants in real-time and make predictions before potential dangers occur, to promptly alert parents. Perhaps it can prevent some irreversible dangers from occurring. Background This system solves the pain point problem of parents having to take care of their babies after working hard. Purpose In this scenario, the system provides a design scheme for an intelligent multimodal fusion hazard prediction system with a temporary care function. The application of this system to baby cribs not only reduces the burden on parents but also ensures the safety and comfort of the baby. Transferability At the same time, the system has transferability, and its design concept can be transferred to other application scenarios, such as hospital care for infants, young children, or elderly patients, as well as childcare stations for abandoned children in society. Significance This innovative system design scheme has a positive significance for family harmony and social development.

Development of the intelligent traffic light system based on image processing and fuzzy control techniques

In Viet Nam's current traffic conditions, congestion and jams—especially at intersections during peak hours—present major challenges. Traditional traffic light systems, which rely on fixed timing principles, often fail to manage traffic flow efficiently, particularly when vehicle density varies significantly across different directions. This research aims to develop an intelligent traffic light system where the signal timings automatically adjust based on the vehicle density at intersections. The study uses an object recognition algorithm to identify, classify, and count vehicles. The data was then fed into a fuzzy logic model to calculate the optimal signal timings. Experimental results demonstrate an accuracy of approximately 88% in vehicle detection. The fuzzy logic model and the programmable logic controller were able to effectively compute reasonable signal timings based on real-time vehicle density. Future developments include expanding the system's functionalities, creating a user-friendly interface, and developing a management application for mobile devices.

Advancing Artificial Intelligence: The Potential of Brain-Inspired Architectures and Neuromorphic Computing for Adaptive, Efficient Systems

Brain-inspired AI architecture, also known as neuromorphic computing, seeks to emulate the structure and functionality of the human brain to create more efficient, adaptive, and intelligent systems. Unlike traditional AI models that rely on conventional computing frameworks, brain-inspired architectures leverage neural networks and synapse-like connections to perform computations more similarly to biological brains. This approach offers significant advantages, including lower power consumption, improved learning capabilities, and enhanced problem-solving efficiency, particularly in tasks that require complex pattern recognition and cognitive processes. This paper explores the key components of brain-inspired AI architectures, such as spiking neural networks (SNNs) and neuromorphic hardware, and reviews the latest advancements in this field. We examine their applications across diverse domains, including robotics, autonomous systems, and medical diagnostics, where brain-like adaptability and real-time learning are critical. Additionally, we analyze the challenges associated with scaling these architectures, including hardware constraints and the complexity of accurately mimicking human brain functionality. The potential for combining brain-inspired AI with current machine learning models is also discussed, highlighting future directions for achieving more advanced, efficient, and human-like artificial intelligence systems. This research contributes to the growing body of knowledge on neuromorphic computing and its promise in shaping the future of AI technologies.Moreover, brain-inspired AI architectures have the potential to surpass traditional AI systems in terms of real-time decision-making and learning efficiency, particularly in environments that require adaptive behavior. The use of spiking neural networks (SNNs) in these architectures allows for more biologically plausible models of neuron activity, which can lead to advancements in sensory processing and autonomous decision-making. As neuromorphic hardware continues to evolve, integrating it with existing AI frameworks could enhance both performance and scalability. However, replicating the brain's full complexity remains a significant challenge, particularly in terms of creating energy-efficient hardware capable of supporting large-scale neural networks. Despite these challenges, the development of brain-inspired AI promises to bridge the gap between artificial and human intelligence, offering transformative possibilities for various industries.

Can ChatGPT provide quality information about fever in children?

Artificial intelligence (AI) systems hold great promise in improving medical care and health problems. We aimed to evaluate the answers by asking the most frequently asked questions to ChatGPT for the prediction and treatment of fever, which is a major problem in children. The 50 questions most frequently asked about fever in children were determined, and we asked them to ChatGPT. We evaluated the responses using the quality and readability scales. While ChatGPT demonstrated good quality in its responses, the readability scale and the Patient Education Material Evaluation Tool (PEMAT) scale used with materials appearing on the site were also found to be successful. Among the scales in which we evaluated ChatGPT responses, a weak positive relationship was found between Gunning Fog (GFOG) and Simple Measure of Gobbledygook (SMOG) scores (r = 0.379) and a significant and positive relationship was found between FGL and SMOG scores (r = 0.899). This study sheds light on the quality and readability of information regarding the presentation of AI tools, such as ChatGPT, regarding fever, a common complaint in children. We determined that the answers to the most frequently asked questions about fire were high-quality, reliable, easy to read and understandable.

