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Summary: This research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole. [ABSTRACT FROM AUTHOR]

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework. [ABSTRACT FROM AUTHOR]

In order to solve the dependence of convolutional neural networks (CNN) on large samples of training data, an intelligent fault diagnosis method based on spectral kurtosis (SK) and attention mechanism is proposed. Firstly, the SK algorithm is used to obtain two-dimensional fast kurtosis graphs from vibration signals, and the two-dimensional fast spectral kurtosis graphs are converted into one-dimensional kurtosis time-domain samples, which are used as the input of CNN. Then the channel attention module (CAM) is added to CNN, and the weight is increased in the channel domain to eliminate the interference of invalid features. The accuracy of fault identification can reach 99.8 % by applying the proposed method on the fault diagnosis experiment of rolling bearings. Compared with the traditional deep learning (DL) method, the proposed method not only has higher accuracy, but also has lower dependence on the number of samples. [ABSTRACT FROM AUTHOR]

Aiming at solving the problems of limited training data, single input information, and limited diagnostic accuracy under the influence of strong background noise in fault diagnosis of rotating machinery, this paper proposes a fault diagnosis method based on the combination of discriminant correlation analysis (DCA) and convolutional neural network (CNN). Firstly, the original vibration signal is divided into several segments in the time domain, and the training data is directly processed by one CNN branch to extract multi-scale time domain features. Simultaneously, the divided data is subjected to discrete wavelet transform (DWT), and processed by another branch of CNN to extract multi-scale time-frequency features. Then, the DCA feature fusion mechanism is adopted to fuse the two-domain features extracted in the parallel branches to improve the model' detection ability. Finally, the fused features are input into the deep CNN for training and learning to extract new features and output the classification results. Through the experimental analysis of two different types of data, the results show that the proposed method can be used for fault diagnosis of rotating machinery effectively. Compared with the single CNN network, the proposed method combines the multi-domain multi-scale feature extraction module with the DCA feature fusion module to enrich the feature information extraction ability. At the same time, the network performance is improved to get higher fault classification accuracy higher. [ABSTRACT FROM AUTHOR]

Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high‐fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current‐based method. [ABSTRACT FROM AUTHOR]

In this study, artificial neural networks (NN) are applied to the design of a transtibial prosthesis to adapt triplanar and bidirectional movements of human locomotion for rehabilitation. NN-based control is used because the prosthesis system is highly nonlinear and has variables with too many uncertainties caused by variations in ankle movements, weight damping, dorsiflexion, and flexion in the amputation area due to biological stimuli. To identify and detect these movements in the transtibial prosthesis, myoelectric signals are used that determine its position and adapt its trajectory through linear and rotary actuators. The input and desired parameters for the NN controller and the backpropagation algorithm are obtained based on the movements of the human ankle and foot based on their trajectory. The prototype is manufactured from different types of plastic using a 3D grapher, which can perform the main stages of human locomotion due to the learning carried out by the NN, reducing the risk of falls, and having a more comfortable and natural gait cycle in the rehabilitation of people. From the output response obtained from the NN controller, only a time delay is obtained without overshoot terms, and the trajectory tracking is adjusted. Simulation and experimental results show that the proposed NN-based control system can ensure the stability of the system and maintain good tracking of human locomotion. [ABSTRACT FROM AUTHOR]

Network disintegration is a fundamental issue in the field of complex networks, with its core in identifying critical nodes or sets and removing them to weaken network functionality. The research on this problem has significant strategic value and has increasingly attracted attention, including in controlling the spread of diseases and dismantling terrorist organizations. In this paper, we focus on the problem of network disintegration with discrete entity resources from the attack view, that is, optimizing resource allocation to maximize the effect of network disintegration. Specifically, we model the network disintegration problem with limited entity resources as a nonlinear optimization problem and prove its NP-hardness. Then, we design a method based on deep reinforcement learning (DRL), Net-Cracker, which transforms the two-stage entity resource and network node selection task into a single-stage object selection problem. Extensive experiments demonstrate that compared with the benchmark algorithm, Net-Cracker can improve the solution quality by about 8∼62%, while enabling a 30-to-160-fold speed up. Net-Cracker also exhibits strong generalization ability and can find better results in a near real-time manner even when the network scale is much larger than that in training data. [ABSTRACT FROM AUTHOR]

As the renewable energy integrates in the power system, the interactions between source, load, and storage equipment in the distribution network become frequent. The original distribution network could not meet the growing demand of the power grid. The use of controlled power electronic devices for flexible interconnection transformation of the distribution network could help improve the controllability, reliability, and safety of the power system and promote the consumption of distributed power sources. It is an important means of evolving towards future intelligent distribution networks. Typical devices for flexible interconnection are discussed of the operating characteristics. In addition, the core technologies of flexible interconnected distribution network system are introduced from the view of point of dispatching. Finally, to achieve a wider application of flexible interconnected distribution networks, the development trend is prospected. [ABSTRACT FROM AUTHOR]

