Intelligent Functions Research Articles

Power Flow Control Strategy of Wind Power Hybrid Power Grid Based on Digital Simulation Cloud Platform Architecture

Wind power hybrid is a comprehensive utilization method of new energy, which can greatly reduce energy pollution, but there is also the problem of tidal flow difference, so the power flow regulation of wind power energy is to be solved at present. In this paper, the digital simulation platform is used to comprehensively judge the grid-connected demand, collect data such as wind speed, power generation and energy storage, and regulate the power flow of the wind power hybrid grid in combination with the problem of wind speed randomness. Secondly, the intelligent control function is used to judge the wind turbine transmission power hybrid grid and the power flow of the distributed power grid to balance the stability of the overall power flow, voltage and current of the wind power hybrid power grid. The results show that the digital simulation cloud platform collects the power flow data of the wind power hybrid power grid, generates abnormal power flow points, and supplements the power flow through alternative methods, and its regulation and control effectiveness is greater than 90%, and the amplitude of maintaining the power flow is between 10~20%. The wind power grid's grid-connected power flow regulation time is within 10s, and there is no damage to the wind turbine and energy storage battery. Therefore, the digital simulation cloud platform can realize the power flow control of the wind power hybrid power grid, maintain its overall stability, and meet the needs of new energy grid expansion.

Coral bionic e-skin for motion monitoring and intelligent recognition of underwater communication commands assisted by deep learning

Continuous and dependable underwater motion monitoring and communication are crucial for ensuring the safety of swimming, divers and underwater workers. We have developed a coral-inspired piezoresistive electronic skin (Melamine sponge-poly(pyrrole)-polydimethylsiloxane, MS-ppy-PDMS e-skin) for motion monitoring and underwater communication. The microstructure of the coral-like poly(pyrrole) and the PDMS acting as “limestone” make MS-ppy-PDMS e-skin superhydrophobic, stable (up to 80,000 cycles and acid and alkali resistance), and sensitive over a wide pressure sensing range (1–2,000 kPa). Moreover, MS-ppy-PDMS e-skin exhibits remarkable breathability, photothermal antibacterial efficacy, and biocompatibility, thereby increasing the comfort of wear while maintaining its outstanding sensing functionality. MS-ppy-PDMS e-skin exhibited dependable electrical signals output in motion monitoring tests on land and underwater. Most crucially, the combination of underwater communication commands captured by MS-ppy-PDMS e-skin with a multilayer perceptron (MLP) neural network allows for rapid and precise analysis and recognition, achieving a 99.38% accuracy rate in identifying underwater communication commands. This enables the realization of intelligent perception function, and is expected to further advance the development of human brain-like cognitive systems. Overall, the coral bionic MS-ppy-PDMS e-skin demonstrates tremendous application potential in the fields of motion monitoring and underwater communication.

A regenerated cellulose fiber with high mechanical properties for temperature-adaptive thermal management

The escalating global population has led to a surge in waste textiles, posing a significant challenge in landfill management worldwide. In this work, ionic liquid 1-butyl-3-methylimidazole acetate ([Bmim]OAc) and DMF (N, n-dimethylformamide) were used as solvents to dissolve waste denim fabric, then vanadium dioxide (VO2) nanoparticles were introduced into the spinning solution, and cellulose fibers were regenerated by dry-wet spinning process, to promote the recycling of waste cotton fabric. Finally, regenerated cellulose fibers with high added value were prepared by dry-wet spinning. Through this innovative strategy, on the one hand, because VO2 can form a large number of hydrogen bonds between the regenerated cellulose molecules, and realize the cross-networking structure of the molecular chains inside the fiber, the mechanical properties of the regenerated cellulose fibers are enhanced. On the other hand, due to the thermal phase transformation characteristics of VO2, it also endows the regenerated cellulose fiber unique intelligent temperature control function. Compared with the pristine regenerated fiber, the tensile stress of the regenerated fiber after adding VO2 nanoparticles (F-VO2) increased by 25.6 %, reaching 158.68 MPa. In addition, the F-VO2 fibric provides excellent intelligent temperature control, reducing temperatures by up to 6.7 °C.