The Art of Dialogue: Synergistic Effects of Language Adaptation and Emotional Intelligence in AI Systems

Artificial intelligence (AI) systems are increasingly applied in areas such as voice assistants, customer service, and healthcare. However, current AI systems often lack semantic understanding and emotional expression capabilities when interacting with humans, leading to poor user experiences. To enable AI systems to converse more naturally with humans, it is necessary to synergistically optimize two key capabilities: language adaptation and emotional intelligence. This paper reviews the latest research progress in AI dialogue systems regarding language adaptation and emotional intelligence. In terms of language adaptation, this paper summarizes technologies such as personalized dialogue, knowledge-enhanced dialogue, and multi-turn contextual dialogue. In terms of emotional intelligence, this paper outlines work in areas such as emotion recognition, emotional dialogue generation, and empathetic dialogue. Additionally, this paper discusses strategies for synergistic optimization of language adaptation and emotional intelligence, as well as challenges in dialogue system evaluation. Finally, this paper looks ahead to future directions for AI dialogue systems.

A student trained convolutional neural network competing with a commercial AI software and experts in organ at risk segmentation

This retrospective, multi-centered study aimed to improve high-quality radiation treatment (RT) planning workflows by training and testing a Convolutional Neural Network (CNN) to perform auto segmentations of organs at risk (OAR) for prostate cancer (PCa) patients, specifically the bladder and rectum. The objective of this project was to develop a clinically applicable and robust artificial intelligence (AI) system to assist radiation oncologists in OAR segmentation. The CNN was trained using manual contours in CT-datasets from diagnostic 68Ga-PSMA-PET/CTs by a student, then validated (n = 30, PET/CTs) and tested (n = 16, planning CTs). Further segmentations were generated by a commercial artificial intelligence (cAI) software. The ground truth were manual contours from expert radiation oncologists. The performance was evaluated using the Dice-Sørensen Coefficient (DSC), visual analysis and a Turing test. The CNN yielded excellent results in both cohorts and OARs with a DSCmedian > 0.87, the cAI resulted in a DSC > 0.78. In the visual assessment, 67% (bladder) and 75% (rectum) of the segmentations were rated as acceptable for treatment planning. With a misclassification rate of 45.5% (bladder) and 51.1% (rectum), the CNN passed the Turing test. The metrics, visual assessment and the Turing test confirmed the clinical applicability and therefore the support in clinical routine.

A GUI Based Application for Breast Cancer Diagnosis from Histopathology Images Using a Sequential Convolutional Neural Network Model

Breast cancer is the most common type of cancer among women globally. Automated breast cancer diagnosis improves healthcare by saving time and providing valuable assistance to pathologists. The focus of this research is the development of a deep learning framework named BCnet (Breast Cancer net), which aims to automatically diagnose breast cancer using histopathological images at various levels of magnification. We train the model from scratch using the BreakHis dataset, incorporating augmentation techniques. BCnet's performance is subsequently evaluated against current architectures that utilize the transfer learning approach. Furthermore, a Graphical User Interface (GUI) application was specifically designed for pathologists to use. BCnet was 95% accurate, while VGG-16, VGG-19, Xception, InceptionResnetV2, and Resnet152V2 were 78%, 79%, 77%, 79%, and 84% accurate, respectively. BCnet hence functions as an intelligent assistance system for pathologists and a potent tool for timely and precise identification, with the capacity to significantly influence patient outcomes.