MOTIVATION: With the enhancement of people's awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users' privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. OBJECTIVE: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users' personal information and security, to enhance the user trust and use experience, to better meet the needs of users. METHODS: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. RESULT: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. CONCLUSION: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy. [ABSTRACT FROM AUTHOR]

The intelligent network information systems, such as smart grid systems, face many security problems in the aspects of sensing, communication and computing. Information security risk assessment is an important way to assess the threats faced by information systems before risk events occur and ensure the security of assets. However, a comprehensive risk assessment of the system is a very resource-consuming process. Many existing risk assessment methods rely on a large number of experts and computing resources. Their assessment results are vulnerable to the differences in experts' subjective judgments. Therefore, we propose FRAMB, a novel man-machine collaborative risk assessment method based on fitting upper and lower bounds. Firstly, we present a risk assessment criterion including four categories and sixteen risk factors following the ISO/IEC 27005:2018 standard. On this basis, we present the DFAHP and CM-NN assessment models to obtain the upper and lower bounds of the risk assessment value, which provides a reference for expert assessment. FRAMB integrates the experts' assessment value and the values of upper and lower bounds, and adjusts the weights of these values to give the final risk assessment value. We introduce the risk assessment process of FRAMB in detail through a case study of the smart grid system risk assessment. We evaluate the effectiveness and accuracy of FRAMB through experiments. The experimental results show that FRAMB can effectively and accurately assess the security risks of the intelligent network information systems. [ABSTRACT FROM AUTHOR]

The internet of medical things (IoMT) has transformed the healthcare sector by facilitating real-time monitoring, remote patient care, and tailored healthcare solutions. However, the challenge of upholding a high standard quality of service (QoS) in IoMT implementations remains a pressing issue. This article delves into the possibilities of utilizing 5G networks and smart techniques to optimize QoS within IoMT systems. By capitalizing on the capabilities of 5G networks, including substantial bandwidth, minimal latency, and extensive connectivity, in conjunction with intelligent methods such as machine learning and predictive analytics, this paper introduces novel strategies to enhance QoS in IoMT environments. The summary underscores the advantages of these methods in elevating network dependability, diminishing latency, enhancing data transmission efficiency, and enabling resource allocation efficiency in IoMT deployments. Additionally, it explores the potential ramifications of these developments on healthcare outcomes, patient contentment, and the overall effectiveness of the healthcare system. The conclusions propose that by maximizing QoS through 5G networks and intelligent techniques, IoMT holds the potential to significantly enhance the delivery of healthcare services, fostering a more interconnected and efficient healthcare ecosystem in the process innovation. [ABSTRACT FROM AUTHOR]

In edge computing environments, limited storage and computational resources pose significant challenges to complex super-resolution network models. To address these challenges, we propose an agile super-resolution network via intelligent path selection (ASRN) that utilizes a policy network for dynamic path selection, thereby optimizing the inference process of super-resolution network models. Its primary objective is to substantially reduce the computational burden while maximally maintaining the super-resolution quality. To achieve this goal, a unique reward function is proposed to guide the policy network towards identifying optimal policies. The proposed ASRN not only streamlines the inference process but also significantly boosts inference speed on edge devices without compromising the quality of super-resolution images. Extensive experiments across multiple datasets confirm ASRN's remarkable ability to accelerate inference speeds while maintaining minimal performance degradation. Additionally, we explore the broad applicability and practical value of ASRN in various edge computing scenarios, indicating its widespread potential in this rapidly evolving domain. [ABSTRACT FROM AUTHOR]

In order to explore the application of neural networks in the intelligent protection of digital music copyrights, this paper initially investigates the key aspects of digital music-related supply chain management (SCM). Subsequently, this paper introduces a fuzzy, comprehensive evaluation-based method for authenticating digital music infringement. Furthermore, it employs a convolutional neural network to analyze digital music's source components. Within this context, an attention mechanism and an adaptive gate model are meticulously designed. Moreover, a selective adaptive cascade is implemented to optimize the structure of the multi-resolution coder–decoder. Through fuzzy authentication, it becomes feasible to accurately and reliably track and verify copyright information pertaining to digital music, thus providing an effective protective mechanism for the digital music industry. Experimental results reveal that in comparison with the SHN-4 series models, the network model embedded with the channel space (CS) attention module achieves improvements of up to 0.04 dB in terms of performance metrics for human voice and accompaniment sources. Furthermore, it outperforms these models by up to 0.19 dB in music source separation metrics. The incorporation of the adaptive attention mechanism profoundly enhances the model's performance in audio source separation tasks, benefiting both vocal and accompaniment sources. This improvement is evident in the form of a 0.44 SDR enhancement for vocal sources and a 0.18 SDR enhancement for accompaniment sources. Additionally, the proposed two-stage music separation-gate self-attention model surpasses the two-stage music separation-gate model in metrics associated with human voice and accompaniment sources. This outcome convincingly underscores the enhanced effectiveness achieved by integrating CS, self-attention, and adaptive connection in network performance, as well as the adaptive gate structure for music copyright protection. This approach heightens the model's autonomous selectivity, minimizes outliers, stabilizes waveform characteristics, and significantly contributes to the safeguarding of digital content. [ABSTRACT FROM AUTHOR]

