Intelligent Processing Research Articles

Optimized Resource Management and Dynamic Routing Protocol for Wireless Sensor Networks Through Load Balancing, Packet Scheduling, and Intelligent Clustering

Heterogeneous Wireless Sensor Networks (HWSNs) are pivotal for providing weather-related event data, enabling universal location access, and facilitating remote monitoring through multi-hop transmission. Efficient energy utilization is critical in ensuring the optimal functioning of HWSNs. Previously, Compressive Sensing (CS) technology was established to enhance communication efficiency within HWSNs. While previous methods were effective in managing energy consumption and reducing transmission delays across network devices, the increased number of devices has impacted their efficacy. Consequently, energy becomes a vital limitation in constructing HWSNs. In order to address these challenges, this study introduces Load Balancing and Packet Scheduling with Intelligent Clustering based Improved Routing Protocol (LPICR). This integrates load balancing, packet scheduling, intelligent clustering, and enhanced routing techniques. The protocol is structured into three main categories: intelligent route selection, load balancing-based Cluster Head (CH) selection, and path scheduling. Initially, an efficient opportunistic routing is conducted by the intelligent route selection process. This routing method minimizes data forwarding during communication and significantly decreases energy consumption in the HWSN. Furthermore, by using a load balancing-oriented procedure for selecting cluster heads, the system achieves efficient determination of cluster heads and construction of clusters, resulting in the most efficient use of energy in communication. Path scheduling reduces the probability of delays by facilitating effective data flow between the source and destination in the HWSN. The NS2 platform is used to implement the proposed LPICR-HWSN protocol. The calculation of the result and comparison analysis is considered for the parameters are Data loss rate, communication time, packet success rate, malicious detection ratio, throughput, Routing overhead and energy efficiency. The results are thoroughly investigated by accounting for factors like the quantity of nodes and the varying speed of the network. To assess the efficacy of this proposed protocol, we conduct a comparative analysis using established methodologies such as CDAS-WSN, EEPC-WSN, TCCS-WSN, and MTODS-HWSN. The results suggest that the proffered LPICR-HWSN model demonstrates superior performance compared to previous methods.

Risk Challenges and Coping Strategies of Youth Value Guidance from the Perspective of Algorithm Recommendation

The deep integration of big data and artificial intelligence technology has given rise to the new information configuration paradigm of algorithm recommendation, which has not only profoundly changed the information-imparting model of the youth, but also exerted a far-reaching influence on the values of contemporary youth. At present, algorithm recommendation, with its intelligent information processing and precise information delivery mechanism, has expanded new space and provided new intermediaries for youth value guidance, but it has also aggravated the solidification of value cognition, eliminated the rationality of value judgment, eroded the subjectivity of value choice, and put forward unprecedented challenges to youth value guidance. Therefore, youth value guidance in the era of algorithm recommendation should be based on three aspects, namely, innovating guidance methods, improving media literacy, and activating value subjectivity, to give full play to the positive role of algorithm recommendation in positive value guidance, and effectively guide the youth to set up correct values.

Intelligent method framework for 3D surface manufacturing in cloud‐edge collaboration architecture

AbstractLarge and complex workpieces are core components in fields, such as aerospace, shipbuilding, and other industrial applications. However, the main challenge of curved plate processing comes from the difficulty in determining the nonlinear rebound features with structural design parameters. An intelligent method framework is proposed for 3D surface manufacturing in cloud‐edge collaboration environment. With the construction of an intelligent generation method for machining parameters, a unified data model is effectively integrated with various discrete data, and an intelligent processing mechanism based on 3D point clouds is constructed. In particular, a prediction method for curved panel rebound is constructed to reduce the manual dependency of the manufacturing process. Finally, a related case study is conducted to verify the framework, and the result shows accuracy, interpretability and reusability advantages over other similar methods.

Efficiency and Cost-Benefit Analysis of the Manual and Intelligent Venous Blood Sampling in Outpatient Area of Hospital.

To evaluate the efficiency and cost-benefit of the manual and intelligent venous blood sampling in outpatient area. The hospital selected had two branches with the outpatient area in Branch A using manual venous blood sampling as the control group and the outpatient area in Branch B using intelligent venous blood sampling as the experimental group. Analyze the differences between the two groups in operation time, cost-benefit, and service efficiency through on-site investigation and project cost methods. Compared with manual venous blood sampling, intelligent venous blood sampling project is more optimized with shorter operation time and higher nursing service efficiency. But both groups have negative returns. The intelligent venous blood sampling process is more optimized, combined with dynamic job implementation, resulting in lower labor costs and higher job service efficiency compared to manual venous blood sampling.