Integration patterns in the use of metadata for data sense‐making during relevance evaluation: An interpretable deep learning‐based prediction

AbstractIntegrating diverse cues from metadata to make sense of retrieved data during relevance evaluation is a crucial yet challenging task for data searchers. However, this integrative task remains underexplored, impeding the development of effective strategies to address metadata's shortcomings in supporting this task. To address this issue, this study proposes the “Integrative Use of Metadata for Data Sense‐Making” (IUM‐DSM) model. This model provides an initial framework for understanding the integrative tasks performed by data searchers, focusing on their integration patterns and associated challenges. Experimental data were analyzed using an interpretable deep learning‐based prediction approach to validate this model. The findings offer preliminary support for the model, revealing that data searchers engage in integrative tasks to utilize metadata effectively for data sense‐making during relevance evaluation. They construct coherent mental representations of retrieved data by integrating systematic and heuristic cues from metadata through two distinct patterns: within‐category integration and across‐category integration. This study identifies key challenges: within‐category integration entails comparing, classifying, and connecting systematic or heuristic cues, while across‐category integration necessitates considerable effort to integrate cues from both categories. To support these integrative tasks, this study proposes strategies for mitigating these challenges by optimizing metadata layouts and developing intelligent data retrieval systems.

A Comprehensive of CPU and GPU Performance and Applications in Autonomous Vehicles

The rapid advancement of autonomous vehicle technology over the past decade has significantly increased the complexity of intelligent transportation systems. This need is clearly reflected in the requirements for high-performance computing in autonomous vehicles — a requirement that artificial intelligence, machine learning and big data analytic have all come to meet through their integration. We need the CPU and GPU power to handle processing in real-time data, sensor fusion and decision-making. We investigate how the CPU and GPU perform in an autonomous driving scenario, concluding that these tasks are complementary to one another describing where each can be best used within a heterogeneous computing architecture. The research delves into how these computing chips can be best used under the demand for real-time processing, reliability and efficiency. The study is to better understand the techniques for improving CPU-GPU collaboration, and applies these new findings on performance-intensive tasks like image recognition or track construction. The technical part involves conducting driving scenarios in the real world on three different types of CPU and several GPU, with key metrics such as processing latency, power consumption and accuracy. The actual experimental results show the advantages of GPU parallel computing and deep learning, while CPU still has a good advantage in multi-tasking ability and logical calculation. The results are important for the implementation of future autonomous driving systems, highlighting heterogeneous computing architectures as a means to optimize and support safe, effective vehicle operations.

Exploring action-oriented models via active inference for autonomous vehicles

Being able to robustly interact with and navigate a dynamic environment has been a long-standing challenge in intelligent transportation systems. Autonomous agents can use models that mimic the human brain to learn how to respond to other participants’ actions in the environment and proactively coordinate with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multimodality, and unobservant intents. Active inference may be defined as the Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior preference of the agent. It enables the agent to choose an action that leads to its preferred future observations. An exploring action-oriented model is introduced to address the inference complexity and solve the exploration–exploitation dilemma in unobserved environments. It is conducted by adapting active inference to an imitation learning approach and finding a theoretical connection between them. We present a multimodal self-awareness architecture for autonomous driving systems where the proposed techniques are evaluated on their ability to model proper driving behavior. Experimental results provide the basis for the intelligent driving system to make more human-like decisions and improve agent performance to avoid a collision.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Intelligence data acquisition based on embedded system in Chinese cuisine cooker (CCICR V1.0)

The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ C$$\\end{document} and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.

Research on trajectory planning based on optimal control modeling

With the advancement of autonomous driving technology, trajectory planning has emerged as one of the core technologies to achieve safe and efficient vehicle driving. An algorithm for single-vehicle trajectory planning based on the Optimal Control Problem (OCP) is proposed in this paper, which can not only plan a safe and efficient path for autonomous vehicles, but also realize the obstacle avoidance function. Firstly, the OCP is modeled for the cycle trajectory planning problem. Secondly, this paper transforms it into a Nonlinear Programming (NLP) with all discrete methods, and uses the interior point method to solve it. Then, the Y-type intersection is selected as the verification scene, and through a series of simulation experiments, the efficiency and safety of the algorithm in different driving environments are verified. The model and algorithm proposed in this paper are not only applicable to Y-type intersections, but also can be extended to other complex traffic roads, providing a new idea and method for trajectory planning in intelligent transportation systems.