Summary: Rapid intelligent transportation systems (ITS) innovations need a reliable MAC protocol to enable massive nonsafety message delivery and high‐priority safety broadcasts. The significant rise in spectrum need is regulated by collaboration in cognitive vehicular networks (CVNs). For QoS improvement, a reliable cooperative MAC for CVNs (CCVN‐MAC) is presented in this study. CCVN allows vehicles to collaborate and share channel status information, allowing for proactive channel switching in case of a legacy user (LU) appearance. To enable transmission mode selection, additional control signals are included. Using the suggested cooperative makeup technique, the helper nodes resend failed transmission. A Markov chain represents the protocol, and NS‐2 is used to evaluate it for several performance characteristics. Compared with conventional MAC techniques, the proposed protocol depicts improved performance, including depreciation of 70% in average latency and an increment of 42.4% in throughput. [ABSTRACT FROM AUTHOR]

Green water electrolysis holds promise for clean energy production. One low-cost variant, Dynamic Magnetic Field (DMF) assisted water electrolysis, shows high hydrogen evolution rates (HER). However, its efficiency diminishes at high magnet rotational speeds, posing a challenge to its widespread adoption. This study aims to enhance the efficiency of DMF-assisted water electrolysis by providing adaptive control for DMF, particularly at high magnet rotational speeds. We employ a Double Deep Q Learning (DDQN) based artificial intelligence (AI) system to design and implement this adaptive control. We utilized a Double Deep Q Learning (DDQN) based artificial intelligence (AI) system to design an adaptive control mechanism for DMF. The DDQN agent learned parameter tuning to adjust the rotational speed of the motor, with a magnet bar attached, for optimal performance. The optimum control behavior was achieved after 52 training episodes, and minor adjustments were made to ensure practical applicability. Our study reveals that the DDQN-based AI control is effective in continuously tuning the rotational speed of the motor with a magnet bar attached, triggering a bifurcation in the electrolyte. This adaptive control mechanism significantly improves and maintains the efficiency of DMF-assisted water electrolysis. The DDQN based AI control is effective to alter and maintain efficiency of DMF assisted water electrolysis. [Display omitted] • The hydrogen production of DDQN controlled DMF is superior to fixed RPM DMF. • DDQN AI provide realtime optimization of DMF electrolysis efficiency. • DDQN ensures correct and applicable control behavior after 52 episodes. • DDQN AI control optimizes DMF electrolysis to sustain the efficiency. • RPM tuning enhances performance by triggers electrolyte bifurcation. [ABSTRACT FROM AUTHOR]

Due to recent technical breakthroughs in wireless communication and the Internet of Things (IoT), the smart grid (SG) has been recognized as a next-generation network for intelligent and efficient electric power transmission. Electric vehicle charging is becoming one of the most popular SG application. However, in SG environment the communication between a vehicle user and smart meter is mostly performed using insecure channel for managing demand response during peak hours. This raises serious security and privacy issues. Motivated from the aforementioned challenges, this paper presents a secure and privacy-preserving framework for IoT-enabled SG environment. The proposed framework first uses a secure mutual authentication scheme to register and exchange session key among SG participants. Second, a deep learning method that uses a stacked sparse denoising autoencoder to convert data into a new encoded format is suggested. The attention-based truncated long short-term memory uses this modified data to identify intrusions. The proposed blockchain architecture uses the proof of authentication consensus mechanism to propagate regular transactions in order to validate data integrity and prevent data poisoning attacks. The suggested framework outperforms several current state-of-the-art solutions in terms of security and numerical discoveries. [ABSTRACT FROM AUTHOR]

Photoelectric sensor, with its high sensitivity and global instantaneous communication ability, has become an important technical support in intelligent higher education. The background of this research is the rapid development of higher education, the rapid progress of intelligent technology and the popularization and application of 5G computing network. This paper investigates how photoelectric sensors can be used to achieve more efficient teaching and learning methods in intelligent higher education. A 5G computer network model including initial mapping model and migration mapping model is constructed. The initial mapping model compresses and encrypts the data before sending, and converts it into a format suitable for transmission in the network, ensuring the security and transmission efficiency of the data. The migration mapping model performs secondary processing on the data when it arrives at the receiving end and converts it into the format acceptable to the receiving end to ensure that the data can be properly received and processed. The findings suggest that photoelectric sensors can be used for real-time monitoring and feedback during the teaching process to provide more accurate assessment results, thereby improving the quality of teaching, and can also be applied to virtual laboratories and distance education to provide students with a wider range of practical and learning opportunities. [ABSTRACT FROM AUTHOR]