Design, Technical Development, and Evaluation of an Autonomous Compost Turner: An Approach towards Smart Composting

In a sustainable circular economy, the composting of organic waste plays an essential role. This paper presents the design and technical development of a smart and self-driving compost turner. The architecture of the hardware, including the sensor setup, navigation module, and control module, is presented. Furthermore, the methodological development using model-based systems engineering of the architecture of concepts, models, and their subsequent software integration in ROS is discussed. The validation and verification of the overall system are carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by requiring it to autonomously follow pre-defined trajectories at the composting plant and perform required composting tasks. The results prove that the autonomous compost turner can perform the required activities. In addition to autonomous driving, the compost turner is capable of intelligent processing of the compost data and of transferring, visualizing, and storing them in a cloud server. The overall system of the intelligent, autonomous compost turner can provide essential leverage for improving sustainability efforts, thus contributing substantially to an environmentally friendly and sustainable future.

Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers

Abstract. The mass balance of the Greenland Ice Sheet is strongly influenced by the dynamics of its outlet glaciers. Therefore, it is of paramount importance to accurately and continuously monitor these glaciers, especially the variation in their frontal positions. A temporally comprehensive parameterization of glacier calving is essential for understanding dynamic changes and constraining ice sheet modeling. However, many current calving front records are limited in terms of temporal resolution as they rely on manual delineation, which is laborious and not appropriate considering the increasing amount of satellite imagery available. In this contribution, we address this problem by applying an automated method to extract calving fronts from optical satellite imagery. The core of this workflow builds on recent advances in the field of deep learning while taking full advantage of multispectral input information. The performance of the method is evaluated using three independent test datasets. For the three datasets, we calculate mean delineation errors of 61.2, 73.7, and 73.5 m, respectively. Eventually, we apply the technique to Landsat-8 imagery. We generate 9243 calving front positions across 23 outlet glaciers in Greenland for the period 2013–2021. Resulting time series not only resolve long-term and seasonal signals but also resolve subseasonal patterns. We discuss the implications for glaciological studies and present a first application for analyzing the effect of bedrock topography on calving front variations. Our method and derived results represent an important step towards the development of intelligent processing strategies for glacier monitoring, opening up new possibilities for studying and modeling the dynamics of Greenland's outlet glaciers.

Intelligent assessment system of material deterioration in masonry tower based on improved image segmentation model

Accurate and timely data collection of material deterioration on the surfaces of architectural heritage is crucial for effective conservation and restoration. Traditional methods rely heavily on extensive field surveys and manual feature identification, which are significantly affected by objective conditions and subjective factors. While machine vision-based methods can help address these issues, the accuracy, intelligence, and systematic nature of material deterioration assessment for large-scale masonry towers with complex geometries still require significant improvement. This research focuses on the architectural heritage of masonry towers and proposes an intelligent assessment system that integrates an improved YOLOv8-seg machine vision image segmentation model with refined 3D reconstruction technology. By optimizing the YOLOv8-seg model, the system enhances the extraction capabilities of both detailed and global features of material deterioration in masonry towers. Furthermore, by complementing it with image processing methods for the global visualization of large-scale objects, this research constructs a comprehensive intelligent assessment process that includes "deterioration feature extraction—global visualization—quantitative and qualitative comprehensive assessment." Experimental results demonstrate that the intelligent assessment system significantly improves the performance of target feature extraction for material deterioration in masonry towers compared to existing methods. The improved model shows improvements of 3.39% and 4.55% in the key performance metrics of mAP50 and mAP50-95, respectively, over the baseline model. Additionally, the efficiency of global feature extraction and visualization of material deterioration increased by 66.36%, with an average recognition accuracy of 95.78%. Consequently, this system effectively overcomes the limitations and subjective influences of field surveys, enhancing the objectivity and efficiency of identifying and analyzing material deterioration in masonry towers, and providing invaluable data support for the subsequent preservation and restoration efforts.

The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era

AbstractThis study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model， a regression analysis explores how different AI‐enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A‐share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production‐based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI‐driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts.

Advanced authentication of IoT sensor network for industrial safety

As industries rapidly advance towards automation and intelligence, the Industrial Internet of Things (IIoT) plays a pivotal role in enhancing factory efficiency and production management. However, the advent of quantum computing introduces significant security challenges, particularly for data transmission and privacy protection, as its immense computational power can compromise the current cryptographic theories' safeguards. We introduce a novel lattice-based cryptographic authentication and key management scheme specifically designed for the IIoT within Wireless Sensor Networks (WSNs), focusing on establishing a secure industrial communication system capable of withstanding quantum threats. The proposed scheme utilizes Crystals-Kyber technology and is built around the Learning With Errors (LWE) problem, providing a robust foundation for security against both traditional and quantum threats. It features innovative methods to safeguard against password guessing, stolen verifier, and replay attacks, enhancing data confidentiality and system integrity. By integrating advanced cryptographic techniques resistant to quantum decryption, this research ensures the secure management and utilization of critical production data, supporting the ongoing transformation towards more intelligent manufacturing processes and sustaining global competitiveness for industries.