Application and Innovation of Information Technology in Rail Transit Courses in Higher Vocational Colleges

The innovation of information technology in the field of rail transit mainly includes big data analysis and intelligent decision making and the application of artificial intelligence technology. Through big data analysis and intelligent decision-making technology, the operational efficiency and passenger experience of rail transit can be improved. Artificial intelligence technology can realize intelligent functions such as train operation prediction, passenger demand analysis, and safety monitoring, so as to improve the safety and reliability of the rail transit system.

Design and Implementation of an Intelligent Ultrasonic System based on 51 Microcontroller

With the rapid development of microelectronics and intelligent technology, microcontroller units (MCUs) are increasingly applied in various fields. This paper proposes the design and implementation of an intelligent ultrasonic system based on a 51 microcontroller, integrating ultrasonic sensors, an LCD display, and a 51 MCU to achieve real-time distance measurement, data display, and intelligent control functions. The system utilizes a CD4069 ultrasonic sensor to transmit and receive ultrasonic waves, measuring the distance between the sensor and an object by calculating the time difference between transmission and reception. The measured distance data is processed and displayed on an LCD screen in real-time. Furthermore, the system features intelligent control capabilities, automatically adjusting and controlling devices based on the measured data, thereby enhancing the system's level of intelligence. The intelligent ultrasonic system based on the 51 microcontroller offers advantages such as low cost, high precision, and strong versatility. It can be widely used in various fields, including intelligent robots, obstacle avoidance systems, and automatic parking systems. The design and implementation of this system not only demonstrate innovative applications of ultrasonic ranging technology but also provide valuable references for the extensive application of microcontroller units in intelligent technology fields.

Sponge-like porous polyvinyl alcohol/chitosan-based hydrogel with integrated cushioning, pH-indicating and antibacterial functions

Developing a packaging material with integrated cushioning, intelligent and active functions is highly desired but remains challenging in the food industry. Here we show that a sponge-like porous hydrogel with pH-indicating and antibacterial additives can meet this requirement. We use polyvinyl alcohol and chitosan as the primary polymers to construct a hydrogel with hierarchical structures through a freeze-casting method in combination with salting-out treatment. The synergy of aggregated polymer chains and the sponge-like porous structure makes the hydrogel resilient and efficient in energy absorption. It also enables rapid movement of molecules/particles and fast reaction due to the large specific surface area of the pore structures and the large amount of free water in it, leading to a sensitive pH-indicating function. The hydrogel shows an obvious color variation within a wide pH range in 3 min. The silver nanoparticles are fixed in the dense polymer networks, enabling a lasting release of silver ions. The porous structure makes the silver ion reach the protected item in a short time, achieving an antibacterial effect against S. aureus and E. coli with little cytotoxicity. This work paves the way for fabricating multifunctional hydrogels for diverse advanced packaging systems.

Multiple hypothesis testing in cognitive IoT sensor network

Recent Internet of Things (IoT) research aims to develop generic objects to learn, reason, and perceive their environment. Therefore, a new area has emerged known as cognitive IoT (CIoT). The cognitive Internet of Things integrates IoT with intelligence and behaves as well as humans through intelligent functionality. Several inferential tasks in CIoT require multiple hypothesis testing. The situation becomes cumbersome when the data is massive and heterogeneous. Thus, this research suggests a novel technique for multiple-hypothesis testing that uses a copula function to deal effectively with massive heterogeneous data. In addition, these data may contain missing or corrupted entries. Hence, it introduced probabilistic clustering, which reduces model inefficiency and takes control over the false discovery rate (FDR). Most of the variance from each cluster was extracted using kernel principal component analysis (KPCA) to reduce the processing burden at the fusion centre. Subsequently, it computes the p-value of each cluster's first principal component data and employs the Bonferroni method for multiple hypothesis testing. Finally, this research study evaluates the performance of the proposed algorithm on six-month environmental data, revealing that the proposed technique is efficient in terms of accuracy and computation time compared to other methods in the presence of massive heterogeneous data.

The Analysis of Intelligent Functions Required for Inland Ships

Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China’s inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships.