COVID‐19 pandemic has impacted many nations, causing physical as well as mental health concerns globally. In most countries, governments enforced strict lockdowns and social distancing, thus affecting people's daily lives. People usually tweet their views on online platforms that is unstructured text with implicit meaning. With the evolution of artificial intelligence in the natural language processing domain, the prediction of sentiments accurately has become a challenge. To contribute as a solution to this, a hybrid approach is proposed for sentiment prediction with the use of an evolutionary‐based approach, transfer‐based learning and machine learning. The proposed approach uses bidirectional encoder representations from transformers (BERT) with genetic algorithm (GA) and support vector machine (SVM), namely, hybrid evolutionary intelligent model (GA‐BERT‐SVM). These approaches aid in extracting important features considering semantics and context present in the text. To avoid the limitations of the backpropagation approach, such as trapping in local minima and overfitting the data, the initial parameters (weights and biases) of the dense layers has been optimized using GA. Additionally, the pretrained BERT layers are utilized without any modification, following a standard transfer learning approach. The BERT embeddings are concatenated with the SVM for training and classification. GridSearchCV and GeneticSearchCV is used for obtaining optimal parameters of SVM. A multi‐classification problem is tackled using a benchmark COVID‐19 dataset, which comprises of Twitter data and is categorized into COVIDSENTI‐A, COVIDSENTI‐B, COVIDSENTI‐C and a combined dataset called COVIDSENTI. Experimental evaluation demonstrates promising results of the proposed model in terms of accuracy, F1‐score, precision and recall, surpassing state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Transferring data in the mobile ad hoc network can be enabled to analyze data transferring and the network that manages the data and route them into the VPN-based routing. Here is the process of maintaining the gateway for the analysis. The main problem here is the routing of the data packets, and the analysis of the nodes in the form of packages is the main issue in this study. To fix this, troubleshooting problems can be enabled for the packets which reach the destinations and the echo response. The primary technique used in this study is energy efficient geographic routing protocol and reward-based intelligent Ad hoc routing is used for the analysis. The energy-efficient geographic routing protocol enables the EGRPM method to reduce the sensor nodes and the WSN. This allows gathering the data and the nodes to maintain the geographic way. Reward-based intelligent Ad hoc routing is used in automatic decision-making, and the analysis of the system to produce the selection action for the research is reinforcement learning. This results from the study of the configuration and the analysis of the data in the ad hoc network. This enables the formation of learning about the routing protocol and facilitates the current data transfer to the research done in the ad hoc networks. This data analysis in the mobile network helps analyze the system and the entire data management. [ABSTRACT FROM AUTHOR]

In the era of Autonomous Networks (ANs), artificial intelligence (AI) plays a crucial role for their development in cellular networks, especially in 5G-and-beyond networks. The availability of high-quality networking datasets is one of the essential aspects for creating data-driven algorithms in network management and optimisation tasks. These datasets serve as the foundation for empowering AI algorithms to make informed decisions and optimise network resources efficiently. In this research work, we propose the IW-IB-5GNET networking dataset: an infrastructure-wide and intent-based dataset that is intended to be of use in research and development of network management and optimisation solutions in 5G-and-beyond networks. It is infrastructure wide due to the fact that the dataset includes information from all layers of the 5G network. It is also intent based as it is initiated based on predefined user intents. The proposed dataset has been generated in an emulated 5G network, with a wide deployment of network sensors for its creation. The IW-IB-5GNET dataset is promising to facilitate the development of autonomous and intelligent network management solutions that enhance network performance and optimisation. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis encounters the challenges of varying working conditions and sample class imbalance individually, but very few approaches address both challenges simultaneously. This article proposes an improvement network model named ICDAN-F, which can deal with fault diagnosis scenarios with class imbalance and working condition variations in an integrated way. First, Focal Loss, which was originally designed for target detection, is introduced to alleviate the sample class imbalance problem of fault diagnosis and emphasize the key features. Second, the domain discriminator is improved by the default ReLU activation function being replaced with Tanh so that useful negative value information can help extract transferable fault features. Extensive transfer experiments dealing with varying working conditions are conducted on two bearing fault datasets with the effect of class imbalance. The results show that the fault diagnosis performance of ICDAN-F outperforms several other widely used domain adaptation methods, achieving 99.76% and 96.76% fault diagnosis accuracies in Case 1 and Case 2, respectively, which predicts that ICDAN-F can handle both challenges in a cohesive manner. [ABSTRACT FROM AUTHOR]

As an important part of the energy transportation system, oil and gas pipelines are developing toward intelligence and digitalization. Vigorously developing and constructing pipeline networks based on intelligent methods such as big data and neural networks will help improve the efficiency of operation and management. This work provides an overview of recent developments of intelligent methods, including machine learning approaches, heuristic algorithms, and mathematical programming, used in the pipeline industry that can provide beneficial support for various applications enhancing operation and maintenance. Several aspects such as operating condition recognition, safety monitoring, pipeline remaining life prediction, and fault detection are reviewed and discussed. The study shows the need to focus on improving management level and ensuring pipeline safety. [ABSTRACT FROM AUTHOR]