Do Not Worry That Generative AI May Compromise Human Creativity or Intelligence in the Future: It Already Has.

Technology alters both perceptions of human intelligence and creativity and the actual processes of intelligence and creativity. Skills that were once important for human intelligence, for example, computational ones, no longer hold anywhere near the same importance they did before the age of computers. The advantage of computers is that they may lead us to focus on what we believe to be more important things than what they have replaced. In the case of penmanship, spelling, or arithmetic computation, such an argument could bear fruit. But in the case of human creativity, the loss of creative skills and attitudes may be a long-term loss to humanity. Generative AI is replicative. It can recombine and re-sort ideas, but it is not clear that it will generate the kinds of paradigm-breaking ideas the world needs right now to solve the serious problems that confront it, such as global climate change, pollution, violence, increasing income disparities, and creeping autocracy.

Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study

BackgroundProviding appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients.MethodA systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline.ResultsForty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models.ConclusionArtificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.

Intelligent process monitoring of smart polymer composites using large area graphene coated fabric sensor.

Herein, we report the development of an online process monitoring system for vacuum-assisted resin transfer molding (VARTM) process using large area graphene coated in-situ fabric sensor. Besides imparting excellent mechanical properties to the final composites, these sensors provide critical information during the composite processing including detecting defects and evaluating processing parameters. The obtained information can be used to create a digital passport of the manufacturing phase to develop a cost-effective production technique and fabricate high-quality composites. The fabric sensor was produced using a scalable dip-coating process by coating 1-, 3- or 5-layers of thermally reduced graphene oxide (rGO) onto glass fabric surface according to the number of dips of the fabrics into GO solution. The electrical resistances from all electrode pairs were simultaneously and continuously recorded during distinct stages of the VARTM process to determine the relative conductance. During the vacuum cycle, the range of relative conductance increased with the number of coated rGO layers, with the 5-layer rGO-coated sensor showing the highest conductance range of 16.9 %. Additionally, it was observed that the 5-layer coated sensor showed a consistent decrease in conductance during the infusion phase due to the fluid flow pressure dominating the resin electrical conductivity.

SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities

The rapid development of generative artificial intelligence has brought significant opportunities for the advancement of digital humanities research. Intelligent processing of ancient texts, as an essential part of digital humanities, is also undergoing a transformation in research methodologies in the wave of AIGC. The integration of generative pre-trained models with Chinese ancient texts, a vital carrier of Chinese culture, allows for deep mining of the content of these texts and provides services that make ancient texts more understandable and accessible to the general public. In this research, we propose a method that combines the most renowned Chinese anthology, the “Siku Quanshu,” with generative pre-trained models. We developed the SikuGPT model, a generative model for ancient text processing tasks, based on GPT-type language models by continued pretraining. This model was tested on two typical tasks of ancient text processing: translation between classical and modern Chinese, and classification of ancient texts. The findings reveal that our model achieves advantages in understanding and generating scenarios of ancient texts. The capability of SikuGPT in processing traditional Chinese texts helps to promote the organization of ancient information and knowledge services, and advances the international dissemination of traditional Chinese culture.

Удосконалення методу оцінки програмного забезпечення контролю параметрів транспортних засобів

The purpose of the conducted research is to improve the method of evaluating the performance of the vehicle parameter control software. It is stated that a new technique for road transport in the field of monitoring its working condition is the creation of information systems for organizational and functional support of rolling stock operation processes with the help of information integration, namely the stages of the life cycle of rolling stock and systems for its technical control and diagnostics. It is shown that nowadays in road transport the basis of life cycle information support can be CALS or IPV/CALS/PLM - technologies (continuous support of the life cycle of vehicles), which are sometimes called product information support systems and are a part of intelligent transport systems. The analysis and clarification of the structure of the intelligent vehicle monitoring system was carried out on the example of the assessment of its environmental safety and its engine parameters. In the work, it is also planned to obtain experimental data from vehicle, the results of processing of which will allow to prove the existence of a real possibility of using the diagnostic complex on a modern automatic transmission system. It should be noted that the system for monitoring environmental safety indicators of vehicle, which is proposed by the authors, also contains in its composition the subsystem of intelligent processing of primary information, which can be used precisely in the role of "intelligent monitoring system". The data obtained and analyzed in the work indicate the need to also improve the efficiency of the neutralization of harmful substances in the engine warm-up mode, which can be implemented by reducing the engine warm-up time and more effective control of the injection/ignition process in transient modes, as well as by introducing an improvement algorithm of the control subsystem composition of the mixture precisely with feedback. The main diagnostic parameters of the vehicle engine during the start-up and warm-up processes, which affect the composition of harmful substances in the exhaust gases, were obtained experimentally. The proposed scheme for monitoring pollution of the roadside environment by traffic flows in the city.