An Educational Chatbot Using AI in Radiotherapy

The surge in demand for information in cancer centers and hospitals, particularly during the pandemic, overwhelmed the limited manpower available. To address this challenge, there arose a need to develop an educational chatbot tailored for diverse user groups in the field of radiotherapy, including patients and their families, the general public, and radiation staff. Objective: In response to the pressing clinical demands, the primary aim of this endeavor is to delve into the intricacies of designing an educational chatbot for radiotherapy using artificial intelligence.Methods: The chatbot is meticulously crafted using a dialogue tree and layered structure, seamlessly integrated with artificial intelligence functionalities, notably natural language processing (NLP). This adaptable chatbot can be deployed across various platforms, such as IBM Watson Assistant, and embedded in websites or diverse social media channels.Results: Employing a question-and-answer methodology, the chatbot adeptly engages users seeking information on radiotherapy, presenting an approachable and reassuring interface. Recognizing that users, often anxious, may struggle to articulate precise questions, the chatbot facilitates the interaction by offering a curated list of questions. The NLP system augments the chatbot's ability to discern user intent, ensuring the provision of accurate and targeted responses. Notably, the study reveals that functional features, including mathematical operations, are preferred in educational chatbots, necessitating routine updates to furnish fresh content and features.Conclusions: The study culminates in the affirmation that leveraging artificial intelligence facilitates the creation of an educational chatbot capable of disseminating information to users with diverse backgrounds in radiotherapy. Furthermore, the importance of rigorous testing and evaluation, informed by user feedback, is emphasized to iteratively enhance and refine the chatbot's performance. Keywords: AI, machine learning, NLP, chatbot, radiotherapy, IoT, healthcare.

Intelligent Liver Function Testing (iLFT): An Intelligent Laboratory Approach to Identifying Chronic Liver Disease.

The intelligent Liver Function Testing (iLFT) pathway is a novel, algorithm-based system which provides automated laboratory investigations and clinical feedback on abnormal liver function test (LFT) results from primary care. iLFT was introduced to NHS Tayside, Scotland, in August 2018 in response to vast numbers of abnormal LFTs, many of which were not appropriately investigated, coupled with rising mortality from chronic liver disease. Here, we outline the development and implementation of the iLFT pathway, considering the implications for the diagnostic laboratories, primary care services and specialist hepatology clinics. Additionally, we describe the utility, outcomes and evolution of iLFT, which was used over 11,000 times in its first three years alone. Finally, we will consider the future of iLFT and propose areas where similar 'intelligent' approaches could be used to add value to laboratory investigations.

Non-subjective artificial intelligence in the system of subject-object relations

Expansion of functional capabilities of artificial intelligence systems actualizes the question of the possibility of their autonomous activity aimed at cognition and creation of the world. A significant factor determining the properties of artificial intelligence is its subjectiveness. If the world is deterministic, its subjectiveness is not necessary to ensure the functionality of artificial intelligence. Subjectiveness, however, is necessary for the initialization of cognitive or creative activity. A reasonable question arises regarding the ability of non-subjective artificial intelligence to act as a subject of cognition. Studies show that two main forms of participation of non-subject artificial intelligence in the system of subject-object relations are possible: as part of an integrated subject of cognition, in which a human being-operator plays the leading role, or fully autonomous functioning of artificial intelligence initialized (formalized or non-formalized) by the tasks of satisfying human being's needs. The analysis of the properties of cognition by means of artificial intelligence and other cognitive systems shows that the ability to act as a subject of cognition is formed not at the formation of subjectness, but at a lower level - the level of formation of self-consciousness, available to relatively uncomplicated cognitive systems, both natural and artificial. The conclusion about the direct connection between self-consciousness and the ability to cognize follows from the definition of cognition in the logic of subject-object opposition, corresponding to the epistemological interpretation of subject-object relations. However, an alternative ontological interpretation is also possible, which identifies being and consciousness. Such an idealistic understanding of the world corresponds to the philosophy of irrationalism, in the framework of which a reliable answer to the question of the necessity of subjectiveness for solving intellectual problems cannot be obtained.

Codesign of FDI Attacks Detection, Isolation, and Mitigation for Complex Microgrid Systems: An HBF-NN-Based Approach.

The primary purpose of this article is to design an intelligent false data injection (FDI) attacks detection, isolation, and mitigation scheme for a class of complex microgrid systems with electric vehicles (EVs). First, a networked microgrid with an EV model is well established, which takes load disturbance, wind generation fluctuation, and FDI attacks into account so as to truly reflect the operation process of the complex system. Then, an intelligent hyper basis function neural network (HBF-NN) observer is designed to accurately estimate the state of the microgrids, learn, and reconstruct the possible attack signal online. Subsequently, a novel HBF-NN-based H∞ controller is skillfully designed to mitigate the negative impact of FDI attacks online, so as to ensure the normal operation of the complex systems in an unreliable network environment. Finally, a two-stage integrated intelligent detection and maintenance algorithm is summarized and one simulation is presented to provide tangible evidence of the feasibility and superiority of the proposed FDI attacks detection, isolation, and mitigation methodology.