With the development of IoT technology and the improvement of people's living standards, smart locks have become prevalent in ordinary households. However, for enterprises and institutions requiring unified management and control capabilities for networked smart locks, the technology has been relatively lagging, and there is a lack of established brands. Existing products are mostly based on wireless low-power network protocols, leading to common issues of low stability, limited network scalability, and high maintenance efforts. This paper addresses the problems existing in current networked lock systems and leverages the centralized office characteristics of enterprises and institutions. By integrating IoT technology and Power over Ethernet- (PoE-) centralized power supply, we have developed a networked smart lock system with unified management and control capabilities. This system is primarily designed for applications in offices, collective dormitories, and hotels of enterprises, institutions, and schools. The test results demonstrate that even with a scale of over tens of thousands of networked locks under unified control, the system maintains fast response times and low packet loss rates. It also incorporates energy-saving and environmentally friendly features, as well as easy automatic updates, facilitating convenient postdeployment maintenance. Thus, it effectively meets the requirements for centralized management and control in different application scenarios with centralized use of rooms. In conclusion, this research has led to the development of a unified management and control networked smart lock system, which is well suited for enterprises, institutions, and schools. Its scalability, speed, energy efficiency, and ease of maintenance make it an excellent solution for various application scenarios requiring centralized management and control of access. [ABSTRACT FROM AUTHOR]

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%. [ABSTRACT FROM AUTHOR]

The sixth generation (6G) represents the next evolution in wireless communication technology and is currently under research and development. It is expected to deliver faster speeds, reduced latency, and greater capacity compared to the current 5G wireless technology. 6G is envisioned as a technology capable of establishing a fully data‐driven network, proficient in analyzing and optimizing end‐to‐end behavior and handling massive volumes of real‐time data at rates of up to terabits per second (Tb/s). Moreover, 6G is designed to accommodate an average of 1000+ substantial connections per person over the course of a decade. The concept of a data‐driven network introduces a new service paradigm, which offers fresh opportunities for applications within 6G wireless communication and network design in the future. This paper aims to provide a survey of existing applications of 6G that are based on deep learning techniques. It also explores the potential, essential technologies, scenarios, challenges, and related topics associated with 6G. These aspects are crucial for meeting the requirements for the development of future intelligent networks. Furthermore, this work delves into various research gaps between deep learning and 6G that remain unexplored. Different potential deep learning applications for 6G networks, including privacy, security, environmentally friendly communication, sustainability, and various wireless applications, are discussed. Additionally, we shed light on the challenges and future trends in this field. This article is categorized under:Technologies > Computational IntelligenceFundamental Concepts of Data and Knowledge > Explainable AITechnologies > Machine Learning [ABSTRACT FROM AUTHOR]

This paper attempts to elucidate the transformative integration of computational techniques within power systems, underscoring their critical role in enhancing system modeling, control, and the efficient integration of renewable energy. It breaks down the two-sided nature of technological progress, highlighting both gains in operational efficiency and new challenges such as real-time processing, data management, and cybersecurity. Through meticulous analysis of query-based research patterns and mathematical frameworks, this study delves into the balancing act between specificity and breadth in scholarly inquiries while evaluating the impact and evolution of research trends through citation analysis. The convergence of interests and transient research trends is evident, particularly in Artificial Intelligence and optimization. This comprehensive narrative anticipates a sophisticated trajectory for power systems, advocating for continuous innovation and strategic research to foster sustainable, resilient, and intelligent energy networks. [ABSTRACT FROM AUTHOR]

In permanent magnet linear synchronous motor systems, the nonlinear system functions in the dynamic model are difficult to obtain accurately, which leads to the reduction of system control performance. In this paper, an adaptive recurrent neural network intelligent sliding mode control (ARNISMC) strategy is proposed. The sliding mode controller is designed to improve the robustness of the system. Secondly, considering the nonlinear system function in the dynamic model of linear motor, it is approximated by recursive radial basis function neural network (RRBFNN). Then, the weight of RRBFNN is learned online by the adaptive algorithm and the approximation error of the nonlinear function is robustly compensated. The stability and convergence of the closed-loop system are proved based on the Lyapunov theory. Finally, the experimental results verify that the proposed ARNISMC not only achieves strong robustness, but also has better control accuracy than the original sliding mode control and radial basis function neural network sliding mode control method. In addition, it also shows the advantages of intelligent control. [ABSTRACT FROM AUTHOR]

This article mainly focuses on the preprocessing method of medical heterogeneous equipment health data sources and the performance measurement of single-layer perceptron network intelligent computing. It structures a data quality evaluation system of medical heterogeneous equipment with five different dimensions: patient personal information, medical basic data, medical testing data, medical treatment data and medical device data. An innovative preprocessing algorithm of data sources is proposed to study the missing data, the error data, the repetition data and the validity data. By constructing a single-layer perceptron network, accuracy, misjudgment rate, precision, recall, true positive rate and false positive rate in intelligent computing are studied, and the corresponding mathematical calculation formulas are established. In the application research, this article collected 157 original data from a medical institution. The algorithm is applied and the models are tested. The research solved the problems of intelligent computing performance measurement of heterogeneous devices based on single-layer perceptron network. [ABSTRACT FROM AUTHOR]