Integration of lean manufacturing system with novel intuitive fuzzy syncretic lean frame work to improve the overall equipment effectiveness

Lean manufacturing embraces the principle of doing more with less by removing non-value-added activities from manufacturing processes to preserve effectiveness, flexibility, and profitability. Predictive maintenance in production stages are more critical since it involves machines which are prone to failure and a post failure maintenance stagnates the production which creates bottlenecks due to machine failures. Hence in this work a novel intuitive fuzzy syncretic lean frame work has been implemented to incorporate the need of intelligent systems. The framework is divided into phases which employs Fuzzy logic with embedded smart sensors for effective utilization of man machine and materials in order to improve the effectiveness of a production floor. A novel time based forecasting is done in the design phase which implements the lean tool Takt time. The manufacturing phase uses the sensors to determine the predictive maintenance of the machines thereby implementing continuous flow as the lean tool and the inspection phase uses smart sensor system for real time continuous monitoring of the machining process thereby incorporating Poka-yoke as a lean tool. Thus by implementing the framework the overall equipment effectiveness is achieved which helps in achieving continuous flow of products and defect free products in any production firm.

Research on Intelligent Energy System and Power Metering Optimization Based on Multi-objective Optimization Decision Algorithm

In this paper, the intricate problem of optimizing power metering within an intelligent energy system, utilizing a multi-objective optimization decision-making algorithm, is thoroughly explored. Given the current energy landscape, achieving efficient energy utilization and environmental sustainability has become a focal point of research. As a pivotal aspect of future energy management, the precision and optimization of power metering in intelligent energy systems directly influence the effectiveness and cost of energy consumption. To begin, this paper delves into the fundamental principles and application backdrop of intelligent energy systems, highlighting the significance of power metering in such systems. Subsequently, addressing the multi-objective optimization challenges in power metering, a novel optimization method based on a multi-objective optimization decision algorithm is introduced. This algorithm achieves comprehensive optimization of power metering, encompassing multiple objectives such as power cost reduction, enhanced energy efficiency, and environmental protection. The experimental results underscore the remarkable performance of this algorithm, which not only elevates the precision of power metering but also achieves substantial savings in energy costs and significantly boosts energy efficiency. Furthermore, the algorithm exhibits robust adaptability, making it capable of addressing power metering optimization challenges across diverse scenarios. Finally, this paper discusses the practical application prospects of the optimization algorithm in intelligent energy systems, and points out the direction of future research. The research in this paper provides new ideas and methods for the optimization of power metering in intelligent energy systems, and has important theoretical and practical significance for promoting the intelligence and refinement of energy management.

Intelligent energy management system of hydrogen based microgrid empowered by AI optimization technique

Microgrids powered by hydrogen often face challenges in effectively managing energy over an extended duration due to the intermittent nature of renewable energy sources (RES) and fluctuating loads. This research emphasizes the use of AI technologies, including machine learning to improve the efficiency of intelligent energy management system (IEMS), hydrogen storage, fuel cell consumption, and power system quality improvement. A novel approach integrating Isolation Forest Ensemble (IFE) and Modified Water Wave Optimization (MWWO) has been proposed to enhance energy management and to improve the power quality. This method combines the population size with precision and accuracy. The effectiveness of this study has been assessed against established methods (AVO, PSO & IPSO, MFO, SAG & XGBoost, MILP, and QTLBO) concerning numerous factors including component sizing, economics, reliability, and efficiency of microgrid. The precision level of the proposed technique is approximately 83 %. The primary benefit of this research is the improvement of power quality and cost-efficiency in hydrogen based microgrids, attained through optimized and intelligent energy management system. This article furnishes evidence indicating the superiority of the anticipated approach. This study utilizes MATLAB/Simulink and PyCharm framework for its investigation and projection.

Recent One Decade Trends in Mathematical Representation Research: Systematic Mapping Study