Intelligent speech elderly rehabilitation learning assistance system based on deep learning and sensor networks

Recently, deep learning has been proved to significantly improve the quality of speech recognition. Convolutional neural networks are often used in speech recognition tasks because of their special network structure and powerful learning function. In order to solve the problem that the traditional convolutional neural network can not reflect the one-dimensional basic attributes of speech signal, this paper proposes to set the number of frames for one dimension of convolution kernel, and use one-dimensional model and two-dimensional convolutional network model for speech recognition. By moving the convolution kernel of time axis and frequency band, it can adapt to the time change of speech signal and maintain the correlation between frequency bands to a great extent. At the same time, this paper also discusses the speech signal preprocessing, feature parameter extraction and regularization algorithm. Due to the lack of hospital resources, the lag of information technology, the poor ability of accompanying and other reasons, the current accompanying service for elderly rehabilitation can not meet the needs of elderly patients. The rapid development and wide use of information technology provide opportunities for the optimization of health services for the elderly. In view of the difficulty of word memory and interpretation in current rehabilitation courses, a VR intelligent teaching system based on intelligent voice technology is developed. The application results show that the system can effectively improve the ability of language expression and word writing. At present, the system of intelligent speech function has not been completed, and lacks speech synthesis function. The next research will focus on the use of speech synthesis technology, in order to realize the man-machine dialogue between people and the system, and show a more real training situation.

人工智能技术在网络信息安全中的应用研究

With the continuous iterative development of computer technology, computer technology has been widely applied in various fields of real society, and artificial intelligence technology is also developing synchronously with computer technology. In the computer network environment, supporting artificial intelligence technology through programming techniques can mimic human neural networks and achieve various intelligent operation functions. However, in today’s society, the network environment is becoming increasingly complex, and the security of network information transmission is gradually being affected. Therefore, it is necessary to ensure the security of the network information environment through the improvement and application of artificial intelligence technology, in order to conduct deeper research on artificial intelligence technology and create a favorable objective environment for its application.

One model to use them all: training a segmentation model with complementary datasets

PurposeUnderstanding surgical scenes is crucial for computer-assisted surgery systems to provide intelligent assistance functionality. One way of achieving this is via scene segmentation using machine learning (ML). However, such ML models require large amounts of annotated training data, containing examples of all relevant object classes, which are rarely available. In this work, we propose a method to combine multiple partially annotated datasets, providing complementary annotations, into one model, enabling better scene segmentation and the use of multiple readily available datasets.MethodsOur method aims to combine available data with complementary labels by leveraging mutual exclusive properties to maximize information. Specifically, we propose to use positive annotations of other classes as negative samples and to exclude background pixels of these binary annotations, as we cannot tell if a positive prediction by the model is correct.ResultsWe evaluate our method by training a DeepLabV3 model on the publicly available Dresden Surgical Anatomy Dataset, which provides multiple subsets of binary segmented anatomical structures. Our approach successfully combines 6 classes into one model, significantly increasing the overall Dice Score by 4.4% compared to an ensemble of models trained on the classes individually. By including information on multiple classes, we were able to reduce the confusion between classes, e.g. a 24% drop for stomach and colon.ConclusionBy leveraging multiple datasets and applying mutual exclusion constraints, we developed a method that improves surgical scene segmentation performance without the need for fully annotated datasets. Our results demonstrate the feasibility of training a model on multiple complementary datasets. This paves the way for future work further alleviating the need for one specialized large, fully segmented dataset but instead the use of already existing datasets.