A cognitive radar (CR) system can offer enhanced target tracking performance due to its intelligence on the perception‐action cycle, wherein a CR adaptively allocates the limited transmitting resources based on its perception of surrounding environments. To effectively manage the transmit waveform resource for the target tracking task, CR resource management problem is formulated under the partially observable Markov decision process framework. The sequential decision‐making and the inherent partial observability for target tracking problem are considered. In the proposed method, a long short‐term memory (LSTM)‐based twin delayed deep deterministic policy gradient (TD3) algorithm is developed to effectively solve the problem. A reward function is designed considering Haykin's cognitive executive attention mechanism for radar systems such that the CR resource management policy has stability in the decision of transmit waveform, which follows the principle of minimum disturbance. Simulation results demonstrate the superiority of the proposed LSTM memory‐based TD3 with improved target tracking performance and increased mean rewards for CR. [ABSTRACT FROM AUTHOR]

Our community is still far away from achieving self‐sustainable ambient intelligence, since it calls for rational energy layouts to satisfy the ubiquitous power demands from diverse terminal products. Harnessing energy directly from the surroundings thus provides ideal solutions. The majority of existing environmental harvesters rely on sophisticated procedures and expensive or toxic materials; while others attempt to streamline the complexity at the cost of compromising performance. This entails transducers that exhibit superb outputs and also employ cost‐effective, even recycled materials and straightforward protocols to render ubiquitous deployments. Here, a high‐efficiency droplet energy nanogenerator (DENG) is devised to satisfy all the requirements. The DENG is fabricated by directly coating a composite layer on a recycled digital video disk surface. It achieves superb electricity generation from one droplet, with an output voltage of >190 V at an instantaneous power density of 65 W m−2, and an energy conversion efficiency of 3.60%. Diverse demonstrations confirm the applicability of the DENG in environmental networks, encompassing self‐sustainable "on plants" sensing systems, smart building windows, and remote environmental monitoring platforms. In light of these superiorities, it is believed that the DENG may open up new alternative routes for future energy strategies. [ABSTRACT FROM AUTHOR]

Analytical diagnostics tools have been proved effective. However, these techniques are unable to be adaptively implemented for diverse machines with various defect modes. Big data-based diagnostics tools have been demonstrated efficient, but they exploit massive data with a high computational cost and time that is impractical for online condition monitoring. Furthermore, the health signals may be acquired under unseen working conditions in real machines. These are the major challenges for previous diagnostics methods and make them infeasible in real-world applications. So, a novel technique is proposed in this research to solve mentioned problems. The main novelty of this method is to fuse information of various processing functions for multi-sensor signals and to apply an efficient feature bank for performing an efficacious feature learning method. The other novelty of this research is to select transferable features for diagnostics of rotating machinery under unseen working conditions. Mean squared error function for the improvement of diagnostics accuracy and transferable feature space with semidefinite dimension for the improvement of diagnostics speed and better visualization for the source and target machines with different working conditions are minimized jointly via weighted neural networks. This approach is verified by two case studies of machinery vibration datasets for the robustness analysis of bearing multi-fault diagnosis problems under various operational conditions for the sensitivity analysis. Results indicate that the proposed algorithm achieves good performance in real rotating machinery diagnostics with unseen working conditions. [ABSTRACT FROM AUTHOR]

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research. [ABSTRACT FROM AUTHOR]

There are two key requirements for medical lesion image super-resolution reconstruction in intelligent healthcare systems: clarity and reality. Because only clear and real super-resolution medical images can effectively help doctors observe the lesions of the disease. The existing super-resolution methods based on pixel space optimization often lack high-frequency details which result in blurred detail features and unclear visual perception. Also, the super-resolution methods based on feature space optimization usually have artifacts or structural deformation in the generated image. This paper proposes a novel pyramidal feature multi-distillation network for super-resolution reconstruction of medical images in intelligent healthcare systems. Firstly, we design a multi-distillation block that combines pyramidal convolution and shallow residual block. Secondly, we construct a two-branch super-resolution network to optimize the visual perception quality of the super-resolution branch by fusing the information of the gradient map branch. Finally, we combine contextual loss and L1 loss in the gradient map branch to optimize the quality of visual perception and design the information entropy contrast-aware channel attention to give different weights to the feature map. Besides, we use an arbitrary scale upsampler to achieve super-resolution reconstruction at any scale factor. The experimental results show that the proposed super-resolution reconstruction method achieves superior performance compared to other methods in this work. [ABSTRACT FROM AUTHOR]

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research. [ABSTRACT FROM AUTHOR]

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%. [ABSTRACT FROM AUTHOR]