Blockchain and game theory enable high-efficiency data sharing in the IIoT

The rapid development of intelligent manufacturing is inseparable from collaboration among smart factories (SFs). In the real unmanned intelligent collaborative production process, each SF exists as an independent individual peer. However, due to the selfishness of SFs and intelligent manufacturing equipment (IME), they do not actively participate in data sharing, which brings serious obstacles to the collaborative development of SF. In order to solve the above problems, this paper combines blockchain technology and game theory technology to propose a high-efficiency data sharing incentive method based on the Stackelberg game. Specifically, We first take the SF as the leader and the IME as the follower, and we define the data sharing incentive problem among SFs as the master–slave game problem. Then, the lower game problem and the upper game problem are solved, respectively, and finally the Nash equilibrium is derived. Then, a high-efficiency data sharing incentive method based on the Stackelberg game is designed and implemented. Finally, we conducted simulation experiments on the method proposed in this paper. Through theoretical and simulation experiments, it has been proven that the proposed method effectively reduces the impact of the selfishness of SFs and IME and improves the efficiency of data sharing among SFs.

Intelligent smelting process, management system: Efficient and intelligent management strategy by incorporating large language model

In the steelmaking industry, enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities. Thus, effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain, reducing management costs and complexities. This paper proposes, for the first time, the integration of Vision-Language Model (VLM) and Large Language Model (LLM) technologies in the steel manufacturing domain, creating a novel steelmaking process management system. The system facilitates data collection, analysis, visualization, and intelligent dialogue for the steelmaking process. The VLM module provides textual descriptions for slab defect detection, while LLM technology supports the analysis of production data and intelligent question-answering. The feasibility, superiority, and effectiveness of the system are demonstrated through production data and comparative experiments. The system has significantly lowered costs and enhanced operational understanding, marking a critical step toward intelligent and cost-effective management in the steelmaking domain.

A STEP-NC complaint and feature-based solution for intelligent process planning

A STEP-NC complaint and feature-based solution for intelligent process planning

An intelligent deep learning approach to spacecraft attitude control: The case of satellites

This paper presents a method using artificial intelligence (AI) and deep learning to transform an on- board camera into an intelligent star sensor. This method overcomes the problems and limitations of conventional star sensors, such as cost, size, weight, power consumption, camera constraints and orientation accuracy. The method proposes an intelligent processing module embedded in the camera to determine the attitude of the spacecraft, and uses two convolutional neural network models to improve the resolution of star images and determine the attitude of the spacecraft from these images. This approach guarantees high spacecraft orientation accuracy and real-time processing, contributing to the success of space missions. The off-line phases of collecting and pre-processing stellar images, building intelligent models and labeling training data are described in detail. The results show that this method offers improved performance over conventional star sensors, and presents significant contributions to scientific research and technological innovation in the field of space remote sensing, contributing to sustainable development and understanding of the universe.

Relationship between Artificial Intelligence and Business Process Optimization: Insights from Selected Banks in Anambra State

This study explored the relationship between artificial intelligence and business process optimization in selected banks in Anambra State. The population consisted of 745 employees from commercial banks in Anambra State, Nigeria. Using purposeful sampling, three banks from each senatorial district in the state were chosen, and 170 questionnaires were distributed to staff members of these selected banks. Out of the 170 distributed questionnaires, 125 were completed and returned. A Pearson correlation critical value table was used to test the assumptions, and the Pearson product- moment correlation coefficient was the statistical instrument for data analysis. The hypothesis results indicated a significant correlation between business process optimization in banks and artificial intelligence, specifically in enhancing customer service relationships and boosting cyber-security in the selected banks in Anambra State. The study recommends that the banking industry should continue to implement artificial intelligence cautiously to maintain a balance between innovative developments and the responsible and ethical use of AI. This approach will ensure improved cyber- security and customer service in banks.

A MULTIDISCIPLINARY ANALYSIS ON THE PERVASIVENESS OF BIG DATA, DISINFORMATION, AND MISGUIDANGE

This is a multidisciplinary study that uses the "paradigm shifts theory" of Thomas Kuhn over ubiquitous data, information, and knowledge which are subjects to the metaverse, misdirection, and social engineering in this age of Cyber-capitalism. Discussions are held to raise awareness of digital pollution and disinformation economy which has become one of the biggest problems of our time, on material and moral values and possible precautions that can be taken from a wisdom approach from IT, AI, ethics, and theology. It is argued that artificial intelligence processes should be provided with opportunities to revise themselves on digital transformation, ethics, cybersecurity, and data analytics.