The Effects and Functionality of Artificial Intelligence in Human Resource Management

Artificial intelligence (AI) technology have revolutionised many elements of corporate operations in recent years, and human resource management (HRM) is no exception. The many functions and impacts of AI in the field of HRM are examined in this paper. This study attempts to shed light on how AI is changing traditional HR practices, the benefits it delivers, as well as the possible obstacles and ethical considerations it presents, by a thorough examination of the available research and empirical investigations. The study will examine how AI is used in HRM in a variety of contexts, such as hiring and selection procedures, performance management, employee engagement, and training and development programmes. It will look at how AI-powered solutions are improving accuracy and efficiency in HR tasks. Examples of these tools include chatbots for candidate conversation, resume screening algorithms, and predictive analytics for finding high-potential individuals. The project will also explore the ways in which AI may support inclusion and diversity in the workplace and enhance the work experience for employees. Additionally, the study will go over the possible drawbacks of implementing AI in HRM, including worries about algorithmic bias, data privacy, and the displacement of human labour. The ethical issues surrounding the use of AI to make critical HR decisions as well as the requirement for accountability and transparency in AI-driven processes will also be covered.

Research and design of urban flood control and drainage dispatching system

In order to solve the problem of frequent flood disasters in urban areas, water conservancy models such as hydrological forecast, flood simulation and operation decision-making were studied and constructed for the waterway-region-urban flood control and drainage engineering facilities, and urban flood control and drainage operation system was designed with the model as the core. The overall structure of the system is "five horizontal and two vertical". The "five horizontal" bottom up are respectively the intelligent perception layer, cloud infrastructure layer, data layer, service layer and application layer, and the "two vertical" are respectively the security system and the standard specification system. On this basis, the function customization of the system is completed to achieve comprehensive display, early warning and forecast, flood simulation, risk assessment, project scheduling and intelligent decision-making functions.

Solar Electric Wheel Chair By IJISRT

The idea of a solar electric wheelchair is presentedin this abstract. It is a state-of-the-art mobility solution that combines the convenience of an electric wheelchair with solar energy technology. With its increased independence and improved convenience, the solar electric wheelchair is made to give those with mobility disabilities with sustainable and environmentally friendly mobility. The wheelchair uses solar energy to power its electric motor through the use of lightweight and efficient solar panels, allowing users to travelfarther between charges. Furthermore, the abstract emphasises the use of intelligent functionalities and modifiable design components, guaranteeing maximum user comfort and flexibility. The solar electric wheelchair fosters better accessibility and autonomy for those with mobilityissues and is a trailblazing move towards environmentally conscientious and user-centric mobility solutions.

Development of Digit in Noise Test in Hindi.

The digit in the noise test is a simple hearing screening tool that can identify hearing loss at the early stage. This screening tool takes less time than traditional pure-tone audiometry. There is a scarcity of hearing health professionals with few resources for carrying out hearing screening on a large scale in India. Hence, a hearing screening tool for the Hindi-speaking population is needed. The study aimed to develop and validate the Digit in Noise test in Hindi (DIN-H). A native Hindi female speaker recorded single digits from 1 to 9 made into triplet combinations, which were binaurally presented to 20 normal hearing subjects having hearing thresholds less than 25 dBHL from 250 to 8000Hz in the presence of broadband speech-shaped noise. The digit triplets were homogenized using speech intelligibility function for similar difficulty levels across stimuli. The homogenized stimuli were evaluated by obtaining the Speech Recognition Threshold (SRT) of 20 normal hearing subjects. The Mean SRT was - 10.4 and - 11.3 dB before and after homogenization. A strong positive correlation existed between test and retest SRTs (0.78). Mean SRT and slope obtained before and after optimization were comparable to other languages like Korean and English. This test can act as a reliable screening tool for assessing individuals. The test was administered to 106 normal hearing participants. The 95th percentile of the SRT value obtained was - 5.6 dB, which was kept as a cut-off score for the screening test. DIN-H can be used as a screening tool for assessing the integrity of the auditory system on a large scale in less time for the Hindi-speaking population.

Application of IoT Android voice assistant based on sensor networks in higher education network mode

In order to make full use of the potential of wireless sensor networks in higher education, this study aims to provide convenient intelligent learning and management functions by constructing an iot Android voice assistant based on wireless sensor networks. The system structure of Android voice assistant based on wireless sensor network is researched, designed and implemented. Through the integration of wireless sensor network and school network platform, the data collection, transmission and analysis functions are realized. The results show that the Android voice assistant based on wireless sensor network effectively provides intelligent learning and management functions in the network mode of higher education. Students and teachers can realize course inquiry, resource acquisition, teaching evaluation and other operations through voice commands to improve learning and teaching efficiency. By combining the data acquisition capabilities of sensors and wireless transmission capabilities, the system provides students, teachers and school administrators with intelligent learning and management functions, which is expected to promote the further development of higher education network environment.