With the advancement in big data and cloud computing technology, we have witnessed tremendous developments in applying intelligent techniques in network operation and management. However, learning- and data-based solutions for network operation and maintenance cannot effectively adapt to the dynamic security situation or satisfy administrators' expectations alone. Anomaly detection of time-series monitoring indicators has been a major challenge for network administrative personnel. Monitored indicators in network operations are characterized by multiple instances with high dimensions and fluctuating time-series features and rely on system resource deployment and business environment variations. Hence, there is a growing consensus that conducting anomaly detection with machine intelligence under the operation and maintenance personnel's guidance is more effective than solely using learning and modeling. This paper intends to model the anomaly detection task as a Markov Decision Process and adopts the Double Deep Q-Network algorithm to train an anomaly detection agent, in which the multidimensional temporal convolution network is applied as the principal structure of the Q network and the interactive guidance information from the operation and maintenance personnel is introduced into the procedure to facilitate model convergence. Experimental results on the SMD dataset indicate that the proposed modeling and detection method achieves higher precision and recall rates compared to other learning-based methods. Our method achieves model optimization by using human–computer interactions continuously, which guarantees a faster and more consistent model training procedure and convergence. [ABSTRACT FROM AUTHOR]

With the development of 5G networks, cellular wireless networks are becoming more diverse and intelligent. As an important part of intelligent network management, wireless network traffic prediction has attracted more attention. Meanwhile, low delay communication is also an important part of the prediction task that needs to be considered. However, traditional deep learning models have many drawbacks in traffic prediction, such as excessive running time and computing resources. To address these issues, especially jointly considering effectiveness and efficiency, we propose a stacked broad learning system with multitask learning method for traffic flow prediction, called MTL-SBLS. Specifically, we use related tasks with similar change patterns as input to the prediction model and share more relevant features through multi-task learning. The multi-layer stacking structure of stacking broad learning system can effectively capture the traffic data features and ensure high prediction performance. When stacking new blocks, the fixed structure and weight of the underlying basic broad learning system blocks ensure that the newly generated stacking broad learning system still has a low computational cost. Finally, the experiments on three real data sets demonstrate that the MTL-SBLS model outperforms the other existing prediction methods (93.38% prediction accuracy on average). Furthermore, the MTL-SBLS model can maintain a running time of less than 10 sec on all three datasets, indicating that it is efficient. Thus, the MTL-SBLS model is proved to improve the accuracy of traffic flow prediction while maintaining low complexity and running time. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is a novel paradigm that consists of a wide network of machines and intelligent devices with the capability of communication and interaction with each other. One of the significant issues for the IoT is its healthcare applications. The positioning systems based on their environment can be classified into two main categories: Indoor and outdoor. Recently, IoT indoor positioning attracts too much attention among researchers for patient monitoring. Hence, in this paper, a stress‐free floor plan indoor positioning algorithm for patient monitoring is proposed, called THIP. The history of the patient's movement is stored in a trajectory history, and this information is employed to estimate the patient's more accurate position. The proposed THIP algorithm is simulated in MATLAB software. Several experiments are conducted to evaluate the performance of the proposed algorithm. Simulation results show that the proposed THIP algorithm raises patient positioning accuracy and declines the distance error cumulative compared to LiFS by about 4.68% and 60.6 m, respectively. Moreover, it can identify the floor level of the moving user. [ABSTRACT FROM AUTHOR]

Minimum of computational cost, bandwidth utility, energy conservation, and memory withstand ability are all aspects of service applications that need to be optimized to be met by a multi-core architecture. The modular nature of Field Programmable Gate Array (FPGA) computing allows for a customizable form factor to be offered to cater to the needs of individual users. Using a deep neural network, such as one obtained from a highly sophisticated optimized network, makes this possible. Consequently, this paper proposes a hybrid framework based on pruning and Int8 quantization that combines the strengths of Recurrent Neural Networks (RNNs) and Long Short-Term Memories (LSTMs). When validating the efficiency of an optimization function, the Golden Eagle Optimization (GEO) is applied to an analysis of the RNN-LSTM framework intelligent optimized network. The optimization algorithm is analysed to determine how best to aim at the GEO, while maintaining a good balance between speed and accuracy. The application of FPGA multi-core applications is hastened by employing machine learning optimization algorithms based on the Amazon Cloud FPGA EC2 instance platform and the ZCU104 FPGA hardware platform. Both FPGA computers produce satisfactory outcomes. Based on the experimental results, the RNN-LSTM framework-based GEO optimization technology can be used to optimize the performance of both FPGA platforms in terms of computational speed, energy consumption, and memory usage. High acceleration speeds can be attained with minimal loss of accuracy using a network structure optimized by pruning and Int8 quantization algorithms. [ABSTRACT FROM AUTHOR]

The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes into account the rational utility of alternatives as well as the emotional alternative attractiveness. The objective of this paper is the comparison of two multistep operational algorithms of the intelligent network: one based on discrete dynamics and the other on continuous dynamics. By means of numerical analysis, it is shown that, depending on the network parameters, the characteristic probabilities for continuous and discrete operations can exhibit either close or drastically different behavior. Thus, depending on which algorithm is employed, either discrete or continuous, theoretical predictions can be rather different, which does not allow for a uniquely defined description of practical problems. This finding is important for understanding which of the algorithms is more appropriate for the correct analysis of decision-making tasks. A discussion is given, revealing that the discrete operation seems to be more realistic for describing intelligent networks as well as affective artificial intelligence. [ABSTRACT FROM AUTHOR]