Research can change over time and can be influenced by technological developments, educational trends, and current social issues. Developments on trends in mathematical representation research over the past 10 years are discussed in this systematic mapping study (SMS). The question of review research with the systematic mapping study method is how the trend of mathematical representation research over the last 1 decade and what new topics have the opportunity to be researched by future researchers related to the themes of mathematical representation. The method used in this review is the PRISMA method which includes 5 stages carried out: 1) defining eligibility criteria, 2) determining information sources, 3) data selection, 4) data collection, and 5) data collection. In this systematic mapping study, it starts from searching for articles on the topic of mathematical representation during the last 1 decade, namely the range of 2013-2023, articles obtained from the scopus database in July 2023 with the keywords "representations" AND : Mathematics" initial data obtained 1907 articles after the exclusion process obtained 120 articles which were then analyzed using R-Studio and Vos-Viewer tools. The results of SMS obtained the relationship of representation topics in the last 10 years of research with the topics of visual, computer software, computer aid instruction, data mining, open source software, network architecture, natural language processing systems, intelligent systems and information analysis. In addition, research over the past 1 decade on the topic of representation has led more to data mining, security of data and STEM. Artificial Intelligence or artificial intelligence is the latest topic that over the past 3 years has grown in various articles that deal with the theme of mathematical representation and accompanied by the theme of multi-representation. The evolution of research topics related to mathematical representations that used to be only related to visual representations and image representations now leads to computer programs, programming algorithms and artificial intelligence, this shows that over the past 1 decade there has been an evolution of research in the implementation of representations in technology.

Breaking through the Key Challenges to Further Electrification of Public Transport in Beijing

In recent years, with the improvement of people's awareness of environmental protection and the increasingly serious problem of urban traffic congestion, the Beijing Municipal Government has actively promoted the development of green transportation, among which electric buses, as a low-carbon, environmentally friendly and energy-saving mode of transportation, are regarded as an important measure to improve air quality and reduce pollution emissions, which has attracted much attention. As electric buses grow, so do the operational challenges of the promoted electric buses, including high initial investment costs, inadequate charging infrastructure, technical limitations in terms of vehicle range and charging speed, and a lack of more supportive policies and regulations. This paper summarizes and analyzes the current situation of electric buses in Beijing. And proposes to solve the challenges encountered in the operation of electric buses by implementing targeted strategies. For example, optimize government financial subsidy measures and implement diversified financial support; The government supports companies to carry out technological innovation and invest in R&D to enhance electric vehicle technology; further promote the construction of charging infrastructure and improve the level of charging services; Effectively deploy battery recycling; promote the integrated development of intelligent vehicles and networking, and accelerate the construction of intelligent public transportation systems; strengthening international cooperation and resource sharing; and the development of a comprehensive policy to promote and facilitate the electrification of public transport in Beijing. Inject new impetus into Beijing's sustainable urban development.

Smart Irrigation System using IOT

The majority of nations rely heavily on agriculture to power their economies. A nation's growth is mostly dependent on agriculture, notably for people's livelihood and the production of food and other raw resources. The rise of agriculture is the main driver of a nation's economy. One type of irrigation system that is frequently utilized in the agricultural industry is the smart irrigation system. In areas of farming where water is scarce, irrigation is used. This intelligent irrigation system is designed with farmers in mind. In this paper, the proposed smart irrigation system uses NodeMCU ESP8266. This system reads the values using sensors and waters the plants based on those values. Also, the system will send notification as well as email to the owner for a certain interval of time depending on the owner specification. The owner can also remotely monitor values by using the mobile application or web application. It is very easy to implement, and will save manpower, leading to a cost-effective solution for efficient water management in agriculture. This system harnesses the power of a microcontroller to monitor and control irrigation process, ensuring optimal water usage.

Developed Internet of Vehicles Architecture: Communication, Big Data and Route Determination Perspectives

Internet of vehicles (IoV) has become an important research topic due to its direct effect on intelligent transportation systems (ITS) development. There are many challenges in the IoV environment, such as communication, big data and best route assigning. In this paper, an effective IoV architecture is proposed. This architecture has four main objectives. The first objective is to utilize a powerful communication scheme in which three tiers of coverage tools — Internet, satellite, high-altitude platform (HAP) — are utilized. Therefore, the vehicles maintain a continuous connection to the IoV environment everywhere. The second objective is to apply filtering and prioritization mechanisms to reduce the detrimental effects of IoV big data. The third objective is to assign the best route for a vehicle after determining its real-time priority. The fourth objective is to analyze the IoV data. The proposed architecture performance is measured using a simulation environment that is created by the NS-3 package. The simulation results proved that the proposed IoV architecture has a positive impact on the IoV environment according to the performance metrics: energy, success rate of route assignment, filtering effect, data loss, delay, usage of coverage tools and throughput.