Information and large number of fault labels are required to achieve intelligent health status assessment of three-phase inverters. However, the current signals of inverters cannot be sufficiently collected since open-circuit faults (OCFs) occur briefly, which makes it difficult to determine the OCF mode of the various power switches. A transfer learning model that effectively uses a small amount of sample data to achieve domain adaptation is proposed to address this problem. First, collected fault-sensitive signals are subjected to a continuous wavelet transform (CWT) to obtain two-dimensional image data with more abundant fault feature information. Second, the source domain and target domain features are projected into the same feature space through a domain adversarial neural network (DANN) to achieve multi-domain feature extraction and adaptation. Then, in the feature extraction module of the DANN, the deep residual network (Resnet) structure is used to replace the typical convolutional neural network (CNN) structure. Finally, an intelligent diagnosis network is used to identify the health status of the inverter samples under variable conditions. Experimental results show that the proposed model can accurately and effectively realize the cross-domain health assessment of three-phase inverters in the case of small samples. The accuracy of the proposed model is better than that of other classical transfer learning models. [ABSTRACT FROM AUTHOR]

With the development of network technology and intelligent robot technology, Robot-assisted teleultrasound has played an important role in clinical fields. The application of real-time remote ultrasound technology has made the ultrasonic diagnosis break through the limitation of time and space distance, and solved the problem of shortage of medical resources to a certain extent. This article introduces the development and application basis of robot-assisted teleultrasound, summarizes the clinical application status, and discusses the advantages and limitations of its current application. In addition, we discuss the value in application scenario, interventional therapy and intracavitary ultrasound in the future. [ABSTRACT FROM AUTHOR]

In intelligent vehicular networks, vehicles should be able to communicate with their surroundings while traveling. This results in more efficient, safer, and comfortable driving experiences, as well as new commercial prospects in a variety of industries. Connected vehicles and autonomous vehicles expect 100% reliable connectivity without any compromise in quality. However, due to challenges such as difficult channel terrains in urban scenarios and dead zones, the reliability of current vehicle-to-infrastructure (V2I) and vehicle-to-vehicle communication systems cannot be guaranteed. The performance of vehicular networks can be considerably enhanced with reconfigurable intelligent surfaces (RIS). Non-orthogonal multiple access (NOMA) allows for massive connectivity with the surroundings. In vehicular networks, the RIS-assisted NOMA can ensure regulated channel gains, better coverage, throughput, and energy efficiency. In this work, a blind RIS-assisted fixed NOMA (FNOMA) system is proposed for a downlink V2I scenario. The closed-form analytical outage probability and throughput expressions are derived by considering RIS as an intelligent reflector and as a roadside unit. It is observed that the analytical and Monte Carlo simulation results are closely related. In simulations, it has been discovered that RIS-assisted FNOMA outperforms the traditional NOMA variants in terms of outage and throughput. Even without precise channel knowledge, blind RIS transmission outperforms traditional NOMA variants due to huge array gain. The increase in the number of reflective elements also results in a significant improvement in signal-to-noise ratio gains. [ABSTRACT FROM AUTHOR]

Intelligent network is the product of deep integration of network technology and artificial intelligence technology, which can make the network have the ability of perception, thinking, learning and action. First, it analyzes the requirements of naval equipment for the development of network intelligence, secondly, it introduces the related concepts and standardization progress of intelligent network, introduces the research progress of intelligent network from the aspects of ICT equipment manufacturers, telecom operators, academic research teams, United States navy, etc. Finally, it summarizes the naval network development, discusses and analyzes the intelligent development of the naval network, and provides support for promo- ting the evolution of the naval network in the direction of high efficiency and intelligence. [ABSTRACT FROM AUTHOR]

Retraction: [Liping Cai, Qiaona Gong, Feng Jiang, Mingzhan Yuan, Zhiyong Xiao, Shuai Zhang, Chengcheng Zheng, Yue Wu, The use of the intelligent Bayesian network method combined with blockchain technology in the optimisation of tunnel construction quality control, IET Software 2023 (https://doi.org/10.1049/sfw2.12109)]. The above article from IET Software, published online on 2 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

Issues of improving algorithms for detecting network attacks in a heterogeneous industrial Internet of Things network based on machine learning technologies for subsequent integration with subsystems of a security operation center are considered. A block diagram of a network attack detection system and an algorithm for the intelligent analysis of network traffic parameters in the task of detecting malicious network activity are developed. Variants of constructing ensembles of classifiers based on machine learning models and heterogeneous neural network models are analyzed. The F1 score for test samples from publicly available datasets of labeled network traffic is as high as 96%. The possibility of embedding the proposed models into software and hardware modules is discussed. A virtual testbed for assessing the effectiveness of machine learning models for detecting network attacks is developed. [ABSTRACT FROM AUTHOR]